Silvoarable Agroforestry For Europe (SAFE)

Transcription

Quality of Life and Management of Living Resources
Silvoarable Agroforestry For Europe
(SAFE)
European Research contract QLK5-CT-2001-00560
SAFE PROJECT FINAL PROGRESS REPORT
August 2004-January 2005
May 2005
Volume 4 :Appendices
SAFE Final Progress Report – Volume 4 (Annexes) – May 2005
1
Quality of Life and Management of Living Resources
Silvoarable Agroforestry For Europe
(SAFE)
European Research contract QLK5-CT-2001-00560
CAPTION OF THE COVER PICTURE
In March 2005, Jérome Feracci, a cereal farmer near the Béziers town in France,
established a 25 ha silvoarable plot with walnut trees. He was convinced to
adopt agroforestry after visiting some SAFE consortium experiments such a
the Restinclières farm near Montpellier.
Such plantations are one of the most evident consequence of the SAFE
project, and will be landmarks for the future development of agroforestry
across Europe.
2
Appendices
TABLE OF CONTENT
This annex includes annexes to contractor reports and some scientific papers
produced by the SAFE consortium during the last year of the project
ANNEX 1.
LIST OF THE SAFE PUBLICATIONS ................................................... 8
Papers published or accepted ..............................................................................................8
Papers submitted .................................................................................................................10
Papers in préparation ..........................................................................................................10
Extension papers .................................................................................................................11
Internal reports .....................................................................................................................11
Posters ..................................................................................................................................12
Communications to congresses.........................................................................................12
ANNEX 2. ECONOMICS OF SILVOARABLE SYSTEMS USING A NOVEL
APPROACH : THE LAND EQUIVALENT RATIO BASED GENERATOR .............. 13
Introduction ..........................................................................................................................14
The LER approach................................................................................................................14
LER-biomass and LER-product......................................................................................................... 14
The LER-based generator ................................................................................................................. 15
A wise hypothesis for the agroforest tree growth .............................................................................. 16
Maximum expectable LER in function of species and final density................................................... 17
Impact of the TGA on the LER results............................................................................................... 19
Data references and main hypothesis................................................................................20
The forestry references ..................................................................................................................... 20
Reference data in agriculture ............................................................................................................ 21
The profitability threshold yield .......................................................................................................... 22
Main management features of the agroforestry systems .................................................................. 23
Economic hypothesis......................................................................................................................... 25
Main results ..........................................................................................................................27
Labour impact for one silvoarable hectare ........................................................................................ 27
Prediction of yield evolution............................................................................................................... 27
Cash flow impact ............................................................................................................................... 29
Profitability of a silvoarable investment ............................................................................................. 32
Main conclusions ............................................................................................................................... 39
Bibliography .........................................................................................................................39
Annex ....................................................................................................................................40
3
Annex 1: Detailed description of the LERbased-Generator .............................................................. 40
Annex 2: Labour, revenues and costs in the 3 types of farms .......................................................... 46
Annex 3: Economic data relative to monocropped or intercropped walnut, wild cherry and poplar in
the 3 farms......................................................................................................................................... 47
ANNEX 3. WHAT LAND STATUS FOR AGROFORESTRY PLOTS IN FRANCE ?
(IN FRENCH) 49
Définition de l’agroforesterie ..............................................................................................50
Problématique liée au statut ...............................................................................................51
Les solutions possibles.......................................................................................................52
Un forfait spécial agroforesterie......................................................................................................... 53
Un forfait distinct au prorata .............................................................................................................. 53
Analyse des solutions présentées .....................................................................................54
D’un point de vue administratif .......................................................................................................... 54
Conséquence pour le calcul de l’impôt foncier.................................................................................. 55
Conséquence pour le calcul de l’impôt sur le revenu........................................................................ 56
Conséquence pour le calcul de l’imposition sur le patrimoine .......................................................... 56
Conséquence pour la gestion de l’exploitation.................................................................................. 56
Solution proposée................................................................................................................57
Faut-il modifier la loi ? .........................................................................................................58
Les surfaces boisées et le code rural................................................................................................ 58
Le bail agroforestier........................................................................................................................... 59
Appendix : rapport du Bureau des Etudes Fiscales du 8 oct. 2004 ................................60
ANNEX 4. QUELLE PLACE POUR LES ARBRES HORS FORET DANS LA
NOUVELLE PAC ? .................................................................................................. 63
Eligibilité des parcelles arborées aux paiements compensatoires.................................69
Place de l’arbre dans l’historique des réformes de la PAC ............................................................... 69
Conclusions au niveau européen ...................................................................................................... 73
Le régime d’application en France .................................................................................................... 75
Les arbres dans le deuxième pilier de la PAC...................................................................82
Le Règlement de Développement Rural ........................................................................................... 82
Les aides disponibles en France....................................................................................................... 85
Bibliographie ........................................................................................................................93
Annexe 1: Article TransRural Initiative de déc 2004 .........................................................94
Annexe 2: Propositions du Groupe Réglementations – SAFE ........................................95
Annexe 3: Lettre de Luc Guyau APCA au MAAPAR – PAC..............................................97
Proposition de modification du projet de règlement européen concernant le soutien au
développement rural (proposition du 14/07/04) ................................................................98
Annexe 4: Chapitre 10 de la circulaire forêt de protection du 7 mai 01 ........................102
4
Annexe 5: Texte MAE Habitats Agroforestiers................................................................105
Annexe 6: Lettre de Luc Guyau APCA au MAAPAR – MAE ...........................................114
ANNEX 5. THE DISTRIBUTION OF SILVO-ARABLE SYSTEMS IN WESTERN
EUROPE AND THEIR ECOLOGICAL CHARACTERISTICS .................................116
Introduction ........................................................................................................................116
A European Stratification System for Resource Assessment .......................................116
A Worked Example of the application of strata in Atlantic Europe ...............................117
Case Studies in Southern Europe ....................................................................................118
Ecological Considerations of Silvo-arable ......................................................................119
Future Work ........................................................................................................................120
Conclusions........................................................................................................................121
ANNEX 6. SILVOARABLE AGRICULTURE IN EUROPE – PAST, PRESENT AND
FUTURE
122
Abstract...............................................................................................................................122
Introduction ........................................................................................................................123
Historical perspective......................................................................................................... 125
Data Collection ...................................................................................................................127
Systems...............................................................................................................................128
Olive tree associations ...................................................................................................... 130
Fruit tree associations ....................................................................................................... 131
Timber tree associations.................................................................................................. 136
Oak tree associations ........................................................................................................ 138
Fodder tree associations ................................................................................................. 143
Conclusions ............................................................................................................................. 150
Acknowledgements............................................................................................................151
References ..........................................................................................................................151
ANNEX 7. THE DEVELOPMENT AND USE OF A FRAMEWORK FOR
CHARACTERISING COMPUTER MODELS OF SILVOARABLE ECONOMICS...163
Abstract...............................................................................................................................164
Introduction ........................................................................................................................165
Materials and methods ......................................................................................................166
Development of a framework for characterising models ................................................................. 166
Use of the framework.........................................................................................................168
Results and discussion .....................................................................................................168
5
Model background ........................................................................................................................... 168
Systems modelled ........................................................................................................................... 170
Objective of economic analysis ....................................................................................................... 171
Viewpoint of the economic analysis ................................................................................................ 174
Spatial scale .................................................................................................................................... 175
Generation and use of biophysical data .......................................................................................... 176
Model platform and interface ........................................................................................................... 177
Input requirements and outputs generated ..................................................................................... 178
Conclusions........................................................................................................................180
References ..........................................................................................................................181
ANNEX 8. FINE ROOT DISTRIBUTION IN DEHESAS OF CENTRAL-WESTERN
SPAIN
192
Abstract...............................................................................................................................193
Introduction ........................................................................................................................194
Material and Methods.........................................................................................................195
Study Area ....................................................................................................................................... 195
Soil cores: Root Length Density ...................................................................................................... 195
Road Cuts: Maximum tree rooting depth and horizontal spread..................................................... 196
Results ................................................................................................................................197
Linear relationships for root length density estimation .................................................................... 197
Vertical profiles of root length density ............................................................................................. 197
Lateral root distribution .................................................................................................................... 198
Effect of soil management on root distribution ................................................................................ 198
Tree roots in road cuts..................................................................................................................... 198
Discussion ..........................................................................................................................199
Herbaceous plants root system ....................................................................................................... 199
Holm-oak root profiles ..................................................................................................................... 199
Lateral root distribution .................................................................................................................... 200
Combined root system: implication on competition for soil resources ............................................ 200
Conclusions........................................................................................................................201
References ..........................................................................................................................201
ANNEX 9. THE DEVELOPMENT AND APPLICATION OF BIO-ECONOMIC
MODELLING FOR SILVOARABLE SYSTEMS IN EUROPE .................................210
Keywords ............................................................................................................................210
Abstract...............................................................................................................................210
Introduction ........................................................................................................................211
Method.................................................................................................................................211
6
Identification of landscape test sites................................................................................................ 212
Characterisation of landscape test sites.......................................................................................... 212
Selection and management of tree and crop species ..................................................................... 213
Biophysical modelling ...................................................................................................................... 213
Plot-scale economic modelling ........................................................................................................ 214
Parameterisation and use of Farm-SAFE ....................................................................................... 216
Results and discussion .....................................................................................................219
Biophysical production in arable and forestry systems ................................................................... 219
Biophysical production in silvoarable systems ................................................................................ 220
Land equivalent ratios ..................................................................................................................... 220
Plot-scale economic results............................................................................................................. 221
Farm-scale feasibility....................................................................................................................... 223
Summary and recommendations......................................................................................224
Conclusion..........................................................................................................................226
Acknowledgements............................................................................................................227
References ..........................................................................................................................227
Tables ..................................................................................................................................231
Captions for Figures ..........................................................................................................237
Figures ................................................................................................................................238
ANNEX 10. YIELD-SAFE:
A
PARAMETER-SPARSE
PROCESS-BASED
DYNAMIC MODEL FOR PREDICTING RESOURCE CAPTURE, GROWTH AND
PRODUCTION IN AGROFORESTRY SYSTEMS...................................................247
ABSTRACT..........................................................................................................................248
INTRODUCTION..................................................................................................................249
MATERIALS & METHODS..................................................................................................253
MODEL DESCRIPTION .................................................................................................................. 253
POPLAR VALIDATION DATA ......................................................................................................... 266
MODEL CALIBRATION FOR POPLAR AND INTERCROPS ......................................................... 267
MODEL VALIDATION FOR POPLAR AGROFORESTRY SYSTEMS ........................................... 269
RESULTS.............................................................................................................................270
AGROFORESTRY EXPERIMENTS WITH POPLAR ..................................................................... 270
MODEL CALIBRATION................................................................................................................... 271
MODEL VALIDATION ..................................................................................................................... 273
SENSITIVITY ANALYSIS ................................................................................................................ 273
DISCUSSION .......................................................................................................................275
ACKNOWLEDGEMENT ......................................................................................................277
REFERENCES.....................................................................................................................277
7
ANNEX 1.
List of the SAFE publications
The full manuscripts of the 5 papers in bold are included in this annex. Most of the
other papers are available on the SAFE web site. Only published or accepted papers
are available in full on the public pages. The other papers are available in the private
pages of the web site (password controlled access)
PAPERS PUBLISHED OR ACCEPTED
Chifflot, V., Bertoni, G., Cabanettes, A. & Gavaland, A. (2004) Beneficial effects of
intercropping on the growth and nitrogen status of wild cherry and hybrid walnut
trees. Agroforestry Systems (in press).
Cubera E., Montero M.J., Moreno G. 2004. Effect of land use on the soil water
dynamic in dehesas of Central-Western Spain. In: Sustainability of Agrosilvopastoral
Systems - Dehesa, Montados -. S. Schnabel and A. Gonçalves (eds.). Advances in
GeoEcology, 37, Catena Verlag, Reiskirchen (in press).
De Filippi R., Reisner Y., Herzog F., Dupraz C., Gavaland A. 2004. Modelling the
potential distribution of Agroforestry systems in Europe, using GIS EnviroInfo 2004,
CERN, p 423-426.
Dupraz C., 2005. From silvopastoral to silvoarable systems in Europe: sharing
concepts, unifying policies. In Silvopastoralism and Sustainable Land Management.
Mosquera-Losada R., Riguerio, A., McAdam J., Eds, CAB International, 432 pages.
Dupraz C., 2006. Entre agronomie et écologie : vers la gestion d’écosystèmes
cultivés. Cahier d’étude DEMETER - Economie et Stratégies agricoles, Paris,
pagination en cours, 16 pages
Dupraz C., Capillon A. 2006. L’agroforesterie : une voie de diversification écologique
de l’agriculture européenne? Cahier d’étude DEMETER - Economie et Stratégies
agricoles, Paris, pagination en cours, 11 pages
Dupraz C., Liagre F., Manchon O., Lawson G., 2004. Implications of legal and policy
regulations on rural development: the challenge of silvoarable agroforestry in Europe.
In : Meeting the challenge : Silvicultural Research in a changing world. IUFRO World
Series Volume 15, Parotta et al, (Eds.), 34-36.
Eichhorn E.P., Paris P., Herzog F., Incoll L.D., Liagre F., Mantzanas K., Mayus
M., Moreno Marcos C., Dupraz C., Pilbeam DJ., 2005. Silvoarable agriculture in
Europe – past, present and future. Agroforestry Systems, in press
Graves, A. R., Burgess, P.J., Liagre, F., Dupraz C. & Terreaux, J.-P. (2005).
Development and use of a framework for characterising computer models of
silvoarable economics. Agroforestry Systems, 65:53–65
Keesman, K.J. and R. Stappers. 2004. Nonlinear set-membership estimation: A
support vector machine approach. Journal of Inverse and Ill-Posed Problems,
Volume 12, No. 1, pp. 27-42.
8
Montero M.J., Obrador J.J., Cubera E., Moreno G. 2004. The Role of Dehesa land
use on Tree Water Status in Central-Western Spain. In: Sustainability of
Agrosilvopastoral Systems - Dehesa, Montados -. S. Schnabel and A. Gonçalves
(eds.). Advances in GeoEcology, 37, Catena Verlag, Reiskirchen (in press).
Montero, M.J., Moreno, G. 2004. Light availability for understory pasture in Holm-oak
dehesas. In: Silvopastoralism and Sustainable land management" published by CAB
INTERNATIONAL (in press). 4 pages.
Moreno G., Obrador J.J., Cubera E., Dupraz C., 2005. Root distribution in
dehesas of Central-Western Spain. Plant and Soil, accepted for publication
Moreno, G., Obrador, J. J., Garcia, E., Cubera, E., Montero, M. J., Pulido, F. &
Dupraz, C. (2005) Competitive and Facilitative interactions in dehesas of C-W Spain.
In Press, special issue of Agroforestry Systems.
Moreno, G., Obrador, J., García, E., Cubera, E., Montero, M.J., Pulido, F. 2004.
Consequences of dehesa management on the tree-understory interactions. In:
Silvopastoralism and Sustainable land management" published by CAB
INTERNATIONAL (in press). 4 pages.
Obrador, J.J., Moreno, G. 2004. Soil nutrient status and forage yield at varying
distances from trees in four dehesas in Extremadura, Spain. In: Silvopastoralism and
Sustainable land management" published by CAB INTERNATIONAL (in press). 4
pages.
Obrador-Olán J.J., García-López E., Moreno G. 2004. Consequences of dehesa land
use on nutritional status of vegetation in Central-Western spain. In: Sustainability of
Agrosilvopastoral Systems - Dehesa, Montados -. S. Schnabel and A. Gonçalves
(eds.). Advances in GeoEcology, 37, Catena Verlag, Reiskirchen (in press).
Palma, J., Graves, A., Bregt, A., Bunce, R., Burgess P., Garcia, M., Herzog, F.,
Mohren, G., Moreno, G. and Reisner, Y. (2004). Integrating soil erosion and
profitability in the assessment of silvoarable agroforestry at the landscape scale. In
Proceedings of the Sixth of the International Farming Systems Association (IFSA)
European Symposium on Farming and Rural Systems at Vila Real 4-7 April 2004.
817-827. The Proceedings are available at: http://home.utad.pt/~des/ifsa/index.htm
Paris P., Pisanelli A., Tadaro L., Olimpieri G. Cannata F. 2004. Growth and water
relations of walnut trees (Juglans regia L.) on a mesic site in central Italy Agroforestry
system (in press).
Parveaud C.E., Sabatier S.A., Dauzat J., Auclair D. 2003. Influence of morphometric
characteristics of the Hybrid Walnut tree crown (Juglans nigra x Juglans regia) on its
radiative balance. In: Hu B.G., Jaeger M. (ed.) Plant Growth Modeling and
Applications. Tsinghua Univ. Press / Springer , Beijing (China). pp. 296-304.
Pulido, F.J., García, E., Obrador, J.J., Montero, M.J. 2004. Effects of management on
acorn production and viability in holm oak dehesas. In: Silvopastoralism and
Sustainable land management" published by CAB INTERNATIONAL (in press). 4
pages.
9
PAPERS SUBMITTED
Graves A.R., Burgess P.J., Palma J.H.N., Herzog F., Moreno G., Bertomeu .,
Dupraz C., Liagre F., Keesman K., van der Werf W., 2005. The development and
application of bio-economic modelling for silvoarable systems in Europe.
Submitted to Ecological Engineering.
Keesman, K.J., W. v.d. Werf and H. v. Keulen, 2005. Mathematical Production
Ecology: Analysis of a Silvo-arable Agro-forestry System. Submitted to Bull. Math.
Biol.
Lambs, L., Muller, E., Chifflot, V. & Gavaland, A. Sap flow measurements of wild
cherry trees (Prunus avium) in an agroforestry system during a dry summer, Southwest of France. Submitted to Annals of Forest Science, 15th November 2004.
Reisner, Y.; Herzog, F. and De Filippi, R. (2005): Target regions for silvoarable
Agroforestry in Europe. Submitted to Ecological Engineering.
van der Werf W., Keesman K., Burgess P., Graves A., Pilbeam D., Incoll L.D.,
Metselaar K., Mayus M., Stappers R., van Keulen H., Palma J., Dupraz C., 2005.
Yield-SAFE: a parameter-sparse process-based dynamic model for predicting
resource capture, growth and production in agroforestry systems. Submitted
to Ecological Engineering.
PAPERS IN PREPARATION
Dufour L., Dupraz C., (2005). Effect of tree competition on durum wheat yield in a
Mediterranean agroforestry system. En preparation pour EJA
Dupraz C, Vincent G., Lecomte I., Van Noordwijk M. (2006) Modelling 3D interactions
of trees and crops with the Hi-SAFE model. En preparation pour Forest Ecology and
Management
Lusiana B., Noordwijk M V., Dupraz C. and de Willigen P., (2006) A process-based
algorithm for sharing nutrient and water uptake between plants rooted in the same
volume of soil II. Nutrients in static root systems
Moreno G., Obrador J.J., García E., Cubera E., Montero M.J., Pulido F. and C.
Dupraz. *Competitive vs Facilitative interactions determined by land use in oak
Dehesas. In preparation for Agroforestry Systems
Mulia R., Dupraz C., (2005). The growth behaviour of plant root system, including
negative-geotropism, in homogeneous and heterogeneous soil resource condition
Mulia R., Dupraz C., (2005). Unusual 3D fine root distributions of two deciduous tree
species observed in Southern France: what consequences for root dynamics
modelling? In preparation for Plant and Soil
Mulia R., Dupraz C., van Noordwijk M. (2005) A 3D model with voxel automata to
simulate plant root growth in heterogeneous soil condition. I. Modelling concepts
10
Noordwijk M. v, Mulia R., Dupraz C., Lusiana B. (2006) A process-based algorithm
for sharing nutrient and water uptake between plants rooted in the same volume of
soil III. Growing root systems
Noordwijk M. v., Lusiana B., Dupraz C.,, Radersma S., Ozier-Lafontaine H., de
Willigen P., (2006). A process-based algorithm for sharing nutrient and water uptake
between plants rooted in the same volume of soil I. Water in static root systems
Palma, J.; Bunce, R.; De Filippi, R.; Herzog, F.; Van Keulen, H.; Mayus M., Reisner,
Y. (2005): Assessing the environmental effects of agroforestry at the landscape
scale. Ecological Engineering. In prep.
EXTENSION PAPERS
Dupraz C., Liagre F., (2006). Agroforesterie pratique. Editions France Agricole, en
préparation, 250 pages environ
Gerardo MORENO. 2004. El árbol en el medio agricola. Foresta (in press).
INTERNAL REPORTS
Chavet M., E. Hallot, C. Pichery, H. Pruvost, J. P. Terreaux, 2004, Agroforestry:
Towards economic land equivalent ratio, Zürich, 2 - 3 novembre 2004
F. Herzog and C. Dupraz, 2005. Agroforestry in Europe – Learning from Tropical
Agroforestry
Graves, A. R., Burgess, P.J., Liagre, F., Terreaux, J.-P. & Dupraz, C. (2003). The
development of a model of arable, silvoarable and forestry economics. Unpublished
draft paper. Silsoe, Bedfordshire: Cranfield University.
Lambs L., E. Muller, V. Chifflot et C. Dupraz 2005 Consommation en eau et
ressources hydriques pour des peupliers en agroforesterie JEF
Pasturel, P. (2004). Light and water use in a poplar silvoarable system. Unpublished
MSc by Research thesis. Silsoe: Bedfordshire: Cranfield University. 143 pp.
Terreaux J.P., M. Chavet, 2002, Problèmes économiques liés à l'agroforesterie,
Cabinet Chavet, Paris, 85 pages.
Terreaux J.P., M. Chavet, 2004, An intertemporal approach of Land Equivalent Ratio
for agroforestry plots, Lameta, DT 2004-15, 18 p.
Terreaux J.P., M. Chavet, T.H. Thomas, 2003, Silvoarable agroforestry: some
economic problems, Powerpoint presentation, Orvieto, Italy, 14-18 octobre 2003, 30
p.
Terreaux JP, Michel Chavet, Anil Graves, Christian Dupraz, Paul Burgess and
Fabien Liagre Evaluating agroforestry investments
Yoda K. Dupraz C., Dauzat J., 2005. Comparison of daily and seasonal variations of
radii among trunk, branch and root in Juglans nigra L. x regia L.
11
POSTERS
Graves A., P. Burgess, F. Liagre, C. Dupraz, J.-Ph. Terreaux, 2004, The
development of an economic model of arable, agroforestry and forestry systems, 1st
World Congress of Agroforestry, Orlando, Florida, 27 June – 02 July, Poster.
COMMUNICATIONS TO CONGRESSES
Agostini, F., Pilbeam, D.J., Incoll, L. D. & Lecomte, I. (2004). Data management for
Decision Support Systems (DSS) in agroforestry. Poster presented (by Dr P
Burgess) at 1st World Congress of Agroforestry, Orlando, Florida, USA, 27th June –
2nd July 2004. Abstract published http://conference.ifas.ufl.edu/wca/Abstracts2.pdf
Auclair D., Laurans M. , Chopard J., Leroy C. Parveaud C.E. 2004. Threedimensional tree architectural analysis and modelling to study biophysical
interactions. In: First World Congress on Agroforestry, Orlando, FL (USA), 27/0602/07 2004. Poster presentation.
Chifflot V., Bertoni G., Gavaland A., Cabanettes A. and Dupraz C., 2004. Improving
growth and nutritional status of highly valuable broadleaf species through
intercropping. 1st World Congress of Agroforestry, Orlando, Florida, USA, 27 June to
02 july 2004.
Chifflot V., Bertoni G., Gavaland A., Cabanettes A. and Dupraz C., 2004. Improving
growth and nutritional status Improving growth and nutritional status of highly
valuable broadleaf species through of highly valuable broadleaf species through
intercropping. Poster presented at the first world congress of Agroforestry, Orlando,
June 2004.
Cubera E., Moreno G., Solla A., 2004. TDR-measurement for the study of the
seasonal variations of soil moisture on quercus ilex dehesas. In: Proceedings:
Workshop on Water Use of Woody Crops: techniques, issues, modelling and
applications on water management. Ílhavo (Aveiro, Portugal) -May 2004. 2 pages.
Dupraz C., Vincent G., Lecomte I., Mulia R, Jackson N., Mayus M., Van Noorwijk M.,
2004. Integrating tree-crop dynamic interactions with the Hi-SAFE model.
Communication presented at the first world congress of Agroforestry, Orlando, June
2004.
Graves A., P. Burgess, F. Liagre, J.-Ph. Terreaux, C. Dupraz, 2004, A comparison of
computer-based models of silvoarable economics, 1st World Congress of
Agroforestry, Orlando, Florida, 27 June – 02 July.
Guido Bongi, Pierluigi Paris (2004) Leaflet heterogeneity in Juglans regia: an unadverted bias in assimilation models. International Walnut Congress, Sorrento, Italy.
October 2004
12
ANNEX 2. Economics of silvoarable systems using a
novel approach : the Land Equivalent Ratio based
generator
T, Borrell1, C. Dupraz1 , and F, Liagre2
1
Institut National de la Recherche Agronomique, Montpellier, France
2
Assemblée Permanente des Chambres d’Agriculture, Paris, France
Mars 2005
13
INTRODUCTION
During the fourth year, Thomas Borrell and Fabien Liagre, in collaboration with
Christian Dupraz - INRA, have realised all the technical and economical simulations
for the French context. APCA participated to the redaction of the French chapter for
the deliverable 7.2 - Plot economics of European silvoarable systems report – leaded
by the partner Chavet.
Face to the late we observed in the results we should have received from yield-safe,
it was decided to go on our simulations using the Ler-Safe data to feed Farm-Safe.
All the results of our simulations have been presented to all the Chambers of
Agriculture which have provided the economical data in the 3 regional meetings we
named before.
THE LER APPROACH
LER-biomass and LER-product
The Land Equivalent Ratio indicates the area of monocultures needed to produce as
much as one intercropped hectare (Vandermeer, 1989). It is calculated as the sum of
relative areas (RA), i.e. productions ratios: for each product, the intercrop production
divided by the monoculture production. In most of the agroforestry cases, there are 2
RAs: the crop RA and the tree RA. For instance, a tree RA of 0.7 means that an
agroforestry plot produces as much timber as a forestry plot of 0,7 ha. A LER of 1.3
thus indicates than intercropping produces 30% more than monocropping.
However, it can be calculated either with total biomass or only with commercial
products, particularly in the case of timber production: the higher rate of thinning in
forestry than in agroforestry implies different tree relative yields whether it is
calculated with or without thinned trees.
This distinction leads to two different indicators: the LER-products, calculated with the
commercial products (bole of timber of the felled trees, grain of the cereals, etc.), and
the LER-biomass, calculated with the total biomass produced on the plot (for their
detailed way of calculation, see Dupraz et al., 2005). Although the likely range of
values for the LER-products is still to be defined with experimental plots and models,
we already know that the expected values of LER-biomass are likely to be comprised
between 1 and 1.4. Indeed, a value below 1 is biologically unrealistic considering that
if one of the intercrops dominates too much the other, it shall perform as in a
monoculture plot and thus produce as much biomass of the same area of
monoculture production. A value above 1.4 seems too much optimistic with regards
to present experimental results and bibliographical documentation (Dupraz et al.,
2005).
14
The LER-based generator
For this study, we used a constrained generator of data: forest, arable and agroforest
time-series are generated in accordance with an expected LER-biomass (see Annex
1: Detailed description of the LERbased-Generator).
The tree RA-biomass is defined according to the densities in forestry and
agroforestry and to the expected increase in tree growth rate at low density. The crop
RA-biomass is then deduced in order to reach the predetermined LER-biomass. The
LER is only divided in a crop component and a tree component (timber); it is thus
impossible to generate data-sets for a third component (fruits for example), such as
for a traditional orchard or double purpose walnuts.
The arable and forest reference data and the values of these two RAs permit to
generate all the time-series under constraint :
o the arable time-series is the repetition of the reference yields in accordance
with the rotation;
o the forest time-series is generated in function of the reference volume of
timber per ha at felling;
o the two agroforest times-series (one for the intercrop, one for the trees) are
generated so that the constraint fixed by the RAs is respected (sum of
productions for the intercrop, volume of timber per ha at felling for the trees).
Amongst the hypothesis made in this generator, we assume that :
o the agroforest trees are felled at the same time as the forest trees, but their
higher growth induces bigger individual pieces of timber; In any case, the unit
volume in agroforestry doesn’t exceed 20 % of the forestry volume one.
o there is no difference in the partition of biomass for the intercrop and a
classical arable crop: the crop RA-products is thus equal to the crop RAbiomass, which shall both be called “crop RA”;
o the intercrops cannot offer higher yields than the arable crops without any tree
(consequently the value of the crop RA cannot be superior to the maximum
intercropping area: 1 – the proportion of area occupied by the tree strips); we
made therefore the hypothesis that the trees don’t affect positively the crop
yield which could be discuss on a long term period (soil erosion and fertility,
wind effect, etc.).
o the width of the intercropped alley can be reduced by successive steps when
the yield decreases (less productive areas are given up), in order to preserve
economically acceptable yields as long as possible. When it cannot be
reduced anymore (at a minimum width), the intercrop is suppressed when it is
no more profitable (profitability threshold yield).
15
A wise hypothesis for the agroforest tree growth
In most of the cases, agroforest designs are at lower density than forest designs. As
a consequence, trees grow quicker. We assume that the growth rate increases when
the density decreases, until to reach a critical final density where the genetic potential
is fully expressed. Below this density, we assume that trees don’t grow more, even if
they are completely isolated.
At this critical density, we assume that trees grow at a rate driven by a coefficient: the
individual tree timber volume growth acceleration in low density AF conditions,
or Tree Growth Acceleration (TGA). At critical density, the volume of an agroforest
individual piece of timber at felling, VAF, is thus calculated as:
VmaxAF =TGA x VF
where VF = volume of a forest individual piece of timber at felling
(in the forestry reference data which is used).
VmaxAF is thus the maximum volume of an individual piece of timber.
Unfortunately, the critical densities and the likely range of values for TGA are not well
documented. Thus these parameters had to be fixed by expert knowledge.
In order to realise wise simulations, we assumed a quite low value of TGA: 1,2 for the
three species (Table I).
Species
Final density in
forestry
VF
TGA
Critical
density
VmaxAF
Walnut
100 trees/ha
1
m3/tree
1,2
50 trees/ha
1,2
m3/tree
Wild
cherry
150 trees/ha
0,8
m3/tree
1,2
60 trees/ha
0,96
m3/tree
Poplar
200 trees/ha
1,5
m3/tree
1,2
100 trees/ha
1,8
m3/tree
VF is the volume of timber of an individual forest tree. TGA is
individual Tree timber volume Growth
Acceleration in
agroforestry at densities lower or equal to the critical density. The critical density is the
highest density at which the maximum volume of an individual piece of timber is reached. VmaxAF is the maximum
timber volume of an agroforest tree, reached at densities lower or equal to the critical density.
Table I: reference values in forestry and values of TGA, the critical density and VmaxAF
for each of three tree species
Nevertheless, some unpublished experimental results are in favour of higher values for TGA:
at M. Jollet’s farm (Les Eduts, Charentes Maritimes, France), INRA’s measurements of the
forest and agroforest trees at the middle of the revolution indicate a TGA of 2 for black
walnuts, at 80 trees/ha (Gavaland, pers. com.). But another thinning will soon accelerate the
growth of the forest trees, and then this estimated TGA is likely to decrease.
16
individual piece of timber (m3/ tree)
There is thus an important difference between our hypothesis and what we could expect
(Figure 1).
2
with TGA = 2
with TGA = 1,2
1,5
1
0,5
0
20
40
60
Final density (trees/ha)
80
100
Figure 1: Volume of the individual walnut timber volume in function of the final density
and of the value of TGA.
As an economic consequence of such a wise hypothesis, the volume of timber at felling is
less important, thus the revenue of the tree component might be under-estimated.
Maximum expectable LER in function of species and final density
As the production of agroforest timber is determined in accordance with the densities,
the critical density and TGA, the tree RA-products and the tree RA-biomass are fixed:
it is impossible to tune them without modifying one of these previous parameters.
Then the range of variation of the LER (biomass or products) corresponds to the crop
RA:
•
As a LER-biomass inferior to 1 is biologically unrealistic, the minimum value of
the crop RA is equal to 1 – tree RA-biomass.
•
As we assume lower or equal yields, the maximum value of the crop RA must
be inferior or equal to the maximum intercropping area. In our optimistic
assumptions, at highest densities (tree lines every 10 m), we assumed a crop
RY at ¾ of the maximum intercropping area.
•
A likely value would be the mean of these two extreme values.
As the proportion of land required by the trees strips rises with the density, the
maximum crop RA decreases when the density gets higher. A first conclusion is that
we obtain acceptable RA with densities which correspond to distances between the
tree lines included between 24 to 40 m.
17
1,1
1
0,9
0,8
0,7
LER max
0,6
LER-optimist
LER-medium
0,5
LER-pessimist
0,4
0,3
0,2
0
20
40
60
80
100
No tree
120
140
Distance between the trees lines (m)
Figure 2: Range of values of the crop RA with walnut, wild cherry and poplar, according the
distance of the tree lines and depending on how optimistic the dynamic of the LER is.
In forestry, the realisation of many thinnings means that a lot of biomass is
synthesised in addition of the trees which shall be conserved until the last fall. As we
assume that the volume of the agroforest trees is maximum 20% bigger than the one
of the forest trees, the production of woody biomass is small compared to the one of
a forestry plot. Thus the ratio of woody biomass, i.e. the tree RA-biomass, is low. At
low density, even an optimistic value of the crop RA is insufficient to compensate
such a low tree RA. Consequently, high LER-biomass cannot be reached for all
densities, in particular for species with a high rate of thinning in forestry such as wild
cherry (Figure 3).
However, very satisfactory LER-products can be reached even with these species.
18
1,6
LER - Products
1,4
1,2
Optimist Crop RA
1
Medium Crop RA
Pessimist Crop RA
0,8
0,6
0
20
40
60
80
100
120
140
Tree final density (trees/ha)
Figure 3: Expectable LER-products for walnut, cherry and poplar, depending on how optimistic
the dynamic of the intercrop is
Impact of the TGA on the LER results
In our simulations, we used a TGA of 1.20. We were cautious in our predictions if we
consider some experimental plots (such in Restinclières in France) or private site
(Farm of Claude Jollet in Charente Maritime) where we observed some TGA which
reach 2. If we had taken this value of 2, the tree RA would have increased between
15 to 30 % in comparison with what we obtained with 1.20.
1
0,9
y = 0,4191x + 0,1123
R2 = 0,9995
0,8
120 trees/ha - products
0,7
y = 0,2745x + 0,0955
2
R = 0,9987
TREE RA
0,6
0,5
120 trees/ha - biomass
50 trees/ha - products
y = 0,2655x + 0,0018
R2 = 0,9989
50 trees/ha - biomass
0,4
y = 0,1773x + 0,005
2
R = 0,9965
0,3
0,2
0,1
0
0,8
1
1,2
1,4
1,6
1,8
2
2,2
Tree Growth Acceleration
Figure 4: Influence of the Tree Growth Acceleration on the tree RA (biomass and
products), according to the tree density.
19
DATA REFERENCES AND MAIN HYPOTHESIS
The forestry references
The revolution duration, timber production and production techniques (initial density,
prunings, thinnings, sward maintenance, and final density) were determined by
expert knowledge, in accordance with available documentation.
The densities correspond to the schedule of conditions of the French circular “Forêts
de production” and to forestry organisms’ advises.
density
(trees/ha)
Revolution duration
(years)
Individual
piece of
timber at
felling
(m3/tree)
initial
Walnut
1
200
100
100
Wild cherry
0,8
800
150
Poplar
1,5
200
200
Volume
at felling
(m3/ha) Good
final
land
unit
Mean annual production
(m3/ha/year)
Land
unit
medium
Bad
land
unit
Good
land
unit
Land
Bad
unit
land unit
medium
46
53
60
2,17
1,89
1,67
120
50
55
60
2,40
2,18
2
300
19
22
25
15,79
13,64
12
Table 1: Densities, revolution duration and mean annual and total productions for walnut, wild
cherry and poplar. With walnut, 2 thinnings of 50 trees/ha are realised at 1/3 and 2/3 of the
revolution; with wild cherry, 3 thinnings are realised at 1/3 (400 trees/ha), half (200 trees/ha)
and 2/3 (50 trees/ha) of the revolution.
Supports for afforestation on agricultural land vary in function of the region and of the
tree species: as the poplar revolution is shorter, the Compensation Payment for
Agricultural Loss (PCPR) is available for 7 years instead of 10.
Type of farm
(region)
Hy-Lc
(Centre)
Ly-Lc
(PoitouCharentes)
Hy-Hc
(FrancheComté)
Walnut and wild cherry
Establishment grant (4 first years)
PCPR farmer (10 first years)
50% of the costs 50% of the costs
240 €/ha
300 €/ha
0
0
Poplar
Establishment grant (4 first years)
PCPR farmer (7 first years)
50% of the costs 50% of the costs
240 €/ha
300 €/ha
0
0
Table 2 : Regional supports for afforestation on agricultural land for walnut, wild cherry and
poplar (year 2003). The PCPR is the Compensation Payment for the loss of agricultural income.
Franche-Comté is a particular region. More than 50% of the area are already
woodlands, thus afforestation is not encouraged: there is no support available for
new forestry plantation.
20
Everywhere in France, newly afforested plots benefit from an exemption from land
tax: for 10 years with poplar, 50 years with walnut and wild cherry. In our simulations,
this land tax is comprised between 30 €/ha (Centre) and 39 €/ha (Poitou-Charentes).
Reference data in agriculture
All arable data come from the Farm observatory ROSACE, a tool produced by APCA.
Thanks to this typology of farms made by the regional Chamber of Agriculture,
several types of farms are defined and described, each one corresponding to the
mean of 5 to 10 farms selected by the Chambers experts. Each year, the economical
inputs are re-calculated (yield, net margin, farm costs, labour and CAP payment). In
addition, all the technical orientations and strategies of the farm are also described.
We selected 3 types of farm, which we shall now designate with 4 initials:
•
Hy-Lc: High yields and Low fixed costs
•
Hy-Hc: High yields but High fixed costs
•
Ly-Lc: Low yields and Low fixed costs
For each of them, the ROSACE typology indicates:
•
The cropping area of the farm, distinguishing tenant farming and property;
•
The crop rotation in function of the quality of the soil (up to 3 Land Units:
best, medium, worst);
•
The mean yields, attributed to the medium Land Unit (for the best and
worst Land Units, we respectively assumed an increase and a decrease of
10% of the mean yields);
•
The variable costs, assignable fixed costs and fixed costs and labour.
•
The prices of the products and sub-products (straws of the wheat) and the
CAP payments of the farm Single Farm Payment, SFP).
To elaborate the selection of each type of farms, various partners from the Chambers
of Agriculture have participated: Camille Laborie, who is in charge of ROSACE in
APCA, Anne-Marie Meudre (Franche Comté), Catherine Micheluzzi (PoitouCharentes) and Benoît Tassin (Centre).
21
Hy-Hc
Ly-Lc
Hy-Lc
72
8
56,4 37,6
Total
tenant
farming
property
Cropping area of
the farm (ha)
80
94
97,5 32,5 130
Fixed
Typical
costs
rotation(s)
(€/farm)
36805
28785
40370
(a) wheat
wheat
oilseed
or
(b) wheat
oilseed
wheat
wheat
sunflower
wheat
oilseed
sunflower
(a) wheat
wheat
wheat
wheat
wheat
maize
or
(b) wheat
wheat
oilseed
Gross
Net
Margi
Marg
n
(€/ha)
(€/ha)
Crop
Mean
yield
(t/ha)
wheat
8
(straw
2 t/ha)
983
776
42,0
oilseed
4
918
711
30,0
set
aside
–
323
116
8,0
wheat
6,5
(straw
2 t/ha)
818
566
42,3
oilseed
3,2
674
422
14,1
area
(ha)
Total
Net
Margin
(€/farm)
18045
17153
sunflow
er
2,5
799
547
28,2
set
aside
–
318
66
9,4
wheat
6,7
(straw
2 t/ha)
794
479
87,8
oilseed
3,5
728
413
14,1
maize
7,5
555
240
28,2
set
aside
–
313
-2
9,4
12031
Table 3 : Main economic data and total net margin (€/farm) for every type of farm.
Rotation (a) corresponds to the best land units, rotation (b) to the worst. Set aside is
realised on 10% of the total farm area.
The Net Margin is equal to the Gross Margin minus the fixed assignable costs (land
tax and machinery costs). The Total Net Margin is equal to the Net Margin minus the
fixed costs (rent of land, amortisation and maintenance of the buildings, social
contributions, banking costs). Labour costs are not taken into account.
The profitability threshold yield
With the development of the trees, the crop yield decreases progressively. Below a
certain level, the crop is not more profitable, above al near the tree area. For each
crop of the three types of farm, the threshold yield was first determined according to
22
the price of the product, the CAP payment and the variable costs, assignable fixed
costs and a part of the fixed costs1. As the results, in proportion of the mean yield of
each crop, were roughly the same in the three farms, we fixed this proportion in order
to facilitate the extrapolation to other types of farm.
crop
Mean yield
Profitability
in the farm threshold yield
Mean yield in
Hy-Hc
Profitability threshold
yield in Hy-Hc
Winter wheat
100 %
50 %
6,7 t/ha
3,35 t/ha
Maize
100 %
70 %
7,5 t/ha
5,25 t/ha
Oilseed rape
100 %
60 %
3,5 t/ha
2,1 t/ha
Table 4: Profitability threshold yield in proportion of the mean yield in the farm and
example for the farm Hy-Hc
The threshold yield is the same in every farm, whatever the land unit is. Thus it shall
be reached more quickly in the worst land unit than in the best land unit.
Main management features of the agroforestry systems
For each type of farm, we simulated the introduction of 2 agroforestry designs in the
3 land units (best, medium, and worst):
•
Plantation at 50 trees/ha, on 40 m spaced tree-lines;
•
Plantation at 120 trees/ha, on 22 m spaced tree-lines.
The tree strip is 2 m wide. The width of the intercropped alley is respectively of 38 m
and 20 m, thus the maximum crop area represents 95% of the initial area at 50
trees/ha and 91% at 113 trees/ha.
With walnut and wild cherry, an early thinning is realised when the timber volume
reaches 0,1 m3 (around the years 10-13), therefore the final densities are different
from the poplars’ one (see Table 5).
1
If the crop is abandoned on a part of the cropping area, we assume that the fixed costs
should decrease a little; thus they must be taken into account in the calculation of this
threshold yield.
23
Agroforestry
Density
(trees/ha)
initial
final
50
40
Forestry
Timber
volume
(m3/tree)
Productio
(m3/ha)
1,2
48
Walnut
Wild
cherry
120
80
1,08
86
50
40
0,96
38
Density
(trees/ha)
initial
final
200
100
800
120
80
0,92
74
50
50
1,8
90
Poplar
200
120
113
1,76
150
200
Timber
volume
(m3/tree)
Tree
RAProductio biomass
(m3/ha)
1
0,8
1,5
Tree RAproducts
0,36
0,48
0,66
0,86
0,22
0,32
0,42
0,62
0,3
0,3
0,66
0,66
100
120
300
199
Table 5: Initial and final densities, volume of an individual piece of timber and
production in forestry and in the simulated agroforestry systems; tree Relative Area
(RA)-biomass and tree RA-products
The crops Relative Areas (RA) have been fixed for 3 hypothesis: optimistic, probable
and pessimistic.
The pessimistic hypothesis means that the LER-biomass is equal to 1. Therefore, the
crop RA is equal to: (1 – tree RA-biomass).
The optimistic crop RA is determined according to 2 constraints:
•
The crop RA must be inferior to the maximum intercropping area
•
We also assumed to fix a ceiling for the LER-biomass of 1.4. Thus the crop RA
is equal to: (1.4 – tree RA-biomass). This ceiling of 1.4 was reached with
walnut and poplar at 120 trees/ha, so the crop RA seems quite low with
regards to the maximum intercropping area.
We assumed a probable crop RA as the arithmetic average of the 2 previous values
(pessimistic and optimistic) (see Table 6 and Table 7).
24
Initial
density
(trees/ha)
Width
between
tree lines
(m)
Width of the
intercropped
alley (m)
Maximum
intercropping
area
Pessimistic
crop RA
Probable
crop RA
Optimistic
crop RA
50
40
38
0,95
0,64
0,79
0,94
120
22
20
0,91
0,34
0,54
0,74
50
40
38
0,95
0,78
0,85
0,93
120
22
20
0,91
0,57
0,72
0,87
50
40
38
0,95
0,7
0,8
0,9
120
22
20
0,91
0,34
0,54
0,74
Walnut
Wild
cherry
Poplar
Table 6: Crop RA in function of the tree species, density and optimism level. Bold
values are those which depend on the ceiling of 1.4 for the LER-biomass.
Width
Width of LER-biomass reached with LER-products reached with
the
the
Initial
between
the
density
tree
intercrop
(trees/ha)
lines
ped alley Pessim. probabl Optimist Pessim. probabl Optimist
(m)
(m)
crop RA crop RA crop RA crop RA crop RA crop RA
Walnut
Wild
cherry
Poplar
50
40
38
1
1,15
1,3
1,12
1,27
1,42
120
22
20
1
1,2
1,4
1,2
1,4
1,6
50
40
38
1
1,07
1,15
1,1
1,17
1,25
120
22
20
1
1,15
1,3
1,19
1,34
1,49
50
40
38
1
1,1
1,2
1
1,1
1,2
120
22
20
1
1,2
1,4
1
1,2
1,4
Table 7 : LER-biomass and LER-products in function of the tree species, density and
hypothesis of optimism for the intercrop bold values are those which depend on the
ceiling of 1.4 for the LER-biomass.
Economic hypothesis
CAP payments
In agriculture, the crops area benefits from the Single Farm Payment (SFP): it was
calculated on the basis of the historical references of each farm, in accordance with
the way France decided to implement the new CAP in 2006.
In the basic scenario, we assumed that the intercrops are eligible to the SFP
proportionally to the area of the plot that they occupy. It is the present situation in
France. The rights corresponding to the tree area could be transferable to another
25
eligible area which doesn’t benefit from a payment right. In our simulations, we did
not attribute them to new plots, considering therefore that these rights were lost for
the farmer.
Tree grants
In our basic scenario, agroforest trees benefit from the same establishment payments
as the forest trees: 50% of the costs of the 4 first years in Poitou-Charentes and
Centre. It corresponds to the present situation, permitted by the circular “Forêts de
protection” which relies on the line i of the French National Rural Development
Programme. However an agroforest plot cannot benefit from neither the PCPR nor
the exemption of land tax.
In France, an agro-environmental measure called “agroforest habitats” can be
contracted under certain conditions, but it still faces administrative difficulties and is
not available in most of the departments, thus it was not taken into account in our
simulations.
Costs and prices
Some key points have to be underlined:
•
The cost of sward maintenance is higher in forestry than in agroforestry. In
forestry, at the beginning of the revolution, sward maintenance is realised
thanks to two grindings instead of one for the maintenance of the tree strip in
agroforestry.
•
The farmer makes all operations himself, except the marking out and
plantation of the young trees. Both of these operations are charged 15 €/h.
The timber prices correspond to standing trees, thus neither the harvesting
cost is taken into account.
•
In a cash flow approach, the basic scenario doesn’t include the labour cost for
the farmer. While in a farming management scenario, we consider an hourly
cost of 7,62 €/h (minimum salary in France). In this last approach, it’s therefore
possible to evaluate the efficiency of the farmer labour.
As it seems impossible to anticipate the future evolution of prices and costs, we
assumed constant values. For instance, a rise or a drop of timber value would
respectively increase or decrease the tree revenue.
26
MAIN RESULTS
Labour impact for one silvoarable hectare
temps de travail
(h/ha/an)
temps de travail
(h/ha/an)
16
16
agriculture
cultures intercalaires
arbres
14
12
agriculture
cultures intercalaires
arbres
14
12
10
10
8
8
6
6
4
4
2
2
0
0
1
6
11
16
21
26
année
31
36
41
46
Case 1: Plantation of 120 trees/ha
1
6
11
16
21
26
31
36
41
46
année
Case 2: Plantation of 50 trees/ha
Figure 5: Labour evolution in the management of a silvoarable plot during the tree
rotation, separating the crop from the tree labour.
An essential condition for adopting agroforestry from the farmers’ point of view is that
they don’t want to devote more time to a new system. If the farmer planted more
trees (case 1), he would need 1 to 1.5 days each year to maintain the trees. But in
the second half of the rotation, the labour decreases progressively due to the fact
that trees don’t need more special maintenance and that the intercrop activity is
reduced. If he plants fewer trees, the impact during the first years is poor. With the
small density, the intercrop activity is longer, because the crop yield is not so affected
by the trees. The labour requested in the second half of the rotation is therefore lower
but very near from the initial scenario.
Prediction of yield evolution
Crop yield evolution
Predicting the crop yield during the second half of the rotation is a perilous venture. If
we know the behaviour of the intercrop during the first half thanks to experimental
measures on existing plots, we asked the bio-economics model to predict the yield
evolution. In our simulation, as we said, we used the LER-Safe prediction. We made
the essential hypothesis that the LER must be include between 1 and 1.4. This
condition helps us to determine a possible range of crop yield evolution, from the
pessimist one to the optimist one (see Figure 6).
27
Tree plantation
Tree Harvest
100
100
Crop yield (%)
Optimist
80
80
60
Pessimist
40
Intercrop Yield
20
Pure Crop
0
60
40
20
0
0
Time
Figure 6: Evolution of the relative intercrop yield according to optimist or pessimist
view about the tree competition. Case of one ha of wild cherry with an initial density of
120 trees/ha for a final density of 80 trees/ha.
In this example of a plantation of wild cherry at 80 trees/ha (final density), which
means a distance between the trees rows of 25m, the crop yield represent more than
90% during the first half of the tree rotation. According to the interaction level, the
crop yield varies between 30 and 75 % of the pure crop yield of reference the year
before harvesting the trees.
The crop yield depends on different parameters:
•
The parameters due to some initial choices: the crop nature (a sunflower will
be more affected by the shadow of the adult trees than a cereal), the density
of the plantation and the distance between the lines, choice of the land unit (a
deeper soil will be more adapted),...
•
The parameters depending on the capacity of the farmer: well pruned trees,
tree root maintenance (root cutting), …
In our economical scenarios, we have tested the different level of interaction.
Tree yield evolution
As for the crop yield estimation, we put forward the hypothesis of different level of
timber productivity. But for our simulations, we only use one prediction of timber
production. To validate our approach, we use a very cautious estimation of
production (see Figure 7). Our results can therefore be considered as the minimum
result we can get from our hypothesis.
28
140
140
Interval
120
100
Basic
scenario
80
Pure
plantation
100
Optimist
80
60
60
40
40
Pessimist
20
20
0
Standing volume (m3/ ha)
120
0
5
10
15
20
25
30
35
40
45
Time
Figure 7: Range of timber volume evolution for an initial plantation of 120 wild cherry.
The figure indicates of the cautious hypothesis of standing volume we used for our
simulations (77 m3 for 80 final trees).
Cash flow impact
To evaluate the impact of the project on the cash flow, we must distinguish first the
investment cost and then the evolution of the annual cash flow depending of the crop
yield evolution and the possible over cost to crop between the trees in comparison
with a pure crop system.
Initial investment
The poor number of trees to plant in an agroforestry system reduces considerably the
investment cost if we compare with a current afforestation cost on agricultural land.
The tree cost is nonetheless higher. The owner will choose a better quality of the
trees and will have to protect each one with a strong protection: each tree has a
possible future value and demands a special attention.
The total cost of a plantation (without subsidy) varies between 500 and 1000
euros/ha according to the tree specie (the walnut plantation being the most
expensive). This cost represents between 20 to 60 % of the average cost in the case
of common land afforestation (see Figure 8).
Afforestation
1 233 €/ha
695 €/ha
Poplar
367 €/ha
120 trees/ha
50 trees/ha
1 518 €/ha
469 €/ha
Wild Cherry
267 €/ha
1 633 €/ha
1 034 €/ha
Walnut
517 €/ha
29
Figure 8: Comparison of the investment in agroforestry and forestry scenario,
WITHOUT subsidy.
In France, it’s current to get a subsidy of 40 to 70% to cover the investment cost and
the maintenance cost of the trees during the 4 first years (except in Franche Comté).
Since 2004, the French Government decided to suspend all economic aids to the
land afforestation, excepted for agroforestry. In our simulations, we decided to
conserve this aid, to be able to compare between the two options (see Figure 9).
Afforestation
617 €/ha
120 trees/ha
348 €/ha
Poplar
184 €/ha
50 trees/ha
759 €/ha
Wild Cherry
235 €/ha
134 €/ha
817 €/ha
517 €/ha
Walnut
259 €/ha
Figure 9: Comparison of the investment in agroforestry and forestry scenario, WITH
subsidy.
Cash flow evolution
Evolution of the cash flow at the plot scale
The cash flow evolution will depend of the crop yield evolution and the LER level we
have selected and the final density. For example, in the Figure 10, we’ve illustrated
the cash evolution for two different densities but for a medium LER level.
100
100
50 trees/ha
90
90
80 to 90 %
% Annual Gross Margin
80
80
70 to 85 %
70
60
70
120 trees/ha
50
30 to 60 %
40
10
30
Silvoarable Gross Margin
Agricultural Gross Margin
0
Plantation
50
40
30
20
60
20
10
0
Time
Trees Harvesting
30
Figure 10: Evolution of the annual cash flow for a probable scenario with wild cherry
(LER=1.07 for a density of 50 trees/ha and 1.15 for a density of 120 trees).
Being cautious in our forecast, we notice nonetheless that at half of the rotation, the
gross margin still represent 80 to 90 % of the agricultural gross margin. We must
underline that in our simulations, we’ve considered that the crop payment area is
reduced progressively by the tree area. In case of the silvoarable area was eligible in
its totality, the impact on the cash would be sensible, above all in some regions
where man get poor crop yield and where the crop payment is essential in the gross
margin calculation (Franche Comté for example).
Let’s also underline the fact that in the INRA experimental plots, the LER reaches
more 1.3 than 1.15 that we have chosen in our simulation with an initial density of
120 trees/ha.
Influence of the CAP payment policy
Inside the first pillar policy, the situation of the agroforestry plots could be different
depending of each country member. In fact, at a European level, the agroforestry plot
could be eligible to the Compensatory Payment. We compare here the possibility to
get the payment on the whole area (Request of the Safe consortium) or only on the
intercrop area (French situation).
The impact of the eligibility given to the whole surface on the profitability is not so
important. In all our simulations, the profitability increases by 3% in the best option for
agroforestry. The impact is more at a cash flow level, when the crop gross margin is
low. That’s typically the case for the farms where:
•
The crop component is lower than the payment component in the gross
margin calculation (Mediterranean area or farm with high cost of production)
•
The yield is decreasing faster in the silvoarable scenario (high density of
plantation or strong impact of the trees on the crop RA) (see Figure 11)
% of the Arable Gross Margin
100%
80%
60%
agriculture
Payment on intercrop area - 50 trees/ha
Payment 100% - 50 trees/ha
Payment on intercrop area - 120 trees/ha
Payment 100% - 120 trees/ha
40%
20%
0%
2%
12%
22%
32%
42%
52%
62%
72%
82%
92%
Time (Tree rotation)
Figure 11: Influence of the different CAP payment policies in agroforestry on the
annual cash flow evolution.
31
Evolution of the cash flow at the farm scale
At the farm scale, one of the first questions of the farmer is about the importance of
the area to plant. Does he have to plant on a big area? In several plots or in a single
plot? There is no only one answer. According to the strategy of the farmer, a large
range of scenarios is available. The choice will depend to the cash flow context and
to know if the farmer can support a strong investment or not, and above all if he aims
to decrease progressively his crop activity or not. The labour availability is also a
strong parameter to decide which area to plant. According to our simulation and
experimental experience, we often recommend not planting more than 10 % of the
cropping area. In that case, the impact on the farm gross margin is less than 3 % in
average on the first half of the tree rotation. A gradual plantation will allow a reduction
of the cash flow impact (see Figure 12).
175
191% 183%
178% 180%
% of Farm Gross Margin without AF
% of Farm Gross Margin without AF
435 %
Farm with 8% silvoarable area
Farm with 100 % of cropping area
150
125
100
75
50
Farm with 8% silvoarable area
175
Farm with 100 % of cropping area
150
125
100
75
50
0%
20%
40%
60%
80%
100%
120%
0%
20%
40%
% of the tree rotation
a. Case of a single plantation
60%
80%
100%
120%
% of the first tree rotation
b. Case of a gradual plantation
Figure 12: Comparison of the cash flow evolution when the farmer plants 8 % of his
cropping area (50/50 Walnut/Wild cherry). We compare the option where the farmer
would plant the silvoarable area in once time or if he decides to plant 2 % every 5
years during 20 years.
A gradual plantation will also allow a soft distribution of the timber income in the time
from the moment where the owner begins to harvest the first mature trees (case b).
From this moment, the timber income is regular. In our example, he can harvest the
trees every 5 years. In this context, the farm gross margin increase by 15 %.
According to the importance of the plantation and of the species he planted, a farmer
could increase his farm income between 10 to 100%. Of course, it can suppose a
long term to wait for the farmer before the first tree harvest…
Profitability of a silvoarable investment
Comparing a silvoarable scenario with agricultural scenario
For our simulations, we have selected 3 kinds of farms:
•
Farm with good crop yields and few fixed costs.
•
Farm with medium crop yields with few fixed costs.
•
Farm with medium crop yields and high fixed costs.
32
For each farm, corresponding to each region of the LTS of the WP8, we have run
different scenarios according to:
•
the tree density: 120 versus 50 for the initial density which corresponds to a
final density of 80/40.
•
the LER level: optimist, probable and pessimist
•
the land unit: good/medium
•
the 3 tree species: poplar, walnut and wild cherry
108 scenarios have been run in total (36 scenarios / LTS). The Figure 13 shows a
synthesis of the Agricultural Values for all these scenarios we have calculated for
each specie according to the level of LER.
% of realised simulations
100%
Walnut
Wild Cherry
Poplar
Agricultural
Value Index
80%
> 1,35
60%
1,20 - 1,35
1,05 - 1,20
40%
0,95 - 1,04
< 0,95
20%
op
tim
i
pr st
ob
ab
le
pe
ss
im
ist
op
tim
i
pr st
ob
ab
le
pe
ss
im
ist
op
tim
i
pr st
ob
ab
le
pe
ss
im
ist
0%
Scenario for intercrop productivity
Figure 13: Profitability of the silvoarable scenarios according to the tree specie and
the LER level.
A first interesting result is that the silvoarable scenarios are at least as profitable as
the agricultural scenario.
Walnut timber is actually the most expensive timber on the market. For a same
duration of rotation, the best results have been logically obtained with the walnut than
the wild cherry. The period of harvesting time is a key parameter in the profitability
calculation (see Figure 14).
33
2,00
1,80
1,60
1,40
1,20
1,00
0,80
0,60
0,40
0,20
0,00
formé
Très
Very
wellbien
pruned Bien
Well pruned
formé
(50 ans)
40 years
50 years
(40 ans)
Mal
formé
Badly
pruned
(60 ans)
60 years
Figure 14: Influence of the maintenance quality on the profitability.
A late in the pruning dates can put the harvesting date back by 10 or 20 years, above
all for some sensitive specie such as the hybrid walnut. In this example, a late of 20
years means a reduction of 60% of the profitability in comparison of the agricultural
profitability.
Influence of the TGA on the Agricultural Value
The value of the Tree Growth Acceleration has a strong impact on the profitability of
the silvoarable scenarios. This impact is stronger for the scenario with higher
densities of plantation. In the following figure, we noticed that the scenario with a
density of 120 ha react much quicker than a scenario with 50 trees.
Again, in our simulations, we used a TGA of 1,20 which could be considered as a
pessimist approach with what we observe in the reality. For example, in the Jollet's
case, the agricultural value would have been increased by 10 to 15 % (see Figure
15).
34
Jollet TGA
Indice of Agricultural Value
1,10
1,05
Hypothesis simulation
1,00
120 wild cherry/ha
0,95
50 wild cherry/ha
0,90
0,8
1
1,2
1,4
1,6
1,8
2
Tree Growth Acceleration
Figure 15: Influence of the TGA on the Agricultural Value
Which density to plant to get the best profitability?
A common question from the farmers is about the number of trees to plant. The
farmers often want to maintain a correct crop yield during the whole rotation but trying
in the same time to get the best investment for timber. Other decides to plant more
trees with the aim to decrease the agricultural activity, even till to suppress the
intercrop. We didn’t take this case in this study.
For each specie, Walnut, Wild cherry and Poplar, according to our production
hypothesis, we simulated the impact of the density to the LER but also to the
Agricultural Value (see Figure 16).
1,6
1,4
LER_optimist
LER_medium
1,2
LER_pessimist
Val-agri_optimist
1
Val-agri_medium
Val-agri_pessimist
0,8
0,6
0
20
40
60
80
100
120
140
WILD CHERRY - Finale Density (trees/ha)
35
1,6
1,4
LER_optimist
LER_medium
1,2
LER_pessimist
Val-agri_optimist
1
Val-agri_medium
Val-agri_pessimist
0,8
0,6
0
20
40
60
80
100
WALNUT - Final Density (trees/ha)
1,6
1,4
LER_optimist
LER_medium
1,2
LER_pessimist
1
Val-agri_optimist
Val-agri_medium
0,8
Val-agri_pessimist
0,6
0,4
0
50
100
150
200
POPLAR - Final density (trees/ha)
Figure 16: Influence of the tree density on the LER value and the Agricultural value for
wild cherry, walnut and poplar.
We observe that for each specie, the best density to get the optimum LER is higher
than the best density to get the optimum Agricultural Value. For the species with a
poor Tree RA (Walnut and wild cherry), the range of density are similar (see Table
8). The best density would vary between 80 to 120 trees/ha to get the highest LER,
while the farmer will get the best profitability with a density included between 60 and
90 trees/ha. Of course, with a higher TGA, this range would increase.
Result
Wild Cherry
Walnut
Poplar
LER
80 - 120
80 - 120
130 - 200
Agricultural Value
60 - 90
60 - 90
100 - 130
Table 8: Range of density to get the optimum LER and Agricultural Value results for
each specie (trees/ha – final density).
36
For the poplar, the optimum densities are higher than for the 2 others species. This
result is due to the fact that the biomass produced by the silvoarable poplar is similar
to the biomass produced by the forestry poplar. The Tree RA is therefore higher for a
given density compared to other species which demand more important fellings.
What could influence these results? As we already said, the TGA level could strongly
influence these results, giving priority to higher densities. The policy schedule and the
price level of the crop and tree component will be therefore the most important
parameters. In the case of the walnut, the choice of a density of 75 trees/ha is a wise
option. Below, the farm doesn’t want to take any risk at a long term period, above he
bets more on the trees.
Comparing a silvoarable scenario with a forestry scenario
We compare also the case where the farmer was hesitating between a forestry
investment rather than a silvoarable investment from a profitability point of view (see
Figure 17).
Agricultural Value Index
1,50
of a silvoarable scenario
of a pure plantation scenario
1,00
1,55
1,21
0,50
1,04
1,00
0,89
0,48
0,00
Poplar
Walnut
Wild Cherry
Figure 17: Comparison of the profitability of the silvoarable and afforestation scenario
with the agricultural scenario. Silvoarable plantation of 120 wild cherry by ha
characterized by a LER of 1,15.
In this example, we explore the case of a probable LER of 1.15 in the silvoarable
option. In almost all our simulations, the silvoarable options are more profitable than
the forestry option. The forestry option may be more profitable in the case where the
crop margin is very poor, above all if it’s possible to plant some valuable species
such as walnut for example.
It’s also interesting to notice that for the poplar, the silvoarable option could be a
possibility to stimulate the poplar market. In France, the poplar area is currently
decreasing because of the price fall of the timber (less than 45 €/m3). Agroforestry
could therefore be a possible strategy to reduce the market risks.
37
Property holdings evaluation in agroforestry
According to his age, a land owner who plants trees, will not necessary benefit from
the harvest… But, as a farmer told us, a farmer has three possibilities of income: the
sale of his products, the stock variation and the possibility to make a capital gain. In
this last option, a silvoarable plot is a capital which could be evaluated if necessary
(inheritance, expropriation, etc). The land evaluation in agroforestry is the
combination of the agricultural land evaluation and the future value of the trees (see
Figure 18).
16 000 €
No commercial value
with commercial value
14 000 €
Euros by ha
12 000 €
10 000 €
8 000 €
6 000 €
4 000 €
2 000 €
0€
10
20
30
40
Age of the trees (years)
Agriculture
agroforestry
Figure 18: Evolution of the monetary value of the silvoarable land according to the age
of the trees. In agroforestry, this value is the sum of the agricultural value plus the
timber future value. If the young trees could have a future value, for example at 10
years old, they don’t necessary have a commercial value in the sense that the
landowner can not expect some income if he cut them.
In this example of a wild cherry plantation, the capital evaluation may represent
between twice and four time the agricultural land value according to the age of the
trees. In the case of a walnut plantation, it may represent till 7 times this value 10
years before the tree harvesting.
38
Photo 1: In this plot of 4 ha, the wild cherries are 30 years old. The value of the
standing volume is estimated to 4 000 €/ha, which represents the same value of the
agricultural land. But the future value of this plantation is much higher and overpass
the 10 000 €/ha.
Main conclusions
To invest in agroforestry represents a light investment in money and labour
comparing with some new systems of diversification. In our simulations, the
profitability reaches 10 to 50 % with walnut, and -5 to +15 % with wild cherry and
poplar, comparing with the agricultural scenario.
A regular calendar of plantation on a few surfaces is a good option for the farmer
(labour and cash flow impact). 10 % represents between 2 and 3 % of reduction of
the farm gross margin. But in the balanced period, the income increases by more
than 15% (mixed plantation of walnut and wild cherry trees). The gross margin could
double if the farmer plants progressively his whole cropping area. But in that case, it
means a stronger impact on the initial cash flow and demands a consequent labour...
If the best bio-physical option is to plant between 80 to 120 trees by hectare (130 to
200 for the poplars), the best economical option is to plant a lower density around 60
to 90 trees by hectare (100 to 130 for the poplar). This means a distance between
the trees lines varying between 24 to 36 m.
All our simulations haven’t taken into account the environmental benefits such the
carbon sequestration, or the impact on the nitrogen pollution. These aspects could be
calculated and to be summed to the whole profitability of the silvoarable systems.
BIBLIOGRAPHY
Borrell, T. (2004) De l’importance des interactions arbres-cultures sur les performances
économiques de l’agroforesterie tempérée. Mémoire de Diplôme d’Agronomie
Approfondie, ENSAM-INRA, Montpellier. 98 p + annexes
Boulet-Gercourt, B. (1997) Le merisier. IDF, 2ème édition. 128 pp.
39
Coulon F, Dupraz C., Liagre F., Pointereau P. (2000) Etude des pratiques agroforestières
associant des arbres fruitiers de haute tige à des cultures et pâtures, Rapport au
ministère de l’environnement, 199 p, Solagro/INRA, Fr
CRPF (1997) Boiser une Terre Agricole. 28 pp.
Dupraz C., Lagacherie M., Liagre F., Boutland A., (1995). Perspectives de diversification des
exploitations agricoles de la région Midi-Pyrénées par l’agroforesterie. Rapport de fin
d’étude commandité par le Conseil Régional Midi-Pyrénées, Inra-lepse éditeur,
Montpellier, 253 pp.
Dupraz C., Lagacherie M., Liagre F., Cabannes B., (1996). Des systèmes agroforestiers pour
le Languedoc-Roussillon. Impact sur les exploitations agricoles et aspects
environnementaux. Inra-Lepse éditeur, Montpellier, 418 pp.
Dupraz, C., Liagre, F. & Borrell, T. (2005) The Land Equivalent Ratio of a silvoarable
agroforestry system. In preparation.
Graves, A.R., Burgess, P.J., Liagre, F., Dupraz, C. & Terreaux, J.-P. (in preparation) The
developmentof an economic model of arable, agroforestry and forestry systems. To be
published soon in Agroforestry Systems.
IDF (1997) Les noyer à bois. 3ème édition, Février 1997. 132 pp.
Liagre F., (1993). Les pratiques de cultures intercalaires dans la noyeraie fruitière du
Dauphiné. Mémoire de Mastère en Sciences Forestières, ENGREF, Montpellier, 80 pp
Segouin O., Valadon A., (1997) Enquête sur les boisements récents de peupliers en Lot-etGaronne, Analyse de pratiques agroforestières ; les cultures intercalaires. Cemagref,
Nogent-sur Vernisson, 45 pp.
Souleres, G. (1992) les milieux de la populiculture, IDF, 310 pp.
Terreaux, J.-P. & Chavet, M. (2002) Problèmes économiques liés à l’agroforesterie :
éléments qualitatifs et quantitatifs. Silvoarable Agroforestry For Europe (SAFE) ;
Cabinet Michel Chavet, Paris – UMR Lameta, Montpellier.
Vandermeer, J. (1989) The Ecology of Intercropping, Cambridge University Press, 225 pp.
ANNEX
Annex 1: Detailed description of the LERbased-Generator
Principle
Farm-SAFE does not have any biophysical module, the time-series must be
generated independently: pure crop and intercrop yields, timber production in forestry
and agroforestry. We used a generator constrained by the LER-biomass: depending
on a previously fixed value and on a quite low number of parameters, these timesseries are produced. The starting and final points are known, the evolution between
them is drawn thanks to a logistic equation.
A key characteristic is that the climatic variability is not taken into account. It would
have necessitate to define the impact of variables (temperature, water, light, etc...)
40
which are not implicated in this type of constrained prediction. Nevertheless, we
assume that except in very particular cases, this variability does not have any impact
on the economic results: on a whole revolution (20 to 60 years), “bad weather” years
are compensated by “good weather” ones, as we are not interested in year-by-year
results but final profitability and global evolution of financial results. Because of
discounting, climate would only have a strong effect if “bad weather” years were
concentrated in a specific part of the revolution, which is very unlikely to happen.
Notations
We use the words “forestry” and “forest trees” for all types of pure trees plantation,
even when the initial density is very low, such as for walnut.
A distinction is made, in forestry and agroforestry, between the trees which are cut at
thinnings and the trees which are maintained until last felling: the firsts are called
“thinned trees”, the others “felled trees”.
We call “timber” the bole of the tree, which has the highest commercial value. The
same word is used for the thinned trees, even if the bole is often too small to be sold
as good timber.
VF is the individual forest tree timber volume at forestry reference density.
VAF is the individual agroforest tree timber volume, depending on the density.
VmaxAF is the maximum individual agroforest tree timber volume.
Parameters
•
Parameters per tree species
-
DC, the critical agroforestry density, i.e. the density at which the tree
growth potential is attained: the individual agroforest tree timber volume is
equal to VmaxAF, the trees cannot be bigger, even at lower densities.
-
TGA, the coefficient of individual Tree timber volume Growth
Acceleration in low density AF conditions, or Tree Growth Acceleration; e.g.
1,2 indicates that the individual agroforest tree timber volume at a lower or
equal density than DC will be 20 % bigger than the one of a forest tree, due to
the positive impact of both low density and intercropping (exceeds of
nitrogen, less competition than the perennial vegetation classically
established between forest trees, etc…).
-
Timber To Biomass in forestry, e.g. the timber contribution to the total
biomass of a young forest tree (TTByoung-F) and of a felled forest tree (TTBfellF);
-
Timber To Biomass of a felled agroforest tree (TTBfell-AF);
maximum value for the forestry ratio: biomass of all the thinned
trees/biomass of all the felled trees;
41
•
Parameters of the logistic curves
-
curvature and inflexion for the individual forest tree timber volume, for
the individual agroforest tree timber volume;
-
curvature and inflexion for the height of the forest trees, of the
agroforest trees;
•
individual tree timber volume of the agroforest tree at thinning.
curvature for the intercrop yields.
Parameters used only as Farm-SAFE entries2
-
final tree height (same in forestry and agroforestry);
-
maximum bole height (same in forestry and agroforestry);
-
fixed value of the ratio: firewood volume/timber volume in forestry, in
agroforestry.
Entries
-
arable rotation, reference yield and threshold yield for profitability for
every crop;
tree species, revolution duration (60 years maximum);
-
forestry: reference production, initial density, years of thinnings
(maximum 5) and numbers of thinned trees;
-
agroforestry: initial density, number of trees cut in the unique thinning,
plot design (distance between tree lines, initial width of the intercropped alley,
width of the intercropped alley reduction step);
-
LER-biomass aimed.
The generation of data-sets
•
The first step is the generation of the time-series of the monocropping
systems:
The time-series of pure agricultural yields are produced simply by repeating the
reference yields as many times as necessary to last the duration of the revolution.
The time series of the timber production of the felled trees are generated with the
following logistic equation:
2
These three parameters are not used in the generation of tree production data-sets
(timber), but they are needed as entries for Farm-SAFE (tree height and production of
firewood).
42
Yt =
Y
initial
−Y
final
courbure

 

t

1 +  T inf lexion 


 
where
+Y
final
Yt is the value of Y at year ;
Y initial and Y final are the initial and final values of Y;
T inflexion is the inflexion date (end of linear growth).
Y initial is equal to zero and Y final to any value, as the curve is then distended in order
to go through a point {X’;Y’}: X’ is the date of fell of the trees and Y’ is the reference
production (in m3/ha of timber at felling).
For forestry, 3 other time series are generated :
-
The timber production of the thinned trees3, as it is considered that they
can be smaller than the felled trees. The same logistic equation is used, with
an Y’ calculated in function of 2 constraints:
(i)
(ii)
-
thinned tree timber volume ≤ felled tree timber volume.
the parameter “maximum value for the ratio: biomass of
all the thinned trees/biomass of all the felled trees”
The biomass of both the felled and the thinned trees, thanks to the ratio
Timber To Biomass. We assume that if TTBF may vary with time t, it is a
linear variation:
TTB F (t ) =
TTB fell − F − TTByoung − F
× t + TTByoung − F
T
where T is the revolution duration.
The biomass of the felled trees and of the thinned trees is thus calculated by dividing
their respective timber time series by TTBF(t).
•
The Relative Areas calculated in function of the Tree Growth Acceleration:
The coefficient of individual Tree timber volume Growth Acceleration in low density
AF conditions, or Tree Growth Acceleration (TGA), permits the calculation of the
agroforest tree timber volume:
- At D ≤ DC, VAF = VmaxAF
- At D = DF, VAF = VF
3
There is only one time series for all the thinnings: late-thinned trees have the same rate of
growth as early-thinned trees.
43
- D > DF is not possible
- At DC < D < DF, VAF = VF + D ×
V max AF −VF
VF − V max AF
− DF ×
DF − DC
DF − DC
Where: D, DC and DF are the actual density, the critical density and the
forest density
VAF, VF and VmaxAF are the actual agroforest tree timber volume, the
forest tree timber volume and the maximum agroforest tree timber
volume, with VmaxAF = TGA x VF
On the basis of the final densities in forestry and agroforestry, the forestry RAproducts can then be deduced.
With TTBfell-AF, we easily know the agroforest trees biomass at f elling.
With regards to the low initial density in agroforestry, we assume that the thinned
trees grow as well as the felled trees: the thinning is early enough to avoid a strong
effect of competition, thus the individual thinned tree timber volume is the same as
the one of a felled tree at that time. And as the number of thinned trees is low and the
thinning quite early, the volume of thinned biomass is poor enough to permit us to
consider a fixed TTBAF in time. Thus the volume of thinned biomass in agroforestry is
calculated by dividing the thinned timber production by TTBfell-AF.
The forestry RA-biomass can then be calculated.
The arable RA-biomass is deduced in function of the aimed LER-biomass. It is equal
to the arable RA-products, as we assume that the proportion of grain in the biomass
of the crop is the same in agriculture and in agroforestry.
•
The generation of the agroforestry data-sets:
The agroforestry timber time-series are generated with the same logistic equation: Y’
is then the forestry reference production multiplied by the forestry RA-products.
Until the thinning, the volume of timber of the thinned trees is taken into account
simply by adding the equivalent number of trees with the same individual tree timber
volume.
The intercrop time-series are also generated with this logistic equation, with an Yfinal
equal to 0: one time-series per crop of the rotation (maize, wheat, oilseed, etc...). For
each crop, the curve is adjusted in function of the threshold yield and the width of the
intercropped alley reduction step: as the yield per total ha decreases with time due to
tree growth and increasing light competition, we assume that the cropped area is
reduced by successive steps (see Figure 19). A reduction of the width of the
intercropped alley happens every time the yield per cropped ha passes under an
economically defined threshold. The last reduction corresponds to the suppression of
the intercrop.
44
w
Figure 19: chronological schema of the intercropped area in the alley in function of
tree growth.
The width of the intercropped strip (w) is reduced at t1 and t2 in order to increase the mean
yield per cropped ha. Its next reduction at t3 corresponds to the suppression of the intercrop.
In the generator, up to 6 reductions can be made.
The intercrop curves are adjusted by modifying their inflexion date so that the sum of
the intercrop productions is equal to the crop reference yield multiplied by the arable
RA-products.
The intercrop time-series are then mixed according to the arable rotation to obtain a
single time-series.
•
The same tree height curve time series in forestry and agroforestry
A last time-series is generated for both forest and agroforest trees : their height
growth. It is not used in the timber volume calculation, but this time-series is needed
in Farm-SAFE for its “autoprune” function.
We use the Boltzmann logistic equation :
Y(t) =
Yinitial − Yfinal
 t −Tinf lexion 
1+ e  curvature 
+ Yfinal − Y(t = 0)
As for the timber time-series, Y initial is equal to zero and Y final to any value, as the
curve is distended in order to go through a point {X’;Y’} : X’ is the date of fell of the
trees and Y’ is the aimed height.
45
Annex 2: Labour, revenues and costs in the 3 types of farms
Hy-Hc
Ly-Lc
Hy-Lc
Crop
Annual labour
(h/ha)
Price
(€/t)
Single
Farm Variable
Payment
costs
(€/ha)
(€/ha)
Assignabl
e
fixed
costs
(€/ha)
wheat
6,
110
(straw 30 €/t)
343
300
207
oilseed
5,5
215
360
302
207
set aside
1,5
–
338
15
207
wheat
6
110
(straw 30 €/t)
345
302
252
oilseed
5,5
220
361
391
252
sunflower
5,5
280
361
262
252
set aside
1,5
–
345
27
252
wheat
7
102,10
(straw 30 €/t)
328
278
315
oilseed
5,5
220
348
390
315
maize
7
85,4
348
434
315
set aside
1,5
–
328
15
315
46
Annex 3: Economic data relative to monocropped or intercropped walnut, wild
cherry and poplar in the 3 farms
Tree timber standing value
Standing value (€/m3)
Tree timber volume
(m3/tree)
Walnut
Wild cherry
Poplar
thinned trees
felled trees
thinned trees
felled trees
felled trees
0.03
0
0
0
0
0
0.04
10
10
10
10
7
0.05
10
10
10
10
7
0.06
10
10
10
10
7
0.07
10
20
10
10
7
0.08
10
20
10
10
7
0.09
10
30
10
10
7
0.1
10
40
10
10
7
0.11
10
60
10
10
7
0.12
10
80
10
10
8
0.13
10
100
10
10
8
0.14
10
126
10
10
8
0.15
20
135
10
10
8
0.16
20
144
10
10
8
0.17
20
153
10
12
8
0.18
20
162
10
15
8
0.19
20
171
10
20
13
0.2
30
180
10
40
15
0.3
30
270
15
55
20
0.4
40
360
20
75
24
0.5
50
450
22
150
28
0.6
100
540
25
250
32
0.7
190
630
35
275
35
0.8
220
720
45
300
37
0.9
300
810
55
325
39
1
400
900
65
350
41
1.1
500
925
75
360
43
1.2
600
950
85
370
45
1.3
700
1000
95
380
46
1.4
800
1000
105
380
47
1.5
900
1000
115
380
48
1.6
1000
1000
125
380
49
1.7
1000
1000
135
380
50
1.8
1000
1000
145
380
51
1.9
1000
1000
165
380
52
2
1100
1100
175
380
53
3
1200
1200
200
380
55
4
1300
1300
200
380
55
47
Establishment costs
agroforest walnut
forest walnut
agroforest wild cherry
forest wild cherry
agroforest poplar
forest poplar
Cost of
plant
Cost of
individual
tree
protection
Labour for
ground
preparation
Labour
for full
weeding
(€/tree)
6.00
4.00
1.00
0.50
4.00
4.00
(€/tree)
1.50
0.50
1.50
0.50
0.50
0.50
(h/ha)
4.00
6.50
4.00
6.50
12.00
16.00
(h/ha)
0.50
1.50
0.50
1.50
0.50
1.50
Labour
Labour for Labour for Labour for
for
planting
tree
localised
marking
trees
protection
weeding
out
(h/ha)
7
4
7
4
7
4
(min/tree)
2
2
2
2
2
2
(min/tree)
2
1
2
1
2
1
(min/tree)
0.50
0.50
0.53
0.53
0.50
0.50
Maintenance and pruning costs
Weeding
period
Annual
labour for
weeding
Annual cost
of herbicide
for weeding
Labour for
annual grass
sward
maintenance
Materials for
annual grass
sward
maintenance
(years)
1-3
1-3
(min/tree)
0.50
0.50
(€/tree)
0.14
0.14
(h/ha)
2.0
4.0
(€/ha)
30
90
(m)
1.00
1.00
(min/tree)
1.00
0.20
(m)
4
4
(min/tree)
7.00
7.00
(min/tree)
4.00
4.00
forest wild cherry
1-3
1-3
0.50
0.50
0.14
0.14
2.0
4.0
30
90
1.00
1.00
1.00
0.18
6
6
6.40
6.40
4.00
4.00
agroforest poplar
forest poplar
1-3
1-3
0.53
0.53
0.14
0.14
2.0
4.0
30
90
1.50
1.50
1.00
1.00
8
8
10.00
10.00
4.00
4.00
agroforest walnut
forest walnut
Height at Minutes per
Minutes per
Height at
Removal of
first tree at first
tree at last
last prune
pruning
prune
prune
prune
Labour for thinning and felling
agroforest walnut
forest walnut
Thinnings
Marking up &
Removal of tree
labour
(min/tree)
(min/tree)
7
5
7
5
Clear felling
Labour
Removal of tree
(min/tree)
4
4
(min/tree)
2
2
forest wild cherry
7
7
5
5
4
4
2
2
agroforest poplar
forest poplar
7
7
5
5
4
4
2
2
Administrative costs
In agroforestry, the land tax is the same as in an agricultural plot. It was thus applied
to the tree strips.
Land tax
Insurance
(€/ha)
(€/ha)
AF plot (agricultural tax)
44
20
forestry plot
30
20
(Franche- AF plot (agricultural tax)
Comté)
forestry plot
52
20
36
20
(Poitou- AF plot (agricultural tax)
Charentes)
forestry plot
58
20
39
20
Hy-Lc
(Centre)
Hy-Hc
Ly-Lc
48
ANNEX 3. What land status for agroforestry plots in
France ? (in French)
Fiche de synthèse dans le cadre de la Loi d’Orientation
Agricole
Assemblée Permanente des Chambres d’Agriculture
Fabien Liagre - Mars 2005
49
Sommaire
1
DÉFINITION DE L’AGROFORESTERIE.............................................. 50
2
PROBLÉMATIQUE LIÉE AU STATUT ................................................ 51
3
LES SOLUTIONS POSSIBLES ........................................................... 52
Un forfait spécial agroforesterie 53
Un forfait distinct au prorata
4
53
ANALYSE DES SOLUTIONS PRÉSENTÉES ..................................... 54
D’un point de vue administratif
54
Conséquence pour le calcul de l’impôt foncier 55
Conséquence pour le calcul de l’impôt sur le revenu 56
Conséquence pour le calcul de l’imposition sur le patrimoine56
Conséquence pour la gestion de l’exploitation 56
5
SOLUTION PROPOSÉE ...................................................................... 57
6
FAUT-IL MODIFIER LA LOI ? ............................................................. 58
Les surfaces boisées et le code rural
58
Le bail agroforestier 59
7
ANNEXE : RAPPORT DU BUREAU DES ETUDES FISCALES ......... 60
DEFINITION DE L’AGROFORESTERIE
L’agroforesterie consiste à associer étroitement des arbres à faible densité avec une
culture ou une pâture sur une même surface. Deux types d’agroforesterie sont
envisageables :
50
L’agroforesterie sur terres agricoles.
Des arbres sont plantés ou maintenus
dans une parcelle agricole. Sous forme
traditionnelle, on peut citer les pratiques
de prés-vergers, de cultures
intercalaires dans les noyeraies du
Dauphiné et du Périgord. Dans le cadre
du programme Européen SAFE, il
s’agissait justement d’étudier les
relations arbres / cultures dans ce type
de schéma agroforestier.
L’agroforesterie sur terres forestières.
Il s’agit ici d’aménager des espaces
boisés afin de rendre possible une
production agricole, souvent de nature
fourragère, tout en conservant une
production de nature sylvicole.
Traditionnellement, il s’agit des systèmes
de sylvopastoralisme ou de pré-bois.
Mais on peut également citer la
production de myrtilles dans les Vosges
ou de vanille à la Réunion.
De plus en plus de projets agroforestiers voient le jour en France. Ainsi, plus de 1500
ha sont à l’étude pour 2005/06. Et il est vraisemblable que l’on doublera cette surface
avant fin 2006. Cette évolution constatée sur l’ensemble du territoire national suscite
souvent des questions quant à la nature fiscale de ce type de parcelle, que ce soit de
la part des porteurs de projets mais également des services fiscaux, un peu
désorientés face à des pratiques innovantes. L’objet de cette note est de donc de
faire un état des lieux de la situation statutaire et de proposer des solutions,
éventuellement dans le cadre de la Loi d’Orientation Agricole.
PROBLEMATIQUE LIEE AU STATUT
Dans le cadre des Boisements de Terres Agricoles ou BTA, à partir du moment où
l’on réalise une plantation d’arbres sur une parcelle agricole, on modifie
complètement le statut de la parcelle qui devient forestière. Dans ce cas, après le
boisement, une notification du propriétaire est envoyée aux services cadastraux pour
signifier le changement de nature de l’occupation du sol. Le changement de statut
est donc essentiellement déclaratif plus que technique à ce niveau. Le propriétaire y
a tout intérêt car la législation actuelle lui permet de bénéficier d’une exonération de
l’impôt foncier pendant une période comprise entre 30 et 70 ans. Cette exonération
lui ouvre également la porte à des réductions des prélèvements fiscaux de ses
revenus agricoles.
En agroforesterie, le propriétaire plante nettement moins d’arbres à l’hectare que
pour une plantation forestière (sauf pour le peuplier et le noyer où les densités
peuvent parfois être similaires). Entre les arbres, un agriculteur (le propriétaire ou un
fermier) cultive pendant 50 % à 100 % de la durée de vie de la plantation. Dans ce
51
cas, le statut de la parcelle est plus difficile à définir car elle relève de deux activités
ou occupations différentes : agricole et forestière.
En agroforesterie, la proportion de surface occupée par les arbres varie dans le
temps au fur et à mesure du développement des arbres (surface en hausse) et du
rythme des éclaircies (surface en baisse). Plus la densité initiale sera forte (entre 100
et 200 arbres/ha par exemple), et plus l’impact des arbres sur la culture sera fort
avec le temps. En absence d’éclaircie forte, et bien que cette éventualité n’est pas
une fin en soi dans les principes de l’agroforesterie, l’agriculteur pourrait décider de
supprimer la culture si celle-ci n’était plus rentable (sauf éventuellement en la
remplaçant par une prairie ce qui permettrait de conserver une activité considérée
comme agricole). Le statut proposé devra tenir compte de cette spécificité et être
assez souple pour pouvoir s’adapter aux différentes évolutions de la parcelle
agroforestière. Une erreur serait sans doute de figer le statut de la parcelle en faveur
d’une des deux composantes, arbre ou culture.
100
% de la surface de la parcelle
% de la surface de la parcelle
100
ARBRES
80
60
40
PATURE
20
0
0
25
50
75
100
ARBRES
80
60
40
CULTURES
20
0
0
% de la durée de vie des arbres
25
50
75
100
% de la durée de vie des arbres
Exemple d’évolution des surfaces agricoles et arborées dans un habitat agroforestier au
cours de la vie des arbres pour une densité de 120 arbres à l’hectare. Avec une densité plus
faible, de l’ordre de 50 arbres à l’hectare, la proportion de la culture est bien sûr plus
importante. En fin de cycle le taux d’occupation de la culture dépasse généralement les 60 %
de la surface totale au sol.
D’autre part, il convient de souligner qu’en agroforesterie, les cultures présentes sont
complètement indépendantes : les cultures agricoles d’une part et les arbres d’autre part.
Ces cultures sont généralement imposées sur une base à l’hectare. On ne peut donc
confondre les productions issues d’une parcelle agroforestière avec les productions d’une
seule culture à vocation multiple (exemple : arbre fourrager produisant à la fois du bois et du
fourrage).
LES SOLUTIONS POSSIBLES
La première question est de savoir s’il faut créer un nouveau statut spécifique à
l’agroforesterie ou si l’on peut s’accommoder d’une combinaison de l’existant.
La deuxième question serait : si l’on s’accommode d’une combinaison de l’existant,
faut-il pour autant modifier la loi française pour prendre en compte cette solution ?
Les solutions proposées ci-après sont le fruit de réflexion d’un groupe de
professionnels, notamment dans le cadre du programme européen SAFE ainsi que
des propositions émises par le Bureau des Etudes Fiscales du MAAPAR.
52
Ces propositions tiennent compte des spécificités des systèmes agroforestiers
énoncées au paragraphe précédent.
Un forfait spécial agroforesterie
L’agroforesterie est ici considérée comme une culture spécialisée. Une nouvelle
classe est créée au niveau cadastral et la base de calcul du bénéfice forfaitaire est
constituée par un bénéfice moyen à l’hectare ou selon les quantités de production
présentes.
Ce statut existe dans le département
de l’Isère où les parcelles
nouvellement plantées de noyers
bénéficient du statut de Terres
Plantées. Ce statut couvre ainsi le cas
des parcelles comportant des arbres
improductifs avec des cultures
intercalaires
Un forfait distinct au prorata
Le rapport des services du Bureau des Etudes fiscales du MAAPAR indique que
« dès lors que sur une même parcelle coexistent deux cultures indépendantes l’une
de l’autre, il pourrait être envisagé d’avoir deux forfaits distincts, l’un forestier, l’autre
propre à la culture elle-même, chacun au prorata de la surface occupée par les
différentes exploitations. »
Sur une même parcelle agroforestière co-existent deux cultures indépendantes l’une
de l’autre en terme de surface et d’imposition fiscale. Le forfait de chaque culture est
déterminé à l’hectare.
A Vézénobres, dans le Gard, suite à
une réflexion départementale, les
services fiscaux ont attribué un statut
fixé au prorata des surfaces occupées
par chacune des composantes,
culture et peuplier, dans le cas d’une
parcelle agroforestière.
Dans cette solution, deux forfaits distincts sont donc appliqués au prorata des
surfaces de chaque culture. Mais, comme nous l’avons vu en introduction, la surface
respective de chaque culture évolue dans le temps. Deux prorata sont donc
envisageables : l’un variable et l’autre fixe.
•
Un prorata variable : le bénéfice de chaque production est ajusté à la surface
respective. L’ajustement est réalisé annuellement ou par périodeUn prorata
53
fixe : le bénéfice de chaque production est ajusté à une surface moyenne
occupée sur toute la durée de l’association. L’ajustement est définitif et
forfaitaireANALYSE DES SOLUTIONS PRESENTEES
Les 3 solutions proposées, le forfait spécial, le prorata variable et le prorata fixe sont
comparées selon diffèrents niveaux :
•
Au niveau administratif d’abord, afin de juger de leur facilité d’application
•
Au niveau de l’imposition du foncier, du revenu et du patrimoine
•
Au niveau des dispositifs réglementaires nationaux
La proposition retenue devra tenir compte de quelques principes de base. Dans la
mesure du possible, il faudra :
•
Proposer des modifications simples à mettre en œuvre et cohérentes, peu
onéreuses à l’Etat ou équilibrées dans le rapport Coût / Economie réalisé pour
l’Etat.
•
Laisser la possibilité d’aménagements ultérieurs des textes officiels pour
encourager la pratique de l’agroforesterie. L’objectif n’est pas ici de rechercher
immédiatement des mesures de soutien ou d’exonération de certaines taxes.
On se tiendra à quelques notions fondamentales qui seront évaluées voir
réajustées au fur et à mesure de leurs applications. Car il semble impossible
de prévoir toutes les implications juridiques de telle ou telle solution.
D’un point de vue administratif
Forfait Spécial
Prorata variable
Prorata fixe
Simplicité des calculs à Prise en compte de la Prise en compte de la
l’échelle de la parcelle
production forestière
production forestière
Système équitable,
adaptable avec l’évolution
des surfaces de chaque
production
Facilité de gestion des
déclarations
Risques de distorsions Suivi plus complexe par Prorata définitif et
entre les départements
les services des impôts
forfaitaire.
Augmentation du nombre
de classes cadastrales
Augmentation du nombre
de comptes existant dans
le cadre du forfait collectif
54
Conséquence pour le calcul de l’impôt foncier
Forfait Spécial
Prorata variable
Risque d’impôt foncier
élevé
Impôt calculé au plus juste
par rapport aux surfaces
Après la coupe des arbres, occupées par les
productions associées
la parcelle redevient
agricole. Si le propriétaire
Possibilité d’exonération
ne replante pas, le statut
partie arbre à l’image de
doit être revu au niveau du ce qui est réalisé en BTA
cadastre.
Le calcul des surfaces
demande à être réalisé
périodiquement.
Cette
réactualisation
pourrait
être réalisée par période
conséquente de 5 à 10
ans par exemple.
Après la coupe des arbres,
la parcelle redevient
agricole. Si la partie
forestière était importante,
voire à 100% en cas
d’arrêt des cultures,
existe-t-il un risque de
procédure complexe pour
repasser en statut
agricole ?
Prorata fixe
Dans une parcelle
forestière, la surface
occupée par la culture est
plus importante en début
de rotation. L’application
d’un forfait fixe, calculé sur
la moyenne d’occupation
des surfaces de chacune
des deux productions, a
comme conséquence de
favoriser l’agriculteur en
début de rotation, et de le
défavoriser en fin de
rotation. Par exemple,
pour un forfait
correspondant à une
combinaison de 50/50,
c’est avantageux pour
l’agriculteur qui cultive sur
90 % de la parcelle les
premières années. Mais
nettement moins, avant la
coupe des arbres où il
continue de payer un
impôt foncier agricole
évalué à 50 % alors qu’il
peut ne cultiver que 20 à
30 % par exemple.
Possibilité d’exonération
de la partie arbre à l’image
de ce qui est réalisé en
BTA
Le calcul du forfait peut
s’avérer complexe, car il
va dépendre de l’essence,
de la densité et de
l’écartement entre les
lignes d’arbres. De plus,
se pose le problème des
plantations à essences
multiples : comment
calculer un forfait fixe avec
des essences à rotation
différentes plantées dans
une même parcelle?
55
Conséquence pour le calcul de l’impôt sur le revenu
Forfait Spécial
Prorata variable
Prorata fixe
Si mode d’imposition au
réel normal, situation
inchangée. L’imposition au
forfait est facilitée par le
forfait spécial
agroforesterie.
réel : situation inchangée.
réel normal : situation
inchangée.
Néanmoins, on retrouve
les mêmes conséquences
que pour le scénario
prorata fixe.
L’imposition de l’activité
forestière est réalisée au
forfait selon la surface
déclarée
forfait, le calcul est réalisé
en fonction des surfaces
respectives déclarées.
Possibilité de bénéficier de
dégrèvements fiscaux sur
le revenu forestier à
l’image de ce qui est
réalisé en BTA
forfait, l’imposition sur la
partie
agricole
sousévaluée les premières
années et surévaluée les
dernières années.
L’imposition forfaitaire sur
la partie boisée est
surévaluée les premières
années et sous-évaluée
les dernières années
Possibilité de bénéficier de
dégrèvements fiscaux sur
le revenu forestier à
l’image de ce qui est
réalisé en BTA
Conséquence pour le calcul de l’imposition sur le patrimoine
Le calcul de l’imposition sur le patrimoine (ISF, transmission) est réalisé sur les
valeurs déclarées par le contribuable.
Chaque année, il évalue la partie vénale de la partie arbre et de la partie agricole.
Il existe des abattements au prorata de la partie agricole et forestière dans le cadre
du calcul des droits de mutation. Les abattements sont plus avantageux pour la
partie forêt que la partie agricole.
Le statut cadastral doit être de nature forestière pour prétendre à ces dispositifs. Un
statut spécial pour une parcelle agroforestière devra être pris en compte au niveau
du dispositif réglementaire. Ce qui n’est pas le cas dans le cas des statuts au
prorata, où les dispositifs existant s’appliqueraient au prorata de la surface forestière.
Néanmoins, dans le cas du statut au prorata fixe, les abattements fiscaux seraient
plus élevés les premières années, compte tenu de l’importance de la surface
forestière réelle, et moins favorable les dernières années.
Conséquence pour la gestion de l’exploitation
Deux exemples sont ici cités pour montrer les implications possibles du choix du
statut sur des aspects de réglementations courantes concernant les exploitations
agricoles ou forestières.
56
o Le calcul des cotisations MSA
Le statut de la parcelle intervient pour le calcul de cotisations sociales des
agriculteurs au forfait uniquement. L’influence du statut cadastral est forte dans le
cas du forfait au prorata fixe. Tout comme pour l’imposition forfaitaire, les premières
années le montant des charges sociales sera plus faible compte tenu de la surface
agricole plus importante que celle retenue dans le calcul du forfait, et inversement en
fin de rotation.
o Statut foncier et sinistres naturels
Les indemnités versées aux propriétaires ou agriculteurs lors de sinistres
exceptionnels sont liées à la nature des sols. Ainsi, les indemnités versées suite aux
tempêtes de Noël 99 ont été conditionnées au statut forestier des parcelles. De
même, les indemnités agricoles suite à un gel ou à une forte grêle sont-elles versées
en fonction du statut agricole des parcelles. Ici aussi, le système le plus équitable en
terme de surface est le statut au prorata variable qui est le seul statut permettant de
distinguer réellement les surfaces agricoles et forestières au moment du sinistre.
SOLUTION PROPOSEE
Seules 3 solutions ont été comparées dans ce document. Nous avons écartés
notamment les autres possibilités qui auraient été le choix d’un statut purement
agricole, voire purement forestier.
Au vu des éléments donnés dans ce document, il semble que la solution la plus
favorable au cas de l’agroforesterie réside dans le choix d’un statut au prorata des
surfaces occupées par la culture agricole d’une part et forestière d’autre part. Afin
d’éviter une réactualisation annuelle de ce statut, qui serait sans doute lourd à gérer
pour l’administration fiscale, pour les propriétaires ainsi que pour les agriculteurs
dans la gestion de leur exploitation, cette actualisation pourrait être réalisée par
période. Pour les essences de moyenne ou longue rotation, supérieure à 30 ans, on
pourrait envisager la mise en place d’un forfait décennal, sur la base déclarative du
propriétaire. Pour des essences à plus courte rotation, comme le peuplier,
l’actualisation pourrait être réalisée tous les 5 ans.
Afin de faciliter d’éventuel contrôles fiscaux, il est proposé que la méthode de calcul
des surfaces soit la plus simple possible. On conseille de calculer la surface agricole
en fonction de la surface réellement semée ou pâturée. Un contrôle de surface par
photographie aérienne est à déconseiller car les surfaces d’emprise des arbres vue
d’altitude sont largement agrandies par rapport à leur surface d’emprise réelle.
Un statut évolutif s’adapte bien aux différentes conséquences fiscales et
réglementaires exposées dans les paragraphes précédents. Cette option ne signifie
pas de coût particulier pour l’administration. Elle rend possible également de soutenir
fiscalement l’agroforesterie sur les mêmes bases que les boisements de terres
agricoles mais uniquement sur les surfaces occupées par les arbres.
Le principe de cette solution est repris en partie dans les conclusions du document
du Bureau des Etudes Fiscales du Ministère de l’Agriculture du 8 oct. 2004. En
57
conclusion de ce document, il est indiqué que « le régime fiscal de l’agroforesterie
pourrait être étudié dans le cadre du projet de Loi d’Orientation Agricole. »
FAUT-IL MODIFIER LA LOI ?
L’objectif de ce document n’est pas de répondre à cette question mais d’apporter des
éléments de réflexion dans cette perspective.
Les surfaces boisées et le code rural
Dans l’article du code rural, on peut lire les articles suivants se rapportant aux
surfaces boisées hors forêt, lorsque qu’une démarche de protection des zones
boisées est engagée:
Article R126-37
L'emprise et l'indication des parcelles cadastrales sur lesquelles sont situés
les boisements linéaires, haies, plantations d'alignement ou vergers de hautes
tiges, dont la protection est prononcée, doivent être matérialisées sur un plan
parcellaire annexé à l'arrêté préfectoral prononçant la protection ou sur le plan
des aménagements fonciers prévu à l'article L. 121-21. L'arrêté précise les
éléments techniques visés à l'article ci-dessus.
Article R126-38
Les boisements linéaires, haies ou plantations d'alignement nouvellement
protégés doivent être portés à la connaissance de l'administration des impôts
dans les formes et délais définis à l'article 1406 du code général des impôts.
Les emprises ainsi créées, matérialisées dans les conditions prévues à l'article
ci-dessus, seront considérées comme nature de culture se rapportant au
groupe des bois.
A ma connaissance, il s’agit du seul texte indiquant clairement la possibilité de
distinguer sur une parcelle agricole une surface de nature forestière. Néanmoins, ces
articles s’appliquent lors d’un processus de protection de formations arborées hors
forêt. En dehors de cette perspective, il ne semble pas que cette éventualité soit
considérée, que ce soit au niveau du code rural comme du code forestier.
Il semble donc souhaitable de débattre de la possibilité d’inclure un paragraphe
décrivant clairement la situation des parcelles agricoles arborées - qui citerait
notamment le cas de l’agroforesterie - où l’on pourrait distinguer les surfaces
cadastrales agricoles d’une part et forestières d’autre part.
Il serait alors spécifié :
o le processus des déclarations à effectuer (date de déclaration et durée de la
période entre deux déclarations)
o le fait que chacune des classes relève des mêmes régimes fiscaux que dans
le cas des parcelles avec cultures pures.
58
Le bail agroforestier
L’agroforesterie mais également l’agriculture moderne ne correspond pas au concept
de bail rural tel que l’on entend aujourd’hui.
En effet, un fermier peut aujourd’hui laisser sa parcelle louée en jachère ou sans
attendre de production et toucher des primes PAC. La notion d’exploitation agricole
du bien loué en bon père de famille tel que cela était définie après guerre, n’est plus
valable aujourd’hui.
De même, dans le cas de l’agroforesterie, il serait souhaitable de modifier les textes
actuels pour rendre possible la création d’un bail agroforestier, où l’agriculteur
pourrait cultiver les surfaces agricoles sans toucher aux arbres du propriétaire. De
même, on pourrait imaginer que le fermier se lance en agroforesterie (avec des
essences à courte rotation par exemple), sans que cela ne remette en cause le
contrat signé avec le propriétaire.
Une analyse plus approfondie des textes actuels et des réformes proposées
permettrait d’inclure le cas particulier de l’agroforesterie dans le cadre de Loi
d’Orientation Agricole.
59
APPENDIX : RAPPORT DU BUREAU DES ETUDES FISCALES DU 8 OCT. 2004
60
61
62
ANNEX 4. Quelle place pour les arbres hors forêt dans
la nouvelle PAC ?
Synthèse et propositions pour les réglementations
européennes et françaises…
Assemblée Permanente des Chambres d’Agriculture
Fabien Liagre - Mars 2005
Résumé des propositions
La réforme des accords de Luxembourg se met en place progressivement dans
chacun des Etats membres. En France, le gouvernement prépare le prochain
règlement d’application de la PAC en vue de 2006, année de mise en place du
découplage des aides.
Parallèlement, la Commission Européenne, après validation du Parlement Européen
et du conseil des ministres de l’Agriculture en début d’année, finalisera le prochain
Règlement de Développement Rural qui interviendra sur la période 2007-2013.
63
Chaque Etat membre débutera alors les travaux en vue de l’élaboration de son
Programme de Développement Rural National.
Dans le cadre de ces évolutions réglementaires, les dispositions en faveur des
formations arborées hors forêt évoluent, certes avec une progression variable selon
les Etats membres mais avec certitude. Pour la première fois, une mesure en faveur
de l’agroforesterie est citée dans le projet de RDR. Jamais une mesure en faveur des
arbres ruraux n’avait été aussi clairement énoncée et appuyée dans un règlement
européen. Dans cette évolution, la France fait figure de précurseur grâce aux
différentes mesures réglementaires prises dès 2001 en faveur de l’agroforesterie
notamment.
L’APCA a souhaité faire un bilan de l’existant, fruit de 3 années de recherche dans le
cadre du programme européen SAFE, afin de clarifier la situation réglementaire et
d’améliorer la lisibilité et la portée des mesures proposées.
Ce travail de réflexion amène aux principales conclusions suivantes qui sont
développées dans le document :
Au niveau européen
1. Compte tenu de la valeur agro-environnementale des formations arborées
hors forêt et de la complexité administrative que suscite leur prise en compte
dans le calcul des surfaces éligibles au paiement compensatoire du 1er pilier, il
est proposé de rendre admissible au paiement unique les parcelles arborées
dans la totalité de leur surface. De même, il est proposé que les surfaces
occupées par les arbres puissent être éligibles au paiement unique. Ces deux
propositions demandent une clarification de la définition d’une parcelle
arborée ainsi que la modification des règlements concernant les accords du
Luxembourg.
2. Sur le modèle de l’article 41 du projet de RDR du 14 juillet 2004, il est
demandé une homogénéisation des mesures en faveur des arbres hors forêts.
Sur le même principe d’éligibilité de l’agroforesterie, une mesure universelle
en faveur de l’arbre rural simplifierait l’adoption de mesure de soutien dans
chacun des pays membre.
Au niveau français
Concernant les aides du premier pilier, il faut distinguer le cas des aides couplées
des aides découplées.
Les propositions pour le régime des aides découplées (DPU) sont :
3. Dans le cadre de l’élaboration de la prochaine circulaire d’application de la
PAC concernant les paiements compensatoires en 2006, la France pourrait
dès à présent autoriser l’éligibilité totale des parcelles arborées dans le cadre
du découplage des aides, pour des raisons agroenvironnementales. Des
critères techniques, faciles à contrôler, sont proposés.
4. L’obtention d’une éligibilité totale permettrait de simplifier les procédures
administratives tant au niveau de l’instruction des dossiers qu’au niveau du
64
contrôle. En effet, les textes font appel aux normes locales pour déterminer les
méthodes de calcul des surfaces d’emprise des arbres en agroforesterie. Hors
aucune norme départementale n’existe en matière d’agroforesterie moderne
et très peu de normes existent pour les autres formations arborées.
5. En cas d’éligibilité des parcelles arborées au paiement unique, il conviendrait
alors de supprimer l’éligibilité des parcelles agroforestières à la Prime de
Compensation à la Perte de Revenu agricole.
Dans le cadre des aides couplées à la production (aides SCOP) :
6. Il est proposé de calculer la surface éligible en fonction de la surface
réellement occupée par la culture intercalaire, et non de la surface obtenue
après déduction de la surface d’emprise des arbres. En effet, dans le cadre
des systèmes de contrôle par vue aérienne, du fait de l’angle de vue, la
surface d’emprise des arbres dépasse la surface d’emprise réelle des arbres.
L’impact des arbres est donc beacoup plus fort que dans la réalité ce qui est
très pénalisant pour l’agriculteur.
Dans le cadre du second pilier :
7. Face à la demande grandissante et aux résultats probants de la recherche en
la matière, il est proposé de modifier la circulaire d’application de la mesure en
faveur de l’agroforesterie afin de lever l’obligation de suivi d’un organisme à
titre expérimental. Une clarification du cahier des charges permettrait
également aux administrations locales d’instruire plus facilement les dossiers.
8. Enfin, afin de mettre à disposition un outil efficace aux collectivités territoriales
souhaitant appuyer des actions agroforestières, il est proposé de revoir le
cahier des charges de la MAE Habitats Agroforestiers afin de la rendre plus
cohérente vis-à-vis des autres mesures. D’autre part, une évolution de cette
MAE en MAE universelle en faveur de l’arbre est souhaitée afin de simplifier la
procédure des Contrats d’Agriculture Durable. Son action porterait alors
exclusivement sur des actions où le caractère environnemental serait
renforcé.
65
Sommaire
1
INTRODUCTION .................................................................................. 69
2
ELIGIBILITÉ DES PARCELLES ARBORÉES AUX PAIEMENTS
COMPENSATOIRES ............................................................................................... 69
Place de l’arbre dans l’historique des réformes de la PAC
L’agenda 2000
69
69
La réforme des accords du Luxembourg
70
Les règles de base ........................................................................................................................ 70
Les règlements d’application ......................................................................................................... 71
Conclusions au niveau européen 73
Une situation ambiguë
73
Le principe de subsidiarité des Etats membre prévaut ................................................................. 73
Un système de contrôle des surfaces inadapté ? ......................................................................... 74
Quelle situation pour les nouveaux membres de l’UE ? ............................................................... 74
Les propositions
74
Le régime d’application en France
75
Evolution de l’historique de l’application des règlements européens
75
Avant la réforme des accords du Luxembourg.............................................................................. 75
Après la réforme des accords du Luxembourg ............................................................................. 76
Conclusions 77
Les agriculteurs agroforestiers pénalisés...................................................................................... 77
Conséquences possibles pour des parcelles arborées existantes ............................................... 77
Une gestion des droits qui incite à l’arrachage.............................................................................. 77
Des normes locales quasi-inexistantes ......................................................................................... 78
Propositions au niveau national
78
Améliorer la définition des normes usuelles.................................................................................. 78
Simplifier le contrôle des surfaces arborées ................................................................................. 78
Admissibilité et éligibilité des parcelles agroforestières ................................................................ 79
Cas des aides découplées ........................................................................................................ 79
Simplifier l’approche administrative ....................................................................................... 79
Une éligibilité totale soumise à conditions............................................................................. 79
66
Conséquences à l’échelle de l’exploitation et au niveau national ......................................... 80
Cas des aides couplées ............................................................................................................ 81
Rendre cohérent l’articulation entre 1er et 2ème pilier ............................................................. 82
Intégrer les surfaces arborées dans le couvert environnemental ............................................. 82
3
LES ARBRES DANS LE DEUXIÈME PILIER DE LA PAC ................. 82
Le Règlement de Développement Rural 82
L’apparition d’une mesure Agroforesterie à l’horizon 2007
Une mesure incomplète ?
82
85
Les aides disponibles en France 85
Le cas des formations arborées hors agroforesterie 85
Le cas des systèmes agroforestiers 87
Les aides à la plantation................................................................................................................ 87
Les textes officiels ..................................................................................................................... 87
Une mesure encourageante mais difficile d’application ............................................................ 88
La compensation à la perte de revenu .......................................................................................... 89
Les textes officiels ..................................................................................................................... 89
Faut-il maintenir cette disposition ?........................................................................................... 90
La MAE Habitats Agroforestiers .................................................................................................... 91
Présentation de la mesure......................................................................................................... 91
Quel avenir pour la MAE Habitats Agroforestiers ?................................................................... 91
Faut-il revoir le cahier des charges ? ........................................................................................ 92
Quelles conséquences avec l’application du prochain RDR ?...................................................... 92
Une reconnaissance facilitée de l’agroforesterie....................................................................... 92
Homogénéiser le soutien aux formations arborées hors forêt ? ............................................... 92
4
BIBLIOGRAPHIE ................................................................................. 93
5
ANNEXES ...........................................ERREUR ! SIGNET NON DEFINI.
Annexe 1: Article TransRural Initiative de déc 2004
94
Annexe 2: Propositions du Groupe Réglementations – SAFE 95
Annexe 3: Lettre de Luc Guyau APCA au MAAPAR – PAC
97
Annexe 4: Chapitre 10 de la circulaire forêt de protection du 7 mai 01
102
Annexe 5: Texte MAE Habitats Agroforestiers 105
67
Annexe 6: Lettre de Luc Guyau APCA au MAAPAR – MAE
114
68
Introduction
Ce document a pour objectif de décrire la situation réglementaire des parcelles
agricoles arborées vis-à-vis de la Politique Agricole Commune, de prévenir des
possibles complications de leur prise en compte et de faire des propositions au
niveau européen et national.
Afin de bien cerner les enjeux actuels dans le cadre de la réforme de PAC en cours,
une analyse de la situation avant 2005 sera effectuée. Une deuxième partie abordera
alors la nouvelle situation générée par la réforme et les questions qu’elle suscite pour
le cas des parcelles arborées.
Nous tenterons de prendre en compte les différentes formes arborées existantes :
arbres isolés, alignement, haies et agroforesterie. Les pré-vergers et les noyeraies
du Dauphiné avec cultures intercalaires ne sont pas oubliés, en tant que forme
traditionnelle d’agroforesterie en France.
ELIGIBILITE DES PARCELLES ARBOREES AUX PAIEMENTS COMPENSATOIRES
Place de l’arbre dans l’historique des réformes de la PAC
Avant d’aborder la prise en compte des arbres hors forêt en France, il est nécessaire
de faire un rapide tour d’horizon des principaux textes européens qui régissent la
PAC. Dans un deuxième temps, on détaillera la position française lors de
l’application des règles de la PAC.
L’agenda 2000
Lors de la réforme de l’agenda 2000, les modalités d’application du SIGC ou
Système intégré de Gestion et de Contrôle des surfaces éligibles étaient alors régies
par le règlement CE No 2419/2001 relatif à certains régimes d'aides communautaires
établis par le règlement (CEE) no 3508/92. Cette réforme de l’agenda 2000
désolidarisait le paiement compensatoire de la production pour l’attribuer en fonction
de la surface occupée par les cultures. Aujourd’hui ces deux règlements ne sont
plus en vigueur mais le règlement 2419/2001 reste souvent une référence des
règlements suivants concernant l’éligibilité des surfaces arborées.
Dans le règlement 2419/2001, l’éligibilité des parcelles arborées est abordée à
l’article 5.
Article 5
a) une parcelle portant à la fois des arbres et une culture prévue à l'article 1er du
règlement (CEE) no 3508/92 est considérée comme une parcelle agricole à
condition que la culture susvisée puisse être effectuée dans des conditions
comparables à celles des parcelles non arborées de la même région;
Extrait art 5 – Règ. 2419/2001
69
Les surfaces en haies peuvent être considérées comme éligible sur une largeur de 2
mètres en fonction des choix de chaque Etat membre correspondant aux pratiques
traditionnelles. Cet aspect est abordé dans l’article 22.
Article 22
Dans les régions où certaines caractéristiques, en particulier les haies, les fossés
et les murs, font traditionnellement partie des bonnes pratiques agricoles en
matière de culture ou d'utilisation, les États membres peuvent considérer que la
superficie correspondante fait partie de la superficie totale utilisée, pour autant
qu'elle ne dépasse pas une largeur totale à déterminer par les États membres.
Cette largeur doit correspondre à une largeur traditionnelle dans la région en
question et ne doit pas excéder deux mètres.
La réforme des accords du Luxembourg
Les règles de base
Dans le cadre de la réforme des accords du Luxembourg en 2003, la Commission
met en place un paiement unique par exploitation pour les agriculteurs de l'UE,
indépendant de la production. Des éléments de couplage limités sont toutefois
maintenus pour éviter l'abandon de la production dans certains pays, comme la
France par exemple.
A ce niveau, il convient de distinguer les notions d’admissibilité et d’éligibilité. Dans le
cadre du régime de Droit à Paiement Unique ou DPU, une première étable est de
déterminer les surfaces admissibles au DPU. En effet, la surface adminissible par
exploitation peut être supérieure à la surface éligible dans le cadre de l’aide à la
production ou l’aide SCOP. Sur ces surfaces, les cultures pratiquées doivent être
éligibles, c'est-à-dire ouvrant droits à paiement.
Le règlement (CE) No 1782/2003 du 29 septembre 2003 établit les règles communes
pour le soutien direct. A l’article 44, il est spécifié que les surfaces comportant des
éléments permanents ne peuvent être comptés dans les surfaces ouvrant droits à
paiement.
Aux fins du paragraphe 2, point b), du présent article, on entend par «superficie
fourragère» la superficie de l'exploitation disponible pendant toute l'année civile,
conformément à l'article 5 du règlement (CE) no 2419/2001 (1) de la Commission,
pour l'élevage d'animaux, y compris les superficies utilisées en commun et les
superficies soumises à une culture mixte. Ne sont pas comptés dans cette
superficie:
— les bâtiments, les bois, les étangs, les chemins,
— les superficies utilisées pour d'autres cultures admissibles au bénéfice d'une
aide communautaire, pour des cultures permanentes ou pour des cultures
horticoles,
70
— les superficies bénéficiant du régime de soutien aux agriculteurs produisant
certaines grandes cultures, qui sont utilisées dans le cadre du régime d'aide
concernant les fourrages séchés ou soumises à un programme national ou
communautaire de gel des terres.
Extrait Article 44 – Règ. 1782/2003
A remarquer qu’il est indiqué que les cultures mixtes peuvent être comptabilisées
dans les surfaces éligibles. A noter également que les bois pâturés ne pourraient pas
être éligibles.
A l’article 51, il est spécifié que l’agriculteur perd ses droits à paiements pour les
surfaces mises en cultures permanentes.
Article 51
Utilisation agricole des terres
Les agriculteurs peuvent utiliser les parcelles déclarées conformément à l'article 44,
paragraphe 3, pour toute activité agricole à l'exception des cultures
permanentes et de la production de produits visés à l'article 1er, paragraphe 2, du
règlement (CE) no 2200/96 du Conseil du 28 octobre 1996 portant organisation
commune des marchés dans le secteur des fruits et légumes (1) et à l'article 1er,
paragraphe 2, du règlement (CE) no 2201/96 du Conseil du 28 octobre 1996 portant
organisation commune des marchés dans le secteur des produits transformés à
base de fruits et légumes (2) ainsi que de pommes de terre autres que celles qui
sont destinées à la fabrication de fécule pour lesquelles l'aide est octroyée au titre
de l'article 93 du présent règlement.
Enfin, certaines surfaces boisées comme les boisements de terres agricoles peuvent
toutefois être comptabilisées en surfaces ouvrant droits à gel (art 54).
Les règlements d’application
La mise en œuvre de la réforme est régie par 3 règlements d’application :
•
Le premier règlement concerne les dispositions relatives à la conditionnalité,
aux contrôles et à la modulation. Ce règlement 796/2004 abroge notamment
le règlement 2419/2001.
•
Le deuxième règlement introduit le paiement unique par exploitation et le
découplage de la production (règlement 795/2004)
•
Le troisième règlement porte sur les secteurs d'aides qui demeureront
spécifiques à certaines productions (2237/2003)
Le premier principe de l’article 8 du règlement d’application 796/2004
spécifie qu’ « une parcelle boisée est considérée comme une parcelle agricole aux
fins du régime d’aide « surfaces » sous réserve que les activités agricoles visées à
71
l’article 51 du règlement (CE) n° 1782/2003 ou, le cas échéant, que la production
envisagée puissent se dérouler comme elles se dérouleraient sur des parcelles non
boisées situées dans la même zone. » Ce principe, qui reprend l’article 5 du
règlement 2419/2001 ouvre la porte à l’éligibilité des parcelles agroforestières mais
ne spécifie en aucune manière si cette éligibilité peut être totale ou partielle (au
prorata de la surface agricole par exemple). A noter que de la notion de parcelle
arborée évolue en parcelle boisée.
L’article 8 reste cependant vague sur la distinction entre parcelle boisée et parcelle
agricole. Aucune proportion de surface ou de production n’est indiquée. En fait, il faut
se référer au document de travail AGRI/2254/2003 pour avoir une première définition
d’une parcelle boisée qui ne serait plus considérée comme agricole et éligible. En
effet, dans ce document, il est précisé qu’une surface arborée est considérée comme
inéligible si le nombre d’arbres dépasse 50 arbres par hectare. Au-delà de ce seuil, la
parcelle devient inéligible au titre du Paiement Unique sauf dérogation pour des
motifs agro-environnementaux. Néanmoins, cette définition ne s’applique qu’aux
parcelles fourragères ou prairies. Rien n’est spécifié pour les parcelles avec grandes
cultures. Il faut également souligner que ce document de travail se rapporte au
règlement 2419/2001. Ce règlement qui a été remplacé par le Règ. 796/2004
concernait la période de l’Agenda 2000 et non celle de la réforme des accords du
Luxembourg et du régime de paiement unique.
A noter également dans ce document, qu’une surface est considérée comme
fourragère si plus de 50 % de la surface est considérée comme jachère.
Les superficies couvertes d'arbres – en particulier d'arbres avec utilisation
potentielle uniquement pour la production de bois – à l'intérieur d'une parcelle
agricole d'une densité supérieure à 50 arbres/ha doivent être considérées comme
inéligibles. Des exceptions peuvent être envisagées pour les classes d'arbres de
cultures mixtes d'arbres fruitiers et autres. Les exceptions éventuelles doivent être
définies à l'avance par les États membres.
Nonobstant une communication spécifique de l'État membre à la Commission,
lorsque des caractéristiques pouvant aller jusqu'à 4 m de large (des murs, des
fossés, des haies) servent de limites entre les parcelles agricoles et font
traditionnellement partie des bonnes pratiques agricoles (par exemple murs de
terrasse, fossés de drainage), ces caractéristiques sont néanmoins considérées
comme étant incluses et une largeur de 2 m peut être attribuée à chaque parcelle
agricole adjacente.
Extrait du document de travail AGRI/2254/2003
Cas des paiements aux vergers
Dans le cadre du règlement 2237/2003, une disposition nouvelle permet un paiement
à la surface pour les vergers de production de fruits à coques. Il est spécifié à l’art.
19, que le verger éligible ne doit pas être entrecoupé de cultures. La surface éligible
doit être supérieur à 0.1 ha. Le nombre d'arbres producteurs de fruits à coque par
hectare de verger ne peut être inférieur à:
•
125 pour les noisetiers,
72
•
50 pour les amandiers,
•
50 pour les noyers,
•
50 pour pistachiers,
•
30 pour les caroubiers.
Ces normes de densité peuvent être revues à la hausse par tout Etat membre.
L’article 19 qui spécifie que le verger ne doit pas être entrecoupé de cultures prête à
confusion. Devons-nous penser que les cultures intercalaires non aidées doivent être
exclues du verger sous peine de voir annuler le paiement pour fruits à coque ? Cette
disposition semble aller à l’encontre de l’article 8 du règ. 796/2004.
Enfin, le document de travail AGRI/2254/2003 spécifiait que chaque Etat membre
pouvaient accorder l’éligibilité aux prés-vergers pour le paiement unique. Si l’on s’en
tient au principe de non cumul des paiements sur une même surface, un agriculteur
pourrait avoir droit au paiement unique à condition de ne pas solliciter le paiement
fruit à coques. Par contre, il semblerait qu’il n’ait pas droit au paiement fruit à coque
dès qu’il y a une présence soit de culture, soit de pâture, aidées ou non.
Aparté sur les normes locales
Dans les différentes réglementations européennes, il est régulièrement fait mention
que les pratiques doivent être réalisées selon les normes locales.
Ainsi, les produits cultivés sur des superficies doivent être entièrement ensemencées
et cultivées conformément aux normes locales. De même que la présence des
arbres en milieu cultivé est reconnu et ne modifie pas l’éligibilité des parcelles si ces
pratiques relèvent des normes locales. Dans l’article 30 du règlement 796/2004, il est
indiqué que la superficie totale d’une parcelle peut être prise en compte à condition
qu’elle soit entièrement utilisée selon les normes usuelles de l’Etat membre ou de la
région concernée. Dans les autres cas, c’est la superficie réellement utilisée qui est
prise en compte.
Si pour certains systèmes traditionnels (bocage, agroforesterie traditionnelle, etc.), il
est possible de recourir à certaines normes, quoique parfois très difficilement,
concernant les systèmes modernes d’agroforesterie, il n’existe aucune norme locale
disponible.
Conclusions au niveau européen
Une situation ambiguë
Le principe de subsidiarité des Etats membre prévaut
Les textes européens laisse toute liberté aux Etats membres de définir eux-mêmes le
niveau d’éligibilité des parcelles arborées. Il est en effet possible pour des raisons
environnementales de rendre admissibles la totalité d’une surface agroforestière
ainsi que de toutes formations arborées hors forêts. Néanmoins, par principe, la règle
73
de base est de déduire la surface d’une culture ou élément permanent des surfaces
ouvrant droits à prime.
D’autre part, le seuil d’arbres par hectare en dessous duquel la parcelle reste
agricole n’est pas clairement identifié au niveau européen ce qui prête à confusion
dans la recherche d’une définition claire d’une parcelle agricole.
Un système de contrôle des surfaces inadapté ?
Dans l’article 20 du règlement 1782/2003 la Commission encourage le recours aux
techniques de couverture d'ortho imagerie aérienne ou spatiale, ce qui suscite
quelques interrogations sur la répercussion que peuvent avoir ces techniques sur le
calcul des surfaces arborées. En effet, par photo aérienne ou satellitaire, l’erreur de
parallaxe est majeure et fausse totalement l’estimation de la surface. Une haie ou
des arbres isolés vus de biais ont une surface plus importante que dans la réalité.
Il conviendrait de définir une méthode standardisée de calcul de la surface occupée
par les arbres dans une parcelle donnée pour faciliter le contrôle, car ce point donne
lieu à des litiges. Dès à présent, des agriculteurs songent sérieusement à rabattre la
hauteur de leurs haies, voire à les supprimer, afin d’en réduire l’impact dans les
calculs des surfaces éligibles...
Quelle situation pour les nouveaux membres de l’UE ?
Les agriculteurs des pays de l’Est sont confrontés à leurs premières déclarations de
surface. Comme lors de l’Agenda 2000, les surfaces comportant des arbres doivent
être déduites des surfaces éligibles déclarées. Face au montant des primes en jeu une exploitation polonaise de 15-20 ha peut recevoir 6000 euros soit l’équivalent
d’une année de salaire (Annexe 1), les agriculteurs sont poussés à enlever haies,
bosquets et bois des parcelles pourtant inclus dans leur gestion agronomique des
surfaces. Si aucune décision n’est prise concernant l’éligibilité des parcelles agricoles
arborées, nous assisterons à une forte diminution du nombre d’arbres telle que celle
observée en Europe Occidentale ces 20 dernières années…
Les propositions
Certains aspects réglementaires mériteraient d’être éclaircis ou ajoutés :
1. Le manque de définition claire pour distinguer une parcelle arborée agricole
admissible au DPU d’une parcelle boisée admissible (les 2 termes sont
employés) prête à confusion. Il conviendrait de réactualiser la définition
donnée pour les surfaces fourragères boisées dans le document de travail
AGRI/2254/2003, lui-même basé sur des règlements qui ne sont plus en
vigueur. La définition pourrait être introduite dans le règlement 795/2004 à
l’article 2 à la suite des autres définitions.
2. Compte tenu du système de calcul des droits à primes basé sur l’historique
des aides reçues, il est proposé de rendre éligible les surfaces occupées par
les arbres dans leur intégralité. Un seuil minimum d’arbres à l’hectare pourra
être proposé par défaut aux Etats membres qui pourront le revoir à la hausse.
Ce seuil serait conforme à la définition de la parcelle arborée proposée cidessus. Pour introduire l’idée de l’éligibilité des parcelles arborées, il est alors
74
proposé de modifier le paragraphe 2 du règlement Art44 du règlement
1782/2003 afin d’intégrer l’éligibilité de la parcelle arborée dans la définition de
l’hectare admissible. Dans un deuxième temps, il est nécessaire de modifier
l’article 51 de ce même règlement afin d’indiquer que les parcelles agricoles
arborées restent éligibles.
3. Quelles normes locales lorsque l’on est face à une pratique innovante telles
que les parcelles agrisylvicoles ? Il serait judicieux qu’au niveau européen, un
avenant réglementaire précise éventuellement ces nouvelles normes. Ainsi
dans l’article 30 du règlement 796/2004, il pourrait être introduit une précision
permettant la prise en compte de la totalité de la surface agroforestière.
Dans ces propositions, nous suggérons d’apporter des rectificatifs aux règlements
1782/2003 ainsi que 795/2004 et 796/2004. Il pourrait également être envisagé de
réactualiser le document de travail AGRI/2254/2003 en conformité avec les
nouveaux règlements. La définition de la parcelle arborée serait précisée ainsi que
son éligibilité aux droits à paiement unique.
Ces propositions reprennent les propositions formulées par le groupe de travail sur
les réglementations du programme européen SAFE (Annexe 2).
Le régime d’application en France
Chaque Etat membre a défini des règlements nationaux d’application des règlements
européens.
Evolution de l’historique de l’application des règlements européens
Avant la réforme des accords du Luxembourg
Avant 2001, les surfaces arborées excluaient toute possibilité de paiement
compensatoire aux cultures intercalaires, excepté dans le cas des jeunes plantations
si ces pratiques correspondaient à des normes locales. Seuls quelques
départements ou quelques pratiques isolées bénéficiaient de ce régime particulier.
Dans les départements de la Drôme et de
l’Isère, les surfaces de cultures intercalaires
entre les jeunes noyers étaient éligibles sous
certaines conditions. Un nuciculteur
percevait un paiement forfaitaire
correspondant à 60 % du paiement
correspondant à la totalité de la surface de la
parcelle pendant 7 ans. A noter que dans le
Périgord, pour le même type de pratique,
aucun paiement compensatoire n’était
possible.
75
Dans certains départements, les cultures
intercalaires entre les peupliers étaient éligibles pour
une durée de 3 ans au prorata de la surface semée,
bien que la parcelle relevait du statut foncier de la
peupleraie.
En 2000, suite à la mobilisation du monde professionnel agricole et forestier, le
gouvernement modifia les modalités de déclaration de surface et de paiements à la
surface. Ces modifications intervinrent dans la circulaire DPEI-C2001-4008 du 8
mars 2001. Les cultures intercalaires sont devenus éligibles, quelque soit l’âge des
arbres et le lieu géographique.
A la page 14 de cette circulaire, il est spécifié :
Lorsque la culture est pratiquée sur une parcelle arborée, la superficie déclarée pour
la culture doit être corrigée proportionnellement au nombre d'arbres, leur emprise
étant calculée selon les normes usuelles de votre département. En tout état de
cause, la culture arable pour laquelle le bénéfice d'un paiement à la surface est
demandé devra pouvoir être effectuée dans des conditions comparables à celles
des parcelles non arborées dans la même région.
Des paiements à la surface au titre des cultures arables peuvent être demandés
pour des surfaces éligibles nouvellement plantées en jeunes arbres après déduction
de l'emprise (que vous établirez forfaitairement et annuellement) des jeunes arbres.
Les parcelles doivent porter des cultures éligibles pratiquées selon les usages
reconnus localement.
Extrait circ. DPEI-C2001-4008
Le premier paragraphe s’inspire directement de l’article 5 du règlement 2419/2001
de la Commission Européenne. Il ajoute néanmoins deux conditions importantes :
•
La surface de la culture éligible doit être réduite de la surface d’emprise des
arbres.
•
La méthode de calcul de la surface d’emprise des arbres doit suivre les
normes usuelles du département
Après la réforme des accords du Luxembourg
Le même principe de calcul des surfaces ouvrant droits à primes est appliqué. Il
s’appuie notamment sur l’article 51 du règlement 1782/2003 qui stipule que toute
76
surface correspondant à une production permanente doit être soustraite de la
surface éligible.
La circulaire DPEI/SPM/SDCPV/MGA/C2004 – N° 4021 du 25 mars 2004 décrit ce
principe d’application et la définition des règles à suivre sur les parcelles arborées
(paragraphe 2.3.2).
La circulaire reprend également en annexe 9 « les normes locales » et demande à
chaque département de prendre les arrêtés définissant ces normes locales.
Suite à la réforme de la PAC, une cellule d’information officielle a été créée au niveau
du MAAPAR. Suite à une question concernant l’éligibilité du maïs intercalaire entre
des peupliers, la note d’Information n°29 aux DDAF, DRAF et DDSV, rédigée par la
Direction des Politiques Economique et Internationale précise :
« Si on peut considérer que la parcelle reste agricole (pour cela, la culture
intercalaire doit pouvoir être effectuée dans des conditions comparables à celles des
parcelles non arborées dans la même région), elle sera admissible. Dans ce cas, la
surface déclarée comme «admissible» devra être corrigée proportionnellement au
nombre d’arbres, leur emprise étant calculé selon les normes usuelles du
département. »
Extrait note d’information°29 - DPEI
Conclusions
Les agriculteurs agroforestiers pénalisés
Bien que la possibilité était laissée à tout Etat membre de rendre éligible les surfaces
arborées pour des motifs environnementaux, la France a décidé de réduire la surface
éligible en fonction de la surface d’emprise des arbres, ce qui pénalise fortement les
agriculteurs souhaitant conserver ou planter des arbres ruraux. Cette décision va à
l’encontre du principe même des Bonnes Pratiques Agricoles. Seules, les surfaces
des haies entretenues peuvent être considérées éligibles à condition qu’elles
correspondent aux normes locales et que leur largeur soit comprise entre 2 et 4
m.
Conséquences possibles pour des parcelles arborées existantes
Le calcul des droits à paiement est réalisé sur un historique des aides reçues. Lors
de cet historique, la surface d’emprise des arbres a normalement été déduite de la
surface primable. Dans le cadre de la réforme, la situation ne devrait pas changer.
SAUF si l’on considère que les arbres se développant, leur surface d’emprise
continue de s’accroître… De ce fait, lors du contrôle, l’agriculteur pourrait voir réduire
ses surfaces éligibles.
Une gestion des droits qui incite à l’arrachage
Si les surfaces d’emprise des arbres sont inéligibles, le propriétaire peut être incité à
les arracher pour percevoir davantage de droits à prime. En effet, compte tenu des
nouvelles possibilités de récupération de droits par le jeu de l’offre et la demande, un
propriétaire peut acheter des droits s’il récupère des surfaces éligibles.
77
De même, en cas de perte de surface éligible suite à des aménagements de
territoires (expropriation), afin de ne pas perdre les droits, l’agriculteur peut
également être tenté de récupérer des surfaces sur son exploitation afin de
conserver ses droits.
Des normes locales quasi-inexistantes
La notion de normes locales est très ambiguë du fait que très peu de départements
ont pris les arrêtés préfectoraux correspondants. Ainsi, une récente enquête
effectuée par l’association Solagro montre que :
•
1 département sur 6 n’a pas pris (ou pas encore) d’arrêté préfectoral
concernant les usages locaux.
•
Dans les arrêtés existants, il existe une grande disparité et imprécision
concernant les méthodes de prise en compte des haies et de leur entretien.
•
Le cas des arbres isolés et des bosquets sont rarement abordés.
•
En tant que système innovant, les parcelles agroforestières associant arbres
non fruitiers et cultures intercalaires ne sont jamais pris en compte.
Propositions au niveau national
Améliorer la définition des normes usuelles
En absence de normes de calcul de la surface d’emprise des arbres, le calcul de la
surface éligible est délicat à mettre en œuvre et reste à préciser dans la plupart des
départements français. Une norme nationale apporterait une simplification
administrative dans le calcul des droits à paiement.
On peut suggérer que l’emprise des arbres corresponde à la surface de la parcelle
qui n’est pas occupée par la culture (surface complémentaire). L’avantage de cette
solution est qu’elle facilite le contrôle et les méthodes de calcul. Elle est valable
quelle que soit la dimension des arbres ou leur disposition. Elle peut cependant
prêter à discussion dans le cas de prairies pâturées, l’emprise des arbres disséminés
devenant négligeable puisque tout est pâturé y compris sous les arbres.
Une autre solution serait que la parcelle soit éligible dans sa totalité, ce qui évite le
recours à des normes locales de calcul.
Simplifier le contrôle des surfaces arborées
En instaurant un système de contrôle des surfaces par photos aériennes, il est
vraisemblable que l’on assiste à une diminution du nombre d’arbres hors forêt ou que
les arbres de hautes tiges disparaissent des haies. Si l’on peut aisément compter le
nombre d’arbres isolés par photos aériennes, la surface d’emprise correspondant à
la projection des houppiers sera plus importante que dans la réalité. On peut
reprendre ici la précédente idée de calculer la surface éligible en fonction de la
surface occupée par la culture. Il convient lors du contrôle de s’assurer que les
cultures soient effectivement menées dans des conditions normales. De même, la
78
deuxième option citée, à savoir l’éligibilité totale de la parcelle arborée, aurait le
mérite de simplifier les procédures de contrôle.
Admissibilité et éligibilité des parcelles agroforestières
La France a décidé d’opter pour un régime mixte en maintenant une aide couplée à
la production (25%) et une aide découplée, en droit à paiement unique (75%).
Cas des aides découplées
Simplifier l’approche administrative
Compte tenu que l’agroforesterie répond :
•
Aux 4 objectifs fixés par
environnementales, à savoir :
les
bonnes
conditions
agricoles
et
o protection contre l’érosion des sols grâce au maillage des lignes d’arbres
enherbées,
o maintien de la matière organique sous le double effet de l’enherbement et
de la décomposition du feuillage et des racines annuelles
o maintien de la structure des sols
o niveau minimum d’entretien, assuré par les animaux dans les zones
sylvopastorales.
•
Aux enjeux définis par les directives européennes sur l’environnement, en
particulier les directives concernant la préservation de la qualité de l’eau
(directive 91/676) et la directive sur le bien-être des animaux (directive 98/58),
L’APCA a proposé au MAAPAR que la totalité de la parcelle agroforestière soit
admissible aux aides découplées (Annexe 3).
L’admissibilité de la parcelle arborée et l’éligibilité des surfaces comportant des
arbres présente le mérite séduisant de simplifier les calculs des surfaces éligibles
ainsi que les procédures de contrôle.
Une éligibilité totale soumise à conditions
Pour être déclarée éligible dans sa totalité, la parcelle arborée devra respecter les
normes usuelles qui la distinguent de la parcelle forestière. La parcelle doit être
majoritairement agricole (culture ou pâture) et la densité d’arbres doit être inférieure
à 200 arbres par ha. La surface agricole au sol doit représenter plus de 50 %.
Les arbres double-fin, cultivés pour le bois et pour leur production fruitière, sont
éligibles à condition que la hauteur de bille soit supérieure à 2 m et nette de tout
point de greffage sur cette hauteur. Conformément à la réglementation, l’exploitant
ne pourra cumuler différentes aides sur cette surface :
o soit, il opte pour la déclaration de la surface dans le cadre du DPU,
79
o soit, il opte pour une déclaration de surface en verger. Dans ce dernier cas, la
parcelle n’est plus éligible aux droits à prime mais peut prétendre aux aides
vergers (ex aides aux fruitiers à coque).
La densité de 200 arbres/ha peut être discutée. Elle correspond en fait au cas d’une
plantation de jeunes arbres sur lesquels il sera pratiqué une éclaircie afin de
sélectionner les plus beaux. Sachant que dans tous les cas, la surface au sol doit
être majoritairement agricole, on ne pourra conserver un grand nombre d’arbres
adulte.
Une alternative serait d’imposer un critère additionnel sur le diamètre des arbres. Le
seuil serait calculé en fonction de la taille des arbres présents. La parcelle resterait
éligible si elle comporte moins de:
o 50 arbres de diamètre supérieur à 30 cm (les "gros")
o 100 arbres de diamètre supérieur à 15 cm (les "moyens")
Les arbres de petits (diamètre < 15 cm) ne comptent pas dans le critère.
Les diamètres sont pris à 130 cm du sol selon les normes dendrométriques
classiques.
En cas de parcelle hétérogène (avec mélange de gros arbres et d’arbres moyens),
une règle simple peut être proposée : 1 gros = 2 moyens. Ainsi, le seuil est égal au
total des gros arbres auquel on rajoute la moitié du total des arbres moyens. Ce
nombre doit être inférieur à 50.
En cas de dépassement du seuil, une surface forfaitaire de 100 m² est retirée pour
chaque gros arbre excédentaire. Ainsi, une parcelle perd toute éligibilité à partir du
moment où elle atteint 150 gros arbres/ha. Et elle garde 50% d'éligibilité pour 100
gros arbres/ha.
Dans tous les cas de figure, la solution proposée devra simplifier les procédures de
calcul et de contrôle. Cette deuxième solution, si elle présente le mérite d’être
rigoureuse, peut s’avérer difficile dans son application sur le terrain. Il convient d’en
discuter avec les services de contrôle (ONIC et CNASEA).
Conséquences à l’échelle de l’exploitation et au niveau national
Pour apprécier l’impact de l’admissibilité des surfaces arborées, il faut considérer le
cas des parcelles arborées existantes des nouvelles parcelles plantées.
o Cas d’une parcelle arborée existante avant la période historique de calcul
Le montant de l’aide unique ne changera pas. Lors de la période ayant servi au
calcul de l’aide, les surfaces des arbres ont été déduites des aides à la productions
ou aides SCOP. Par contre, l’agriculteur va récupérer une surface admissible ouvrant
droit à paiement unique correspondant la surface des arbres de cette période. S’il a
la possibilité, il peut donc racheter des droits disponibles.
80
Au niveau national, il n’y aura pas d’impact puisque le nombre de droits est fixe.
Dans les régions de bocage ou de pré-vergers, il y aura une grande surface
admissible, auparavant non éligible aux aides SCOP, qui va générer une forte
demande de la part des agriculteurs pour acheter des droits. Le montant des droits
devrait augmenter dans ces régions du fait du jeu de l’offre et la demande.
o Cas d’une parcelle nouvellement plantée
Dans ce cas, la plantation des arbres ne modifie pas le nombre de droits de
l’exploitation qui reste le même qu’avant la plantation.
Cas des aides couplées
Contrairement aux aides découplées, il n’est pas demandé ici de rendre éligible la
totalité des surfaces occupées par les arbres.
La raison est simple et relève de la gestion nationale des aides :
o Dans le cas des parcelles arborées existantes, les exploitants pourraient
prétendre à des aides couplées qu’il n’avait pas ou qu’ils avaient perdues lors
de la plantation (css moins fréquent). Dans certaines régions, ces surfaces
occupées par les arbres (arbres isolés, haies, pré-vergers) peuvent être très
importantes. L’ensemble des ces surfaces bénéficieraient d’aides couplées.
Au niveau national, sachant que le montant total des aides couplées reste
identique, on assistera alors à un transfert des aides des zones céréalières
non arborées (comme la Beauce ou la Picardie) vers ces régions plus
arborées. Cette décision, très politique, aurait peu de chance d’aboutir car les
groupements céréaliers ne seraient sans doute pas disposés à partager les
aides couplées dont ils bénéficiaient depuis le départ.
o Dans le cas des jeunes plantations, l’idée pourrait être de laisser l’éligibilité
totale. Mais, d’une part, cette situation serait alors inéquitable avec les
exploitations possédant déjà des surfaces arborées non éligibles. D’autre part,
cela compliquerait la gestion administrative des aides. En effet, si cette
solution parait simple aujourd’hui, qu’en sera-t-il dans 5 ans, où lors des
contrôles, il faudra distinguer les arbres de plus de 5 ans, non éligibles, des
moins de 5 ans éligibles… Dans le cas des plantations avec différents âges de
plantation, on induit des conditions qui vont rendre difficile tout contrôle ou
simplement toute instruction de dossiers d’aide.
Nous proposons toutefois une amélioration de la situation actuelle. En effet, les
calculs des surfaces éligibles aux aides couplées sont difficiles à mettre en œuvre
comme nous l’avons vu précédemment. Il est en effet très difficile de calculer les
surfaces d’emprise des arbres en absence de normes locales clairement définies. De
plus, les nouvelles méthodes de contrôle par photos aériennes déforment les
surfaces d’emprise.
Il est donc proposé, dans le cadre du calcul de la surface éligible à l’aide
couplée, que soit pris en compte la surface réellement occupée par la culture,
et non la surface agricole de laquelle on déduit la surface d’emprise des
arbres.
81
Rendre cohérent l’articulation entre 1er et 2ème pilier
Actuellement, les agriculteurs doivent soustraire les surfaces d’emprise des arbres
isolés. Dans le cadre du deuxième pilier, et notamment des CAD, ils peuvent
prétendre à une compensation dans certains départements calculée au prorata du
nombre d’arbres ou de la surface arborée. Ce que l’agriculteur perd d’un côté, il le
regagne de l’autre, mais auquel il faut rajouter un surcoût représenté par le temps
passé, l’instruction des dossier tant par l’intéressé que par l’administration. En
accordant l’éligibilité totale des surfaces arborées, on améliore l’efficacité du système
d’aide. Il serait logique dans ces conditions de simplifier les procédures de
compensation du deuxième pilier, ce qui constitue également une avancée vers une
simplification administrative. On ne conserverait alors que les MAE avec une réelle
justification environnementale. Ainsi, il faudra sans doute revoir le contenu de la MAE
Habitats Agroforestiers afin de la réserver qu’à des territoires à forts enjeux
environnementaux (zone de captage par exemple). Son cahier des charges pourrait
revu et le montant de la compensation diminué.
Intégrer les surfaces arborées dans le couvert environnemental
Parmi les dispositions que la France a prises au titre de la conditionnalité des aides,
figure l’obligation d’implanter des bandes enherbées le long des cours d’eau, puis au
delà sous forme de couvert environnemental, jusqu’à 3% des terres arables.
Au delà de l’obligation d’implantation des bandes enherbées le long des cours d’eau,
les surfaces au sol des arbres hors forêt doivent pouvoir être considérées comme
couvert environnemental au titre de l’obligation de 3 %.
LES ARBRES DANS LE DEUXIEME PILIER DE LA PAC
Le Règlement de Développement Rural
L’apparition d’une mesure Agroforesterie à l’horizon 2007
Les aides à la plantation et à l’entretien des arbres hors forêt proviennent de
différentes mesures du Règlement de Développement Rural (2000-2006),
essentiellement les lignes f (MAE), h et i (mesures forestières).
Chaque pays, en fonction de ses priorités, opte pour un choix de mesures dans le
cadre du Programme de Développement Rural National (PDRN). Nous ne ferons pas
de tour d’horizon des mesures en faveur des arbres hors forêt qui ont été retenues
dans chacun des pays membres, ce qui serait beaucoup trop long compte tenu de la
diversité des situations. Néanmoins, il convient de signaler que les dispositions en
faveur des haies et alignements d’arbres étaient envisagées, ce n’était pas le cas
pour les systèmes agroforestiers. Cette situation va certainement évoluer lors du
prochain RDR.
En effet, le projet de RDR portant sur la période 2007-2013 dont l’approbation par le
Parlement Européen et le Conseil des Ministres de l’Agriculture est prévue en mai
2005, intègre le soutien à l’agroforesterie comme nouvelle mesure.
L’apparition de cette mesure est le résultat du succès de la Recherche
Développement en France notamment dans le cadre du programme européen SAFE.
82
Ainsi, on peut lire dans le Règlement du Conseil concernant le soutien au
développement rural par le Fonds européen agricole pour le développement rural
(FEADER) du 14 juillet 2004, une description des enjeux à la création d’une mesure
de soutien à l’agroforesterie.
Considérant (38)
Les systèmes agro-forestiers ont une valeur élevée du point de vue écologique
et social puisqu'ils combinent des systèmes d'agriculture extensive et des
systèmes sylvicoles, qui ont pour objectif la production de bois et d'autres
produits sylvicoles de grande qualité. Il y a lieu de favoriser leur mise en place.
Considérant 38 du projet de RDR du 14/07/04
Cette considération amène à la proposition d’une mesure en agroforesterie. Celle
mesure se situe dans l’axe 2 du RDR au titre de l’aménagement de l’espace. Cet axe
2 comporte deux sous-sections :
1. Les mesures axées sur l’utilisation durable des terres agricoles
2. Les mesures axées sur l’utilisation durable des terres sylvicoles
La mesure de soutien à l’agroforesterie sur terre agricole est incluse dans les
mesures concernant les terres sylvicoles (sous-section 2).
La liste des mesures est spécifiée dans l’article 34. On remarquera que la mesure
agroforesterie se situe juste après la mesure de boisement de terres agricoles, dans
la même sous-section.
83
L’aide prévue au titre de la présente section concerne les mesures suivantes :
a) Mesures axées sur l’utilisation durable des terres agricoles grâce à :
i) des paiements destinés aux exploitants agricoles pour les handicaps
naturels en zone de montagne;
ii) des paiements aux exploitants agricoles situés dans des zones présentant
des handicaps, autres que ceux des zones de montagne;
iii) des paiements NATURA 2000;
iv) des paiements agroenvironnementaux et en faveur du bien-être animal;
v) un soutien aux investissements non productifs.
b) Mesures axées sur l’utilisation durable des terres sylvicoles grâce à :
i) un soutien au premier boisement de terres agricoles;
ii) un soutien à la première installation de systèmes agro-forestiers sur
des terres agricoles;
iii) un soutien au premier boisement de terres non agricoles;
iv) des paiements NATURA 2000;
v) des paiements environnementaux forestiers;
vi) un soutien à la restauration du potentiel de production sylvicole et à
l'introduction de mesures de prévention;
vii) un soutien aux investissements non productifs.
Art 34 du projet de RDR du 14/07/04
Chaque mesure est présentée dans un article spécifique. La mesure Agroforesterie
fait l’objet de l’article 41.
Première installation de systèmes agroforestiers sur des terres agricoles
1. Le soutien prévu à l’article 34, point b) ii), est accordée aux exploitants agricoles
qui mettent en place des systèmes agroforestiers combinant des systèmes
d’agriculture extensive et des systèmes de sylviculture.
L’aide couvre les coûts d'installation.
2. Par «systèmes agro-forestiers», on entend les systèmes d’utilisation des terres
qui combinent la croissance d’arbres et l’agriculture sur les mêmes terres.
3. Les sapins de Noël et les espèces à croissance rapide cultivées à court terme ne
sont pas admissibles au bénéfice de cette aide.
4. Le soutien est limité aux plafonds fixés à l'annexe I.
Article 41 du projet de RDR du 14/07/04
84
Pour la première fois dans un document officiel de la Commission Européenne, le
terme agroforesterie apparaît. Notons qu’aucune mesure n’est spécifique aux autres
formations arborées hors forêt, telles que haies, alignement ou bosquet. En fait, les
mesures de soutien prises au niveau des PDRN pourront dépendre d’une mesure de
type agroenvironnementale ou en faveur du bien-être des animaux (article 37). Cette
mesure se rapporte aux mesures sur terres agricoles (cf. art 34).
Une mesure incomplète ?
L’incorporation de la mesure en faveur de l’installation de systèmes agroforestiers
parmi les mesures sur terres sylvicoles suscite une certaine ambiguïté. Alors que le
considérant 38 reconnaît la vocation mixte des parcelles agroforestières, la mesure
Agroforesterie proposée aurait pu être incluse parmi les mesures concernant les
terres agricoles et non sylvicoles.
En fait, deux mesures en faveur de l’agroforesterie auraient pu être proposées
comme le suggère l’APCA dans la lettre au MAAPAR (voir Annexe 3) :
o Une mesure de soutien à l’agroforesterie sur terres agricoles classée dans les
mesures sur terres agricoles (1ère sous-section) : il s’agit ici d’un soutien à
l’investissement des pré-vergers hautes tiges et des parcelles agrisylvicoles.
o Une mesure de soutien à l’agroforesterie sur terres forestières classée dans
les mesures sur terres sylvicoles (2ème sous-section) : il s’agit ici d’un soutien à
la mise en place de systèmes agroforestiers dans des bois ou forêts
existantes dans un objectif de production fourragère (sylvopastoralisme) ou de
production agricole associée aux arbres (fruits forestiers, champignon, …).
Enfin, étant donné le manque d’aide clairement exprimée concernant les autres
formations arborées hors forêt, peut-être serait-il judicieux à l’avenir d’envisager une
mesure unique en faveur de l’arbre agricole dont l’application serait universelle, se
rapportant aussi bien aux haies, pré-vergers qu’aux systèmes agrisylvicoles.
Les aides disponibles en France
Les aides à la mise en place et à l’entretien des formations arborées ne sont pas
soumises aux mêmes lignes budgétaires en fonction de leurs caractéristiques.
Le cas des formations arborées hors agroforesterie
Les mesures d’aide à la plantation et l’entretien des haies et arbres isolés relèvent
généralement des MAE qui sont retenues dans les Contrats d’Agriculture Durable. Il
n’existe pas de cahier des charges national sur ce type de mesure. Selon les
départements, des enjeux prioritaires sont fixés par territoire. L’adoption d’une MAE
en faveur des haies n’est donc pas systématique et de nombreux départements
n’offrent aucune possibilité de soutien à la création de haie.
Néanmoins, il existe également différentes possibilités de financement sur des fonds
nationaux forestiers, grâce à la circulaire concernant les conditions de financement
des projets d’investissements forestiers ou d’actions forestières à caractère
protecteur, environnemental et social du 7 mai 2001 (circ. DERF/SDF/C2001-3010).
Dans cette circulaire, que nous appellerons par la suite circulaire Forêt de Protection,
85
il est spécifié que les haies et alignements peuvent bénéficier de subventions si la
largeur des plantations est supérieure à 10 m, en accord avec l’article R.126-36 du
Code Rural. Cet article mentionne en fait les caractéristiques des haies et
alignements pouvant être protégés par arrêté préfectoral. On peut se poser la
question de la légitimité de ce renvoi à cet article, dont l’objectif n’est peut-être pas
celui des personnes sollicitant une demande d’aide via la circulaire de protection. Le
planteur d’une haie ne recherche peut-être pas forcément sa mise en protection par
décision préfectorale.
Article R126-36
Les boisements linéaires, haies et plantations d'alignement susceptibles d'être
protégés en application de l'article L. 126-6 du code rural:
a) Sont constitués d'espèces ligneuses buissonnantes et de haute tige figurant sur
une liste fixée par arrêté du ministre chargé des forêts. Ils sont structurés selon
des modalités fixées par ce même arrêté;
b) Doivent avoir une surface minimale de 500 mètres carrés. La surface des haies
est égale au produit de leur longueur par une largeur forfaitaire, fixée à cinq
mètres pour les haies constituées d'espèces buissonnantes et à dix mètres pour
les haies d'arbres de haute tige.
Extrait de l’article R126-36 du code rural
Par le biais de la circulaire forêt de protection, il est ainsi possible de financer des
plantations dans le cadre de projets dont les enjeux sont purement
environnementaux (protection des ressources en eau, aménagement du paysage,
aménagement de brise-vent, etc.…).
Il convient de souligner que le financement de cette mesure dépend de la ligne i du
PDRN et non de la ligne h qui a été suspendue par le MAAPAR jusqu’en 2006. Il est
donc possible de solliciter une aide à la plantation pour des haies ou des bosquets
contrairement au cas des Boisements de Terres Agricoles conventionnels qui
dépendent de la ligne h.
Pour les opérations de boisement ou de reboisement, les prescriptions de la
circulaire DERF/SDF/C2000/3021 du 18 août 2000 s’appliquent aux opérations de
protection des ressources en eau et des sols, sous les conditions de surface
définies ci-dessous.
La surface minimale d’un projet de boisement ou de reboisement susceptible d’être
aidé dans le cadre de la protection de l’eau et des sols est de 1 ha d’un seul tenant
pour les bosquets et les boqueteaux. Les alignements et les bandes boisées devront
couvrir une surface minimum de 500 mètres carrés soit une longueur minimale de
50 mètres (l’article R.126-36 du code rural, relatif aux boisements linéaires, haies et
plantations d’alignement susceptibles d’être protégés, fixe en effet une largeur
minimale de 10 mètres pour ces structures)
Extrait circulaire DERF/SDF/C2001-3010 du 7 mai 2001 – Chap. 7
86
Le respect de ces critères entraîne automatiquement l’inéligibilité de ces surfaces
arborées dans le cadre du droit à prime du premier pilier puisque la largeur se situe
au dessus du seuil de 4 mètres. On constate donc que peu d’agriculteurs ne
souhaitent financer la plantation des haies via cette circulaire et font plutôt appel,
lorsque cela est possible à des fonds issus des CAD, plus souples en matière de
cahier des charges.
Néanmoins, ces plantations arborées sont éligibles à la prime de compensation à la
perte de revenu agricole (PCPR). Cette éligibilité est définie dans la circulaire du 8
août 2001 à la page 10.
- Haies, bosquets, boisements linéaires, plantations truffières
Les plantations qui visent d’autres fins que la production de bois à titre principal,
telles que les haies, bosquets, boisements linéaires, plantations truffières, réalisés
dans des conditions ouvrant droit au soutien financier de l'Etat (notamment circulaire
DERF/SDF/C2000-3010 du 7 mai 2001) ou de collectivités territoriales, sont
également éligibles à la prime.
Le montant de la prime sera fixé par calcul de la surface équivalente avec une
largeur forfaitaire de 10 m par rang de plantation.
Extrait de la Circulaire DERF/SDF/C2001-3020 - DEPSE/C2001-7034 du 08 août
2001
Par contre, cette mesure est financée grâce à la ligne h du PDRN qui est donc
suspendue jusqu’en 2006.
Le cas des systèmes agroforestiers
Les aides à la plantation
Les textes officiels
Bien que le RDR correspondant à la période 2000-2006 ne prévoyait pas de mesure
spécifique en faveur de l’agroforesterie, le gouvernement français a fait figure de
précurseur en imaginant une aide à la plantation, basée sur le même principe que les
aides aux boisements des terres agricoles. Cette disposition allait fortement
influencer la création de l’article 41 du prochain RDR.
Ainsi dans la même circulaire forêt de protection citée au paragraphe précédent
(circulaire DERF/SDF/C2001-3010 du 7 mai 2001), après une description de l’intérêt
l’agroforesterie, une mesure spécifique de soutien est spécifiée dans le chapitre 10
(Annexe 4).
Elle autorise l’application d’une subvention comprise entre 20 et 50% du montant des
travaux à la plantation et des 3 premiers entretiens (établi selon un forfait régional ou
sur devis estimatif). Ce montant peut-être majoré jusqu’à 20% en fonction du zonage
et s’il s’agit d’un projet collectif.
87
- la plantation d’arbres, à titre expérimental, capables de donner du bois de qualité,
dans des parcelles agricoles, dans le cadre d’un projet agroforestier formalisé à l’échelle
de l’exploitation agricole, et suivi par un organisme de recherche (INRA, Cemagref,
AFOCEL) ou de développement (IDF, CRPF, chambre d’agriculture…).
Nota : les caractéristiques de ces expérimentations liées à l’agroforesterie, incluant
l’engagement écrit du bénéficiaire de l’aide concernant les soins apportés aux arbres
(protections contre les animaux, si besoin est, entretiens, tailles de formation et élagages
pendant 15 ans) sont adressées au Cemagref de Nogent sur Vernisson (45) par le DDAF du
département d’implantation. Cinq à dix ans après la clôture financière de l’opération, la
DDAF adresse à la direction en charge de la politique forestière, à la DRAF et au Cemagref,
un rapport technique sur les résultats de ces expérimentations.
Extrait circulaire DERF/SDF/C2001-3010 du 7 mai 2001 – Chap. 10
Une mesure encourageante mais difficile d’application
Pour la première fois en France, le terme « Agroforesterie » est cité dans un texte
officiel. Pour la plupart des acteurs agroforestiers, il s’agit d’un grand pas en avant
dans la reconnaissance de cette pratique.
Tout agriculteur ou propriétaire en France peut donc solliciter une demande de
financement pour la mise en place d’un projet agroforestier.
Mais deux dispositions dans la mesure d’aide ont considérablement freiné son
application dans les différents dossiers qui ont été présentés dans les départements
français :
o La dérogation à titre expérimental : de nombreuses DDAF se replient derrière
cette disposition soit pour refuser le financement, soit, et ce qui est légitime,
en demandant un suivi par un des organismes cités. La demande de suivi est
souvent liée à l’obligation d’une convention de suivi entre l’organisme et le
porteur du projet. Or, rien n’est spécifié sur le contenu du suivi ni sur les
modalités de financement qui pourrait finalement incomber au propriétaire.
o Le financement de cette mesure est accordé au titre des mesures
environnementales. La vocation de production des parcelles agroforestières
n’est pas reconnue dans ce schéma de financement.
D’autre part, le manque d’un cahier des charges explicite dans la circulaire
concernant les plantations agroforestières, soulève souvent des questions de la part
des techniciens encadrant le projet : le projet doit-il être d’un seul tenant ? Y a-t-il une
surface minimale par projet et par îlot ? Peut-on autoriser le mélange pied à pied
d’autorisation plus contraignante administrativement dans le cas des boisements
forestiers conventionnel ? Peut-on autoriser plus de 4 essences objectifs ? Doit-on
forcément se référer à la liste des essences de la circulaire forêt de production
DERF/SDF/C2000-3021 du 18 août 2000 pour un projet agroforestier ? Une question
également soulevée par les techniciens concerne la surface à prendre en compte
dans une parcelle agroforestière en cas de contrôle : la surface totale ou la surface
occupée par les arbres ?
88
En fait, il est dit clairement que tout projet doit respecter les directives définies par la
circulaire DERF/SDF 2000/3021 du 18 août 2000 sauf pour les conditions de
surfaces minimales. Celles-ci sont définies pour les haies (50 m de longueur pour 10
m de large) ou pour les bosquets (1 ha) mais par pour les surfaces agroforestières.
Les directives techniques applicables aux BTA qu’il est demandé de suivre vont
même à l’encontre de ce qui est généralement proposé en agroforesterie. Ainsi, un
mélange pied à pied des essences est tout à fait possible en agroforesterie. D’autre
part, un projet agroforestier n’a pas forcément vocation à former un massif continu
avec un bois ou une forêt adjacente. Enfin, en agroforesterie, il est possible d’élargir
le choix des essences à planter et la liste proposée dans la circulaire
DERF/SDF/C2000-3021 est plutôt contraignante pour les porteurs de projets
agroforestiers.
Devant les résultats acquis par la recherche prouvant la faisabilité technique et
économique de ce type de système d’une part, et la forte demande des agriculteurs
en France d’autre part, l’APCA souhaite l’annulation de l’obligation de suivi de la part
d’un organisme professionnel. D’autre part, afin de régler certains litiges techniques,
il serait souhaitable de clarifier davantage le cahier des charges de l’agroforesterie et
de ne pas le calquer sur celui des BTA dont le concept et les objectifs sont très
différents.
Enfin, il convient de souligner que le financement de cette mesure dépend comme
pour les autres formations hors forêt de la ligne i du PDRN et non de la ligne h qui a
été suspendue par le MAAPAR jusqu’en 2006. Il est donc possible de solliciter une
aide à la plantation contrairement au cas des Boisements de Terres Agricoles
conventionnels qui dépendent de la ligne h.
La compensation à la perte de revenu
Les textes officiels
Parallèlement à l’adoption d’une mesure de soutien à l’investissement, le
gouvernement français a également modifié la mesure de compensation de revenu
agricole pour boisement sur terres agricoles afin d’inclure une disposition en faveur
des plantations agroforestières. Cette modification, comme pour l’éligibilité des haies
à la PCPR précédemment abordée, intervient dans la circulaire DERF/SDF/C20013020 - DEPSE/C2001-7034 du 08 août 2001 à la page 11.
89
- Boisements agroforestiers
La circulaire DERF/SDF/C2000-3010 du 7 mai 2001 précise les modalités de
financement par l’Etat et/ou les collectivités territoriales de projets de boisements
agroforestiers. Ces derniers sont éligibles à la prime de compensation de perte de
revenu. Par ailleurs, la circulaire DPEI/SPM/C2001-4008 du 8 mars 2001 prévoit
que des primes à la surface au titre des cultures arables peuvent être versées pour
des terres en partie plantées d’arbres, dont l’emprise est déduite des surfaces
agricoles éligibles.
Les surfaces éligibles au boisement agroforestier correspondent, dans le respect
des itinéraires techniques définis régionalement, à la somme des surfaces boisées,
celles-ci ne bénéficiant pas d’un paiement à la surface agricole.
Le demandeur s’assurera annuellement pour chaque parcelle cultivée en
agroforesterie que la surface boisée déclarée, cumulée avec la surface agricole
déclarée, n’est pas supérieure à la surface totale de la parcelle.
Comme pour les surfaces agricoles éligibles aux paiements à la surface, les
surfaces conduites en agroforesterie feront chaque année l’objet d’une déclaration
de surface éligible à la prime de compensation de perte de revenu découlant du
boisement de terres agricoles. La déclaration sera adressée chaque année par le
bénéficiaire à la DDAF avant le 30 avril.
En raison de la variation annuelle de la surface couverte par le boisement, les
projets agroforestiers seront intégrés dans l’analyse de risque en vue de la sélection
des dossiers à contrôler sur place.
Extrait de la Circulaire DERF/SDF/C2001-3020 - DEPSE/C2001-7034 du 08 août 2001
Le montant de la prime pour les agriculteurs (prime A) est le double de celui de la
prime des propriétaires non agriculteur (prime B). Il est compris entre 100 et 350
euros pour la prime A, entre 50 et 175 euros pour la prime B.
Le seuil financier minimum pour la constitution d’un dossier de demande de prime
annuelle est fixé à 100 euros, sauf pour les projets de plantation de peupliers et
noyers éligibles aux aides à l’investissement de l’Etat. Pour ces dernières essences,
la surface minimale par projet est de 1 ha avec 0.5 ha par îlot pour le noyer.
Faut-il maintenir cette disposition ?
Avant toute chose, comme pour les haies et bosquets ou les BTA, cette mesure est
financée grâce à la ligne h du PDRN qui est donc suspendue jusqu’en 2006.
Aujourd’hui cette aide est donc inaccessible à tout porteur de projet y compris
agroforestier.
Tout comme pour les haies, la compensation auquel a droit l’agriculteur est
relativement faible compte tenu de la surface en jeu.
En agroforesterie, les surfaces occupées par les arbres représentent entre 5 et 10 %
de la surface de la parcelle. Si la prime est de 100 €/ha (montant minimum) cela
90
représente entre 5 et 10 € de compensation par ha agroforestier. Si la prime est de
350 €/ha (montant maximum), cela représente entre 17.5 et 35 € par ha
agroforestier. Pour atteindre les seuils minimum de présentation d’un dossier qui est
de 100 €/dossier, il faut donc réaliser un projet:
o De 10 à 20 ha lorsque la prime est de 100 €
o De 3 à 6 ha lorsque la prime est de 350 €
L’intérêt de présenter un dossier de sollicitude de la PCPR (si celle-ci venait à être
rétablie), dépend donc du type de projet et du montant de la prime à l’hectare boisé.
Mais, au-delà de ces aspects financiers, si l’on considère l’agroforesterie comme
une pratique agricole, et surtout si l’on attribuait l’éligibilité aux paiements
compensatoires de ces formations arborées, l’obtention de la PCPR ne se justifie
plus. Pour des raisons de simplifications administratives, il est souhaitable de ne pas
modifier l’éligibilité de la parcelle plutôt que de réduire le paiement compensatoire
pour solliciter ensuite un paiement de compensation à la perte de revenu…
La MAE Habitats Agroforestiers
Présentation de la mesure
En novembre 2001, le comité STAR de Bruxelles approuve la MAE Habitats
Agroforestiers, comportant deux volets : les MAE 2201 et 2202.
« Cette mesure consiste, pour l'agriculteur volontaire, à créer et/ou entretenir des
habitats agroforestiers dans des parcelles où les activités agricoles - cultures ou
élevage - sont pratiquées en présence d'arbres espacés disséminés sur l’ensemble
de la parcelle. »
Comme toute MAE, la mesure compense un surcoût pour l’agriculteur que
représente l’adoption d’une mesure environnementale (voir Annexe 5: Texte MAE
Habitats Agroforestiers).
La MAE Habitats Agroforestiers distingue le soutien en faveur des jeunes plantations
(volet 2201) du soutien en faveur de l’entretien d’habitats de plus de 5 ans (volet
2202).
Quel avenir pour la MAE Habitats Agroforestiers ?
La MAE Habitats Agroforestiers est une MAE reconnue comme mesure nationale
dans le cadre des Contrats Territoriaux d’Exploitation (CTE). Tout comme les 5
autres mesures nationales de 2001, tout porteur de CTE pouvait adopter la MAE
agroforesterie, quelque soit sa position géographique. Le cahier des charges était
identique sur tout le territoire national.
Suite à l’adoption des Contrats d’Agriculture Durable et à l’abandon des CTE, la MAE
Agroforesterie tout comme deux autres MAE ont été écartées des mesures
nationales. Son application n’est aujourd’hui possible que si les régions l’inscrivent
parmi les mesures utilisables dans les CAD départementaux. Cette éventualité a
rarement été le cas vu le faible nombre de mesures possibles pouvant être retenues
91
au niveau de chaque département. L’APCA a d’ailleurs signalé cette difficulté et
demandé au Ministre de l’Agriculture de réintégrer la MAE Agroforesterie comme
mesure prioritaire au niveau national. Cette demande a pour l’instant était refusée
(voir Annexe 6: Lettre de Luc Guyau APCA au MAAPAR – MAE).
Il est vraisemblable que sans décision allant dans le sens de la demande de l’APCA,
le nombre de bénéficiaires de cette mesure restera extrêmement faible…
Faut-il revoir le cahier des charges ?
Dans l’hypothèse où les parcelles agroforestières pourraient bénéficier de l’éligibilité
au paiement compensatoire, il serait envisageable de modifier la MAE existante afin
de l’affecter uniquement à des systèmes agroforestiers à objectifs clairement
environnementaux.
Dans cet objectif, il serait judicieux de revoir le contenu de la MAE Habitats
Agroforestiers afin de la réserver qu’à des territoires à forts enjeux environnementaux
(zone de captage par exemple). Son cahier des charges pourrait être revu en
proposant par exemple des conditions d’enherbement au pied des arbres sur une
largeur qu’il faudrait déterminer. Le montant de la compensation serait également à
revoir. A priori, il serait légitime de soustraire du montant existant un montant au
moins égal au montant du paiement compensatoire pour la surface occupée par les
arbres…
Quelles conséquences avec l’application du prochain RDR ?
Une reconnaissance facilitée de l’agroforesterie
L’application de la mesure Agroforesterie de l’article 41 du RDR devrait favoriser une
meilleure clarté dans le soutien à l’agroforesterie en France. Les projets
agroforestiers pourront être éligible à une subvention à l’investissement sans être
considérés comme environnementaux ni expérimentaux.
Homogénéiser le soutien aux formations arborées hors forêt ?
Lors de l’élaboration de la circulaire Forêt de Protection, le gouvernement français
avait fait un premier pas pour tenter d’homogénéiser les mécanismes de financement
en faveur des formations arbores hors forêt. Mais l’application des différents cahiers
des cahiers des charges entraient souvent en collision avec le cahier des charges
des règlements d’application de du premier pilier. D’autre part, les sources de
financement pour certaines formations arborées, comme les haies dépendaient de
deux sources de financement possibles (circulaire protection et CAD).
Lors des premiers travaux concernant le prochain PDRN, il semble que nous allons
évoluer vers une programmation à deux volets : un volet national avec des mesures
d’application national et un volet déconcentré où les mesures seraient d’application
régionale. Une politique de soutien intéressante aux arbres hors forêt pourrait être
l’établissement d’une seule mesure, de type nationale, en faveur de l’arbre rural. Une
subvention individuelle à l’arbre, avec un plafond délimité par des conditions de
densité à l’hectare permettrait sans aucun doute de simplifier les méthodes de
soutien actuel. En tout état de cause, cette mesure devra être nationale. Il semble
92
essentiel que tout agriculteur puisse recevoir un soutien pour un projet agroforestier
ou de plantation de haies, quelque soit le lieu géographique.
BIBLIOGRAPHIE
Clos, Blanchard, (2001) Rapport au Ministère de l’Agriculture « CTE et relation en
agriculture et Forêt », 6 p
Coulon F, Dupraz C., Liagre F., Pointereau P. (2000) Etude des pratiques
agroforestières associant des arbres fruitiers de haute tige à des cultures et pâtures,
Rapport au ministère de l’environnement, 199 p, Solagro/INRA, Fr
Dupraz C., Lagacherie M., Liagre F., Boutland A., (1995). Perspectives de
diversification des exploitations agricoles de la région Midi-Pyrénées par
l’agroforesterie. Rapport de fin d’étude commandité par le Conseil Régional MidiPyrénées, Inra-lepse éditeur, Montpellier, 253 pp.
Dupraz C., Lagacherie M., Liagre F., Cabannes B., (1996). Des systèmes
agroforestiers pour le Languedoc-Roussillon. Impact sur les exploitations agricoles et
aspects environnementaux. Inra-Lepse éditeur, Montpellier, 418 pp.
Grousset E, Pointereau P, (2005) Rapport sur la prise en compte de l’arbre
champêtre dans les soutiens européens, 25 p, SOLAGRO, Fr
Liagre F., (1993). Les pratiques de cultures intercalaires dans la noyeraie fruitière du
Dauphiné. Mémoire de Mastère en Sciences Forestières, ENGREF, Montpellier, 80
pp
Mémorandum Agroforestier (1999) 6 p.
Rapports de Recherche du programme européen SAFE Partner 9 : APCA (20022005)
Rapports de Recherche du programme européen SAFE Work Package 9 (20022005)
Roux V., (1996). Les formations boisées hors forêt: aspects juridiques et fiscaux.
APCA et Ministère de l’Agriculture et de la Pêche, éditeurs, Paris, 144 pp.
SCAFR, (1999) Mise en place d’un statut spécifique pour les parcelles
agroforestières. Rapport final, Ministère de l’Agriculture, DERF, Paris, avril 1999.
Segouin O., Valadon A., (1997) Enquête sur les boisements récents de peupliers en
Lot-et-Garonne, Analyse de pratiques agroforestières ; les cultures intercalaires.
Cemagref, Nogent-sur Vernisson, 45 pp.
SRFB Languedoc-Roussillon, (1998). Recherche d’un statut pour les parcelles
agroforestières. Rapport final du groupe de travail sur l’agroforesterie, 8pp + annex.
Site du programme SAFE : www.montpellier.inra.fr/safe
Appendices
93
ANNEXE 1: ARTICLE TRANSRURAL INITIATIVE DE DEC 2004
94
ANNEXE 2: PROPOSITIONS DU GROUPE REGLEMENTATIONS – SAFE
Changes in the CAP due to be introduced on 1 January 2005 have major implications
for future uptake of agroforestry and the following lobbying statement was agreed.
Safe members should translate and send to appropriate authorities in their countries.
The CAP mid-term review approved on 26th June 03 led to Regulation 1782/03 (29th
September 03) defining the conditions for the Single Payment Scheme (SPS). This
includes a provision that areas of 'woodland' should be excluded from the area of the
farm eligible for SPS. Recommendation: this definition of woodland should be
clarified to ensure that it does not lead to the removal of trees from farmed
landscapes, and accompanying landscape and environmental damage.
Guidance Document (AGRI/2254/2003) recommends that the threshold of 'woodland'
is > 50 stems per ha. The specific wording is 'areas of trees - particularly trees with a
potential use only for wood production - inside an agricultural parcel with density of
more than 50 trees/ha should, as a general rule, be considered as ineligible <for the
Single Payments Scheme>. Exceptions may be envisaged for tree classes of
mixed-cropping such as orchards and for ecological/ environmental reasons.
Eventual exceptions must be defined beforehand by the member states'.
Recommendation: ‘mixed-cropping’ is an imprecise term which also covers
herbaceous mixtures and should be replaced by in AGRI/2254/2003 by
‘agroforestry’.
Article 5 of Regulation 2419/01 indicates that: 'a parcel that both contains trees and is
used for crop production covered by Article 1 of Regulation (EEC) No 3508/92 shall
be considered an agricultural parcel provided that the production envisaged can be
carried out in a similar way as on parcels without trees in the same area'. This article
is the basis of the dispensation in AGRI/2254/2003. It is important that agroforestry
remains classified as ‘agriculture’. Recommendation: wording of Article 5 of
Regulation 2419/01 should be retained in any replacement Regulation.
There are internationally accepted definitions of ‘forest’ or ‘forest land’ used by the
UN-ECE/FAO and the UNFCCC which use threshold values of crown cover, tree
height at maturity, minimum area and bounding areas. However ‘woodland’ as used
in EU Regulation (1782/03) is less well defined. Recommendation: 50 trees per
ha is an acceptable definition of ‘woodland’ for the purposes of 1782/03, but
should be clarified to say ’50 trees per ha of more than 15cm diameter at breast
height’.
Crop and pastoral production can maintain acceptable production beneath wellpruned trees at densities greater than 50 trees per ha. Recommendation: a) for
silvoarable systems - SPS can be paid for the cultivated proportion of a parcel
provided that at least 50% of the parcel is cultivated; b) for silvopastoral
systems – SPS payments can be maintained provided that more than 50% of
the non-shaded pasture production is maintained.
Some countries declare parcels as x% covered by one ‘activity’ or y% covered by
another ‘activity’ or even ‘owner’. Recommendation: the EU should make clear to
all EU countries that they have the flexibility to allow multiple activities within
95
parcels in their national IACS systems (e.g. ‘forestry’ and ‘cropping’ in the
same parcel)
A farmer will loose SPS payments if he introduces a non cereal, pasture or fodder
crop, including ‘perennial crops’. Recommendation: it should be made clear that
that trees planted at agroforestry spacings do not constitute a ‘perennial crop’.
Farmers obtaining the SPS are obliged to demonstrate that they maintain the farm in
‘good agricultural and environmental condition’. Annex IV of Regulation1782/03
indicates that one condition is ‘avoiding encroachment of unwanted vegetation on
agricultural land’. Recommendation: national definitions of ‘good agricultural
and environmental condition’ could include the phrase ‘well-managed
agroforestry is recognized as a mechanism of improving landscape and
environmental diversity’.
Regulation 2237/03 Chapter 5 indicates that payments to nut trees orchards will NOT
be made if these are intercropped. Recommendation: the annual per nut-tree
payment should only be removed if the intercrops are subsidized as part of the
Single Payment Scheme.
96
ANNEXE 3: LETTRE DE LUC GUYAU APCA AU MAAPAR – PAC
Monsieur Hervé GAYMARD
Ministre de l’Agriculture, de l’Alimentation,
de la Pêche et des Affaires Rurales
78, rue de Varenne
75007 PARIS
Paris, le 16 novembre 2004
Monsieur le Ministre,
L’agroforesterie connaît un écho grandissant auprès des agriculteurs et des
collectivités territoriales. Cet intérêt croissant se traduira, en 2005, par 1000 ha
supplémentaires de parcelles forestières sur terres agricoles.
L’agroforesterie représente, en effet, un atout au sein de l’exploitation agricole. Elle
en enrichit la valeur patrimoniale, améliore les performances agro-environnementales
du système d’exploitation et permet à l’agriculteur de maintenir son revenu réel, tout
en investissant pour l’avenir.
L’agroforesterie constitue, donc, une voie de développement qui répond à des
exigences économiques et environnementales, mais aussi sociétales, du fait de son
empreinte dans le territoire et de son impact paysager.
A cet égard, nous nous félicitons que le projet de règlement européen du 14 juillet
2004 ait prévu de soutenir l’agroforesterie, se basant sur les actions de recherche
menées conjointement par l’INRA et les Chambres d’Agriculture. Toutefois, la
rédaction de l’article 34 introduit une ambiguïté en limitant la mesure du soutien à
l’agroforesterie sur les terres forestières exclusivement. Nous vous proposons un
amendement afin de clarifier le contenu en distinguant les deux types de système
agroforestier : forestier et agricole.
Par ailleurs, les avantages environnementaux tirés de l’agroforesterie sont
multiples (biodiversité, paysage, protection climatique, des sols et des eaux), il est
donc indispensable que la totalité de la parcelle agroforestière soit admissible aux
aides découplées, et que les rangées d’arbres de parcelles agroforestières soient
considérées comme couvert environnemental au titre de l’obligation de 3 % dans les
bonnes conditions agricoles et environnementales.
La France a acquis une avance reconnue au niveau européen. Il serait donc
regrettable que la déclinaison française de l’Accord de Luxembourg et que le
nouveau règlement développement rural viennent annihiler l’investissement et les
efforts entrepris depuis plusieurs années.
En espérant que ces propositions retiendront toute votre attention, je vous prie
d’agréer, Monsieur le Ministre, l’expression de ma haute considération.
Luc GUYAU
PJ : annexes pour propositions d’amendement
97
APCA-SDPAR
PROPOSITION
DE MODIFICATION DU PROJET DE REGLEMENT EUROPEEN
CONCERNANT LE SOUTIEN AU DEVELOPPEMENT RURAL (PROPOSITION DU
14/07/04)
L’axe 2 du RDR au titre de l’aménagement de l’espace comporte deux sous-sections
:
1. Les mesures axées sur l’utilisation durable des terres agricoles
2. Les mesures axées sur l’utilisation durable des terres sylvicoles
La sous-section 2 comprend la mesure de soutien à l’agroforesterie sur terre
agricole.
Cette rédaction est ambiguë. En effet, cette mesure qui soutient l’agroforesterie sur
terre agricole se situe dans la sous-section des mesures forestières. Afin d’éviter
toute confusion, il est proposé de distinguer les deux types de systèmes
agroforestiers et d’intégrer une mesure de soutien à l’agroforesterie sur terre
agricole dans la première sous-section et une mesure de soutien à l’agroforesterie
sur terre forestière dans la deuxième sous-section.
Cette distinction demande une modification des articles 34 et 41 ainsi que
l’introduction d’une nouvelle mesure donnant lieu à un nouvel article.
Modification de l’article 34
Il est ajouté un point vi à l’article 34 a). La rédaction de l’article 34 a) serait la
suivante (modifications proposées en gras) :
Article 34
L’aide prévue au titre de la présente section concerne les mesures suivantes:
a) Mesures axées sur l’utilisation durable des terres agricoles grâce à :
i) des paiements destinés aux exploitants agricoles pour les handicaps naturels en
zone de montagne;
ii) des paiements aux exploitants agricoles situés dans des zones présentant des
handicaps, autres que ceux des zones de montagne;
iii) des paiements NATURA 2000;
iv) des paiements agroenvironnementaux et en faveur du bien-être animal;
v) un soutien aux investissements non productifs.
vi) un soutien à la première installation de systèmes agroforestiers sur des
terres agricoles.
98
b) Mesures axées sur l’utilisation durable des terres sylvicoles grâce à :
i) un soutien au premier boisement de terres agricoles;
ii) un soutien à la première installation de systèmes agro-forestiers sur des terres
forestières;
iii) un soutien au premier boisement de terres non agricoles;
iv) des paiements NATURA 2000;
v) des paiements environnementaux forestiers;
vi) un soutien à la restauration du potentiel de production sylvicole et à l'introduction
de mesures de prévention;
vii) un soutien aux investissements non productifs.
Le point a – vi) donne lieu à nouvel article.
Proposition d’article pour l’agroforesterie sur terres agricoles
Dans la sous-section 1 (Conditions relatives aux mesures en faveur d’une utilisation
durable des terres agricoles), on ajoute un nouvel article rédigé comme suit :
Article 39
Première installation de systèmes agroforestiers sur des terres agricoles
1. Le soutien prévu à l’article 34, point a) vi), est accordée aux exploitants agricoles
L’aide couvre les coûts d'installation.
2. Par «systèmes agro-forestiers», on entend les systèmes d’utilisation des terres
qui combinent la croissance d’arbres et l’agriculture sur les mêmes terres.
3. Les sapins de Noël et les espèces à croissance rapide cultivées à court terme ne
4. Le soutien est limité aux plafonds fixés à l'annexe I.
Modification de l’article 41 (qui devient 42)
L’article 41 de la sous-section 2 (Conditions relatives aux mesures en faveur d’une
utilisation durable des terres sylvicoles), concerne la mesure en faveur de
l’agroforesterie sur terres forestières. Il convient d’adapter le contenu actuel de
l’article.
99
Proposition de rédaction :
Article 42
Installation de systèmes agroforestiers sur des terres sylvicoles
1.Le soutien prévu à l’article 34, point b) ii), est accordée aux exploitants agricoles
L’aide couvre les coûts de l’aménagement.
2.Par «systèmes agroforestiers», on entend les systèmes d’utilisation des terres qui
combinent la croissance d’arbres et l’agriculture sur les mêmes terres.
3.Les sapins de Noël et les espèces à croissance rapide cultivées à court terme ne
4.Le soutien est limité aux plafonds fixés à l'annexe I.
Propositions concernant l’agroforesterie dans le cadre de l’application des
accords du Luxembourg
1. Agroforesterie et DPU
Le document de travail AGRI/2254/2003 recommande que le seuil pris en compte
pour caractériser une parcelle arborée soit de 50 tiges par ha. Au-delà, la parcelle
devient inéligible au titre du PU sauf dérogation pour des motifs agroenvironnementaux.
Il est également spécifié dans le règlement 1782/03 que l’agriculteur perd ses droits
à paiements pour les surfaces mises en cultures pérennes (article 51).
Néanmoins, le premier principe de l’article 8 du règlement d’application 796/2004
spécifie qu’ « une parcelle boisée est considérée comme une parcelle agricole aux
fins du régime d’aide « surfaces » sous réserve que les activités agricoles visées à
l’article 51 du règlement (CE) n° 1782/2003 ou, le cas échéant, que la production
envisagée puissent se dérouler comme elles se dérouleraient sur des parcelles non
boisées situées dans la même zone. »
Proposition
Compte tenu que l’agroforesterie répond aux :
4 objectifs fixés par les bonnes conditions agricoles et environnementales, à
savoir :
protection contre l’érosion des sols grâce au maillage des lignes d’arbres enherbées,
maintien de la matière organique sous le double effet de l’enherbement et de la
décomposition du feuillage et des racines annuelles
100
maintien de la structure des sols
niveau minimum d’entretien, assuré par les animaux dans les zones sylvopastorales.
enjeux définis par les directives européennes sur l’environnement, en
particulier les directives concernant la préservation de la qualité de l’eau (directive
91/676) et la directive sur le bien-être des animaux (directive 98/58),
Il est proposé que la totalité de la parcelle agroforestière soit admissible aux aides
découplées.
Pour cela, la parcelle agroforestière devra respecter les normes usuelles en
agroforesterie qui la distinguent de la parcelle forestière, la parcelle doit être
majoritairement agricole (culture ou pâture) et la densité d’arbres doit être comprise
entre 50 et 200 arbres par ha.
Les arbres double-fin, cultivés pour le bois et pour leur production fruitière, sont
éligibles à condition que la hauteur de bille soit supérieure à 2 m et nette de tout
point de greffage sur cette hauteur. Conformément à la réglementation, l’exploitant
ne pourra cumuler différentes aides sur cette surface :
soit, il opte pour la déclaration de la surface dans le cadre du DPU,
soit, il opte pour une déclaration de surface en verger. Dans ce dernier cas, la
parcelle n’est plus éligible aux droits à prime mais peut prétendre aux aides vergers
(ex aides aux fruitiers à coque).
2. Agroforesterie - BCAE
Parmi les dispositions que la France a prises au titre de la conditionnalité des aides,
figure l’obligation d’implanter des bandes enherbées le long des cours d’eau, puis au
delà sous forme de couvert environnemental, jusqu’à 3% des terres arables.
Proposition
Au delà de l’obligation d’implantation des bandes enherbées le long des cours d’eau,
les rangées d’arbres de parcelles agroforestières doivent pouvoir être considérées
comme couvert environnemental au titre de l’obligation de 3 %.
101
ANNEXE 4: CHAPITRE 10
01
DE LA CIRCULAIRE FORET DE PROTECTION DU
7
MAI
10 - CREATION OU RESTAURATION DES FORMATIONS ARBOREES HORS
FORET
La politique forestière, tout en privilégiant clairement les investissements en forêt, a
été progressivement amenée à s’intéresser aux formations arborées hors forêts,
telles que les haies, les bosquets et boqueteaux, ainsi qu’à participer à des
expérimentations qui peuvent préfigurer d’une nouvelle association entre l’agriculture
et la forêt, comme l’agroforesterie. Dans les zones faiblement boisées, de telles
formations arborées hors forêt peuvent en effet contribuer à préserver ou restaurer
la diversité biologique, à structurer le paysage, à fixer les sols, tout en jouant un rôle
de production de bois d’œuvre (pour des essences précieuses) et de feu pour les
propriétaires. Elles peuvent donc, sous certaines conditions, bénéficier des aides
aux investissements forestiers à caractère protecteur, environnemental et social.
Nota : les dispositions prévues au III (Aides directes) de la circulaire
DERF/SDEF/N°3016 du 27 septembre 1995 sont abrogées.
10.1 CONDITIONS GENERALES D’ELIGIBILITE
10.1.1 OPERATIONS ELIGIBLES
Au titre du PDRN le cofinancement est assuré par la mesure i.1, sont éligibles :
- les opérations de plantation destinées à créer de nouvelles haies arborées, selon
des critères techniques fixés au niveau régional, sur proposition des préfets de
département, et s’inscrivant dans des usages locaux traditionnels, en particulier les
haies brise-vent destinées à limiter l’évapotranspiration, ainsi que le renforcement du
réseau de boisement linéaire ;
- les opérations de boisement ou reboisement, dans les zones faiblement boisées,
de bosquets ou boqueteaux présentant un fort intérêt au titre de la diversité
biologique et des paysages, compatibles avec une politique raisonnée d’occupation
de l’espace rural, et répondant à des critères techniques fixés au niveau régional sur
proposition des préfets de département ;
- la plantation d’arbres, à titre expérimental, capables de donner du bois de qualité,
dans des parcelles agricoles, dans le cadre d’un projet agroforestier formalisé à
l’échelle de l’exploitation agricole, et suivi par un organisme de recherche (INRA,
Cemagref, AFOCEL) ou de développement (IDF, CRPF, chambre d’agriculture…).
Nota : les caractéristiques de ces expérimentations liées à l’agroforesterie, incluant
l’engagement écrit du bénéficiaire de l’aide concernant les soins apportés aux arbres
(protections contre les animaux, si besoin est, entretiens, tailles de formation et
élagages pendant 15 ans) sont adressées au Cemagref de Nogent sur Vernisson
(45) par le DDAF du département d’implantation. Cinq à dix ans après la clôture
financière de l’opération, la DDAF adresse à la direction en charge de la politique
forestière, à la DRAF et au Cemagref, un rapport technique sur les résultats de ces
expérimentations.
102
Les travaux éligibles dans le cadre de la mesure i.1 sont :
- élimination de la végétation préexistante
- préparation du sol
- fourniture et mise en place de graines et plants d’une espèce ou d’une provenance
génétique adaptée à la station en conformité avec la réglementation sur le matériel
forestier de reproduction en vigueur
- les trois premiers entretiens
- les travaux annexes indispensables (fossés, protection contre le gibier, les insectes
ravageurs et les champignons pathogènes) dans la limite des plafonds fixés au
niveau régional
- maîtrise d’œuvre des travaux et leur suivi par un expert forestier ou un homme de
l’art agréé, avec un montant maximal de 10% du coût total des travaux
- desserte interne au chantier et son raccordement sur une voirie opérationnelle
- étude préalable d’impact environnemental ou d’insertion paysagère pour un
montant maximal de 10% du coût total des travaux.
10.1.2 CONDITIONS D’OCTROI DES AIDES
Outre le niveau minimum d’investissement financier requis pour rendre recevable
une demande d’aide (1000 Euros) et le respect des directives définies par la
circulaire DERF/SDF 2000/3021 du 18 août 2000, en dehors des conditions de
surface, les opérations devront couvrir une surface minimum de 500 mètres carrés
soit, pour les haies arborées, une longueur minimale de 50 mètres (l’article R.126-36
du code rural, relatif aux boisements linéaires, haies et plantations d’alignement
susceptibles d’être protégés, fixe en effet une largeur minimale de 10 mètres pour
ces structures). Pour les bosquets et boqueteaux la surface minimale éligible à une
aide est de 1 ha d’un seul tenant. Sont considérées comme contiguës les formations
arborées séparées par un chemin public ou privé ou par un ruisseau. Les formations
arborées protégées en application de l’article L.126-6 du code rural ou de l’article
L.130.1 du code de l’urbanisme ou d’une décision préfectorale sont prioritaires à
l’octroi des aides.
En application du principe d’exclusion, l’obtention des aides sera uniquement
envisagée si les autres possibilités de financement de l’Etat ne peuvent être
retenues.
10.1.3 CONDITIONS RELATIVES AUX PEUPLEMENTS
Les opérations de boisement, reboisement ou reconstitution de formations
dégradées devront prévoir l’utilisation d’espèces traditionnelles convenant au type
de formation souhaitée. Les essences utilisées seront adaptées au sol et au climat
de la zone concernée. Les espèces végétales qui ont un comportement envahissant
sont à proscrire. Pour la strate arborée des formations arborées faisant l’objet d’une
103
aide à l’investissement, les essences objectif sont celles définies par la circulaire
DERF/SDF 2000/3021 du 18 août 2000.
10.2 CONDITIONS PARTICULIERES DEFINIES AU PLAN REGIONAL
Hormis le cas des expérimentations, les conditions techniques et financières de mise
en oeuvre de ces opérations sont arrêtés par le préfet de région, après consultation
de la commission régionale de la forêt et des produits forestiers. Elles sont en
cohérence avec les priorités et les programmes d’actions définis par les orientations
régionales forestières. Les orientations définies à cet égard par la circulaire précitée
du 27 septembre 1995 restent valables.
Dans le cas des haies, il appartient au préfet de région de définir la liste des
essences accessoires et des essences d’accompagnement qui seront retenues au
niveau régional sur propositions des préfets de départements. Cette liste sera
déterminée à partir de l’annexe 1 de la circulaire DERF/SDEF n° 3016 du 27
septembre 1995.
104
ANNEXE 5: TEXTE MAE HABITATS AGROFORESTIERS
MESURE TYPE NATIONALE
n° 2201 et 2202
CREATION (2201) ET GESTION (2202) D'HABITATS AGROFORESTIERS
I - PRINCIPE
Cette mesure consiste, pour l'agriculteur volontaire, à créer et/ou entretenir des
habitats agroforestiers dans des parcelles où les activités agricoles - cultures ou
élevage - sont pratiquées en présence d'arbres espacés disséminés sur l’ensemble
de la parcelle.
II - AVANTAGES ESCOMPTES POUR L'ENVIRONNEMENT
Selon les zones d'implantation, les essences présentes et les activités agricoles
auxquelles sont associés les arbres, les bénéfices escomptés pour l'environnement
sont de divers ordres. Tous contribuent à améliorer le caractère durable du système
de production agricole en jouant sur la complémentarité des arbres et des cultures,
obtenue par un choix judicieux des associations et une gestion technique
appropriée. Les différents avantages environnementaux relèvent de 7 catégories :
•
Protection des sols
-protection physique contre l’érosion hydrique (amélioration de la macro-porosité du
sol par le système racinaire des arbres, permettant une meilleure infiltrabilité ;
ralentissement des écoulements de surface par les alignements d’arbres) et
éolienne (ralentissement du vent par le maillage d’arbres) ;
-amélioration de la qualité des sols (enrichissement en matière organique par le turnover racinaire des arbres et l’incorporation de leur litière)
-récupération d’éléments nutritifs minéraux en profondeur par le système racinaire
profond des arbres (pompe à nutriments) ;
-stimulation de l’activité des micro organismes du sol : les extrêmes climatiques sont
modérés par l’ombrage du houppier des arbres ;
•
Protection des eaux
-réduction des risques de pollution diffuse des nappes et rivières par interception des
lixiviats, notamment de l’azote, par les racines des arbres, soit sous la zone
d’enracinement des cultures, soit dans les écoulements hypodermiques (parcelles
en pente) ;
-effet brise-vent et humidificateur de l’air des arbres limitant l’évapotranspiration,
donc les besoins en irrigations de la culture;
•
Stimulation de la biodiversité
105
-Maintien ou reconstitution d’une large biodiversité, par les refuges et les milieux de
lisière variés que les arbres procurent au sein d’agrosystèmes cultivés ou pâturés
intensifs, en particulier pour les groupes d’espèces suivants :
-végétation au sol sous l’emprise des arbres ;
-bryophytes, lichens, épiphytes : les arbres constituent des milieux souvent
obligatoires pour de nombreuses espèces devenues rares ;
-oiseaux (perchoirs pour oiseaux chasseurs, lieux de nidification, refuges contre les
prédateurs, protection climatique) ;
-chiroptères (arbres repères pour les déplacements nocturnes) ;
-petits mammifères (rongeurs, insectivores, et leurs prédateurs) ;
-insectes : plus de la moitié de la faune d’insectes est inféodée aux arbres, et de
nombreuses espèces ont régressé suite à la généralisation de traitements
insecticides à large spectre sur les cultures ; les arbres sont des refuges où de
nombreuses espèces peuvent échapper aux traitements et peuvent être des
réservoirs d’auxiliaires pour la lutte biologique contre les ravageurs des cultures.
-Gibier : les arbres isolés offrent des refuges en milieu cultivé.
•
Fixation du carbone
Fixation à long terme du carbone dans les arbres, sans baisse significative du stock
de carbone des sols de la parcelle (important dans le cas de prairies qui peuvent
perdre une part de leur carbone organique lors d’un boisement en plein par
exemple).
•
Bien-être animal
Les arbres offrent une protection contre le soleil, le vent, la pluie, réduisant les
dépenses énergétiques corporelles des animaux.
•
Qualité des paysages
-création et maintien de paysages semi-arborés, ouverts, pittoresques et sécurisants
-création d’îlots verts en zones de grandes cultures intensives ;
•
Protection contre les incendies
Les habitats agroforestiers sont incombustibles par nature : pas de strate basse
combustible, arbres espacés, culture intercalaire ou pâture entretenue. Dans les
zones méridionales, ces habitats peuvent contribuer efficacement à l’entretien des
coupures vertes, avec des cultures intercalaires de vigne par exemple.
III - CONDITIONS D'ÉLIGIBILITÉ
106
Pour la création d’habitats agroforestiers, les surfaces pour lesquelles l’aide est
demandée doivent être situées:
en priorité, dans des zones peu arborées (moins de 5% de la SAU de
l’exploitation occupée par des arbres hors forêt);
dans des zones où les bénéfices agri-environnementaux attendus de la
création ou l'entretien d'habitats agroforestiers sont confirmés par le service
environnemental de la DDAF;
dans des zones de forte déprise agricole lorsque la présence d'habitats
agroforestiers permet de maintenir une activité agricole sur des surfaces menacées
de se boiser naturellement en générant des risques naturels (fermeture du paysage,
incendie, etc.)
Pour la gestion d'habitats existants, les surfaces doivent répondre aux conditions
techniques définissant un habitat agroforestier.
IV - ENGAGEMENTS DU CONTRACTANT
Le contractant qui crée un habitat agroforestier s'engage pour une durée de cinq ans
à:
•choisir dans la liste annexée des essences d’arbres adaptées aux conditions
pédoclimatiques de la parcelle et compatibles avec les pratiques agricoles (engins,
animaux), notamment par un port arboré de hauteur suffisante (2 mètres au moins
de hauteur de tronc sans branche); ce choix doit être validé par la DDAF.
•ne pas planter des espèces envahissantes;
•se conformer à la réglementation en vigueur pour les essences dont la plantation
est encadrée;
•respecter un espacement de 10 à 40 mètres entre les lignes d’arbres, de 4 mètres
minimum entre les arbres sur la ligne de plantation ;
•planter entre 50 et 200 arbres/ha. Pour le cas particulier des peupliers et des noyers
(à bois ou double fin), la densité de plantation sera comprise entre 50 et 100
arbres/ha.
•planter une surface minimale de 0,5 ha;
•planter au cours de la première année du contrat au moins le nombre d'arbres pour
lequel l'aide a été accordée;
•Conduire les arbres de manière à obtenir à terme un arbre adulte avec un tronc
sans branches de 2 m de hauteur au moins (arbres de basse ou moyenne tige
exclus)
•pratiquer une culture ou une pâture intercalaire entre les arbres;
107
•remplacer les plants n'ayant pas pris pour maintenir au moins 50 arbres/ha;
•protéger les troncs en fonction des contraintes agricoles (petits animaux, gros
animaux, gibier, machines);
•entretenir les arbres pour maintenir la compatibilité avec les pratiques agricoles
dans les conditions habituelles à la région (élagage du tronc, entretien du sol de la
bande des arbres);
Il s'engage en outre à respecter les bonnes pratiques agricoles définies dans le
PDRN sur l'ensemble de l'exploitation.
Le contractant qui gère un habitat agroforestier existant s'engage pour cinq ans à:
•maintenir un tronc sans branches d’au moins deux mètres de hauteur ;
•pratiquer une culture ou une pâture intercalaire entre les arbres;
•maintenir au moins 50 arbres/ha (regarnis possibles à tout moment, y compris la
première année pour atteindre le seuil de 50 arbres/ha à partir d’un habitat de trop
faible densité);
•respecter un espacement de 10 à 40 mètres entre les lignes d’arbres, de 4 mètres
minimum entre les arbres sur la ligne;
•protéger les troncs en fonction des contraintes agricoles (petits animaux, gros
animaux, gibier, machines);
•entretenir les arbres pour maintenir la compatibilité avec les pratiques agricoles
dans les conditions habituelles de la région (allégement du houppier, entretien du sol
de la bande des arbres).
Il s'engage en outre à respecter les bonnes pratiques agricoles définies dans le
PDRN sur l'ensemble de l'exploitation.
V - MONTANT DE L’AIDE
Pour la création d'habitats agroforestiers, le montant de l’aide est de :
Avec culture intercalaire
Action 2201 A
Aide de base : 240€/ha/an
Aide si CTE : 288€/ha/an
Marge Natura 2000 : 20%
Avec pâturage de petits animaux
Action 2201 B
108
Avec pâturage de gros animaux
Action 2201 C
Le montant de l’aide est calculé pour la mise en place de l’habitat en première année
et pour 4 années de soins aux arbres, pour une densité de 100 arbres/ha.
L’agriculteur peut planter à ses frais des arbres supplémentaires, dans le respect
des fourchettes techniques définies ci-dessus (maximum de 200 arbres, de 100
arbres pour les peupliers et les noyers).
Inversement, si la densité plantée est inférieure à 100 arbres/ha, dans le respect des
fourchettes techniques, l’aide sera calculée au prorata du nombre réel d’arbres
plantés (minimum 50 arbres/ha).
Pour la gestion des habitats agroforestiers, le montant de l'aide est indépendant
du nombre d'arbres, à condition que l'habitat comprenne au moins 50 arbres. En
revanche, le montant de l'aide est fonction de la nature de la pratique agricole
intercalaire ainsi que de l'âge des arbres :
Avec
culture Avec pâturage Avec pâturage
intercalaire
petits animaux gros animaux
Age des arbres
<20
Action numéro
2202A 2202B 2202
C
2202D 2202E 2202F
102
140
95
114
114
114
122
168
114
137
137
137
20 %
20 %
20 %
20 %
20 %
20 %
Aide de base
Aide si CTE
€/ha/an
€/ha/an
Marge Natura 2000
>20
<20
>20
<20
>20
Si des aides des collectivités territoriales ou professionnelles sont disponibles pour
la création ou la gestion d'habitats agroforestiers, le montant de l’aide au titre de la
MAE sera calculé en déduisant des valeurs précédentes le montant des autres aides
accordées.
VI - JUSTIFICATIONS DU MONTANT DES AIDES DE LA MESURE "CREATION ET
GESTION D'HABITATS AGROFORESTIERS "
109
L’aide est calculée sur la base de la création d’habitats de 100 arbres/ha ou de
l’entretien d’habitats de 50 arbres adultes par hectare.
CREATION D'HABITATS AGROFORESTIERS
L’introduction de 100 arbres par hectare dans une parcelle agricole cultivée ou
pâturée entraîne des surcoûts pour l’activité agricole qui sont les suivants :
augmentation des frais de culture en intercalaire: 750F/ha/an (soit 2,5 heures
de travail mécanisé par hectare .) correspondant au ralentissement des opérations
mécanisées par les arbres (manœuvres en bout de parcelle, passage entre les
arbres, précautions pour préserver les racines, précautions lors des traitements aux
cultures, épandage de précision des intrants).
augmentation des frais de conduite des pâtures en intercalaire : 250 F/ha/an
(soit 1 heure de travail mécanisé par hectare) correspondant au ralentissement des
opérations mécanisées de fauchage des refus, épandage des amendements
organiques, contrôle des infestations de plantes nuisibles sous les arbres qui
risquent de gagner sur le pâturage, entretien des zones de piétinement par les
animaux près des arbres. Cette estimation est un minimum correspondant aux
terrains plats. Un temps plus long est nécessaire dans les terrains en pente.
gestion de l’emprise au sol des arbres pour favoriser la biodiversité sans nuire
aux pratiques agricoles intercalaires : 320 F/ha/an quelle que soit la pratique agricole
intercalaire ;
achat, pose et entretien des protections des plants d’arbres contre les
activités agricoles :
Le coût des fournitures : les prix des manchons à l'unité sont respectivement de 10F
pour les cultures (manchon de 120 cm, piquet de 150 cm); 30F pour les pâturages
de petits animaux (manchon de 170 cm, piquet de 150 cm, contre-piquet de
blocage); 60F pour les pâturages de gros animaux (manchon de 230 cm; 2 piquets
de 250 cm, spirale de barbelé) .
La mise en place des protections : en 1 heure on pose respectivement 30
protections (culture), 15 (pâturage de petits animaux) et 8 (pâturage de gros
animaux), y compris le temps de distribution du matériel auprès de chaque arbre.
Pour un coût de l’heure de travail de 150F, le coût de pose d’une protection est de 5
F avec culture, 10 F avec pâturage de petits animaux et 20 F avec pâturage de
grands animaux.
L’entretien des protections : il faut, au cours des années qui suivent la plantation,
retendre les attaches, redresser les manchons inclinés, remplacer les manchons
détériorés. On estime ce coût à 2, 3 et 4 F/manchon et par an pour les trois types de
protection respectivement, soit 160, 240 et 320 F/ha/an (coût de 4 entretiens
annuels répartis sur 5 années). Cela correspond à un temps de travail de 1h20, 2h
et 2h40/hectare/an selon les trois types de protection.
conduite des arbres : pour assurer le bon développement de l'arbre, il faut
chaque année, pendant les cinq premières années, entretenir le tronc dans le
110
manchon (dépose, soins à l'arbre, repose du manchon). On estime que 3 heures de
travail par hectare et par an sont un minimum. On propose une aide de 18 F/arbre
sur 5 ans, soit 360F/ha/an.
Création d’habitats agroforestiers
Action 2201A
Action 2201B
Action 2201C
Différents types de coûts
Avec pâturage Avec pâturage
Avec culture
de
petits de
gros
intercalaire
animaux
animaux
Augmentation des frais de culture en
750
intercalaire
250
250
Gestion de l’emprise des arbres au sol 320
320
320
Achat des protections4
200
600
1200
Mise en place des protections1
100
200
400
Entretien des protections5
160
240
320
Formation des arbres compatible avec
360
la pratique intercalaire
360
360
Total des surcoûts
(F/ha/an)
1890
1970
2850
1575
1642
2375
Aide
Aide si CTE
de
base
(F/ha/an)
Note : Les coûts de plantation (achat des plants, préparation du sol) et le manque à
gagner résultant de la diminution de surface cultivée ne sont pas pris en compte
dans le calcul de l’aide. Le cas échéant, ces aspects pourront bénéficier de mesures
prévues à cet effet.
GESTION D'HABITATS EXISTANTS
Surcoûts générés pour l’activité agricole par la présence de 50 arbres/ha âgés de
plus de 5 ans. Les coûts sont calculés pour 50 arbres par hectare, les coûts générés
par un nombre plus élevé d’arbres sont à la charge de l’exploitant.
augmentation des frais de culture en pratique intercalaire quel que soit l‘âge
des arbres : temps de travail accru des opérations mécanisées (manœuvres en bout
de parcelle, passage entre les arbres, précautions pour préserver les racines,
précautions lors des traitements aux cultures, épandage de précision des intrants,
fauchage des refus liés aux arbres). Lorsque les arbres atteignent une taille
4
Coût en première année réparti sur 5 ans
5
Coût réparti sur 5 ans de 4 opérations annuelles d’entretien.
111
importante l'agriculteur peut avoir à modifier son équipement en machines. Le temps
de travail est le même que dans le cas de la création d'un habitat mais la surface
réellement cultivée est plus faible, ce qui ramène le surcoût réel à 450F/ha/an . Pour
les pâtures, les surcoûts sont les mêmes que pour la création d'habitat (250
F/ha/an), le contrôle des espèces nuisibles dans les pâtures étant encore plus
délicat avec des arbres de forte taille.
Gestion de l’emprise au sol des arbres pour favoriser la biodiversité sans
nuire aux pratiques agricoles intercalaire, quel que soit l’âge des arbres : 250F/ha/an
(gyrobroyage de la végétation naturelle de la bande des arbres);
protection des troncs des arbres âgés de moins de 20 ans contre les activités
agricoles, calculés sur la base de 50 arbres protégés par hectare :
Pour le pâturage, le remplacement des manchons rigides par des filets à large maille
est nécessaire dès lors que le tronc de l'arbre atteint une taille presque égale au
diamètre du manchon. La protection des troncs est indispensable tant que les arbres
ne sont pas autodéfensables. L’amortissement et l’entretien des filets est estimé à 5
F/arbre/an avec de petits animaux et 8F/arbre/an avec de grands animaux, soit des
coûts respectifs de 250 et 400 F/ha/an.
Pour les cultures, la protection des arbres consiste à faire des traitements
cicatrisants pour les blessures occasionnées aux troncs par le passage des
machines (elle est calculée sur la base de 2F/arbre/an, soit 100 F/ha/an, ce qui ne
couvre pas la totalité des dépenses prévisibles);
entretien des houppiers d’arbres âgés de plus de 20 ans : on estime que deux
interventions de 8mn par an et par arbre sont nécessaires en cinq ans pour relever
la base de la couronne des arbres. Les branches des arbres isolés, même élagués à
4 mètres, ploient vers le sol et finissent souvent par toucher le sol. Dans ce cas, le
passage des machines devient impossible. Il faut donc régulièrement écimer les
branches retombantes pour éviter d'avoir une trop grande surface inaccessible pour
les travaux. Les travaux nécessaires pour une bonne conduite des travaux agricoles
sont: élagage latéral, allégement des branches basses, émondage, éclaircissage du
houppier. Cela représente un coût de 400 F/ha/an pour 50 arbres.
Gestion d’habitats agroforestiers
Différents types de coûts
Avec culture Avec pâturage Avec pâturage
intercalaire
petits animaux gros animaux
Age des arbres
<20
Action numéro
2202A 2202B 2202C 2202D 2202E 2202F
>20
<20
>20
<20
>20
Augmentation des frais de culture en 450
intercalaire
450
250
250
250
250
Gestion de l’emprise des arbres au 250
sol
250
250
250
250
250
112
Protection des troncs (arbres de 100
moins de 20 ans)
250
400
Entretien des houppiers (arbres de
plus de 20 ans)
400
Total des surcoûts Aide si CTE 800
(F/ha/an)
1100
750
900
900
900
917
625
750
750
750
Aide de base (F/ha/an)
667
400
400
Note : le manque à gagner résultant de la diminution de surface cultivée ou pâturée
à cause des arbres n'est pas pris en compte dans le calcul de l’aide. Le cas échéant,
il pourra être pris en compte au titre d'autres mesures prévues à cet effet.
VII - SUIVI DE LA MESURE
L'application de la mesure fera l'objet d'un suivi pendant trois années (2002 - 2004).
Au terme de cette période, un bilan sera établi sur les points suivants;
1.
Le nombre d'hectares d'habitats créés et d'habitats gérés qui bénéficient de la
mesure, dans les trois catégories prévues (cultures, pâturage de petits animaux,
pâturage de gros animaux);
2.
Pour chacune des catégories, une typologie des associations activité
agricole/essences présentes;
3.
La part de la surface agricole utile de l'exploitation occupée par les habitats
agroforestiers;
4.
Les caractéristiques techniques des habitats (densité, espacement des lignes,
espacement des arbres sur les lignes, habitats monospécifiques ou plurispécifiques,
etc.);
5.
L'évolution des pratiques et techniques agricoles liée à la présence des
arbres, y compris les innovations techniques introduites par les exploitants;
6.
Les incidences sur l'économie de l'exploitation.
Les éléments nécessaires au bilan seront recueillis au niveau départemental et
transmis à la DERF qui en fera la synthèse.
113
ANNEXE 6: LETTRE DE LUC GUYAU APCA AU MAAPAR – MAE
Monsieur Hervé GAYMARD
Ministre de l’Agriculture, de l’Alimentation, de la Pêche et des Affaires Rurales
78, rue de Varenne
75007 PARIS
Paris, le 23 avril 2003
Monsieur le Ministre,
Je me permets d’attirer votre attention sur les conséquences dommageables du
nouveau dispositif des CAD sur l’agroforesterie.
Cette pratique qui consiste à associer sur une même surface une production agricole
(culture ou élevage) et une production sylvicole (arbres plantés à faible densité)
intéresse les agriculteurs à plusieurs titres. Elle permet :
de constituer un patrimoine de valeur par la plantation d’arbres ayant vocation à
produire des bois de qualité, tout en maintenant un revenu agricole ;
de préserver l’environnement. En effet, la présence régulière, sur des parcelles
agricoles, d’arbres à faible densité améliore la structure des sols et freine l’érosion ;
de concilier la constitution d’un capital bois sans abandon de l’activité agricole
d’origine, présentant ainsi une alternative au boisement des terres agricoles. Les
parcelles ainsi plantées sont de plus facilement réversibles ;
de créer des paysages originaux très appréciés.
L’agroforesterie reçoit, de plus, un accueil très favorable auprès de la Commission
Européenne et la France est en pointe sur ce dossier.
Dans ce contexte, je voulais vous faire part de mon étonnement à la lecture de la
première circulaire, DEPSE/SDEA/C 2003-7007 du 12 mars 2003 sur les modalités
d’élaboration des contrats types dans le cadre des CAD. En effet, celle-ci ne fait
aucune mention quant au mode d’application de la MAE “Habitats agroforestiers”.
Approuvée par Bruxelles en 2001, cette MAE qui permet de soutenir la création et
l’entretien de parcelles agroforestières avait, ensuite, été inscrite au PDRN en tant
que mesure nationale.
Mais cette mesure a été retirée de la liste des mesures nationales. Pourtant,
l’agroforesterie intéresse toute exploitation, sans véritable restriction géographique.
Son application ne sera donc possible que si les régions l’inscrivent parmi les
mesures utilisables dans les CAD locaux. Ceci est peu probable, compte tenu de la
liste réduite des mesures retenues pour chaque CAD et de la nouveauté de cette
pratique.
114
Je vous propose, donc, de réintégrer la MAE “Habitats agroforestiers” en tant
qu’action agroenvironnementale d’application nationale dans le contrat type
départemental aux côtés des mesures de conversion à l’agriculture biologique et de
préservation des races menacées.
En espérant que vous pourrez tenir compte de notre proposition, je vous prie
d’agréer, Monsieur le Ministre, l’expression de ma haute considération.
Luc GUYAU
115
ANNEX 5. The distribution of silvo-arable systems in
western Europe and their ecological characteristics
Attachment 1 to WP8 Report
R G H Bunce
Alterra Green World Research
P 0 Box 47, 6700 AA Wageningen
The Netherlands
INTRODUCTION
The inherent nature of silvo-arable systems are that it is necessary not only to know
the characteristics of the tree cover, but also the crop or ground cover beneath them.
The term is therefore a combination of land cover and land use, in the general use of
these terms. lt follows that remote sensed images can only give an indication of their
occurrence on the ground, although local knowledge can assist in interpretation.
Thus the “open sclerophyllous forest“ class of the CORINE land cover map can be
used to indicate dehesas or montados but cannot give information as to whether
there is fallow land, crops or grass between, or beneath, the trees. Aerial photo
interpretation can give more detail e.g. on the density of the trees, but again cannot
determine what is growing on the ground. Field visits are therefore necessary not
only to determine the presence of silvo-arable systems but also to obtain
measurements of their extent and characteristics.
Expert local knowledge can be used to describe the principal characteristics of silvoarable systems and an overview of their extent. However, whilst this approach gives
a good overview of local conditions, it cannot provide objective overall estimates
because the relationship of the local area to the whole domain is not known. The
present chapter therefore provides a worked example of a procedure that could be
applied to the whole of Europe and its applications to Atlantic Europe. Some case
studies are then described for southern Europe before suggesting a possible future
approach for obtaining estimates.
A EUROPEAN STRATIFICATION SYSTEM FOR RESOURCE ASSESSMENT
The need for detailed field survey on the one hand and an accompanying policy
requirement for strategie estimates has long been recognized in landscape ecology.
Such apparently opposing needs make it essential to use sampling and then to have
a system of relating the samples to the whole population - comparable to socioeconomic surveys of voting intention or opinion polls. Such an approach was initiated
at a regional level in the mid 70s by Bunce (1975) with Sheail & Bunce (2003)
describing its eventual development at a European scale. The approach is based on
the regression principle of ecological parameters being related to environmental
factors. At a regional and European scale, altitude and climate data can be recorded
and classified using modern statistical methods into relatively homogenous classes
which can then be used as strata for sampling. The methodology hus been utilized in
116
GB to assess ecological resources since 1978 (Haines-Young et al. 2000) and at a
national level in Spain for habitat change using aerial photographs in the SISPARES
project. European strata have now been produced (Mucher et al. 2004 and Metzger
et al. (in press)) and were used in the worked example for Altantic Europe described
in the next section.
A 1km square sampling unit was used in GB as representing a scale suitable for field
sampling but also enabling all units to be classified at a national level. ElenaRossello (1997) developed an approach at different scales according to the
heterogeneity of the topography but modern statistical packages have enabled the
European strata to be constructed in one analysis at the 1km square scale. In GB
dispersed random 1km squares were drawn from die environmental strata and
surveyed in the fleld for land cover, habitats, vegetation and soil. National estimates
of extent were then made using standard statistical procedures. Once such
representative field samples are available, they can be used for modeling exercises
for example, for assessing the potential for wood energy plantations in GB (MitchelI
et al. 1993) by estimating potential yield and value of trees and comparing it with
current agriculture, using a similar procedure to the one adopted in SAFE. Bunce et
al. (1987) showed how such a procedure could be used in Spain and Jones et al.
(1995) how the framework could be used to model potential changes in agricuitural
enterprises.
A WORKED EXAMPLE OF THE APPLICATION OF STRATA IN ATLANTIC EUROPE
A good example of resource assessment required by a policy customer, involving a
comparable requirement for fleld survey and strategic estimates is provided by the
Veteran tree survey of Atlantic Europe as described by Smith & Bunce (2003, 2004).
This project was initiated because of controversy concerning the extent of veteran,
i.e. those over 150 years old in GB, as compared with elsewhere in Atlantic Europe.
The customer, English Nature needed such figures to establish an appropriate policy
for maintaining the resource. For practical reasons, the extent of the survey was
restricted to the Atlantic Zone, as described by Mucher et al. (2003) which is
hierarchically drawn from linked strata of the full survey of 84classes, with other
regions such as Alpine (south) and Mediterranean (north) not being sampled,
although they could be subsequently included using the same procedure. 31 sites
were taken at random from these classes with three 1km squares surveyed at each
using a standard list of habitats based on the GB Countryside Survey (Haines-Young
et al. 2000) recorded. Further details were added for wood pastures, which are silvopastoral systems and their current states such as whether they were still in use or
abandoned. Details of the veteran trees were recorded as described by Smith &
Bunce (2004) and the strata used to obtain estimates of tree resource and their
distribution in the landscape. The results showed that the majority of veteran trees
were outside GB - which was the opposite view of what was believed before the
survey. However, GB did have more of the largest size category, trees which were
mainly in parklands, rather than field boundaries. Whilst these results are not
important to SAFE they do show how the stratification system can be used and that
the results do not always reflect expert judgement.
The area of wood pastures (silvo-pastural) recorded was very low but there were
some records. There was no silvo-arable in the sample, although one plantation of
117
Walnuts was recorded in northern France but did not have a crop between the trees.
Effectively therefore, silvo-arable systems are absent in Atlantic Europe, although
they are known to occur in experimental sites and other isolated cases.
In addition 11 1km squres taken at random in Asturias, north-west Spain were
surveyed using the same methodology, but were not included in the analyses. There
was no silvo-arable and only a few small areas of silvopastural. In the Picos de
Europa, north-west Spain 30 1km squares were surveyed as described by Bunce et
al (1996), no silvo-arable was present but orchards of walnut and apples over grass,
sometimes cut for hay. During work over almost 20 years in this region‚ no more
silvo-arable has been seen.
This project therefore demonstrates the procedure of applying the European
stratification to estimate a land use resource.
CASE STUDIES IN SOUTHERN EUROPE
Following the SAFE Meeting in Toulouse, it was decided to extend the sampling to
Southern France and Spain. However sufficient time and resources were only
available to carry out some individual site surveys, which are effectively case
studies. These are described below.
1.
Castelnandry (Southern France). Three 1km squares were surveyed using the
same procedure as the veteran tree survey. No silvo-arable systems were recorded,
although one square had several hectares of walnut plantations.
2.
Port Vendres (Southern France). Three 1km squares were surveyed, with
many vineyards but no silvo-arable.
3.
Cadalso los Vidrios, Navaluenga and Almorox, Gredos mountains (Central
Spain). Three 1km squares were surveyed, with vineyards and fruit trees, but no
silvo-arable, although one field had a line of lives among the cereals. There were
also about 20 hectares of dehesa but with grassland beneath the trees. To the south
of one of the actual samples, there were extensive areas of dehesa with crops
between and beneath the trees. Further south there were also extensive areas of
cereal dehesas with varable tree densities.
Other site visits were made during the course of excursions elsewhere in Spain and
Portugal, and whilst they cannot be used in any quantitative way they are illustrative
of the occurrence of silvo-arable in southem Europe.
1.
Matute, central Delamada mountains, Rioja (Northern Spain). Two areas of
about 5 hectares: (a) walnut and cherry over vegetables; (b) poplar over wheat and
vegetables. Both areas were in narrow strips beside small rivers and were irrigated.
Above and below the two sites there were extensive plantations of Poplar, all of
which were intensively managed with mainly bare ground between the trees. All the
plantations were in narrow corridors beside the river and occupied only a small part
of the total landscape, which was otherwise cereal fields or Mediterranean scrub and
forest.
118
2.
Ezcaray, west Delamada mountains, Rioja. Where the river from the
mountains joined the main valley, there was about 20 hectares of Walnut about 20
years old in rows over a wheat crop. Once in the lowland plains there was only
intensive cereals. Above the silvo-arable site there were a series of Poplar
plantations extending along the river until at higher altitudes the steep valley sides
were covered in Beech forest.
Because of the intensive management of the Poplar, there was little ground
vegetation that there is likely to be few ecological benefits from the vegetation point
of view. The silvo-arable sites were also intensively cultivated so were comparable.
The benefits are therefore from additional diversity in the landscape and for cover for
birds and insects. The scale of the small river valleys means that there is likely to be
limited effect on restricting erosion. In both areas the silvo-arable sites were within
an existing network of plantations, whereas new sites in cereal dominated
landscapes would have a greater impact both in visual and ecological terms.
Both these sites were so restricted in area that they would be unlikely to be picked
up by dispersed random samples. This is in contrast to the next site which is
comparable to Extramadura in the extense of dehesa with crops.
3.
Monticola, (south-east Portugal). This is an area in south-east Portugal with
extensive montado, an open forest landscape with various densities of scattered
trees. Anna Keersma has studied this area using aerial photographs and with these
categories of tree cover - over 30% canopy cover; 10-30% and less than 10%.
Although there is no difference between the proportion of arable/fallow in the
different categories, there is over 1000 ha of the three categories under crop or
fallow covering over 25% of the land surface.There is also about 10% of the land
under crops with the rest being mainly different types of forest and scrub. As in
Extramadura therefore a high proportion of the landscape is an active silvo-arable
system. Pineda (2003) gives a figure of 6-8 M Ha of dehesa in Spain but figures are
not available to separate that into silvo-arable and silvo-pastural. If the Momticola
case was representative, then about 25 % could be silvo-arable, which from general
observation could be realistic, then there could be over 2 M Ha in Spain alone. The
SAFE case studies in Spain, Italy and Greece will provide more details.
In general terms however it can be definitely stated tht silvo-arable systems cover
large areas in Mediterranean Europe but only a few patches are present in the
Atlantic Zone.
ECOLOGICAL CONSIDERATIONS OF SILVO-ARABLE
The visits to the Silsoe and Montpellier sites, together with experience of Walnut and
Poplar plantations elsewhere, indicates that a well managed silvo-arable site is
comparable in its ecology to a crop monoculture with additional trees. In areas less
intensively managed along the lines of trees, there were residual patches of weed
species, both annual and perennial which could contribute to biodiversity both in
terms of fauna and flora especialiy if the latter contained some of the rarer arable
weeds. Limited
-
information can be gained from most existing poplar plantations without crops, as
these are usually managed in a different way. There is also a major difference
119
between new poplar plantations within an existing matrix of older patches, which
adds to the present network, as opposed to new silvo-arable patches in an otherwise
intensively formed landscape. These are likely to have a much greater, and perhaps
beneficial, contribution to both biodiversity and landscape complexity. In particular,
they may act as stepping stones and refugia for anirnals, especially birds, which are
moving through otherwise ceral dominated land. The current states of the landscape
is therefore important in determining the potential contribution of new silvo-arable
patches.
The Situation is very different in dehesas and montados. Here there is already an
existing functioning network of mature silvo-arable systems with well established
high-levels of biodiversity in both flora and fauna. This is recognized in many texts
e.g. Pineda (2003) and Gomez-Sal (2003) and by die establishment of agrienvironment schemes to maintain such systems in Extremadura.
FUTURE WORK
Characterisation
In many parts of southern Europe, lines of trees, whether vines, olives, nut trees or
fruit trees, are important elements in the landscape. lt is essential, as emphasized by
Haines-Young (2000) and Sheail & Bunce (2003) that consistent definitions are
required for any objective baseline survey. The procedure developed in the BioHab
project (a framework for linking Biodiversity and Habitats) is suitable for this and is
described on www.biohab.alterra.nl. As far as silvo-arable features are concerned,
three habitats are involved, Crops (woody), Crops (annual), Annuals (fallow) and
Forest. These would link directly to the typologies developed in SAFE for silvo-arable
systems. Rules are provided for determining all patches over 400m2 and at least 40m
long, in order to include both linear and aerial features, with records being made at a
1km square, as described above.
Distribution
The results described above indicate that there is little point in further sampling in
Atlantic Europe because of the scarcity of silvo-arable at the current time. Rather,
efforts should be concentrated on southern Europe in order to obtain better
estimates of the resource in this part of Europe. Screening exercises using aerial
photographs in conjunction with the Environmental Stratification System could be
used to target any field surveys in the most efficient way. Such procedures have
already been developed to target key habitats in GB. This system would enable
silvo-arable to be fitted into other land uses as has been tried and tested over the
last 25 years in GB.
Ecological Characteristics
Some general characteristics of the likely ecological features of silvo-arable systems
have been described, but more detailed studies are required to quantify these. There
is likely to be major differences between new sites in monocultures compared with
those within an existing matrix or within a landscape with other patches of woodland
and scrub already present. Such a study would be very interesting in landscape
ecological terms and could provide additional benefits for silvo-arable schemes.
120
Existing sites within the SAFE network could be used but monitoring new sites from
their inception would also likely to be of considerable value.
CONCLUSIONS
The sample survey already carried out for Atlantic Europe shows that silvo-arable
systems are very rare. In contrast, in southern Europe, existing knowledge indicates
that sivo-arable systems are widespread, although these have to be quantified and
separated from silvo-pastoral systems as these are confused in many current data
sets. Existing methodology could be used to fill this gap but significant financial
support would be required - although if student labour was used this would be
achievable.
Finally, there are likely to be significant ecological benefits associated with new silvoarable sites. especially in monotonous landscapes dominated by arable crops.
121
ANNEX 6. Silvoarable agriculture in Europe – past,
present and future
M.P. Eichhorn1,*, P. Paris2, F. Herzog3, L.D. Incoll1, F. Liagre4, K. Mantzanas5, M. Mayus4,
G. Moreno Marcos6 C. Dupraz4 and D.J. Pilbeam1
1
School of Biology, University of Leeds, Leeds LS2 9JT, UK; 2 Istituto per l’Agroselvicoltura
Villa Paolina, Via G. Marconi 2, 05010 Porano (TR), Italy;
3
Eidgenössische
Forschungsanstalt für Agrarökologie und Landbau (FAL), Reckenholzstr, 191CH-8046
Zürich, Switzerland;
4
INRA Montpellier, UMR Systèmes de Culture Méditerranéens et
Tropicaux, 2 Place Viala, 34060 MONTPELLIER Cedex, France; 5 Laboratory of Rangeland
Ecology, Aristotle University, 54006 Thessaloniki, Greece; 6 Centro Universitario Plasencia,
Forestry School , Avd. Virgen del Puerto 2, 10600 Plasencia – Cáceres, Spain
*Author for correspondence: e-mail: [email protected]
Key words: dehesa, pré-vergers, Streuobst, Hauberg Waldsystem, piantata, orchards
ABSTRACT
Combinations of trees and crops have formed key elements of the landscape of
Europe throughout historical times, and many such systems continue to operate
in the present day. In many cases they represent traditional systems in decline,
and a number of formerly widespread practices have already become extinct or
exist only in a threatened state. The causes for this include both practical and
economic considerations. The agricultural subsidy regime within the European
Union is presently unfavourable with regard to silvoarable practices, and this
has been a major factor in their recent decline.
The silvoarable systems of Europe can be split into two zones – northern
Europe and the Mediterranean. The latter contains not only a greater area of
silvoarable cultivation, but also a greater diversity due to the broader range of
commercial tree and crop species available. In general the systems of northern
Europe are limited by light, whilst those in the Mediterranean are limited by
water availability.
122
Innovative new combinations have been developed, some of which have the
potential for future expansion. An appreciation of the legacy of previous
systems of agroforestry is necessary when developing novel approaches or
seeking solutions to present problems.
Mixed systems of agriculture present an opportunity for the future of European
rural development, and have the potential to contribute towards the
enhancement of biodiversity and increased sustainability of agriculture, whilst
also preserving landscapes that are both culturally important and aesthetically
pleasing. The adoption of a consistent definition of silvoarable agriculture
within Europe is recommended.
INTRODUCTION
Silvoarable agroforestry consists of widely spaced trees inter-sown with annual
crops. Such systems have traditionally formed key elements of the European
landscape, and have the potential to make a positive contribution towards
sustainable agriculture in Europe in the future.
Trees have traditionally served three purposes in the agrarian economy – the
production of fruits, fodder and wood (for fuel, litter or timber). In addition,
they have amenity value, providing shade and shelter to labourers and livestock,
and they combat erosion by wind and water. When grown in combination with
crops, trees are known to compete for key resources, and hence the modern
convention is to separate forestry and agriculture into discrete activities. To
focus upon the deleterious effects of trees upon associated crops is however
overly simplistic and ignores a range of effects that are both positive and
negative in their influence on arable productivity (Jose et al., 2004).
Trees compete with surrounding crops for soil water, which can inhibit arable
production in dry climates. Despite this, trees also intercept driving rain and aid
the condensation of water droplets from fog and dew (Grove & Rackham,
123
2001). By acting as windbreaks, they slow air movement and can reduce
evaporative stress on crops (Hawke & Wedderburn, 1994; Jose et al., 2004).
The deeper rooting systems of trees are thought to impede drainage of rainfall
from thin soils, and via the process of hydraulic lift they may draw water from
lower soil levels and release it into the upper soil where it benefits shallowrooted plants (Burgess et al., 1998; Dawson, 1993; Jose et al., 2004; van
Noordwijk et al., 1996). Similarly, although the shade cast by trees may limit
the growth of crops, there is a benefit to reduced irradiance (and hence
transpiration) in arid areas, especially when growing sensitive vegetable subcrops, and in some cases there may be a moderate yield benefit to shading (Lin
et al., 1999). In colder climes the canopy insulates against ground frosts.
Although competition for nutrients may occur, the deeper rooting systems of
trees also bring up nutrients from lower soil layers, and reduce the leaching of
topsoil. These nutrients are then recycled via leaf litter and root turnover and
increase the overall resource-use efficiency of the system (Jose et al., 2004; van
Noordwijk et al., 1996). Litter can itself act as a buffer against wind and water
erosion and as such increases the sustainability of agriculture by protecting
topsoil when crops are absent. Trees may also attract sheltering livestock, which
are therefore more likely to deposit manure beneath them (Grove & Rackham,
2001). Scattered trees in croplands and pastures are likely to improve soil
structural characteristics beneath the tree canopy.
In order to minimise the potential for negative interactions between trees and
crops, it is necessary to carefully select combinations of trees and associated
arable crops that have positive interactions (facilitation). The most efficient and
sustainable systems are those which are able optimise the use of spatial,
temporal and physical resources by avoiding competition between components
(Jose et al., 2004). Research has indicated that mixtures of crops can in some
circumstances be more productive than monocultures, especially if the trees can
124
obtain resources that would otherwise be unavailable to the crops (Cannell et
al., 1996), and may reduce the need for agrochemicals (Vandermeer, 1989). By
developing an understanding of enduring traditional systems and practices we
may gain insight into the potential future applications of silvoarable techniques
and their advantages.
Historical perspective
When the original forests of Europe were cleared, trees with high value were
retained in the landscape. These included various fruit trees in the Rosaceae,
oaks (Quercus spp.) and beech (Fagus spp.) for their production of acorns and
mast as animal forage, and ash (Fraxinus spp.) from which lopped branches
were used as fodder (Dupraz & Newman, 1997). These formed elements of
early agroforestry systems and were continually replaced throughout history as
they did not obstruct manual cultivation techniques.
The earliest evidence for planned agroforestry in Europe dates back as far as the
Copper Age (c. 2,500 BC). Stevenson & Harrison (1992) identified a change in
the composition of pollen cores collected from south-western Spain, with mixed
oak and pine forests being replaced by scattered oaks and herbaceous
vegetation. A large proportion of the identifiable pollen was from weeds of
cultivation. They define this shift as the beginnings of the dehesa, a land-use
system characterised by intermittent cultivation, grazing and burning. Some
have argued that in fact the transition occurred during a period of climatic
change when the region was becoming more arid (Grove & Rackham, 2001). A
similar and concurrent change in Italy has been identified as a possible shift
towards wood-pasture land usage (Potter, 1979).
The earliest stages of agriculture involved systems of shifting cultivation, with
intercalated agricultural and forestry land use. As civilisation progressed
towards more stable patterns of agriculture woodland grazing and silvopastoral
125
systems were abundant, and there was a continuous transfer of fertility from
woods to cultivated lands via manure. The maintenance of soil fertility was
based upon a strict connectivity between agriculture, husbandry and forestry.
Eckert (1995) estimated that in the Neidlingen valley (Baden-Wurttemberg,
Germany) up until 1500, 75% of the nitrogen and 90% of the phosphorus
required for arable fertilisation came from woodlands in the form of fodder,
litter or wood for domestic fires. This input was vital to maintaining the
sustainability of agriculture.
A further reason for the maintenance of trees in the landscape was the
production of fruit for human consumption. Fruit was an essential part of the
diet, being a crucial source of many vitamins (Herzog, 1998a), and culturally
important for the production of alcohol. Many economically valuable tree
species are dual-purpose, producing an annual fruit crop and an ultimate timber
end product (e.g. cherry, walnut), often in addition to litter and fuel wood.
In the Middle Ages, with the introduction of sustainable crop rotations, soil
fertility became less dependent upon woods and trees. This shift in the role of
trees was accelerated during the 19th century by the introduction of chemical
fertilisers and the mechanisation of agriculture. Nowadays forestry, agriculture
and husbandry are discrete activities with few chemical and energetic
relationships. Nevertheless, many historical agroforestry practices have been
retained, and continue to be maintained in a traditional fashion.
At present, information on the status of agroforestry in general, and of
silvoarable systems in particular, is quite poor in Europe. This is due to a bias
towards single crop systems in both research activities and institutional
interests. Throughout the last century there has been a marked decline in the use
of silvoarable agroforestry systems across Western Europe. In many countries
this decline can be attributed to the same basic causes:
126
• Scattered trees in arable landscapes impeded mechanised agriculture and
have been deliberately removed or were damaged by machinery.
• The post-war drive for increased yields led to a focus on maximising
productivity through mono-cultural systems.
• A reduction of manpower in agriculture limited the commercial viability
of labour-intensive systems, e.g. full stature fruit tree orchards.
• Consolidation of fragmented land holdings into larger single farms and
fields removed boundary trees and reduced the scope for landscape
diversity
• The subsidy regime of the Common Agricultural Policy (CAP) indirectly
led to a reduction in crop associations by favouring single crop systems.
• Wooded areas were for many years ineligible for direct subsidy
payments, and in many cases trees were grubbed out to increase subsidy
income.
• A stricter quality norm applied to dessert fruit (EEC regulation 1641/71)
tended to standardise their production in intensively managed orchards.
Traditional silvoarable systems have gradually been abandoned in marginal
agricultural areas, while on more productive soils they have been replaced by
crop monocultures. There is no motivation provided to farmers to maintain
silvoarable systems, and they have often been perceived as an obstacle to
modernisation via mechanisation.
DATA COLLECTION
This paper collates contributions from seven European countries (France,
Greece, Germany, Italy, Spain, the Netherlands and the United Kingdom).
127
These are the result of information collection from bibliographical and other
sources (e.g. personal contacts and internet sites). In addition, inventories of
silvoarable systems from local to national levels were conducted within the
Silvoarable Agroforestry For Europe (SAFE) project during the first year of its
activity (2001-02). In Germany, Greece and the Netherlands these represented
the first attempts to quantify the existing silvoarable systems in that country and
are therefore limited to what could be achieved within a single year.
Member countries faced consistent difficulties in their attempts to document
both the types and extent of silvoarable practices within their borders. These
included a lack of official statistical data, largely due to a failure to distinguish
between silvoarable agroforestry and conventional forestry plantations within
land use surveys. The existing literature on silvoarable agroforestry is largely
confined to local journals and magazines, and is inaccessible to conventional
literature searches. There were also logistical difficulties in locating and
contacting individual farmers to verify reports.
SYSTEMS
The combinations of trees and crops employed by European farmers are
immensely varied. In this review we will concern ourselves only with those
systems that are currently extensive, have been in the recent past (i.e. the last
century), or have clear potential for commercial expansion in the future. The
major extant systems within Europe are summarised in Table 1. Mixed systems
of agriculture remain common in garden plots, and very small fields with trees
on the boundaries are effectively silvoarable. These, however, are generally too
small and inconsistent in their composition to be considered coherent systems in
their own right. They represent the needs of individual farmers to maximise
returns from a small area, and tend to be composed of fruit trees and vegetables
for domestic consumption rather than economic returns.
128
The climate of northern Europe imposes greater constraints on silvoarable
productivity than in tropical or Mediterranean systems. Lower photon flux
densities (PFD) at higher latitudes make it increasingly difficult to support an
economically viable ground crop beneath tree canopy cover. During the later
stages of tree development, canopy closure prohibits the growth of many crops
unless the tree rows are widely spaced, which in turn increases the pruning
requirements of the trees. In linear systems, once the tree height exceeds the
width of the row the system is often no longer suitable for alley cropping.
The incentives for silvoarable systems in northern Europe therefore need to be
clearly defined, as it is likely that the total economic output of a mixed system
will be less than might be achieved with a single crop. They are invariably
planted, and have not arisen directly from semi-natural vegetation (as with some
Mediterranean systems). In this sense they differ from landscape-level systems,
where the economic assessment is based upon the advantages of maintaining
pre-existing trees. In almost all cases the principle advantages of silvoarable
systems are in yield diversification and the production of a short-term return on
land while planted trees are still small. Their potential roles in agricultural
sustainability and maintenance of biodiversity are an area of active research
(Gordon & Newman, 1997).
In Mediterranean Europe the silvoarable systems present in northern Europe are
supplemented by a number of additional types. This is due to the greater
diversity of economically valuable trees in the region, along with a natural
tendency towards savannah-type vegetation in arid areas, since the relative size
of the root system required to support the above-ground parts of the tree is
greater, causing the trees to be naturally dispersed. In contrast to northern
Europe the limiting factor in most systems is water rather than light. The main
additional tree species present in Mediterranean regions are olive (Olea
129
europaea var. europaea L.), evergreen oaks (Quercus spp.), carob (Ceratonia
siliqua L.) and a broader range of fruit trees.
Over large portions of Mediterranean Europe, silvoarable agroforestry remains a
common system of land use. In contrast to northern Europe this almost
exclusively results from the maintenance of traditional systems that have
persisted for thousands of years rather than the development of novel or
modernised systems. In many Mediterranean areas agricultural land is still
divided into fragmented smallholdings, unlike northern Europe, where for the
most part these have been consolidated into larger, more efficient units of land.
Small fields result in more boundaries, and therefore a greater number of trees
remain in the landscape. These are seldom of a single dominant species, and are
often spread throughout fields with no planned pattern or density.
In other areas, particularly the olive groves of central Italy, or the dehesas of
SW Spain and Portugal, it is the trees themselves that define the landscape, and
they form a consistent component within a variety of arable or pastoral land
uses. A great diversity of tree/crop associations therefore exists, and it is likely
that all possible permutations occur, albeit only on small scales, where they may
be planted according to the specific needs of local farmers. The various systems
are discussed here in terms of the key tree products, although often they are
mixed in function.
Olive tree associations
Olives form a continuous landscape element in many parts of southern Europe,
with diverse crops sown between the stems. This practice is thought to date
back to pre-Roman times, when wheat was cultivated between rows of olive
trees on alternate years, since this was known to increase their yield in the
following year and thereby splitting the grove increased overall productivity
(Lelle & Gold, 1994). In Greece olives cover an estimated 650,000 ha in total
130
with intercropping of cereals, vegetables and fodder crops. Olive trees are
typically planted in rows, although they may also be irregularly scattered when
groves have been thinned. Oaks, carob, walnut (Juglans regia L.), almond
(Prunus dulcis (Miller) D. A. Webb) and other fruit trees often form a minor
mixed component.
In the central Italian regions of Umbria and Lazio the silvoarable system formed
by olive trees is the most abundant in the country, covering some 79,000 ha. As
in Greece, they are commonly intercropped with cereals or fodder legumes. The
olives form a consistent component of the landscape in contiguous silvopastoral
and horticultural fields, either as scattered trees or in rows with 5-10 m between
stems. A similar landscape survives in some parts of Spain. Grape vines are
sometimes grown along the rows of trees as part of a formerly extensive system
known as piantata (see below).
Fruit tree associations
Silvoarable systems based upon fruit production have covered extensive tracts
in central Europe as recently as the last century. The pré-vergers are areas of
low-density fruit tree plantations which double as grazing land, and are
abundant in northeast France. The fruit trees are often dual-purpose and produce
a timber end product, especially walnut, pear (Pyrus communis L.) and apple
(Malus domestica Borkh.). Some of these plantations are intercropped during
the early years of tree growth, especially walnut plantations in the regions of
Dauphiné and Périgord, covering some 15,000 ha. Around 4,000 ha may be
silvoarable at any given time (Liagre, 1993a). Typically crops are grown
between 5 and 15 years into an approximately 30-year cycle (Liagre, 1993b;
Mary et al., 1998) with a variety of crops including maize and other cereals,
sorghum, soybean, oil-seed rape, sunflower, tobacco, alfalfa, lavender and bush
fruits (Ribes spp.). In Dauphiné around 20% of walnut orchards are
intercropped (80% of those below 10 years of age) (Dupraz & Newman, 1997).
131
Walnuts are commonly found irregularly scattered amongst cereal crops
throughout France, a practice dating back at least 300 years in Burgundy
(Dupraz & Newman, 1997).
A comparable but less regimented style of silvoarable orchard is the central
European system of Streuobst, defined as ‘tall trees of different types and
varieties of fruit, belonging to different age groups, which are dispersed on
croplands, meadows and pastures in a rather irregular association’ (translated
from Lucke et al., 1992). Streuobst was formerly a widespread land use system,
and was typically practised in areas with highly productive arable land. The
system was sub-divided into silvoarable (Streuobstäcker) and silvopastoral
(Streuobstwiesen) forms. Streuobstäcker generally consisted of two rows of
fruit trees, intercropped close to the tree trunks, with relatively low branches to
facilitate fruit harvest. The most common fruit trees were apple, pear, plum
(Prunus domestica L.) and mazard cherry (P. avium L.) planted at a density of
20-100 stems ha-1 (Herzog, 1998b).
Initially, during the 16th and 17th centuries, German national and regional
policies encouraged the planting of fruit trees and creation of Streuobst, which
was maintained throughout the following centuries. A fruit tree survey of 1938
recorded approximately 800,000 ha of active Streuobst (Herzog, 1998a). This
area declined dramatically during the second half of the century (Rösler, 1996)
due to replacement by intensively-managed orchards with narrow grassed alleys
between rows of dwarf fruit trees (Figure 1). These permit greater
mechanisation but exclude intercrops. Streuobst eradication programmes were
originally subsidised under the CAP in favour of more standardised means of
production. Those that remain operate at a loss due to high manual labour costs.
In more recent years there have been subsidised schemes for protection on local
to national scales. Nevertheless, the majority of sites maintain Streuobstwiesen
132
rather than the arable Streuobstäcker, which is presently limited to a few very
small fields (< 0.5 ha). Similar orchards formerly existed in the Netherlands,
albeit on a smaller scale than the German Streuobst, but the remaining
fragments are maintained solely as a cultural heritage.
One of the reasons for the development of the pré-vergers in the 16th century,
and the former abundance of Streuobst in Central Europe during the 18th
century, was that long term fruit production could be combined with annual
income from the arable crops in the system (Herzog & Oetmann, 2000).
Reasons for continued preservation may include landscape restoration,
combating erosion and nature conservation or recreation, but in such cases they
are not managed for economic returns. A recent fashion for hobby fruit
production has led to small plots being maintained by individuals as a leisure
pursuit.
A similar orchard system was reinvented and subsequently lost in the United
Kingdom. During the early 20th century it was common practice to grow crops
between saplings of fruit trees in orchards, particularly apple (not for production
of alcohol) and cherry, especially in the Kent region (Hoare, 1928). Soft fruits
(e.g. blackcurrant, gooseberry, raspberry, strawberry) or vegetables (e.g.
asparagus) were the most common intercrops (Roach, 1985). Although at their
peak such orchards covered c.110, 000 ha in 1951-55, rotations were lengthy
(50-100 years) and the area intercropped at any one time represented only a
small proportion of this (Roach, 1985).
There is a necessary distinction to be made between the comparable Streuobst
and pré-verger systems of northern Europe and those that are present further
south on similar sites but which also incorporate grape vines. They differ from
the majority of silvoarable systems in that the trees are no longer the focal
element and chief economic resource. Mixed vineyards have a venerable and
133
well-documented history in the European landscape, with trees incorporated as
living mechanical supports for the vines and to increase the economic return
from the land through diversification. Meiggs (1982) collates historical records
from the classical period of a wide variety of trees intercalated with grape rows,
and the diverse functions they served. He found no evidence of the planting of
trees for timber alone, and assumes that timber was sourced from trees grown
between and amongst other crops. Ancient authorities on agriculture such as
Cato (234-149 BC) did not recommend using any land specifically for the
growth of trees, but advocated their inclusion within grape systems.
In flat fertile areas of Italy (e.g. the Po Valley), poplars (Populus spp.) and fruit
trees (Rosaceae spp.) were used as support for the vines, organised into rows
and intercropped (piantate), a system dating back to the Etruscan period (699464 BC). In the most productive regions, such as Campania, the rows of vines
could reach as high as 10 m. In hilly regions the vines tended to be supported by
smaller stature trees including ash (Fraxinus spp.), maple (Acer spp.) and
mulberries (Morus spp.). Special consideration was given to those crops least
likely to compete strongly for water during the dry season (May-October) such
as wheat or fodder legumes including clovers (Trifolium spp.) and vetches
(Vicia spp.). Fragments of the system remain in areas of Sicily.
In southern France the formerly prevalent system referred to as Joualle was
composed of rows of grapevine with peach, walnut and olive trees inserted. In
some cases the trees were used as support for the vine (hautain). In order to
maximise returns from the land, the gaps between rows would often be sown
with annual crops (usually cereals). This system has greatly declined due to
mechanisation, which makes the manual harvesting of such narrow crop rows
uneconomical, and the French national agricultural policy of separating
agriculture from forestry. A similar system continues to operate in Greece, with
olive, walnut, various oak species and wild pear incorporated amongst the vines,
134
and in Sicily intercropped vineyards still cover some 153,000 ha. In Spain the
intercropping of vineyards continues only in restricted areas. In general,
specialised intensive vineyards have replaced the traditional mixed form, and
the system persists only as scattered fragments.
In northern Spain intercropped small orchards (less than 0.5 ha) combining fruit
trees with vegetables remain abundant, but do not cover a substantial land area,
estimated at 6,200 ha in total (INE, 2002). Throughout the Mediterranean region
small orchards of walnut, almond, peach (Prunus persica (L.) Batsch), apricot
(Prunus armeniaca L.) and olive are intercropped with vegetables and cereals.
Small-scale silvoarable plots still exist in eastern Germany (e.g. Magdeburger
Börde), usually for household consumption rather than as commercial systems.
Commonly cherry trees (Prunus avium L.) are undercropped with turnip, but
also with alfalfa, potatoes, oats and formerly asparagus. Its continued survival in
the former DDR can be attributed to the need to maximise returns from the
small amount of private land assigned to each farmer following expropriation,
and the high fertility of land in the region.
In the Languedoc-Roussillon province of southern France a modern intensive
agroforestry system combines peach trees with intercropped vegetables. The
system is highly profitable and efficient in light use as the vegetables are able to
grow through winter and spring before the trees come into leaf, although it
requires irrigation.
The greatest expanse and diversity of fruit-producing silvoarable systems is
found in Greece. There is substantial regional variation in the dominant fruit
tree species, although in all areas a mixture occurs. In northern and central areas
pear (Pyrus spp.) dominates, with cereals, vegetables or tobacco cropped
between them. Walnut is preferred in montane areas, grown also for timber,
while mulberry (Morus spp.) is favoured in Thrace. Silvoarable combinations
135
with figs (Ficus spp., also grown for fodder) occur in Crete and the Aegean
Islands. Cereals (wheat and barley) are the most common intercrops in all
systems.
Walnut is a major component of silvoarable systems in Italy, where again it is
used as a dual-purpose fruit and timber tree in montane regions. In Campania it
is intercropped with vegetables, and often mixed with hazel (Corylus avellana
L.) grown for wood and nuts and as a trainer tree to improve the form of the
walnut trunks. On the fertile volcanic soils of the region tree growth is fast, and
a low plantation density (50 stems ha-1) permits crop cultivation for a number of
years. The system is very much in decline, due to competition from foreign
imports (especially from California), the greater profitability of vegetables when
planted alone, and the high value of land for development in a densely
populated region.
Timber tree associations
The increased demand for high-quality timber in Europe, coupled with the
reduced availability of tropical hardwoods, has led to the development and
expansion of a number of silvoarable systems designed specifically for the
production of high-grade timber. A number of experimental approaches have
been adopted in different countries, with great potential for increased
application. In contrast to fruit trees, it is thought that there is no critical stage
for diameter growth of timber trees, and therefore they may be more easily
incorporated into silvoarable systems without deleterious competition from
crops (Dupraz, 1994).
Silvoarable systems combining hybrid poplars grown for timber with cereal
crops were pioneered in northern Italy and have since been adopted throughout
northern Europe. The practice continues in the Po Valley region on flat fertile
soils. Maize, soybean and cereals are grown between tree rows during the first
136
two years of a ten-year cultivation cycle, although with intensification on fertile
soils this may be reduced to as little as seven years. Lapietra et al. (1991)
estimated that 4.1% of the total poplar area is intercropped at any given time.
The system is presently in decline in Italy due to the European grant policy of
subsidising tree planting on arable land, but which did not permit concurrent
intercropping.
In France the practice became fashionable during the 18th century, and continues
to be practised in well-irrigated alluvial regions throughout the country,
covering some c. 6,000 ha. Typically cereals are intercropped for the first three
years. In northern Greece cereals, vegetables or fodder crops may be grown
among the trees. High levels of irrigation are usually required, which precludes
the use of this strategy in more arid regions. Fertilisation is also necessary,
along with intensive weed control and pruning of the trees. Similar systems in
France are not managed with the same intensity.
In the United Kingdom during the 1950s Bryant & May Forestry Ltd. managed
large-scale plantations of hybrid poplar in southern England for the manufacture
of matches (Beaton, 1987; Dupraz & Newman, 1997). Alleys were cropped
with cereals for eight years, with an under-sown grass/clover mixture in the
final year. The plantations were then used for grazing cattle until year 20, when
canopy closure prevented the formation of pasture. The poplars were harvested
at 25 years old. The availability of cheap Scandinavian lumber and the crisis in
cereal prices caused these plantations to be abandoned in the 1970s. In recent
years similar trial plots combining hybrid poplars with various crops have been
established in the Netherlands (Edelenbosch & Dik, 1995) and the United
Kingdom (Beaton et al., 1999; Incoll et al., 2002).
Other linear combinations of timber trees and crops exist with a limited
distribution, but none have been so widely adopted. Silvoarable methods have
137
been incorporated into systems that were formerly the preserve of pure forestry.
The wider spacing between rows of trees in silvoarable systems increases rates
of growth and can, with an appropriate pruning regime, increase the value of the
timber through enhanced form and increased timber length.
In France, a farm in Aude combines the leguminous timber tree black locust
(Robinia pseudacacia L.) with cereals on 20 ha of land, with the aim of
maintaining soil fertility while reducing the need for fertilisation. In the UK a
commercial silvoarable system for the production of furniture timber includes
five tree species (Fraxinus excelsior L., Juglans nigra L., Prunus avium,
Quercus rober L. and Acer pseudoplatanus L.) with alley cropping of cereals or
pulses. The tree rows contain additional specimen trees for early transplanting
to urban parks, gardens and streets, negating the need for row thinning and
improving the overall efficiency of the system.
In the Netherlands innovative combinations of trees grown for high-grade
timber and ground-level flower production have been attempted. The Stichting
Robinia Foundation in Lelystad, an organisation promoting sustainable timber
production, runs a small demonstration plot (0.5 ha) with several tree species
(Catalpa bignonioides Walt., Alnus glutinosa (L.) Gaertner, Prunus avium and
Gleditsia triacanthos L.) intercropped with hyacinth (Liliaceae) for flowers and
bulbs. In Fryslân the Boslandbouw Foundation has experimented with cedar
(Cedrus spp.) intercropped with flowering quince (Chaenomeles spp.) for
flower and fruit production, and a number of further plots. The potential of
these systems for wider commercial application has yet to be established,
although they have great aesthetic appeal.
Oak tree associations
In certain regions of Europe the landscape is defined by the presence of
scattered oaks, forming contiguous arable and pastoral associations. This is
138
most characteristic of the dehesas of SW Spain and Portugal, a system of land
use that may have been practised for as long as 4,500 years (Stevenson &
Harrison, 1992). The dehesa is the dominant agroforestry system in Spain, and
probably the largest such system in Europe. Estimates of present extent vary
(Carruthers, 1993), largely due to inconsistencies in its definition, with
operational dehesas in the strictest sense thought to cover more than two million
hectares in SW Spain and 0.2 million hectares in Portugal, where it is known as
montado (Joffre et al., 1988)(Figure 2). Similar systems occur in northern
Greece, and cover much of Crete and Sardinia, but have almost entirely
disappeared on the Italian mainland (Grove & Rackham, 2001).
Although some linear dehesas exist, in the majority of cases the trees are
scattered at relatively low densities (10-40 stems ha-1). The shapes of the trees
confirm that they have developed in an open environment, suggesting that the
savannah is at least partly natural, and that is has not developed by subtraction
of trees from a pre-existing forest, although mixed oak and pine forests are
thought to have once dominated the region (Stevenson & Harrison, 1992). The
constituent trees have been actively selected for sweet acorn production, and
consist mostly of evergreen oak (Quercus ilex L.), but also cork oak (Q. suber
L.) and Pyrenean oak (Q. pyrenaica Willd.).
The ground beneath the trees is sown with cereals, fodder crops or sunflower, or
is used as wood-pasture. The lengths of the rotations vary from 2 to 12 years
depending on the maturity of the system. The system is therefore sometimes
referred to as being agro-silvo-pastoral, since it combines a range of different
practices, with arable cultivation shifting somewhat irregularly over successive
years. Pigs, sheep, grain, acorns and fuel wood are the main products (Grove &
Rackham, 2001) although cork can be a valuable commodity where suitable
trees are found.
139
Within the dehesas only a small proportion is cropped in any given year,
between 10.3% (MAPA, 1985) and 16% (Escribano & Pulido, 1998).
Moreover, crops are only harvested from 30% of the cultivated land, with the
remainder being grazed directly by cattle or used as fodder, supplemented by
acorns from the trees (Escribano & Pulido, 1998). In most cases the principle
aim of cropping is not to produce a commercial product but to control
encroachment of shrubs and to improve the soil and pasture.
The dehesa system exists primarily for the production of the fine hams that are a
speciality of the region, since pigs are known to fatten better in savannah than
woodland. This is because savannah oaks produce more acorns than woodland
trees, combined with the availability of understorey grasses and herbs for
grazing, which are beneficial for their protein content. The economic
importance of acorns in dehesa is far greater than the relative value of pannage
in northern European woodlands, and in 1957 it was estimated that acorns
comprised one sixth of the value of all forest products in Spain (Balabanian,
1984; Parsons, 1962). Acorns were formerly a common human food (belotas),
and not only in times of famine, although acorn-bread is now seldom baked
(Grove & Rackham, 2001).
The minimum size of an operational dehesa estate is thought to be around 400
ha (Grove & Rackham, 2001). Management is seasonally labour-intensive, with
branch lopping and acorn gathering being physically demanding tasks.
Increased labour costs in Europe therefore threaten this way of life. Cork cutting
is lucrative employment, but highly seasonal, and labourers are assisted by
European grants for the remainder of the year.
Lopped branches from the trees are used for firewood or charcoal production.
The trees tend to be lopped in a distinctive pattern, particular to different
140
regions, which determines their crown shape. Although valuable trees in many
other regards, savannah oaks tend to produce low quality timber.
A subtle change in dehesa management practices has also occurred over the last
30 years (Romero Candau, 1981). The traditional rotation was five years of
cereal cropping followed by five to ten years of pasture management. Ploughing
prevented the establishment of inedible shrubs (such as Cistus spp., Erica spp.,
Arbutus unendo L.) and maintained high quality pasture for succeeding years.
The crisis in cereal production in the 1970s led to this practice being abandoned
for some 20 years, following which severe bush encroachment has reduced the
value of the pasture (Dupraz & Newman, 1997) and created a substantial
uncontrolled fire hazard. In other areas, localised overgrazing has led to the loss
of the most favoured shrub species such as Medicago arborea L. and Colutea
arborescens L.
Dehesas are in a less threatened state than many traditional land use systems,
due in part to protective legislation. Approximately a million hectares were lost
between 1950 and 1980 when EU subsidy made cereal cropping more
profitable. Only water shortages restricted the ability of irrigation schemes to
convert more dehesa into purely arable cropland. Since 1984 state law has
forbidden substitution of oak woodland in Extremadura. Nevertheless, there is
some evidence of a more recent decline from 2.3m ha to 1.7m ha between 1985
and 1998 (Miguel et al., 2000), although the reliability of these data is
compromised by the lack of a firm definition of dehesas and an absence of
systematically collected information. A more subtle change in structure may be
occurring through alterations in tree density; a decline of 23% between 1951
and 1981 has been documented (Miguel et al., 2000). Almost no oaks have been
planted for the last century, which has led to concerns over the long-term
regeneration of the system (Grove & Rackham, 2001).
141
Figure 3 illustrates the decline of intercropped open woodland in Spain, which
largely refers to traditional dehesa systems (in approximately 90% of cases). It
must be noted that open woodland is not in decline in Spain, with new woods
developing on abandoned agricultural land in marginal areas. Instead it is the
actual practice of arable cultivation that has been greatly reduced.
Silvoarable oak systems are widespread in Greece, with cereals grown
commercially between scattered trees at densities ranging from 10 to 100 stems
ha-1. A variety of oak species (Quercus pubescens Willd., Q. petraea
(Mattuschka) Liebl., Q. cerris L. and Q. trojana Webb in Loudon) are found in
northern and central areas, associated with cereals, tobacco, sunflower and
fodder crops. In southern and western areas Valonia oak (Q. macrolepis
Kotschy) is most abundant, usually with intercropped cereals.
Similar associations persist in marginal areas of central and southern Italy and
Sardinia, with scattered oaks at densities between 7-250 stems ha-1. The system
is referred to as seminativo pascolo arborato. Various oak species are involved,
most commonly Q. pubescens and Q. cerris, although cork oak occurs in
Sardinia, where the system is similar to dehesa. Wild pear sometimes forms a
minor element. The trees were formerly used for fuel wood production, but
increasingly they are not managed and are retained purely as landscape
elements, or to reduce erosion. A rotation of wheat and clover is grown beneath
the trees, with oats being a less common intercrop.
In northern Europe the Hauberg Waldsystem of west-central Germany was an
ancient agroforestry system, practised for around 2,500 years. The system
combined the growth of trees for fuel wood with crop production and grazing
on a long rotation. The dominant tree species were oak and birch (Betula spp.).
According to historical records, trees in the Hauberg system were cut for
firewood and charcoal every 16 to 20 years, with the stumps left in the ground
142
to allow re-growth. After tree felling any remaining ground vegetation was
removed or burnt, and for the following years a crop was sown between the
stumps. Typically this was either buckwheat in June or rye in autumn as an
over-wintering crop. After about nine years of silvoarable farming, when the
trees had reached a certain size and could tolerate the presence of livestock, the
area was grazed with sheep and cattle. This silvopastoral land use continued
until the next felling.
The Hauberg was a collective system of land management, farmed by the entire
village community. At the end of the 19th century the Hauberg co-operations
began conversion of low forest into high forest as timber wood became more
valuable than firewood. This transformation occurred at a slower rate than may
have been expected, and in 2000 between 6,000 and 7,000 ha of traditional low
forest still survived. The reasons for its maintenance include the need for
protection against erosion and drainage control, preservation of biodiversity,
and the maintenance of a landscape recognised as being historically important
and culturally unique. It is however no longer a commercially viable land-use
option since there is a limited market for the wood products, and intercropping
is excessively labour intensive.
Fodder tree associations
In several of the examples given above, most notably the dehesa system, the
trees provide an important source of fodder leaves. There are also many cases,
both historical and contemporary, of trees being managed exclusively for their
leaves as a source of valuable nutriment for livestock during seasons when
ground vegetation for grazing is sparse (Dupraz & Newman, 1997; Lachaux et
al., 1988). This applies especially to the more arid areas of the Mediterranean.
In Greece, where the growing season for grasses is short, leaves are shredded
from deciduous oaks and dried to feed sheep throughout the rest of the year. In
143
Crete a distinctive form of pollarding creates ‘goat pollards’, low platforms with
dense shrubby regrowth that goats clamber amongst and forage upon. These
trees only reach full stature if grazing is withheld for several years, and in
practice they remain at ground level in perpetuity.
Cato (234-149 BC) was an advocate of the maintenance of trees as a source of
fodder alone. Leaves of elm (Ulmus spp.) were considered best, followed by
poplar: ‘If you have poplar leaves mix them with the elm to make the latter last
longer; and failing elms, feed oak and fig leaves’. All four continue to be used
to the present day. Columella (1st century AD) regarded poplar, elm and ash as
providing the best fodder (Meiggs, 1982). In ancient times these trees were most
likely planted within vineyards in mixed systems as described above.
Crete and the Aegean Islands contain silvoarable combinations with figs, with
carob also favoured in Crete, where the pods provide an important source of
stored fodder. In Sicily, carob is grown over some 20,000 ha and intercropped
with cereals and fodder legumes. The pods are also utilised as raw materials in
the food processing industry.
Non-native trees have more recently been considered as fodder sources in the
Mediterranean. The use of honey locust (Gleditsia triacanthos), a leguminous
tree native to America, has been promoted for many years (Wilson, 1993). A
number of other species have been considered, including Amorpha fruticosa,
Robinia pseudacacia, Colutea arborescens, Corinilla emerus, Medicago
arborea and Morus latifolia (Dupraz & Newman, 1997). Species characteristic
of dry regions, such as Acacia spp. and Atriplex spp., have also been trialled in
various locations (Correal, 1987; Dupraz & Newman, 1997).
Present status of agroforestry in Europe
144
There are a number of obstacles faced by those farmers and institutions who are
current or potential practitioners of silvoarable agroforestry, and who may
benefit from increased knowledge and awareness of its potential applications.
There is a lack of received knowledge on former agroforestry systems which
have now largely disappeared, and a focus on single crop systems within
research institutions reduces the advice and training available to farmers
wishing to manage trees in an agricultural environment. In terms of agricultural
subsidies, EEC Regulation 2080/92 provides grant funding for tree planting on
arable land, but does not permit intercropping. The current political climate is
therefore generally unfavourable and mixed agriculture requires more powerful
promotion at the regional level.
In this context, a recently proposed European Council Regulation on support for
rural development by the European Agricultural Fund for Rural Development
(EAFRD) states that "Agri-forestry systems have a high ecological and social
value by combining extensive agriculture and forestry systems, aimed at the
production of high-quality wood and other forest products. Their establishment
should be supported" (EAFRD, 2004). Should this recommendation be
approved, the prospects for the adoption of silvoarable methods within Europe
would improve greatly.
In three of the northern European countries included in this review (Germany,
the Netherlands, and the United Kingdom) silvoarable agroforestry no longer
has a significant role in the agrarian economy, and multifunctional land use
generally persists only on a small scale. France alone retains silvoarable systems
of any notable importance. The Hauberg Waldsystem, formerly practiced
extensively in west-central Germany, has almost completely vanished. The
intercropped fruit orchards of central Europe, Streuobst and pré-vergers, are
much diminished in extent and largely silvopastoral. Other ancient types of
145
silvoarable agroforestry are often very poorly documented and little is known
about their former content or extent.
Attitudes towards mixed agriculture among governmental bodies have altered in
recent years. In France, a census of silvoarable practices, commissioned by the
Environment Ministry, was conducted in 2000 by the SOLAGRO Association
and INRA, Montpellier (Coulon et al., 2001). An informal network of interested
parties has formed to lobby for the reform of agricultural and forestry laws to
favour agroforestry systems and has succeeded in changing the application of
subsidies.
Since
2002,
intercrops
are
eligible
for
CAP
subsidies
(DPEI/SPM/C2001-4008, 8th March 2001), agroforestry plantations receive
forestry subsidies (DPEI/SDF/C2001-3010, 7th May 2001) and the area planted
with trees is eligible for the European PCPR subsidy for lost arable income
(DERF/SDF/C2001-3020, 8th August 2001). Agroforestry is therefore currently
strongly favoured by the regulations within France.
Current silvoarable practices in France are relatively well-documented and there
are movements to preserve existing practices and encourage novel approaches.
As an example of a system operated as a going concern, Claude Jollet
(Charentes Maritimes) maintains 56 ha of walnut and wild cherry trees (c. 25
years old), intercropped with barley and sunflower. It is estimated that by 2005
there will be more than 80 similar silvoarable projects in France.
In the Netherlands the centralised system of agrarian research and the
established network of farmers’ associations have encouraged innovation and
enabled effective dissemination of research findings, although at present most
systems operate only on very small scales. In the UK a number of trial plots
have been set up under the aegis of the Farm Woodland Forum, an organisation
actively promoting agroforestry options.
146
Silvoarable agriculture remains of great importance in many regions of the
Mediterranean. The systems are usually relicts of formerly extensive
agricultural practices now restricted to more marginal areas, especially olive
grove intercropping and dehesas. Despite their former importance, and the
threats posed to them by the expansion of modern intensive agriculture, they
have been relatively poorly studied, both in terms of their economic importance
and their role in preserving local and regional biodiversity.
The greatest diversity of silvoarable systems is found in Greece, where a large
variety of combinations of trees and crops exists. These are generally
characterised by a small plot area, with a number of different tree species
present, often dispersed throughout the field and at the margins but without a
fixed pattern or spacing. A range of understorey crops with different
management are often planted side by side. This poses an obstacle to the strict
categorisation of systems. At present in Greece there is no regional or national
policy to improve silvoarable systems and make them economically viable. A
particular problem here is that the typically short lengths of land tenancies do
not encourage farmers to initiate novel long-term management practices such as
agroforestry. The trees belong to the landowner rather than the tenant.
Silvoarable agroforestry remains widespread in Italy, although it suffered a
decline in the latter part of the last century. In many cases, silvoarable systems
survive in regions where the terrain and climate have impeded intensification
(e.g. in Umbria, a relatively dry, hilly region)(Bertolotto et al., 1995). Recent
interest in silvoarable techniques has been stimulated by the demand for highquality local timber for domestic furniture manufacture. Many of the systems
described above have been in existence for centuries in Italian agriculture, but
have substantially declined.
147
In Spain, regular national agricultural census data throughout the last 50 years
enable a clear picture of extant silvoarable systems and the trends in their
distribution to be identified, although the census categories are often broad and
do not specify tree or crop species within associations. For example, irregularly
structured dehesas are recorded in the same category as linear poplar/maize
silvoarable systems. Despite the continued survival of the dehesas, silvoarable
systems are a minority land use relative to conventional arable cropland, and are
mostly found on more marginal arable soils.
During the latter half of the 20th century the most pronounced was the reduced
intercropping of fruit trees, which fell by 97% between 1962 and 1999. During
the same period intercropped olive systems were reduced by 94%. This trend
has continued still further (Table 2). Despite some inconsistencies in
classifications between years, the general pattern of severe decline is evident.
Factors specific to Spain include the migration of people from marginal
agricultural land (causing silvoarable fields to revert to woodland), the
consolidation of fragmented land holdings into larger single farms and irrigation
projects that reduce the need for shade trees among crops.
Future prospects for silvoarable agriculture
A modern focus on sustainable agriculture and the conservation of nature and
landscapes in Europe has increased the interest in silvoarable systems, and
encouraged the establishment of research projects. Multifunctional land use has
been identified as a potential means of increasing the biological species richness
of farmland through increased habitat diversity as well as protecting against
erosion and reducing the need for agrochemical input (Jose et al., 2004;
Vandermeer, 1989).
There is a pressing need in Europe for a local source of high quality hardwoods
to replace tropical sources (Smith, 1990). Europe is now almost self-sufficient
148
in other wood and wood-based products, largely derived from sustainable
forests in Scandinavia, but high-grade timber retains a high market value.
Deciduous broad-leaved species are preferred to conifers, principally to satisfy
market needs, but also for environmental, cultural and aesthetic reasons (Dupraz
& Newman, 1997). The economic potential for timber silvoarable systems is
difficult to assess due to a lack of research and great variability in the local
incentives provided to farmers for tree planting.
Timber trees are thought to have greater potential than fruit trees in silvoarable
systems, as there are no constraints posed by fruit harvesting that might limit the
choice of intercrops. In addition, fruit trees are sensitive to competition during
the earlier stages of growth, whereas timber trees are more resilient and there is
no critical period for determining diameter growth rates (Dupraz, 1994; Dupraz
& Newman, 1997). Market demands for a standardised form of fruit also favour
their production in intensively managed and dedicated orchards.
There has been an increase in recent years in the use of trees purely for fodder
production. This has been researched in the Rougier des Camarès area in
southern France as a means of combating erosion in fields that were previously
sown purely with annual fodder crops (Dupraz & Newman, 1997). Trees may
have a valuable role to play in maintaining the integrity of soils and combating
erosion in other areas of Europe.
The search for alternative energy sources has led to silvoarable systems being
considered as a source of bio-fuels (Hall, 1997; Herzog, 1994), or else they may
have a potential role in the reduction of atmospheric CO2 (Herzog, 1994).
Poplar short-rotation coppice (SRC) would seem to be the most viable option
(Newman et al., 1991), although willow (Salix spp.) is also being investigated.
Such ‘carbon-neutral’ fuel sources have been highlighted as potential
alternatives to fossil fuels for energy production at local levels (Newman et al.,
149
1991). An experimental intercropping has been attempted at Long Ashton
Research Station, Bristol, UK (Nichols et al., 2000), but there is no historical
precedent for combined coppice and arable production.
Although there has been a recent surge in research interest in silvoarable
agroforestry, all experimental findings on novel systems are of necessity
preliminary, since modern scientific research has yet to cover the lifespan of a
cohort of trees in a plot.
It is also necessary to be cautious in claiming environmental benefits for
silvoarable systems in general, especially in the context of increased
sustainability of agriculture. Considerable experience accumulated in the tropics
has shown that the management of intercropped systems is often intensive. The
high cost of manual labour in Europe is likely to lead to a greater reliance on
agrochemical input, especially when unfavourable combinations of trees and
crops are employed. The combined peach and vegetable systems of southern
France, which require intensive fertilisation and irrigation, are an illustration of
this.
Conclusions
Despite the limitations of this review, it is clear that there are two distinct
geographical and climatic zones with respect to European silvoarable
agroforestry –northern Europe and the Mediterranean. The latter contains a
broader range of systems, reflecting the higher diversity of commercial crops
and plant resources. In general, the form and structure of systems in northern
Europe are determined by light limitation, whereas in the Mediterranean water
is the key resource.
In their review of agroforestry practices in temperate regions around the globe,
Newman and Gordon conclude that the most successfully optimised systems are
those for which there is a clearly defined market for a tree product (Newman &
150
Gordon, 1997). In assessing the prospects for the preservation of traditional
silvoarable systems, and the scope for novel and innovative approaches to
combinations of trees and crops, we should therefore focus upon the economic
value of the trees.
Although extant silvoarable practices in Europe are mostly residual elements of
formerly widespread systems, there is still a considerable diversity in existence.
The precise quantification of silvoarable systems in Europe is difficult due to
lack of documentation. The application of a consistent definition of silvoarable
agroforestry in land use surveys and recognition of their unique characteristics
would go some way towards an accurate appraisal of their present extent and
importance in the landscape of Europe. Their productive role in the European
countries studied is not yet fully understood and deserves more attention,
especially in the context of the diversification of farm income and the
development of sustainable farming systems, two issues of immense strategic
importance to the future of European agriculture. There are economic,
environmental and aesthetic reasons to encourage their adoption in all regions
of the European Union.
ACKNOWLEDGEMENTS
This research was carried out as part of the SAFE (Silvoarable Agroforestry For
Europe) collaborative research project. SAFE is funded by the EU under its
Quality of Life programme; contract number QLKS-CT-2001-00560.
REFERENCES
Balabanian, O. (1984) Problemas agrícolas e reformas agrárias no Alto
Alentejo e na Estremadura Espanhola, Lisboa.
Beaton, A. (1987) Poplars and agroforestry. Quarterly Journal of
Forestry, 81, 225-233.
Beaton, A., Incoll, L.D., & Burgess, P.J. (1999) Silvoarable agroforestry.
Scottish Forestry, 53, 28-32.
151
Bertolotto, U., Pisanelli, A., & Cannata, F. (1995) Pratiche agroforestali
nella regione Umbria. Monti e Boschi, 2, 5-11.
Burgess, S.O., Adams, M.A., Turner, N.C., & Ong, C.K. (1998) The
redistribution of soil water by tree roots systems. Oecologia, 115, 306311.
Cannell, M.G.R., van Noordwijk, M., & Ong, C.K. (1996) The central
agroforestry hypothesis: the trees must acquire resources that the crop
would not otherwise acquire. Agroforestry Systems, 34, 27-31.
Carruthers, S.P. (1993) The dehesas of Spain - exemplars or
anachronisms? Agroforestry Forum, 4, 43-52.
Correal, E. (1987) Trees and shrubs in the fodder and pastoral
mediterranean systems. FAO European Network on Pastures and
Forage Crop Production Bulletin, 5, 46-53.
Coulon, F., Dupraz, C., Liagre, F., & Pointereau, P. (2001). Étude des
pratiques agroforestières associant des arbres fruitiers de haute tige à
des cultures ou des pâtures. Solagro/Inra, Ministère de l'Aménagement
et du Territoire et de l'Environnement.
Dawson, T.E. (1993) Hydraulic lift and water use by plants: implications
for water balance, performance and plant-plant interactions. Oecologia,
95, 565-574.
Dupraz, C. (1994). Les associations d'arbres et de cultures intercalaires
annuelles sous climat tempéré. In Revue Forestière Française, pp. 7283.
Dupraz, C. & Newman, S.M. (1997). Temperate Agroforestry: The
European Way. In Temperate Agroforestry Systems (eds A.M. Gordon &
S.M. Newman), pp. 181-236. CAB International.
EAFRD (2004). Proposal for a council regulation on support for rural
development by the European Agricultural Fund for Rural Development
(EAFRD), Rep. No. COM(2004)490 final. Commission of the European
Communities, Brussels.
Eckert, G. (1995) Untersuchungen zur Geschichte der Landnutzung und
zur Landschaftspflege auf brachgefallenen Wacholderheiden und
Steinobstwiesen im Neidlinger Tal (Kreis Eßlingen) Verlag Ulrich E.
Grauer, Stuttgart.
152
Edelenbosch, N.E. & Dik, E.J. (1995). Mengteelt van populier met
suikerbieten, snijmais en gras. Deel 1. Economische evaluatie van
mengteelt van bomen met landbouwgewassen. Report IBN-DLO 181,
Wageningen, the Netherlands.
Escribano, M. & Pulido, F. (1998). La dehesa de Extremadura:
estructura económica y recursos naturales. Consejería de Agricultura y
Comercio, Junta de Extremadura, Merida (Spain).
Gordon, A.M. & Newman, S.M. (1997) Temperate Agroforestry Systems
CAB International, Wallingford, UK.
Grove, A.T. & Rackham, O. (2001) The nature of Mediterranean Europe:
an ecological history. Yale University Press, New Haven, USA.
Hall, D.O. (1997) Biomass energy in industrialised countries - a view of
the future. Forest Ecology and Management, 91, 17-45.
Hawke, M.F. & Wedderburn, M.E. (1994) Microclimatic changes under
Pinus radiata agroforestry regimes in New Zealand. Agricultural and
Forest Meteorology, 71, 133-145.
Herzog, F. (1994) Agroforestry and Land Use Change in Industrialised
Nations. Schweizerische Zeitschrift für Forstwesen, 145, 761-763.
Herzog, F. (1998a) Agroforestry in temperate Europe: History, present
importance and future development. In Mixed Farming Systems in
Europe. Workshop Proceedings, Dronten, The Netherlands, 25-28 May
1998 (eds H. van Keulen, L.E. A. & v.L.H. H.), Vol. 2, pp. 47-52. A. P.
Minderhoudhoeve-Series.
Herzog, F. (1998b) Streuobst: a traditional agroforestry system as a
model for agroforestry development in temperate Europe. Agroforestry
Systems, 42, 61-80.
Herzog, F. & Oetmann, A., eds. (2000) Communities of Interest and
Agro-Ecosystem Restoration: Streuobst in Germany. Interactions
between Agroecosystems and Rural Human Community. Advances in
Agroecology. Springer, New York.
Hoare, A.H. (1928) The English Grass Orchard and The Principles of
Fruit Growing. Ernest Benn, London.
Incoll, L.D., Rogers, D.G., Clements, R.O., Newman, S.M., Beaton, A.,
Burgess, P.J., Acworth, W.F., Wolfe, M.S., Russell, K., Jones, M.,
Sinclair, F.L., Teklehaimanot, Z., Jones, B.L., Watson, C.A., Sibbald,
153
A.R., Hulbert, I.A.R., & McAdam, J.H. (2002) UK Agroforestry Forum: a
survey of agroforestry in the British Isles, 2001. Agroforestry Forum
Newsletter, 3, 2-63.
INE (2002). Censo Agrario de España 1999. Instituto Nacional de
Estadística, Madrid.
Joffre, R., Vacher, J., De Los Llanos, C., & Long, G. (1988) The dehesa:
an agrosilvopastoral system of the Mediterranean region with special
reference to the Sierra Morena area of Spain. Agroforestry Systems, 6,
71-96.
Jose, S., Gillespie, A.R., & Pallardy, S.G. (2004) Interspecific
interactions in temperate agroforestry. Agroforestry Systems, 61, 237255.
Lachaux, M., De Bonneval, L., & Delabraze, P. (1988) Pratiques
anciennes et perspectives d'utilisation fourragère des arbres. Fourrages,
81-104.
Lapietra, G., Coaloa, D., & Chiarabaglio, P.M. (1991) Rapporto annuale
sulla pioppicoltura 1990. Cellulosa e Carta, 3, 20-23.
Lelle, M.A. & Gold, M.A. (1994) Agroforestry systems for temperate
climates: lessons from Roman Italy. Forest and Conservation History,
38, 118-126.
Liagre, F. (1993a) Les practiques de cultures intercalaires dans la
noyeraie fruitière du Dauphiné. Mémoire de mastère en Science
Forestières, ENGREF-INRA, Montpellier.
Liagre, F. (1993b) Les pratiques de cultures intercalaires dans le
noyeraie fruitière du Dauphiné. MSc, ENGREF, Montpellier.
Lin, C.H., McGraw, R.L., George, M.F., & Garrett, H.E. (1999) Shade
effects on forage crops with potential in temperate agroforestry
practices. Agroforestry Systems, 44, 109-119.
Lucke, R., Silbereisen, R., & Herzberger, E. (1992) Obstbäume in der
Landschaft Eugen Ulmer Verlag, Stuttgart.
MAPA (1985). Anuario de Estadística Agraria 1985. Ministerio de
Agricultura, Pesca y Alimentación, Madrid.
Mary, F., Dupraz, C., Delannoy, E., & Liagre, F. (1998) Incorporating
agroforestry practices in the management of walnut plantations in
154
Dauphiné, France: an analysis of farmers' motivations. Agroforestry
Systems, 43, 243-256.
Meiggs, R. (1982) Trees and Timber in the Ancient Mediterranean World
Oxford University Press, Oxford, UK.
Miguel, E., Pointereau, P., & Steiner, C. (2000). Los árboles en el
espacio agrario. Importancia hidrológica y ecológica. Banco Santander
Central Hispano, Madrid.
Newman, S.M. & Gordon, A.M. (1997). Temperate Agroforestry:
Synthesis and Future Directions. In Temperate Agroforestry Systems
(eds A.M. Gordon & S.M. Newman), pp. 251-266. CAB International,
Wallingford, UK.
Newman, S.M., Park, J., Wainwright, J., Oliver, P., Acworth, J.M., &
Hutton, N. (1991) Tree productivity, economics and light use efficiency of
poplar silvoarable systems for energy. In Proceedings of the 6th
European Conference on Biomass Energy Industry and Environment,
Athens.
Nichols, A.R., Kendall, D.A., & Iles, D.R. (2000) The agronomic and
environmental implications of a combined food and energy system.
Aspects of Applied Biology, 58, 363-372.
Parsons, J.J. (1962) The acorn-hog economy of the oak woodlands of
southwestern Spain. Geographical Review, 52, 210-235.
Potter, T.W. (1979) The changing landscape of South Etruria. Elek,
London.
Roach, F.A. (1985) Cultivated Fruits of Britain. Their Origin and History.
Blackwell, Oxford.
Romero Candau, L. (1981) La dehesa como forma de explotacion
agraria. Problemas actuales. Estudios territoriales Ceotma, 3, 149-152.
Rösler, M. (1996) Marktwirtschaftliche Bedeutung des Streuobstbaus. In
Auswirkung des EU-Streuobstforums im Rahmen der 2. Rhöner
Apfelmesse., pp. 3-10. Rhöner Apfelbüro, Zella.
Smith, J. (1990). La Forêt et le Bois dans le Marché Unique. Etude du
club de Bruxelles.
Stevenson, A.C. & Harrison, R.J. (1992) Ancient forests in Spain: a
model for land-use and dry forest management in south-west Spain from
155
4000 BC to 1900 AD. Proceedings of the Prehistoric Society, 58, 227247.
van Noordwijk, M., Lawson, G., Soumaré, A., Groot, J.J.R., & Hairiah, K.
(1996). Root distribution of trees and crops: competition and/or
complementarity. In Tree-Crop Interactions: A Physiological Approach
(eds C.K. Ong & P. Huxley), pp. 319-364. CAB International,
Wallingford, UK.
Vandermeer, J. (1989) The Ecology of Intercropping. Cambridge
University Press, Cambridge.
Wilson, A. (1993) Silvopastoral agroforestry using honeylocust. In
Proceedings of the Third North American Agroforestry Conference, pp.
265-269, Ames, Iowa, 16-18 August 1993.
26
24
Millions of trees
22
20
18
16
14
12
10
1900
1920
1940
1960
1980
2000
Year
Fig. 1. Number of fruit trees in Streuobst systems in Baden-Wurttemberg
(in 1900 and 1912 trees in home gardens are included). Redrawn from
Herzog & Oetmann (2001).
156
Fig. 2. Distribution of dehesas within Spain and Portugal. Reproduced
from Blanco-Castro et al. (1997) –.
157
Area of intercropped woodland (ha)
800000
700000
600000
500000
400000
300000
200000
100000
1960
1970
1980
1990
2000
Year
Fig. 3. Area of intercropped woodlands (equivalent to planted dehesas)
from 1962-2001 in Spain. Data from Anuario de Estadística
Agraria (Annual of Agricultural Statistics) 1962-2001.
158
Extent (ha)
Stems
ha-1
Layout
Olea europaea var.
europaea
Italy
20,000
25 - 100
S/L
Y
Rosaceae
France
3,000
25 - 300
S/L
Greece
Spain
650,000
15,030
50 - 100
50 - 100
S/L
L
Fodder
Location
Fruit
Tree species
Timber
Component
trees
Firewood
System name
Annual crops
Olive systems
Olive groves
Olive
Other fruit
trees
Y
Y
C - V - FL - FG
Y
Y
Y
Y
Y
Y
C
C - M - FL - GV GF
C
Orchard
systems
Joualle
Almond
Prunus dulcis
Sicily
18,000
50 - 100
S/L
Y
C - FL - FG
Mixed
Rosaceae
France
2,000
50 - 300
S/L
Y
V - GV - GF
Peach
Walnut
France
100
200 - 300
L
Y
GV
Olive
Prunus persica
Juglans regia
Olea europaea var.
europaea
Mulberry
Morus nigra
N Greece
500
10 - 50
S/L
Y
M - FL - V
Fig
Ficus carica
Crete, Aegean
Islands
10,200
10 - 50
S/L
Y
Common pear
Pyrus communis
N & C Greece
7,000
20 - 50
S/L
Mixed
Rosaceae
Spain
13,484
40 - 200
S/L
Y
Y
Y
C
Y
C - T - V - GV
Y
C - M - V - BF
Timber trees
159
Fodder
Timber
Tree species
Location
Poplar
plantations
Poplar
Populus cv.
N Italy
France
Greece
Walnut
Hazel
Juglans regia
Corylus avellana
C & S Italy
10,000
25 - 100
L
Y
Y
Y
C - V - FL
Walnut
Walnut
Juglans nigra, J. regia
Juglans nigra, J. regia
France
Greece (montane)
15,000 *
7,600
80 - 120
10 - 25
L
S
Y
Y
Y
Y
Y
Y
All crops
C - T - FL - GV
Oak
Pear
Quercus spp.
Pyrus spp.
C & S Italy, Sardinia,
Sicily
180,000
10 - 100
S
Y
Y
C - FL
Oak
Quercus spp.
France
100
50 - 150
S/L
Y
Y
FG
Valonia oak
Q. ithaburensis subsp.
macrolepsis
S & W Greece
29,600
10 - 50
S
Y
Y
C
N & C Greece
1,470,000 * 10 - 100
S
Y
Y
C - T - Sun - FL GV
W & SW Spain
2,300,000 * 10 - 40
S
Y
Y
C - Sun - FL
Walnut
plantations
12,500
200
6,300 180 - 220
Unknown 10 - 50
Layout
L
L
L
Fruit
Component
trees
Extent (ha)
Stems
ha-1
Firewood
System name
Y
Y
Y
Annual crops
C-M-S
C-M
M - FL - V
Oak systems
Quercie
camporili
Les plantades
Downy oak
Sessile oak
Turkey oak
Macedonian
oak
Dehesas
Evergreen
oak
Q. pubescens
Q. petraea
Q. cerris
Q. trojana
Q. ilex subsp. ballota
160
Q. suber
Q. pyrenaica
Carob
Ceratonia siliqua
Extent (ha)
Stems
ha-1
Layout
Sicily
Crete
20,000
7,900
100 - 125
25 - 100
S/L
S/L
Fodder
Cork oak
Pyrenean oak
Location
Fruit
Tree species
Timber
Component
trees
Firewood
System name
Annual crops
Fodder trees
Carob
Y
Y
Y
Y
C - FL
V - GF
* only a proportion is intercropped in any single year.
Table 1. Extant silvoarable agricultural systems in Europe, their composition, present extent, structure and main economic
products. Layout of stems is either scattered (S) or linear (L). Annual crops sown between the stems include maize (M),
other cereals (C), vegetables (V), oil seed rape (OSR), soya (S), tobacco (T), sunflower (Sun), fodder legumes (FL), fodder
grasses i.e. hay (FG), bush fruits such as Ribes spp. (BF), ground fruits (GF) and grape vines (GV).
161
System
Fruit + annual crop a
1962
1972
402,005
Vineyard + annual crop a
Olive + annual crop a
242,628
Woodland tree + annual crop b
685,893
478,375
1982
1989
1999
78,999
27,562
13,484
21,677
8,175
8,359
39,092
20,219
15,030
433,000
357,000
213,100 c
a
Data derived from National Agriculture Census (INE, 1963, 1975, 1985,
1991 and 2002).
b
Data derived from Annual Report of Agricultural Statistics (MAPA, 1985 and
2001). Refers to annual crops with presence of some mature woodland trees
covering between 5 – 20 % of the surface (open woodland). This type of
intercropped system refers mainly to dehesas (in more than 90% of cases).
c
MAPA (2001) gives a value of 600,000 ha in 1999, which highlights the lack
of an adequate definition of agroforestry.
Table 2. Recent trends in the extent of silvoarable systems (in hectares)
in Spain.
162
ANNEX 7. The development and use of a framework for
characterising computer models of silvoarable
economics
Accepted for publication in Agroforestry Systems
Running head (shortened title): Agroforestry economic models
A.R. Graves 1, P. J. Burgess 1, F. Liagre 2, J-P. Terreaux 3 & C. Dupraz 4
1
Cranfield University, Silsoe, Bedford MK45 4DT, UK;
2
Assemblée Permanente des Chambres d’Agriculture, 9 Avenue Georges V, 75008
Paris, France;
3
Cabinet d’expertises forestières, Chavet, Paris, France;
4
Institut National de la Recherche Agronomique, 2 Place Viala, 34060 Montpellier,
France
Full address for correspondence:
Mr A.R. Graves, Cranfield University, Silsoe, Bedford, MK45 4DT, U.K
Telephone number:
+44-(0)1525-863107
Facsimile number: +44-(0)1525-863344
E-mail address:
[email protected]
Key words: ARBUSTRA, Agroforestry Estate Model, Agroforestry Calculator, farm,
POPMOD, WaNuLCAS
163
The development and use of a framework for characterising
computer models of silvoarable economics
ABSTRACT
A review of existing computer models of silvoarable economics was undertaken for a
project, entitled ‘Silvoarable Agroforestry for Europe’ (SAFE), which aims to reduce
uncertainty regarding the introduction and management of silvoarable systems in
Europe. Because the published literature describing and comparing such models is
sparse, a framework was developed and then used to characterise five computer
models: POPMOD, ARBUSTRA, the Agroforestry Estate Model, WaNuLCAS, and
the Agroforestry Calculator. The key characteristics described for each model were:
the background, the systems modelled, the objective of the economic analysis, the
economic viewpoint, the spatial and temporal scales, the generation and use of
biophysical data, the model platform and interface, and the input requirements and
outputs. Each of the models could produce a partial budget of the profitability of a
silvoarable, arable or forestry system at a one-hectare level using discounted costbenefit analysis. Whilst the research models undertook the analysis from a viewpoint
of a generic farmer, the models developed for decision-support also included
appraisals from the perspectives of tenants, share-croppers and participants in a
joint-venture. The two farm-scale models, ARBUSTRA and the Agroforestry Estate
Model, could also be used to examine the feasibility of silvoarable systems on an
existing business, and to determine the effects of heterogeneous land types and
phased planting. The framework allows users to identify the pertinent issues for
selecting or developing a particular model.
164
INTRODUCTION
Computer-based models of silvoarable economics (CMSEs) are coherent, numerical
representations of the economic structure and processes of silvoarable enterprises,
which can be manipulated to compare economic opportunities, determine feasibility,
optimise management practices, or predict economic behaviour. The importance of
agroforestry economics has often been stated (Singh et al., 1998, Nelson et al.,
1998; Chianu et al., 2002), but there is little published information describing CMSEs
and their development.
Biophysical issues continue to dominate agroforestry
research, despite the observation that many agroforestry projects fail as a result of
inadequate attention given to socio-economic factors (Mercer et al., 1998).
In the 1960s, the first crop simulation models were developed on mainframe
computers to estimate light interception and photosynthesis (Bouman et al. 1996;
Loomis & Williams, 1962). At a similar time, the first forestry simulation models were
also developed, using distance-dependent growth models to understand the effect of
management practice on stand development (Fries, 1974). Biophysical simulations
of agroforestry systems commenced in the 1980s, and included evaluations of the
potential of agroforestry on grazing land in New Zealand (Arthur-Worsop, 1984), and
the intercropping of crops with pine in North Carolina in the USA (McNeel & Stuart,
1984).
Early computer models of agroforestry economics tended to focus on silvopastoral
systems and used forestry models to simulate the returns from trees (Arthur-Worsop,
1984; Cox et al., 1988). In China, computer “models” were used to optimise the
intercropping of Paulownia with arable crops, according to economic, ecological and
social objectives (Jiang et al., 1986; Qun, 1991). Etherington and Matthews (1984)
describe a computer programme that was used for developing partial and whole-farm
165
budgets for land-use systems involving trees.
Subsequently there has been the
development of a range of computer models of silvoarable economics including
POPMOD (Thomas, 1991), ARBUSTRA (Liagre, 1997) and the Agroforestry Estate
Model (Knowles and Middlemiss, 1999)
Since 1992, the European Union has introduced a series of measures to promote the
integration of trees within existing farm businesses.
In 2001, a project entitled
‘Silvoarable Agroforestry for Europe’ (SAFE) was initiated by the European
Commission to reduce the uncertainties regarding silvoarable systems in Europe. An
important objective of the project was the development and use of a computer-based
model of silvoarable economics to compare the profitability of silvoarable, arable and
forestry systems at a one-hectare scale and to determine the feasibility of silvoarable
systems at the farm scale. The aim of this paper is to describe the development and
use of a framework for characterising existing models of silvoarable economics. This
can then be used to provide a coherent means of considering the key characteristics
required in the new model.
MATERIALS AND METHODS
Development of a framework for characterising models
In order to provide a consistent approach for describing and comparing existing
models of silvoarable economics, it was necessary to develop a framework for
characterising those models.
Because, to our knowledge, there is no existing
framework for the categorisation of computer models of silvoarable economics, the
framework was derived from criteria used for other categorisations of agroforestry
systems, economic analyses and computer models.
The importance of providing adequate background to the model was evident from the
register of ecological models described by Kassel University and GSF (2004) and the
166
review of bio-economic models by Brown (2000). The bibliographic framework for
categorising of the economic analysis of agroforestry technologies, developed by
Swinkels and Scherr (1991), provide background in terms of the surname of the
author, the language and the geographical location.
In a review of crop-soil
simulation models, Matthews (2002) grouped models according to their use in
research, decision-support or education.
An important feature in many of the categorisations is a description of the systems
modelled. This is included in the framework used by Swinkels and Scherr (1991) and
Brown (2000). Swinkels and Scherr (1991), and Antonopoulou (2003) in a review of
decision-support systems for crop growth and management, also describe the crops
that are modelled.
Brown (2000) notes the importance of understanding the objectives of the model,
taking note of the mathematical approach used and defining the temporal and spatial
scales. Swinkels and Scherr (1991) also describe six types of economic analysis
including cost-benefit analysis and optimisation, and seven levels of analysis
including the research plot, the farm, the project, and the region. As the models are
computer-based, the provision of technical information on the software and a
description of the input and output of data are also important (Kassel University and
GSF, 2004; Brown, 2000).
The final framework (Table 1) comprised nine major divisions. These were: (1) the
model background, (2) the systems modelled, (3) the objective of the economic
analysis, (4) the economic viewpoint, (5) the spatial scale, (6) the temporal scale, (7)
the generation and use of biophysical data, (8) the model structure and interface, and
(9) the input requirements and the outputs generated.
167
Table 1
USE OF THE FRAMEWORK
The framework described in Table 1 was used to characterise five contrasting
computer models of silvoarable economics. The first two models, which have been
used extensively by the authors, are POPMOD and ARBUSTRA. POPMOD was
developed by the Bio-economic Agroforestry Modelling Project at the University of
Wales, in Bangor, North Wales (BEAM project, 2002; Thomas, 1991). ARBUSTRA
was developed by the “Equipe de Recherches en Agroforesterie” within the Institute
National de la Recherche Agronomique (INRA) in Montpellier, France (Liagre, 1997).
The third model to be considered was the Agroforestry Estate Model developed by
Forest Research in New Zealand (Knowles and Middlemiss, 1999, Forest Research
2002). The fourth model was WaNuLCAS (Water Nutrients and Light Capture in
Agroforestry Systems), which was developed by the International Centre for
Research on Agroforestry (ICRAF) under the Southeast Asia Programme (van
Noordwijk and Lusiana, 1999, 2000, 2003). Although it is primarily a biophysical
model, it can undertake economic evaluations and its inclusion broadens the scope
of the comparison. The last model to be considered was the Agroforestry Calculator
(Department of Agriculture, 2002) developed by Campbell & White Associates Pty
Ltd in Australia under the “Decision-support for Adoption of Agroforestry Project”.
Although it was originally developed to focus on silvopastoral economics it can also
be used to evaluate silvoarable systems.
RESULTS AND DISCUSSION
Model background
The date of first mention in white or grey literature, the country of origin and the
language provide a context for each model. This is important as many of the inputs,
168
such as grant payments, are “closed” or pre-defined (Thomas and Willis, 1997). This
is the case in POPMOD and ARBUSTRA. A “closed” approach is useful if the model
is used in scenarios for which it was developed, as the user receives guidance on the
input data required.
However it can create difficulties if the model is used in a
different context. For example both ARBUSTRA and POPMOD require modification
to model the current silvoarable grant system in Spain. The Agroforestry Estate
Model is an example of an “open” model which can be easily used for different
economic scenarios. The language of the model is also important as this determines
the number of users who understand and operate the model.
For example
ARBUSTRA is available in French, whereas WaNuLCAS is available in English,
Bahasa Indonesian and Portuguese (Van Noordwijk and Lusiana, 2003).
Models are generally developed for the purpose of research, decision-support or
education (Matthews 2002; Graves et al., 2002).
As research tools, models of
silvoarable economics can be used to compare systems and management practices.
This may help to identify knowledge gaps, generate and test hypotheses, and
determine key parameters. As decision-support tools, models can be used to identify
preferred enterprises or scenarios or to optimise resource-use amongst a given set of
enterprises. In education, models can allow students to investigate the long-term
interactions of silvoarable systems, without the time requirements or financial costs of
real experiments.
Three of the selected models, POPMOD, ARBUSTRA and WaNuLCAS, were
primarily developed as research tools. POPMOD was initially developed to compare
agrosilvopastoral systems with poplar in England (Thomas, 1991) and to examine the
effect of recently-introduced fast-growing poplar hybrids on the profitability of
silvoarable systems (Willis et al. 1993). The aim of ARBUSTRA was to evaluate the
169
effect of introducing silvoarable systems to farms in the Midi-Pyrénées. A major
objective of WaNuLCAS was to synthesise existing knowledge and hypotheses on
above and below-ground resource-use by trees and crops at the “patch-scale” (van
Noordwijk and Lusiana, 1999).
The Agroforestry Estate Model and the Agroforestry Calculator were initially
developed with the aim of decision-support. The Agroforestry Estate Model was
developed to evaluate the physical and financial impact of agroforestry projects on
existing farms in New Zealand, Australia, Canada and the United States (Knowles
and Middlemiss, 1999). The Agroforestry Calculator was developed to give farmers
and consultants an easy way of estimating the profitability of plot (one-hectare) scale
agroforestry projects and comparing these with existing enterprises.
Once developed, a model can be used for other purposes. For example ARBUSTRA
has also been used as a decision-support tool to advise farmers on the economic
impact of silvoarable projects in France. Similarly POPMOD, the Agroforestry Estate
Model and WaNuLCAS have also been used in graduate education.
Because
decision-support and education models need to be readily understood by new users,
they tend to be better “finished” and more clearly presented than research models.
Decision-support models will also tend to distinguish between the specific
requirements of different types of user, such as owner occupiers and tenant farmers.
Systems modelled
Agroforestry systems can be described by their components (trees, crops, and
animals), and their temporal (ranging from coincident to sequential) and spatial
arrangement (Nair, 1985). Each of the five models was able to model agroforestry
systems where trees and crops are grown simultaneously, while the Agroforestry
Estate Model, WaNuLCAS and ARBUSTRA are also able to model sequential
170
systems.
POPMOD can be used to model one arable, silvoarable and forestry
system concurrently, which makes rapid comparison between different systems
possible; ARBUSTRA can model as many as six arable, silvoarable and forestry
systems simultaneously. By contrast, WaNuLCAS can model only one system at a
time, so that comparisons between forestry, silvoarable and arable systems need to
be made by consecutive runs.
The Agroforestry Estate Model and Agroforestry
Calculator model a “current scenario” and the impact of agroforestry on the “current
scenario”.
Objective of economic analysis
The objectives for undertaking an economic analysis of a silvoarable system can
include comparison, an assessment of feasibility, optimisation, and prediction of
actual farmer behaviour.
Each of these can also be subjected to uncertainty
analysis.
Comparison
Each of the models can be used to compare the economic effect of different
silvoarable, arable, or forestry systems, using a partial budget. When one arable
system is compared with another, a partial budget is usually undertaken on the basis
of the gross margin (revenue minus variable costs) on a per hectare basis. The
variable costs are those costs, such as seed, fertiliser and sprays, which are specific
to a system and vary in proportion to the area. Although labour and machinery costs
may be regarded as ‘fixed’ over a short period of time, it is often possible to assign
them to a specific system. As such they can be termed ‘assignable fixed costs’. In
forestry, the costs of labour and machinery are typically included, and therefore for
comparisons of arable and forestry systems, it is best to compare the ‘net margin’
(revenue minus variable and assignable fixed costs) (Willis et al., 1993).
171
A second implication of comparing silvoarable, arable and forestry systems is the
need to aggregate the economic evaluation over a defined period of time. Whereas
an economic comparison of two arable crops is often undertaken on an annual basis,
the economics of a forestry plantation is generally considered over a rotation which
lasts many years.
Because of inflation, the opportunity cost of money and the
increased flexibility of having money available now, most people will ‘discount’ the
value of future income. Discounting is a method that allows the user to directly
compare money realised at different periods of time (Barnard and Nix 1979).
By
discounting the value of, say timber production in year 50, it is possible to calculate
the present value of the timber, as if it was available in year 1. In each of the five
models, a cash-flow is dynamically simulated over time, so that the user can compare
annual and perennial systems over specified time horizons, using the net present
value (NPV) at a selected discount rate.
Feasibility
A second potential objective for an economic model is to determine if a combination
of systems is feasible within a specified context. The approach is similar to that used
for comparison, but the combined effect of more than one system on a specified
resource, such as timber output, labour or cash-flow, is also ascertained. Because
the inputs and the outputs of forestry and silvoarable systems can be “lumpy”,
farmers may decide to smooth input requirements and output flows by planting trees
over a number of years (phased or multiple planting). Both the Agroforestry Estate
Model and ARBUSTRA are capable of determining the feasibility of silvoarable
systems in a whole-farm context.
172
Optimisation
A third potential objective for economic models is to inform the user how to optimise
a given system, when faced with a particular or numerous objectives, within specified
constraints (Mendoza et al., 1987). For example: determining the optimum area of
agroforestry to maximise profit and output, given a limited budget.
None of the
models reviewed here were specifically designed for optimisation.
Prediction of actual farmer behaviour
Economic models can also be used to predict the actual response of farmers to
changes in cost, prices or policy - for example, determining the likely change in the
area of agroforestry following a change in government grants. In relation to forestry
or arable systems, the possible approaches used have included positive
mathematical programming (Judez et al. 2001) and positivistic mathematical
programming (C. Yates, pers. comm. 2001). However none of the selected economic
models use these approaches.
Uncertainty analysis
All of the reviewed models are deterministic in that each input is typically described
by a mean and a given set of inputs leads to a uniquely definable outcome.
However, biophysical and economic predictions are subject to uncertainty and the
use of single values can be misleading. One alternative approach is to develop a
stochastic model, where the variability of selected inputs is defined, for example, by
standard deviations or the random selection of historical data. The resulting output
values can also be described in terms of a mean and standard deviation. A second
alternative approach is use the model to undertake an analysis of the sensitivity of a
specified output to changes in the value of an input. ARBUSTRA allows a sensitivity
analysis of the effect of the discount rate, the price of wood, the agricultural gross
173
margin and tree subsidies on the NPV.
The Agroforestry Calculator allows a
sensitivity analysis of the effects of the tree returns and the response of the
silvoarable crop to trees on the NPV. Sensitivity analysis can also be achieved using
POPMOD, the Agroforestry Estate Model and WaNuLCAS, but this requires the user
to manually change each input, re-run the model and store the outputs. However
even with these models there may be the potential to link them to additional software
that allows the automation of such analyses.
Viewpoint of the economic analysis
An important model characteristic is the viewpoint of the economic analysis. Each of
the five reviewed models primarily provides a micro-economic, farmer-based analysis
of the modelled systems.
From such a viewpoint government subsidies are
considered as revenue and taxes can be considered as a cost.
Amongst the five sampled models, those models that have been used for decisionsupport tend to include a wider range of viewpoints than those developed for
research. For example ARBUSTRA and the Agroforestry Calculator allow economic
analyses from the perspective of a tenant or an owner involved in a share-cropping
arrangement respectively.
The Agroforestry Estate Model extends the possible
viewpoints to include joint-venture agreements between a farmer and a landowner,
and cutting right agreements, where the right to harvest the trees at the end of the
rotation is bought before the trees are clear-felled.
An alternative viewpoint is the macro-economic perspective or that of society as a
whole. This may require adjustment to some of the market prices to reflect the public
value.
Likewise taxes could be included as revenue and subsidies as costs. A
consideration of the environmental costs and benefits of the system may also be
included. The reviewed models are not specifically designed to provide a macroSAFE Final Progress Report – Volume 4 (Annexes) – May 2005
174
economic analysis, but there are options to alter prices and to set the level of grants
to zero. ARBUSTRA can also be used to determine the cost of a project “to society”
and the Agroforestry Calculator includes the economic effects of soil degradation.
Spatial scale
The spatial scale for the analysis of silvoarable economics may be at the onehectare, field, farm, or even the regional, national or global scale (Figure 1). The
smallest scale is typically “one-hectare” on which a single system (i.e. arable, forestry
or silvoarable) completely occupies a homogenous area.
A “field-scale” analysis
differs from a “one-hectare-scale” analysis, in that the revenues and costs related to
headlands and field boundaries can be included. Whereas each of the five models
can generate results at the ‘one-hectare-scale’, ARBUSTRA is also capable of
incorporating the effects of headlands within the silvoarable calculations.
Figure 1
A “farm-scale” analysis is used to determine the effect of a combination of arable,
forestry or silvoarable systems on the resources of a specified farm business. Of the
models examined, only ARBUSTRA and the Agroforestry Estate Model are designed
for economic analysis at a farm-scale. Both models include fixed-costs, can model
several systems simultaneously, and allow an evaluation of the overall effect of
introducing new systems. Both also allow the user the option of defining more than
one possible planting date for silvoarable and forestry systems. An additional feature
of the ARBUSTRA farm-model is the inclusion of heterogeneous land types on an
individual farm. The inclusion of six areas of different fertility with arable and/or
silvoarable and/or forestry systems on each allows for variations in productivity
across the farm.
175
Above the farm-scale, analyses could also be taken at a project-, catchment-,
community-, regional-, national- or international-scale (Swinkels and Scherr, 1991).
None of the models examined were specifically designed for such scales, but they
could be developed for such a use if integrated with spatial biophysical and economic
data within a Geographical Information System.
Temporal scale
The temporal scale relates both to the time-increment for the economic calculations
and the maximum time period that can be considered. Each of the five selected
models, except WaNuLCAS, run on an annual time-step; WaNuLCAS can be run on
an hourly, or daily time-step. The original version of POPMOD was designed to run
over a 30 year period, which was the typical rotation period for poplar in the UK. At
the other extreme, ARBUSTRA can run simulations over 120 years. In ARBUSTRA,
there is also a mathematical procedure for calculating an “infinite” NPV.
Generation and use of biophysical data
A distinguishing characteristic of computer models of silvoarable economics is the
method by which biophysical data are generated and used. The simplest structure is
a stand-alone economic model that operates without a biophysical module (Figure 2).
This is the structure used by ARBUSTRA and the Agroforestry Calculator.
In
ARBUSTRA, the arable systems and the silvoarable crop are described solely in
terms of a gross margin entered by the user. In the Agroforestry Calculator only a
gross margin value is required for the “current enterprise”.
Figure 2
The second and third forms are effectively bio-economic models in that they include
both biophysical and economic modules.
The biophysical module may feed
176
biophysical data to the economic component of the model in a one-way flow of
information or may allow the economic component of the model to feed information
back to the biophysical module in a two-way flow (Brown, 2000). In the simplest
version of WaNuLCAS, the production data are fed to the economic module and
there is no feedback. In POPMOD, there is a two-way flow of information. For
example, if the silvoarable crop component is no longer profitable, then the economic
module can instruct the biophysical module to stop “planting”. In turn, the cessation
of arable cropping can affect the productivity of the tree component of the silvoarable
system.
The biophysical component of a model can be characterised as empirical, functional,
or mechanistic (Passioura, 1996). It is worth noting that all models reach empirical
boundaries at some point and that these boundaries are effectively a matter of
“scale”. For example POPMOD uses an empirical biophysical model of annual crop
yield and it derives timber output from yield tables for poplar at stated stand densities
and yield classes.
By contrast WaNuLCAS includes a number of mechanistic
biophysical modules. Although this allows tree and crop yields to respond directly to
changes in the climate and soil conditions, it does require additional inputs and
functions.
Model platform and interface
The choice of the software platform for the model is dependent on availability and
costs, the suitability for the task, the knowledge of the developers, and the ability to
transfer the models between users.
Both POPMOD and ARBUSTRA were
developed as spreadsheet models in QUATTRO PRO (Borland International Inc,
Scots Valley, California). The Agroforestry Estate Model was originally developed in
Microsoft® Excel (Microsoft Corporation, Seattle, Washington, USA) and has recently
177
been re-written in Visual Basic.
WaNuLCAS was developed in a graphical
development environment called Stella (High Performance Systems Inc, Hanover,
New Hampshire, USA), but the biophysical and economic inputs and outputs can be
developed or obtained in linked Microsoft® Excel spreadsheets. The Agroforestry
Calculator is a spreadsheet model developed in Microsoft® Excel.
The model interface can determine how easily a model can be operated by a new
user. ARBUSTRA and the Agroforestry Estate Model, which have been used for
decision-support, ease the user’s task by providing a graphical user interface (GUI)
for data input and model navigation. In contrast POPMOD allows direct access to
logically grouped input cells in the spreadsheet. Such transparency in the working of
the model can be particularly useful to researchers and developers.
Input requirements and outputs generated
The ability of the user to interact with the model depends in part on the user’s
familiarity with the modelling platform described in the preceding section. However it
also depends on the input requirements, the availability of associated databases, and
the provision of outputs.
Inputs required
One factor determining the level of inputs is whether the model operates at a onehectare- or a farm-scale. POPMOD and the Agroforestry Calculator, which are onehectare-scale models, generally require fewer inputs than farm-scale models such as
ARBUSTRA and Agroforestry Estate Model. The input requirements also increase if
there are a wide range of options.
For example, in both ARBUSTRA and
Agroforestry Estate Model there is the capacity to use different machine and labour
costs for different silvicultural operations, i.e. silvicultural operations undertaken by
the farmer may be costed at a different rate than work undertaken by contractors.
178
A second factor determining the input requirements for an economic model is the
requirement to provide or generate biophysical data. For example, the two standalone models, ARBUSTRA and the Agroforestry Calculator, describe timber
production from a discrete value provided by the user for the timber yield in the year
of harvest.
By contrast, bio-economic models, such as POPMOD and the
Agroforestry Estate Model, require a continuous set of timber volume values for each
year of the tree rotation. These data may come from field measurements, historical
records or biophysical models. Of the five models, WaNuLCAS requires the most
inputs, because of the many data required for mechanistic modelling.
Availability of databases
Although peripheral to the model itself, the provision of databases can be important
in determining the ease with which the model can be used. Both WaNuLCAS and
Agroforestry Estate Model allow access to databases of inputs, and the Agroforestry
Calculator allows users to add or change databases within the model itself.
Outputs generated
Each of the five models calculates a Net Present Value for the modelled system.
Other values that are commonly calculated include the annuity value, the benefit:
cost ratio, the payback period and a description of the cash-flow of the modelled
systems. The generation of further economic outputs is partly determined by the
spatial scale of the model.
Hence the two farm-scale models, ARBUSTRA and
Agroforestry Estate Model, provide a farm Net Present Value and describe the
amount of land occupied by different systems and the effect on farm labour
requirements.
The production of physical outputs from the economic model is primarily determined
by the relative importance of the biophysical component of the model. Hence bioSAFE Final Progress Report – Volume 4 (Annexes) – May 2005
179
economic models such as POPMOD and Agroforestry Calculator can provide annual
timber yields. However, the greatest range of biophysical outputs, including the soilwater and nutrient status, is provided by WaNuLCAS.
CONCLUSIONS
A framework of analysis (Table 1) was developed and used to provide a consistent
approach for describing and comparing the selected models.
The results of a
comparison of five models are summarised in Table 2. This was done to help inform
the development of an economic model for a project to reduce the uncertainty of
introducing and managing silvoarable systems in Europe.
Table 2
The first stage of the framework was to describe the background for each model, in
terms of the country of origin and initial purpose. The models developed for research
tended to provide a more generic analysis than those developed for decision-support,
which included, for example, appraisals of share-cropping, tenant farming, leaseholding, joint-ventures and cutting right schemes.
A common feature of each model was the need to combine short-term, e.g. annual
crops, and long-term, e.g. timber production, enterprises within the same partial
budget. This was typically done on the basis of a net margin. In each of the models,
the long-term nature of silvoarable and forestry systems was accounted for by
discounting future costs and revenues to derive a net present value at a specified
discount rate.
The principal objectives of the reviewed models were to compare the economic effect
of alternative silvoarable systems relative to agriculture or forestry or to determine the
feasibility of silvoarable enterprises in a farm context. Comparisons of the profitability
180
of the systems were made using partial budgets at a one-hectare scale.
Examinations of feasibility were made with farm-scale models which could include
spatial heterogeneity within the farm, multiple planting schemes and the interactions
between different systems.
The models intended for decision-support tended to have a clearer interface for new
users than those developed for research. However those models which operate
solely at a spreadsheet, such as POPMOD, are transparent and are relatively easy
for a researcher to manipulate. The actual choice of a model may also depend on
the cost and the availability of the software.
The full range of features described here may be difficult to satisfy in one model and
compromises are often made in selecting or developing a model. The framework
developed here allows model developers and users to identify the pertinent issues
and prioritise what is most important for their needs.
Acknowledgements
This research was carried out as part of the SAFE (Silvoarable Agroforestry for
Europe) collaborative research project. SAFE is funded by the EU under its Quality
of Life programme, contract number QLF5-CT-2001-00560, and the support is
gratefully acknowledged.
We also acknowledge the valuable comments on this
paper from Leith Knowles and Lars Hansen.
REFERENCES
Antonopoulou E (2003) DSSs in major field crops: classification and performance.
In: Harnos Z, Herdon M and Wiwczaroski TB (eds) Proceedings of the 4th Conference
of the European Federation for Information Technology in Agriculture, Food and the
Environment (EFITA).
Volume 1 pp 103-113. University of Debrecen, Hungary.
(Accessed 19 January 2004). http://www.date.hu/efita2003/centre/pdf/0110.pdf
181
Arthur-Worsop MJ (1984) An economic evaluation of agroforestry: the national
viewpoint. In: Bilbrough GW (ed) Proceedings of a Technical Workshop on
Agroforestry pp 61-70. New Zealand Forest Service.
Barnard CS and Nix J (1979) Farm Planning and Control. 2nd Edition. Cambridge
University Press, Cambridge, UK.
BEAM project (2002) BEAM models – POPMOD (poplar model). BEAM project,
University of Wales, Bangor, Wales.
(Accessed 19 January 2004).
http://www.bangor.ac.uk/~afs117/popmod.htm
Bouman BAM, van Keulen H and Rabbinge R (1996) The 'School of de Wit' crop
growth simulation models: a pedigree and historical overview. Agricultural Systems
52: 171-198.
Brown DR (2000) A review of bio-economic models. Cornell African Food Security
and Natural Resources Management Program, Cornell University, USA. 101 pp.
(Accessed
19
January
2004)
http://aem.cornell.edu/faculty_sites/cbb2/grad/Brown/BrownReviewofBioeconomicMo
dels.pdf
Chianu JN, Akintola JO and Kormawa PM (2002) Profitability of cassava-maize
production under different fallow systems and land-use intensities in the derived
savanna of southwest Nigeria. Experimental Agriculture 38:51-63.
Cox O, McGregor M and Maclaren P (1988) Agroforestry components of the radiata
pine stand model. In: Maclaren P (ed) Proceedings of the Agroforestry Symposium,
Rotorua, 24-27 November 1986. pp 175-182. Forest Research Institute-Bulletin 139,
New Zealand.
Department of Agriculture (2002).
Agroforestry Calculator.
Department of
Agriculture,
Western
Australia.
(Accessed
19
January
2004).
http://agspsrv34.agric.wa.gov.au/environment/tools/trees/Agroforestry_Calculator.ht
m
Etherington DM and Matthews PJ (1984). MULBUD User’s Manual. National Centre
for Development Studies, Australia National University, Canberra. 115 pp.
Fries J (1974). (ed) Growth models for tree and stand simulation. Research Notes
30. Department of Forest Yield Research, Royal College of Forestry, Stockholm,
Sweden. 379 pp.
Forest Research (2002). Agroforestry Estate Model. Forest Research, Rotorua,
New
Zealand.
Accessed
19
January
2004.
http://www.forestresearch.co.nz/default.asp
Graves AR, Hess T, Matthews RB, Stephens W and Mason T (2002) Crop
simulation models as tools in education. Journal of Natural Resources and Life
Sciences Education 31: 48-54
Jiang J, Zhu J, Wu L and Liu T (1986) Comprehensive benefit analysis and
optimisation of Paulownia intercropping combinations. In: Hsuing W and Chandler
182
PF (eds) Agroforestry research and practice, pp 163-173. China Publishing House,
Beijing.
Judez L, Chaya C, Martinez S and Gonzalez. AA (2001) Effects of measures
envisaged in "Agenda 2000" on arable crop producers and beef and veal producers;
an application of positive mathematical programming to representative farms of a
Spanish region. Agricultural Systems 67: 121-138
Kassel University and GSF (2004). The Register of Ecological Models. (Accessed
19 January 2004). http://eco.wiz.uni-kassel.de/ecobas.html
Knowles L and Middlemiss P (1999) Evaluating Agroforestry Options: A Continuing
Professional Development Course. Forest Research, Rotura, New Zealand.
Liagre F (1997) ARBUSTRA Manuel de l’utilisateur. User manual for ARBUSTRA,
Centre Régional de la Propriété Forestière (CRPF) and l'Institut National de la
recherche Agronomique (INRA) Montpellier, France. 71pp.
Loomis RS and Williams WA (1962) Maximum crop productivity: an estimate. Crop
Science 3: 67-72.
Matthews RB (2002) Introduction. In: Matthews RB and Stephens W (eds). Cropsoil simulation models: applications in developing countries, pp 1-5.
CAB
International, Wallingford, UK.
McNeel JF and Stuart WB (1984) Feasibility of agri-silviculture for pine plantations in
the south. Paper, American Society of Agricultural Engineers, No. 84-1609, 14 pp.
Mendoza, GQ, Campbell GE and Rolfe GL (1987). Multiple objective programming:
an approach to planning and evaluation of agroforestry systems: Part 2 – an
illustrative example and analysis. Agricultural Systems 23: 1-18.
Mercer DE, Miller RP, Nair PKR and Latt CR (1998) Socioeconomic research in
agroforestry: progress, prospects, priorities. Agroforestry Systems 38: 177-193.
Nair PKR (1985) Classification of agroforestry systems. Agroforestry Systems 3: 97128.
Nelson RA, Cramb RA and Mamicpic MA, (1998) Erosion/productivity modelling of
maize farming in the Philippine uplands. Part III: Economic analysis of alternative
farming methods. Agricultural Systems 58:165-183.
Passioura JB (1996)
Simulation models: science, snake oil, education, or
engineering? Agronomy Journal 88: 690-694.
Qun H (1991) Economic evaluation of intercropping with paulownia – optimisation
and choice of intercropping models. In: Zhaolua Z, Mantang C, Shiji W and Youxu J
(eds) Agroforestry systems in China. pp. 163-173. The Chinese Academy of
Forestry People’s Republic of China and International Development Research
Centre, Canada.
Singh K, Rao BRR, Singh CP, Bhattacharya AK, Kaul PN, and Rajeswara Rao BR
(1998) Production potential of aromatic crops in the alleys of Eucalyptus citriodora in
183
semi-arid tropical climate of south India. Journal of Medicinal and Aromatic Plant
Sciences 20:749-752.
Swinkels RA and Scherr SJ (1991) (compilers) Economic analysis of agroforestry
technologies: an annotated bibliography. International Council for Research in
Agroforestry, Nairobi, Kenya. 215 pp
Thomas TH (1991) A spreadsheet approach to the economic modelling of
agroforestry systems. Forest Ecology and Management 45: 207-235.
Thomas TH and Willis RW (1997) Linking bio-economics to biophysical agroforestry
models. Agroforestry Forum 8(2): 40-42.
van Noordwijk M and Lusiana B (1999). WaNuLCAS 1.0, a model of water, nutrient
and light capture in agroforestry systems. Agroforestry Systems 45: 131-58.
van Noordwijk M and Lusiana B (2000) WaNuLCAS 2.0 Background on a model of
water, nutrient and light capture in agroforestry systems. International Centre for
Research in Agroforestry (ICRAF), Bogor, Indonesia.
van Noordwijk M and Lusiana B (2003) Welcome to the world of WaNuLCAS. A
model of water nutrient and light capture in Agroforestry Systems. ICRAF South East
Asia
Programme,
Bogor,
Indonesia.
(Accessed
19
January
2004).
http://www.worldagroforestrycentre.org/sea/Products/AFModels/WaNulCAS/
Willis RW, Thomas TH and van Slycken J (1993) Poplar agroforestry: a reevaluation of its economic potential on arable land in the United Kingdom. Forest
Ecology and Management 57: 85-97.
184
Table 1. The framework used to characterise computer models of silvoarable
economics.
Model characteristic
Examples of options
1.
Background
1.1
First reference
Date
1.2
Country of origin
Country
1.3
Language
English, Portuguese, French
1.4
Initial primary use
Research, decision-support or educational
2.
Systems modelled
2.1
Components
system
of Agriculture, forestry,
agrosilvopastoral
2.2
Number
systems
of Number of systems modelled simultaneously
3.
Objectives
economic
of Comparison of two or more possible designs
Feasibility:
resources
analysis
effect
of
silvoarable,
combinations
silvopastoral
with
or
specified
Optimisation: identification of ‘best’ design on basis of
criteria
Prediction of actual behaviour
Uncertainty analysis
4.
Viewpoint
analysis
of
Micro-economic (i.e. perspective of owner occupier or tenant)
Macro-economic (i.e. perspective of society)
5.
Spatial scale
One-hectare: sub-field level analysis of a homogenous
area
Field-scale: includes the effect of headlands
Farm-scale: fixed costs included
Catchment, regional, national or international-scale
6.
Temporal scale
6.1
Time-step
Hourly, daily or annual
185
6.2
Time
considered
7.
use
period Maximum rotation
Generation
of
and
biophysical
data
7.1
Biophysicaleconomic links
Stand-alone economic model
Bio-economic model with one- or two-way information
flow
7.2
Nature
biophysical model
of Empirical: single relationship models
Functional: use of several empirical relationships
Mechanistic: models which include growth processes
8
Platform
interface
and
8.1
Model platform
Spreadsheet, programming language, graphical environment or
database
8.2
Model interface
Direct or a specifically-designed graphical user interface
9.
Inputs
outputs
and
9.1
Inputs requirements
Low, moderate or high
9.2
Databases
Availability of linked databases
9.3
Outputs
Biophysical, economic or both
186
Table 2. Characterisation of five computer models of silvoarable economics.
POPMOD
ARBUSTRA
Agroforestry WaNuLCAS
v 2.0
Estate
Model v 4.0
Agroforestry
Calculator
1989
1995
1996
1996
1999
UK
France
New Zealand
Indonesia
Australia
Langauge
English
French
English
English,
Indonesian,
Portuguese
English
Initial primary
Research
Research
Decisionsupport
Research
Decisionsupport
Widely-spaced
poplar, arable
and
silvoarable
Forestry,
arable
and
silvoarable
Forestry,
arable,
livestock,
silvoarable
and
silvopastoral
Forestry,
arable
and
silvoarable
Arable
and
silvoarable
Yes
Yes
Yes
No
Yes
3.
Objective of
economic analysis
Comparison
Comparison
and feasibility
Comparison
and feasibility
Comparison
Comparison
4.
Economic
viewpoint
Owneroccupier
Owneroccupier
tenant
Owneroccupier
joint-ventures,
cutting rights
Owneroccupier
Owneroccupier,
leasehold
or
share-cropping
One-hectare
One-hectare,
field and farm
One-hectare
One-hectare
One-hectare
1.
Background
First
reference
Country of origin
use
2.
modelled
Systems
Systems
More
one system
than
or
5. Spatial scale
Scale
and farm
Farm
heterogeneity
N/a
Yes
Yes
N/a
N/a
No
Yes
Yes
No
No
Time-step
Annual
Annual
Annual
User-defined
Annual
Time period
30 years
120 years
User-defined
User-defined
50 years
Multiple
planting
6.
scale
Temporal
187
Empirical
model,
twoway
information
flow
No biophysical
model, direct
yield data for
trees
Empirical
model,
oneway
information
flow
Mechanistic
Spreadsheet
Spreadsheet
Visual Basic
Graphical
development
environment
Spreadsheet
Input into cells
Graphical user
interface
Graphical user
interface
Graphical user
interface
Semi-graphical
user interface
Moderate
High
Moderate
Very high
Low
Databases
Tree data
None
Tree data
Various
Tree data
Outputs
Mostly
economic
Mostly
economic
Biophysical
and economic
Mostly
biophysical
Mostly
economic
7. Generation and
use of biophysical
data
model,
oneway
information
flow
No biophysical
model, direct
yield data for
trees
8.
Platform/inte
rface
Platform
Model
interface
9.
Inputs
outputs
and
Input
requirements
Note: N/a = not applicable
188
Figure 1. Schematic representation of one-hectare, field, farm, and regional scales
of modelling of silvoarable economics.
189
Country,
region,
community or project
scale
A defined geographical
area comprising more
than one farm business.
Farm scale
An area managed as one
business.
Fixed costs
and
a
range
of
enterprises and land
types
are
typically
Field scale
The effect of headlands
and boundaries may be
included and the field
may be given a specific
One-hectare scale
Homogenous area with
economic data typically
based on gross or net
margins per hectare
190
Figure 2. Three possible types of information flow within computer models of
silvoarable economics.
Stand alone
economic model
Economic
model
Bio-economic model
with one-way
information flow
Bio-economic model
with two-way
information flow
Biophysical
module
Biophysical
module
Economic
module
Economic
module
191
ANNEX 8. Fine root distribution in Dehesas of CentralWestern Spain
G. Moreno1, J.J. Obrador1, E. Cubera1, C. Dupraz2
1
I.T.Forestal, Centro Universitario, UEX. Plasencia 10600. Cáceres. Spain
2
INRA, UMR-SYSTEM, 2 Place Viala. 34060 MONTPELLIER Cedex, France.
1
Corresponding author:
Gerardo MORENO MARCOS
I.T. Forestal, Universidad de Extremadura,
Avda. Virgen del Puerto, Plasencia, 10600 (CÁCERES, Spain)
Phone: + 34.927427000
Fax: + 34.927425209
Email: [email protected]
Running Title: Rooting system in dehesas
Nº of text pages: 17
Nº of tables: 1
Nº of figures: 5 (+ 1 page with figure captions)
192
Fine root distribution in Dehesas of Central-Western Spain
G Moreno1*, JJ Obrador1, E Cubera1, C Dupraz2
I.T.Forestal, Centro Universitario, UEX. Plasencia 10600. Cáceres. Spain
2
INRA, UMR-SYSTEM, 2 Place Viala. 34060 MONTPELLIER Cedex, France.
6
ABSTRACT
A Dehesa is a structurally complex agro-silvo-pastoral system where at least two
strata of vegetation, trees and herbaceous plants coexist. We studied the root
distribution of trees (Quercus ilex L.) and herbaceous plants, in order to evaluate tree
and crops competition and complementarity in Dehesas of Central Western Spain. 72
soil cores of 10 cm diameter (one to two metre deep) were taken out around 13
trees. Seven trees were intercropped with Avena sativa L. and six trees were in a
grazed pasture dominated by native grasses. Soil coring was performed at four
distances from the tree trunks, from 2.5 (beneath canopy) till 20 m (out of the
canopy). Root length density (RLD) of herbaceous plants and trees was measured
using the soil core-break method. Additionally, we mapped tree roots in 51 profiles of
7 recently opened road cuts, located between 4 and 26 m of distance from the
nearest tree. The depth of the road cuts varied between 2.5 and 5.5 m. Herbaceous
plant roots were located mostly in the upper 30 cm, above a clayey, dense soil layer.
RLD of herbaceous plants decreased exponentially with depth until 100 cm depth.
Holm-oak showed a much lower RLD than herbs (on average 2.4 versus 23.7 km.m-3,
respectively, in the first 10 cm of the soil depth). Tree RLD was surprisingly almost
uniform with depth and distance to trees. We estimated a 5.2 m maximum depth and
a 33 m maximum horizontal extension for tree roots. The huge surface of soil
explored by tree roots (around 7 times the projection of the canopy) could allow trees
to meet their water needs during the dry Mediterranean summers. The limited vertical
overlap of the two root profiles suggests that competition for soil resources between
trees and the herbaceous understorey in the Dehesa is probably not as strong as
usually assumed.
Keywords:
Agroforestry, grasses, open woodland, Quercus ilex, root length density, rooting
system, core-break.
6
* Correspondence to: [email protected]
193
INTRODUCTION
Dehesas are multi-purpose open woodlands where at least two strata of vegetation
coexist. They have been common in the Iberian Peninsula, at least since the middle
ages (Montero et al., 1998). At present, Dehesas cover 3.1 million hectares in
western Spain and Portugal, and they are considered as habitats to be preserved
because of the high biological diversity they support (Díaz et al., 1997). However, in
the last decades, a significant decrease in extension and tree density has been
occurring as a consequence of increased mechanisation, changes in land use and
death of trees in over-aged stands (Plieninger et al., 2003). A better knowledge of the
role of trees on dehesa functioning and sustainability could contribute to improve its
management and conservation.
Some pioneer studies on the effect of trees in dehesa functioning have shown the
positive effects of trees on soil nutrient contents (Escudero, 1985), soil water storage
capacity (Joffre and Rambal, 1988), water stress for the underlying herbaceous
stratum (Joffre and Rambal, 1993), and pasture production (Puerto et al., 1987).
Several authors have also shown the positive effect of tree clearance on the
remaining trees physiological status (e.g., Infante et al. 1999 and Montero et al.,
2004) and productivity (e.g., Diaz et al., 1997). The improved physiological status of
the dehesa trees could be due to an increase in the available soil volume, and thus
water and nutrients, for each individual tree. Joffre et al. (1999) stressed the need for
better knowledge of the extension of the tree root system to understand the
implications of soil water balance on the stability of the dehesa (tree-tree and treeunderstorey interactions), and therefore to predict the consequences of long-term
climatic and land use changes.
Much of the competition among plants takes place underground (Casper and
Jackson 1997), and below-ground competition knowledge is a major difficulty for
understanding simultaneous agroforestry systems (van Noordwijk et al., 1996). The
use of a higher proportion of below ground resources can be achieved if deep
networks of tree roots are able to capture water or nutrients draining or leaching
below the rooting zone of the crops (van Noordwijk et al., 1996).
Understanding and predicting ecosystem functioning (e.g. nutrient cycling, carbon
and water fluxes) requires an accurate assessment of plant rooting distribution
(Jackson et al., 1996). A realistic map of root length density, both horizontal and
vertical, is needed to model possible interactions (facilitation, competition and
complementarity) between plants. However, many models (e.g. HyPAR: Mobbs et al.,
1999; WaNuLCAS: Van Noordwijk and Lusiana, 2000) assume a simple shape of the
tree rooting system: an exponential decrease with soil depth and distance from the
tree trunk is often considered. This simplicity is explained by the difficulty and
complexity of root studies, which have resulted in a lack of quantitative information on
rooting systems (Smith et al., 1999; Jose et al., 2001).
In the last decades some new methods have been proposed to describe the root
system: soil windows, minirhizotrons, soil cores washing, (Smit et al., 2000).
However, all them have any limitation in term of collecting representative samples
and of cost or time required (Smit et al., 2000). To address the issue of
representativeness, some authors applied a combination of methods, e.g. trenches
plus soil cores (e.g. Jones et al., 1998, Silva and Rego, 2003), in order to
194
characterise the horizontal and/or vertical root extension and to determine the root
density. Additionally, to reduce the labour needs and speed up the process, van
Noordwijk et al. (2000) proposed an indirect method to estimate the root length
density by counting roots emerging from the horizontal planes of broken soil cores.
To our knowledge, only two studies have been carried out to characterise the root
systems in dehesas (Barrera et al., 1987; Joffre et al., 1987), and both were limited to
natural grasses roots, in the first 30 and 60 cm of soil, respectively. None of these
two works dealt with the tree root distribution. The present study focus on the root
distribution (fine root length density) of both tree (holm-oak) and herbaceous
vegetation (cereal crop or natural grasses) considering both vertical and horizontal
dimensions. Additionally, we have documented the effect of soil tillage on the root
density of the trees. To achieve that we used two different methods, based on the
study of soil cores and on the record of root maps in recently opened road cuts.
MATERIAL AND METHODS
Study Area
The study has been carried out in two dehesas (Cierra lobata? CL and xxx ST) of CW Spain (39º 41’ N - 6º 13’ W; altitude: 380 m.a.s.l.), with an average tree density of
35 tree ha-1 (Quercus ilex L in both grazed and cropped plots. Average tree
dimensions were 44.9 cm for DBH (diameter at breast height), 10.4 m for canopy
width and 7.8 m for tree height. In grazed plots the main grasses were Lolium rigidum
Gaudin, Plantago lanceolata L., Erodium sp L., Taraxacum obovatum (Willd.) DC.
and Echium plantagineum L. These species were also abundant as weeds in the
intercropped (oats) plots given that herbicides are not applied in dehesas crops. The
aboveground biomass of the intercrop was low : 5.2 and 2.9 Mg of dry weight per ha
in CL and ST respectively. The difference reflected the difference of the fertilisation
schemes : 200 and 50 kg of NPK ha-1 in CL and ST farms, respectively.
The climate is semi-arid Mediterranean, with an annual rainfall of 579 mm, mean
annual temperature of 16.2 ºC, and mean annual potential evapotranspiration of 864
mm. Climate is classified as subtropical Mediterranean, following the Papadakis
classification (1966), with dry, warm and cold (with frost) periods of 4, 3 and 5
months, respectively. Soils are chromic Luvisols (FAO, 1998) in both farms,
developed over tertiary sediments with abundant gravels and stones of quartzite, one
or several very red argic horizons, with slight brown and silty-sand texture in the
surface horizon and a very sandy layer in depth (below 100 cm). Soils also showed
poor internal drainage, resulting in variegated colours and/or pseudo-gleic, low soil
chemical fertility, and occasional CaCO3 accumulation between 1.5 and 2 m depth.
Soil cores: Root Length Density
Soil cores were taken with a stainless steel soil column cylinder with a cutting shoe
and a removable cover (diameter 10 cm, length 1000 mm), inserted into the soil with
a heavy electrical powered percussion hammer (Makita HM 1800, provided by
Eijkelkamp, Giesbeek, The Netherlands). Between 23rd - 28th April 2002 thirty-six soil
cores of 1 m-depth were extracted at 2, 5, 8 and 12 m distance from the tree trunk of
4 intercropped holm-oaks, at two orientations (three orientations in one tree).
Between 10th - 20th May 2003, thirty-six soil cores of 2 m-depth (at maximum) were
taken at 2, 5, 10 and 20 m of distance from the tree trunk of 3 intercropped holmSAFE Final Progress Report – Volume 4 (Annexes) – May 2005
195
oaks and 6 ‘intergrazed’ holm-oaks. Soil cores were covered by a gutter (two halves
of a PVC tube) and transparent plastic to avoid damage during transport to the
laboratory, where they were stored at 6°C in a cold chamber to keep the roots fresh
until analysis.
Cylindrical soil cores were divided into 10 cm-length samples. Each sample was then
broken by hand in two parts, and the number of tree and herbaceous plants fine roots
(using a 2 mm diameter threshold for tree fine roots) were recorded in both sides of
the sample parts. Decayed tree roots were excluded. Holm-oak roots were identified
by their black cork, while grasses roots were white. Very new tree roots (growing tips)
are also white, but much thicker than crop/grass roots. Differentiating crop and weed
roots was not possible.
The method of soil core-break allowed us to estimate the Root Length Density (RLD)
from the number of roots sticking out of two soil surfaces of a horizontally broken soil
core (Van Noordwijk et al., 2000). A total of 65 randomly selected samples were
washed each year to provide a direct calibration of root length versus counts (Van
Noordwijk et al., 2000). To wash samples, different filters between 2 and 0.125 mm
mesh size were used. This was done to avoid losing fine roots. All samples had tree
fine roots, and only 46 had herbaceous roots. Roots obtained from the washing
activity were laid on plastic paper and then photocopied and the length of the fine
roots was measured manually for each soil core. Data are expressed as Root Length
Density (km m-3 of soil) because root length is a better indicator of root system
functions in terms of uptake of water and nutrients than root weight and root number
(Jones et al., 1998).
Road Cuts: Maximum tree rooting depth and horizontal spread
We took advantage of current works in the road that cross the study area, to check
the maximum distance and depth of the holm oak rooting system. Road cuts had
been recently (3-4 months) opened. We counted the number of emerging tree roots
(fine and coarse). Roots were counted in 51 profiles, located every 5 meters in 7
different road cuts. To identify the position of the root (depth and distance from the
nearest tree), we used a metallic square of 50 cm size, divided into small squares of
100 cm2. The maximum depth of the profiles varied between 250 and 550 cm. The
slope of the road cuts was measured to calculate the actual depth of roots.
We were able to confirm (by means of recent aerial photographs) in four of the road
cuts that no trees had been removed during the works. In these cases we measured
the distance to the nearest tree. For the other three road cuts we confirmed the
previous existence of some trees where the road cuts had been created and data
were only used to describe the root distribution in depth. We therefore have 51
profiles for the description of vertical root distribution, and only 34 for horizontal root
distribution. Results are expressed as number of roots per m2. Schenk and Jackson
(2002a) have shown that patterns of rooting profiles based on root length and root
count do not differ. Thus, results of both methods (soil cores and road cuts) could be
compared.
Data Analysis
A simple linear regression was found to be fit for calibrating the soil core-break
method (relationship between RLD and the number of counted roots), as suggested
196
by van Noordwijk et al., 2000. Root density has been regressed with depth (linear
and non-linear regressions) or with both depth and distance (multiple regression) in
order to describe rooting patterns of both trees and herbaceous plants.
Differences between treatments and position in RLD were assessed by analysis of
variance (ANOVA). Two-way ANOVAs were applied to detect differences in mean
values of RLD (dependent variable) between distance and depth (as independent
variables) for both herbaceous plants and trees. Two-way ANOVAS were also
applied to contrast the effect of soil management (cropped versus grazed) on RLD at
different distances or depths. Results are expressed as F values (and degree of
freedom) and significance level (p).
Depth of 50% and 95% cumulative root density (d50 and d95, respectively) were
calculated according to the Gale and Grigal (1987) model. Following these authors, a
non-linear regression was used to fit the function fc = 1 – βd to the profile of
cumulative root fraction (fc), from the soil surface to depth d (cm). β is the fitted
“extinction coefficient”. Values of d50 and d95 were then calculated from d50 = Ln (0.5)
/ Ln (β) and from d95 = Ln (0.05) / Ln (β), respectively.
RESULTS
Linear relationships for root length density estimation
The calibration curves between Nroot and RLD were determined separately for
herbaceous and tree fine roots (Figure 1). The relationships was better for
herbaceous roots than for tree roots (R2 = 0.85 vs 0.42, respectively). This difference
is partly explained because the range of Nroot and RLD was much lower for tree (03800 roots m-2 and 0-8 km m-3) than for herbs (0-28000 roots m-2 and 0-44 km m-3). A
further explanation is probably linked to the patchy pattern of tree roots : tree roots
are less evenly distributed in the soil core sample volume, resulting in a less accurate
prediction from the core-break count. Both regressions were however highly
significant, with p < 0.001 (n = 46 and 65 for herbs and trees, respectively).
Vertical profiles of root length density
Figure
1 and
Herbaceous RLD was very high in the first cm of the soil (Fig. 2), decreasing very
-0.999
2
sharply, exponentially with depth: RLDkm.m-3 = 122.1 * Depthcm
(R = 97.0; F1, 2
18 =
714; p <0.00001). At 40 cm depth, RLD was ten times lower than in the first 10 cm.
The depth of 50% cumulative root length (d50) was found at 10.7 cm (Fig. 2 and
Table 1). Results of a two-way ANOVA (depth and distance as independent
variables) showed significant differences between consecutive depths till 60 cm (F9,
442 = 94.46; p < 0.001); then, differences were not significant. Below 90 cm,
herbaceous plant roots were only found very occasionally, reaching a maximum
rooting depth of 100 cm.
197
By contrast, a linear but non-significant decrease in tree RLD was observed from 0 till
200 cm of soil (F9, 442 = 0.36; p < 0.952). At 2 metres depth the holm-oak RLD was
still about half with respect to the uppermost soil layer (Fig. 2). From the regression
between tree RLD and depth (RLDkm.m-3 = 2.24 – 0.0056 x Depthcm; R2 = 0.56; F1, 18
= 22.9; p < 0.00015), the expected maximum rooting depth for holm-oak in this area
was estimated at 400 cm, and the d50 value at 96.4 cm (Fig.2 and Table 1).
Lateral root distribution
Table 1
RLD varied significantly with distance to the tree trunk, for both trees and herbaceous
plants (Figure 3a). Herbaceous plants RLD was significantly higher at 10 and 20 m of
distance than at 2.5 and 5 m of distance (F3, 442 = 10.26; p < 0.0004). Tree RLD
decreased smoothly with the distance to the tree. Significant differences were
detected between 2.5 and 5m and between 5 and 10m (F3, 442 = 7.64; p < 0.001).
In spite of the differences with distance, the RLD profile shape did not vary with
distance, neither for herbaceous plants nor for holm-oak (Fig. 3b). In fact, we did not
find any significant interaction between both factors (Distance x Depth) either for
herbaceous plants (F27, 442 = 1.16; p < 0.289) or for trees (F27, 442 = 0.80; p < 0.702).
Only a slight increase in d50 value with the distance for herbaceous plant roots has
been estimated (9.6, 9.3, 10.6 and 14.4 at 2, 5 10 and 20 m of distance); the
opposite was observed for tree roots (67, 69, 57 and 55 cm at 2, 5 10 and 20 m of
distance, considering only the roots of the first 200 cm of soil).
Effect of soil management on root distribution
Two main differences have been observed in the root distribution when two different
Figure 3a
soil management types (cropped or grazed) are compared (Fig. 4a). Root length
and 3b
density of herbaceous plants was much lower in grazed plots (native grasses) than in
intercropped plots (oats + weeds) (F1, 249 = 5.55; p = 0.004), at any distance (notsignificant interaction: F6, 249 = 0.062; p = 0.991). d50 value was also clearly deeper in
intercropped plots (cm) than in grazed plots (13.9 and 7.4 cm, respectively; Table 1).
The profile of RLD was similar for both cropped and grazed plots (Fig. 4b).
Tree RLD was however very similar in both types of management, cropped and
grazed plots (Fig. 4a). The only difference was observed in the top soil, where
intercropped trees had a lower RLD than grazed trees (Fig. 4b), although the
differences were not statistically significant (F1, 104 = 1.18; p = 0.31). d50 value was
only slightly deeper in intercropped plots (cm) than in grazed plots (69.1 and 64.6 cm,
respectively, considering only the two first metres of depth).
Tree roots in road cuts
Figure 4
Table 2
This study confirmed results obtained with soil cores. The maximum depth where
roots were found was 450 cm, with a d50 value of 81.2 cm vs a d50 value of 96.4
found in the soil core study (Table 1).
A significant decrease in the number of tree roots with distance was also found (F3, 27
= 96.81; p = 3.0E-05). Significant differences were found between 0-10 m and 10-15
m, between 10-15 m and 15-20 m, but not between 15-20 m and >20 m. The
maximum measured distance was 26 m, where roots were found even at 3 m of
depth (data not shown).
198
Considering the relationship between the number of roots and both distance and
depth, the following equation was found: Nroot (m2) = 76.2– 0.14* Depth(cm) –
2.23*Distance(m); with R2 = 0.56 (F2, 155 = 36.24; p < 0.0001). According to the
stepwise analysis, the amount of variability explained by both parameters (their
contribution to R2) was 0.39 % and 0.18 %, for distance and depth, respectively.
From this equation, 33 m was estimated as the maximum distance, and 520 cm as
the maximum depth.
DISCUSSION
Herbaceous plants root system
Most of the herbaceous plant roots were located in the first centimetres of the soil, a
common pattern for most of the herbaceous plants in the world (Jackson et al.,
1996). Native grasses showed a very shallow root system, with d50 at 7.4 cm, and
with 94% of the root length in the first 30 cm of soil. The maximum rooting depth was
evidenced at about 80 cm. The oat crop had a deeper rooting pattern than native
grasses, with ‘only’ 78% of the root length in the first 30 cm, and with a maximum
rooting depth of 100 cm. Other authors have reported deeper root systems for
temperate grassland and crops than those found in this study (e.g., Canadell et al.,
1996 and Jackson et al., 1996; see Table 1). The apparent low capacity of oats and
native grasses to go deep in our study area could be explained by the presence of a
very clayey soil layer between 40-80 cm depth. Nevertheless, a very shallow root
system for native grasses of dehesas of Quercus ilex has also been reported by
Barrera et al. (1987) and Joffre et al. (1987).
The highest RLD of cropped plants as compared to natural pasture grasses was a
surprise. This result does not coincide with the reported values by Jackson et al.
(1996), who found that crops showed very low root density when compared to most
other biomes (even a tenth part respect to temperate grassland). The denser and
deeper root system in oat crops with respect to native grasses could induce an
additional competition for soil resources (mainly water) with trees when compared to
the pasture.
Holm-oak root profiles
The root profiles of mature holm-oaks were surprisingly almost uniform with depth.
Most reported tree root profiles feature a significant decrease with depth, and this
decrease often follows an exponential negative pattern (Jackson et al., 1996).
Nevertheless, a linear or quasi-linear decrease of root density has been also reported
by few authors (e.g., Kummerov and Mangan, 1981 and Schulze et al., 1996).
As a consequence, we put in evidence an unusual deep rooting system for Quercus
ilex in this dehesa study, with a d50 value between 96,4 cm and 81,3 cm, and with a
d95 value between 417 cm and 351 cm (with soil cores and road cuts methods
respectively). Schenk and Jackson (2002a), after an exhaustive review of 475 root
studies, concluded that most of the plants have at least 50% of the roots in the first
30 cm of the soil, even in the desert. For sclerophyllous Mediterranean plants, these
authors reported mean values of 19 and 171 cm for d50 and d95 (Table 2). Rather
shallow root systems, with most of the roots in the first 50 cm of the soil depth, have
also been reported in the Iberian Peninsula for forests of Quercus ilex (Canadell and
Rodá, 1991 and López et al., 2001), for Quercus coccifera (Cañellas and San Miguel,
199
2000), for a sand dune shrub community (Martínez et al., 1998), and for Erica and
Ulex species (Silva and Rego, 2003) .
A deep-root pattern is often found in water-limited situations, mainly for species with
taproots in desert, savannah, tropical evergreen forest and sclerophyllous shrubland
and forest (Canadell et al., 1996). We have found a maximum rooting depth for holmoak at around 5 m, meanwhile Canadell et al. (1996) and Canadell and Rodá (1991)
reported maximum rooting depth of only 3.7 and 1 m, respectively, for close forests
of Quercus ilex. Regarding other evergreen Quercus species there are several
references with maximum rooting depth between 5 and 10 m (see Canadell et al.,
1996). These authors reported a mean value of 5.2 m for sclerophyllous shrubland
and forest of the world.
Lateral root distribution
In semiarid conditions, the survival of trees facing severe drought conditions is only
possible if the tree root system can extend beyond the influence of the tree canopy
(Joffre and Rambal, 1999). Lateral root spread influences how many neighbours
compete for resources available to plants in an ecosystem (Schenk and Jackson,
2002b). In the present study, the maximum lateral rooting (estimated at 33 m) was
slightly larger than the average distance between trees (26 m). This pattern may be
common in semiarid open woodland. Schenk and Jackson (2002b) pointed out that
larger lateral root spreads were found in plants growing at low density in dry
environments, where plants can explore the soil in interspaces between plants.
These authors reported several cases of trees with maximum lateral root spread
above 20 m.
An outstanding consequence of this result is that lateral roots can explore the whole
inter-tree space, allowing full use of the soil volume by mature trees in dehesas. The
surface of explored soil by roots was around 7 times the projected area of the
canopy. The dynamics of soil water content in the same plots (Cubera et al., 2004)
showed that soil water beyond the tree canopy was depleted throughout the summer,
while no grasses were active, confirming that water was extracted by trees. Our
results support thus the hypothesis that mature tree density in dehesas could be
water-availability dependent (Joffre and Rambal, 1999).
Combined root system: implication on competition for soil resources
To reduce competition with crops/grasses for below-ground resources, tree should have a
deep root system and little root proliferation near the top of the profile, thereby enabling the
herbaceous plants to utilise resources from near the soil surface, while trees have sole
access to deeper layers (Schroth 1995). We have shown such a pattern of spatial separation
between herbaceous plants and tree root systems. Trees had a much deeper root system,
with a rather low RLD in the upper layers of the soil, and herbaceous vegetation did not reach
deep layers, where tree roots were still abundant.
This rooting pattern contributes to reducing below-ground competition, thereby probably
falling into the general category of ‘niche separation’ (Casper and Jackson, 1997). Thus,
although water limitation is an important feature in most dehesas (including our study area),
this does not necessarily mean that competition for water is high. Many authors have shown
that woody plants took up more water from deeper layers than herbaceous ones (e.g.
Ehleringer et al., 1991 and Sala et al., 1989) avoiding thus a direct competition. In fact, this
two-layer model appears to be most appropriate in drier regimes and in systems with
200
substantial winter precipitation (Schenk and Jackson, 2002b), as it is the case of Iberian
dehesas. The possible existence of this water partitioning in dehesas should be addressed in
future research, and is only relevant when the rainfall pattern allows deep soil layer to be
systematically refilled during the cold season.
The very high herbaceous RLD in the first cm of soil could induce a strong
competition for nutrients with trees, as a result of the fact that nutrients (mainly N)
may be available only in the upper soil layers (Jackson et al., 1996). Finally, this
extensive and deep rooting system of the trees may indicate a very good capability to
avoid any nitrate leaching from the cropped area. Most leached nitrates can be
captured by the tree roots, as the evergreen oak trees are active throughout the year.
CONCLUSIONS
If we follow the assumption that roots grow only as deeply and as far as needed to
fulfil the plant resource requirements (Schenk and Jackson, 2002b), it is obvious that
mature holm-oaks need a huge volume of soil to capture below-ground resources in
oligotrophic soils, under a semi-arid climate with a long summer drought. As Joffre et
al. (1999) have pointed out, dehesas have to cope with the high variability of the
Mediterranean climate; the tree extensive root system undoubtedly must contribute
both in adapting to natural conditions and in overcoming unpredictability.
Holm-oak RLD decreased very slowly with distance and depth. This rooting pattern
should have important consequences in modelling the coexistence of tree and
grass/crop. It is at odds with the commonly assumed pattern of tree fine roots
distributions, that is often described with an exponential decrease with soil depth and
distance from the tree trunk.
We have found a limited vertical overlap of tree and herbaceous understorey root
systems, and this feature is probably a key to the stability and productivity of this
agro-silvo-pastoral system. However, we documented the rooting pattern only in
spring, and more information is still required on temporal dynamics of the fine roots of
both holm-oak and herbaceous plants in the dehesa.
Acknowledgements
This study was supported by the European Union (SAFE project : Silvoarable Agroforestry For
Europe QLK5-CT-2001-0560), by the Spanish Ministerio de Ciencia y Tecnología (MICASA project)
and by the Consejería de Educación de Extremadura (CASA project). Elena Cubera has been
awarded a grant by the Consejería de Educación de la Junta de Extremadura (Spain) and Jesús
Obrador has been awarded a grant by ANUIES (México).
REFERENCES
Barrera I, Galindo P and Gómez J M 1987. Modelo de distribución de la biomasa radical
en función de la profundidad. Anuario del CEBA de Salamanca 12, 313 - 323.
Canadell J, Jackson R B, Ehleringer J R, Mooney H A, Sala O E and Schulze E-D 1996
Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583595.
Canadell J and Rodá F 1991 Root biomass of Quercus ilex in a montane Mediterranean
forest. Can. J. For. Res. 21, 1771-1778.
201
Cañellas I and San Miguel A 2000 Biomass of root and shoot systems of Quercus
coccifera shrublands in Eastern Spain. Ann. For. Sci. 57, 803-810.
Casper B B and Jackson B J (1997) Plant competition underground. Annu. Rev. Ecol.
Syst. 28, 545 - 570.
Cubera E, Montero M J and Moreno G 2004 Effect of land use on soil water dynamics in
dehesas of Central-Western Spain. In Advances in GeoEcology 37: Sustainability of
Agrosilvopastoral systems –Dehesas, Montados-. Eds. S Schnabel and A Ferreira.
pp. 109-123. Catena Verlag, Reiskirchen.
Díaz M., Campos P., Pulido F.J. 1997. The Spanish dehesas: a diversity in land-use
and wildlife. In: Pain D.J. and Pienkowski M.W. (eds) Farming and Birds in
Europe: The common Agricultural Policy and its Implications for Bird Conservation.
Academic press, Cambridge, UK, pp 178-209.
Ehleringer J R, Phillips S L, Schuste W F S and Sandquist D R 1991 Differential
utilization of summer rains by desert plants: implications for competition and climate
change. Oecologia 88, 430-434.
Escudero A 1985 Efectos de árboles aislados sobre las propiedades químicas del
suelo. Rev. Ecol. Biol. Sol 22(2), 149-159.
FAO 1998 World reference base for soil resources. FAO, ISRIC and ISCC. Rome. pp.
109.
Gale M R and Grigal D F 1987 Vertical root distributions of northern tree species in
relation to successional status. Can. J. For. Res. 17, 829-834.
Infante J M, Damesin C, Rambal S and Fernández-Alés R 1999 Modelling leaf gas
exchange in holm-oak trees in southern Spain. Agr. For. Meteorol. 95, 203-223.
Jackson RB, Canadell J, Ehleringer J R, Mooney H A, Sala O E and Schulze E.-D 1996
A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389-411.
Joffre R, Leiva Morales M J, Rambal S and Fernández Alés R 1987 Dynamique
racinaire et extraction de l’eau du sol par des graminées pérennes et annuelles
méditerranéennes. Acta Oecol. Oecol. Plant. 8(22), 181-194.
Joffre R and Rambal S 1988 Soil water improvement by trees in the rangelands of
southern Spain. Oecol. Plant. 9, 405-422.
Joffre R and Rambal S 1993 How tree cover influences the water balance of
Mediterranean rangelands. Ecology 74: 570-582.
Joffre R, Rambal S and Ratte J P 1999 The dehesa system of southern Spain and
Portugal as a natural ecosystem mimic. Agroforest. Syst., 45, 57-79.
202
Jose S, Gillespie A R, Seifert J R and Pope P E, 2001. Comparison of minirhizotron and
soil core methods for quantifying root biomass in a temperate alley cropping system.
Agroforest. Syst. 52, 161-168.
Jones M, Sinclair F L and Grime V L 1998 Effect of tree species and crown pruning on
root length and soil water content in semi-arid agroforestry. Plant Soil 201, 197-207.
Kummerov J and Mangan R 1981 Root system in Quercus dumosa Nutt. dominated
chaparral in southern California. Acta Oecol. 2, 177-188.
López B, Sabaté S and Gracia C 2001 Vertical distribution of fine root density, length
density, area index and mean diameter in a Quercus ilex forest. Tree Physiol. 21,
555-560.
Martínez F, Merino O, Martín A, García Martín D and Merino J 1998 Belowground
structure and production in a Mediterranean sand dune community. Plant Soil 201,
209-216.
Mobbs D C, Lawson G J, Friend A D, Crout N M J, Arah J R M, Hodnet M G 1999
Hypar Model for agroforestry system. Technical Manual. DFID Forestry Research
Programme.
Montero G, San Miguel A and Cañellas I 1998 System of Mediterranean Silviculture “La
Dehesa”. In Agricultura Sostenible. Eds. R M Jiménez Díaz and J Lamo de Espinos.
pp 519-554. Mundi Prensa, Madrid.
Montero M J, Obrador J J, Cubera E and Moreno G 2004 The role of dehesa land use
on tree water status in Central-Western Spain. In Advances in GeoEcology 37:
Sustainability of Agrosilvopastoral systems –Dehesas, Montados-. Eds. S Schnabel
and A Ferreira. pp. 125-136. Catena Verlag, Reiskirchen.
Papadakis J 1966 Climates of the World and Their Agricultural Potentialities. Ed. J
Papadakis. Buenos Aires.
Plieninger T, Pulido F J and Konold W 2003 Effects of land-history on size structure of
holm oak stands in Spanish dehesas: implications for conservation and restoration.
Environ. Conerv. 30, 61-70.
Puerto A, García J A and García A. 1987. El sistema de ladera como elemento
esclarecedor de algunos efectos del arbolado sobre el pasto. Anuario del CEBA de
Salamanca 12, 297 - 312.
Sala O E, Golluscio R A, Laurenroth W K and Soriano A 1989 Resource partitioning
between shrubs and grasses in the Patagonian steppe. Oecolgia 81, 501-505.
Schenk H J and Jackson R B 2002a The global biogeography of roots. Ecological
Monographs 72(3), 311-328.
203
Schenk H J and Jackson R B 2002b Rooting depths, lateral root spreads and belowground/above-ground allometries of plants in water-limited ecosystems. J. Ecol. 90,
480-494.
Schroth G 1995 Tree root characteristics as criteria for species selection and systems
design in agroforestry. Agrofor. Syst. 30, 125-143.
Schulze E-D, Mooney H A, Sala O E, Jobbagy E, Buchmann N, Bauer G, Canadell J,
Jackson R B, Loreti J, Oesterheld M, and Ehleringer J R 1996 Rooting depth, water
availability, and vegetation cover along an aridity gradient in Patagonia. Oecologia
108, 503-5111.
Silva J S and Rego F C 2003 Root distribution of a Mediterranean shrubland in
Portugal. Plant Soil 255, 529-540.
Smith D M, Jackson N A, Roberts J M and Ong C K 1999 Root distribution in a Grevillea
robusta-maize agroforestry system in semi-arid Kenya. Plant Soil 211, 191-205.
Smit A L, Bengough A G, Engels C, van Noordwijk M, Pellerin S and van de Geijn
2000 Root Methods: A Handbook. Springer Verlag. Berlin Heidelberg. pp. 510.
Van Noordwijk M, Brouwer G, Meijboom F, Do Rosario G, Oliveira M and Bengough A
G, 2000. Trench Profile Techniques and Core Break Methods. In Root Methods: A
Handbook. Eds. L Smit, A G Bengough, C Engels, M van Noordwijk, S Pellerin and
van de Geijn. pp. 211-233. Springer-Verlag, Berlin Heidelberg.
Van Noordwijk M, Lawson G, Soumare A, Groot JJR and Hairiah A 1996 Root
distribution of trees and crops: competition and/or complementarity. In Tree-crop
interactions: A Physiological approach. Eds. C K Ong and P Huxley. Pp. 319-364.
CAB International, Wallingford.
Van Noordwijk M and Lusiana B 2000 WaNuLCAS 2.0, Background on a model of
water nutrient and light capture in agroforestry systems. International Centre for
Research in Agroforestry (ICRAF), Bogor, Indonesia. 186 pp.
LEGEND of FIGURES
Figure 1. Linear regressions between the number of roots crossing a horizontal
plane (Nroot) and root length density (RLD) for (a) herbaceous plants (oats and
native grasses), and (b) Holm-oak (only fine-roots).
Figure 2. Variation of root length densities with soil depth for holm-oak and
herbaceous plants in dehesas developed over chromic Luvisols in CW Spain. The
inset shows the cumulative fractional root distribution plotted against the soil depth.
Figure 3. (A) Mean values of RLD of holm-oak and herbaceous plants (oats and
native grasses) measured at different distances to the tree trunk in dehesas. (B)
Distribution of RLD plotted against the distance to the tree trunk and the depth for
both herbaceous plants and tree.
Figure 4. Distribution of the tree (holm-oak) and herbaceous plants (oats and native
grasses) RLD at differences distances (A) and depth (B) under two different types of
dehesa management: cropped and grazed.
Title: Fine Root distribution in dehesas of Central-Western Spain
204
Authors: G Moreno, JJ Obrador, E Cubera, C Dupraz
FIGURE 1
50
40
-3
RLD (km m )
Herbaceous
Plants
RLD = 0.0012 Nh
2
R = 0.85
30
20
10
A
0
0
5000
10000
15000
20000
25000
30000
-2
Nroot (m )
RLD = 0.0015 Nh
R2 = 0.42
8
Holm-oak
-3
RLD (km m )
10
6
4
2
B
0
0
1000
2000
3000
4000
-2
Nroot (m )
205
FIGURE 2
-3
0
5
Root lenght density, km m
10
15
20
0
0,0
100
125
0,4
0,6
0,8
1,0
50
100
Depth, cm
Depth,cm
75
0,2
0
25
50
25
Cumulative root fraction
150
200
250
300
350
400
150
175
200
Herbaceous plants
Holm oak
206
FIGURE 3
8
A
6
-3
RLD (km . m )
Herbaceous plants
Holm-oak
4
2
0
2,5m
5m
10m
20m
Distance to tree trunk
0
5
Distance, m
10
15
20
25
0
25
Depth, cm
50
75
100
125
150
175
B
200
RLD of Herbaceous plants
RLD of Holm-oak
207
FIGURE 4
Herbaceous plants (Cropped)
Herbaceous plants (Grazed)
Holm-oak (Cropped)
Holm-oak (Grazed)
-3
16
14
12
10
8
6
4
A
2
0
0
5
10
Distance
15
20
25
-3
0
5
10
15
20
25
30
35
0
Depth, cm
50
100
Herbaceous plants (Cropped)
Herbaceous plants (Grazed)
150
Holm-oak (Cropped)
Holm-oak (Grazed)
B
200
TABLE 1
208
Vegetation type
β
0.937
Herbaceous plants
0.951
Oats + weeds
Native grasses (mostly
0.911
annual)
0.993
Holm-oak (soil cores)*
0.992
Holm-oak (road cuts)
Mediterranean woody plants
0.943(b)
Temperate grassland
0.961(b)
Crops
#
R2
d50#
(m)
d95#
(m)
96.0
97.4
0.11
0.14
0.46
0.60
Maximum
rooting
depth (m)
1
1
98.8
0.07
0.32
8
98.9
99.5
0.96
0.81
0.19(a)
0.12(b)
0.17(b)
4.16
3.51
1.71(a)
0.51(b)
0.75(b)
4
4.5
5.2(c)
2.6(c)
2.1(c)
82.0
94.3
d50 and d95 indicate the depth in cm corresponding to 50 and 95%, respectively, of the cumulative
root fraction. Both values are estimate from the Gage and Grigal (1987) model: Y = 1-βd, where Y is
the cumulative root fraction from the surface to depth d (cm), and β is the fitted “extinction coefficient”.
* The equation RLDkm.m-3 = 2.24 – 0.0056 x Depthcm (see text) was applied to get values of RLD from
200 to 400 cm depth.
Table 1. Comparison of the root profiles of holm-oak and herbaceous vegetation in
dehesas of Central-Western Spain with average values from recent comprehensive
reviews. (a) Schenk and Jackson (2002a) averaged from 475 root studies. (b) Jackson et al. (1996)
averaged data from many different species from all over the world. (c) Canadell et al. (1996) data
averaged data from 82 species of temperate grassland.
209
ANNEX 9. The development and application of bioeconomic modelling for silvoarable systems in Europe
A.R. Graves1, P.J. Burgess1, J.H.N. Palma2, F. Herzog2
G. Moreno3, M. Bertomeu3, C. Dupraz4, F. Liagre5, K. Keesman6, W. van der Werf6
A. Koeffeman de Nooy7, J.P. van den Briel7
1
Institute of Water and Environment, Cranfield University, Silsoe, UK
2
3
Agroscope FAL Reckenholz, Zürich, Switzerland
Universidad de Extremadura, Centro Universitario de Plasencia, Plasencia, Spain
6
4
Institut National de la Recherche Agronomique, Montpellier, France
5
Assemblée Permanente des Chambres d’Agriculture, Paris, France
Wageningen University, P.O. Box 43, 6700 AA, Wageningen, the Netherlands
7
Stafkantoor Gelders Particulier Grondbezit, Wageningen, the Netherlands
A.R. Graves
Tel: +44 (0)1525 863107
Fax: +44 (0)1525 863001
KEYWORDS
Agroforestry, silvoarable, arable, forestry, modelling, biophysical, economics, FarmSAFE, Yield-SAFE, temperate, walnut, poplar, wild cherry, oak, stone pine
ABSTRACT
The European Union has introduced measures to promote the integration of trees
within farm businesses. Although silvoarable agroforestry is one method by which
this can be achieved, the implications at a plot- and farm-scale are poorly
understood. From 2001 to 2005, the Silvoarable Agroforestry for Europe project
therefore developed computer-based tools to evaluate both the biophysical and
economic performance of arable, forestry and silvoarable systems under different
European conditions. A biophysical model called “Yield-SAFE”, based on light and
water competition, was developed to predict long-term arable, forestry and
silvoarable yields for given sets of climate and soil conditions. The output from this
model was then used in a plot- and farm-scale economic model called “Farm-SAFE”
to determine profitability and resource use. Both models were parameterised and
used for selected regions of France, Spain and the Netherlands. The analysis in
France suggests that walnut and poplar silvoarable systems could provide a
profitable alternative to arable and forestry systems, while in Spain a modest
restructuring of the amount and delivery of agricultural payments would increase the
attractiveness of silvoarable systems of holm oak and stone pine.
In the
210
Netherlands, low timber value and the opportunity cost of losing arable land for slurry
manure application made both silvoarable and forestry systems uncompetitive with
arable systems.
INTRODUCTION
Agroforestry is a form of multi-cropping involving at least one woody-perennial
species and significant ecological and economic interactions. Agroforestry systems
can be described by their components (crops, animals and trees) and their spatial
(dispersed or zoned) and temporal (coincident to sequential) arrangement (Nair
1985, Sinclair 1999). Silvoarable agroforestry, defined as the practice of growing an
arable crop between spatially-zoned trees (Dupraz and Newman, 1997, Burgess et
al., 2004b), is a form of agroforestry that could be undertaken on mechanised arable
farms in Europe.
The majority of research on agroforestry systems has been undertaken to evaluate
their biophysical performance despite the observation that it is often socio-economic
constraints that limit their adoption (Graves et al., 2004, Mercer et al., 1998). Since
there are potentially many biophysical and socio-economic interactions between the
tree and crop components of silvoarable systems (Dyack et al., 1999) there is a need
to consider both the biophysical and socio-economic aspects together. However
both the biophysical and the socio-economic analysis of such systems are
constrained by lack of experimental data describing the effect of different
permutations, for example spacing and different tree species. There are also
problems in describing the socio-economic integration and the interaction between
the short- and long-term components over the length of a tree rotation.
Computer simulations provide a means of systematically undertaking biophysical
and economic analyses of silvoarable systems in the absence of empirical data.
Various biophysical and economic models have been developed for monocultures of
arable and forestry systems, but few have been developed for silvoarable
agroforestry (Graves et al., 2005a). The current bio-economic models of silvoarable
systems range from detailed biophysical models with limited economic analysis to
economic models that use biophysical data from an external source (Graves et al.,
2005a). Bio-economic models have been used to examine the profitability (Thomas
1991, Willis et al. 1993, Thomas and Willis, 1997; Burgess et al. 2000) and feasibility
(Dupraz et al., 1995) of silvoarable systems in Europe. Profitability is normally
assessed at a one-hectare scale and performance is compared to competing
enterprises such as arable agriculture and forestry. Feasibility is often determined
at a farm-scale to view how silvoarable agroforestry affects cash-flow and resource
use. This paper describes the integrated use of a bio-physical and an economic
model, at both a one-hectare- and farm-scale, to determine the potential profitability
and feasibility of silvoarable agroforestry in Europe.
METHOD
The study focused on three countries (Spain, France and the Netherlands) with
differing climates, tree and crop species, and levels of practical experience in
implementing agroforestry. Potential sites for the uptake of silvoarable agroforestry,
termed landscape test sites, were identified in each country using a geographical
information system. Annual yields of trees and crops were derived using a bioSAFE Final Progress Report – Volume 4 (Annexes) – May 2005
211
physical model called “Yield-SAFE” (van der Werf et al., 2005) and profitability and
feasibility were determined using an economic model called “Farm-SAFE” (Graves et
al., 2005).
Identification of landscape test sites
An initial requirement was to identify landscape test sites where silvoarable systems
could be used as an alternative to arable systems. A geographical information
system (ArcGIS - ArcInfo© and ArcInfo WorkStation© 8.3) was used to select the
three dominant environmental classes in each country from an environmental
classification of Europe, based on a statistical analysis of climate and topography
(Mücher et al. 2003). In Spain and France three classes were identified in each
country, but in the Netherlands there was only one class. The location of arable land
was derived using a land cover classification from the Pan-European Land Cover
Monitoring (PELCOM) project (Mücher et al. 2000). From the combined dataset,
three landscape test sites measuring 4 km x 4 km were selected for each
environmental class to give nine, nine and three landscape test sites in Spain,
France and the Netherlands respectively; two landscape test sites in France were
later discarded for lack of associated data, bringing this total to seven landscape test
sites. In Spain the sites ranged from Alcala la Real in Andalucia in the south to St
Maria del Paramo in Castilla y Leon in the north. In France, the landscape test sites
ran across central France from Champdeniers in Poitou Charente in the west to
Champlitte in Franche Comté in the east (Table 1). In the Netherlands the landscape
test sites were located in the central and eastern part of the country.
Characterisation of landscape test sites
To provide input data for the biophysical model, daily mean values of air temperature,
total short-wave radiation, and rainfall were generated for each landscape test site
using CLIGEN 5.2 (United States Department of Agriculture, 2005) with reference
values from the nearest weather station (Global Data Systems, 2005). The annual
values for mean air temperature and total radiation were highest in Spain (9.1-15.5oC
and 5480-6600 MJ m-2) and lowest in the Netherlands (8.8-9.0oC and 3690-4830 MJ
m-2) (Table 1). Mean annual total rainfall was lowest in Spain (320-530 mm) and
highest in France (590-1080 mm). In Spain, much of the rainfall occurred in winter
with minimal rainfall in the summer months. In France, this seasonality of rainfall,
although greatly reduced, was still evident while in the Netherlands rainfall was
generally consistent throughout the year.
Each landscape test site was also characterised in terms of soil depth and texture
and using a classification of hydraulic properties of European soils (Wösten et al. 1999),
the available soil water content was calculated using van Genuchten’s equation
(1980). Further data layers for elevation and land cover were developed. Preexisting electronic data were used where available or digitised from paper sources,
for example, topographic maps. The radiation throughout each landscape test site
was calculated with digital elevation models in the Digitales Gelände-Modell
(DiGeM©) (Conrad, 2002), and the relative radiation obtained using the value from a
flat un-shaded pixel as the reference for one hundred per cent. Finally, field visits
were made to each site to confirm existing interpretation, improve existing data, and
provide missing data.
(Table 1)
212
A cluster analysis of the available soil water content and the percentage solar
radiation within each landscape test site was then used to develop between one and
four “land units” (Table 2). The land available for silvoarable agroforestry in each
landscape test site was assumed to be equivalent to the land available for arable
production and this was selected by excluding non-arable land. The area of a
specialist cereal farm in each landscape test site was determined from regional data
in the Farm Accountancy Data Network (FADN) (European Commission, 2005) in
Spain, the Agricultural Economics Research Institute in the Netherlands (2005) and
from the Réseau d’observation des systèmes d’exploitation (ROSACE) (Assemblée
Permanente des Chambres d’Agriculture, 2005) in France. Where there were no
data relating to a specialist cereal farm, farm size was related to the most frequently
occurring farm types for the landscape test site region, in the case of Alcala la Real in
Spain, an olive farm, and in the case of the Netherlands, pig, dairying and general
field cropping farms. At each landscape test site, the proportion of the area of each
land unit relative to the total area of the land units was used to represent the
proportion of each land unit within a hypothetical farm.
(Table 2)
Selection and management of tree and crop species
Annual yields of trees and crops in arable, forestry and silvoarable systems were
required for each land unit as inputs for the economic analysis. The tree and crop
species for forestry and arable production were chosen to reflect the most the likely
practice at each landscape test site. In France and the Netherlands, the trees were
selected because they were timber trees; in Spain the choice of tree also reflected
policy constraints and issues of ecological importance. The forestry systems
selected for Spain comprised holm oak (Quercus ilex) and stone pine (Pinus pinea).
In France wild cherry (Prunus avium), walnut (Juglans spp.), and poplar (Populus
spp.) were chosen and walnut and poplar were selected in the Netherlands. The
arable systems in Spain were based on wheat, sunflower and fallow. In Poitou
Charentes and Centre in France, they were based on wheat and sunflower and in
Franche Comté, on wheat, oilseed and grain maize; in the Netherlands on wheat and
forage maize. The silvoarable systems integrated the forestry tree species and
arable crop species and rotation for each land unit.
The management of the forestry systems at each landscape test site was based on
local practice. In Spain, planting densities, thinning and pruning for oak were derived
from Pulido et al., (2003) and for stone pine from Yagüe (1995) and Montero and Cañella
(2000). In France management for forestry systems was developed from the Institut
pour le Développement Forestier (1997), Souleres (1992), Boulet-Gercourt (1997)
and the Centre Régional de la Propriété Forestière (1997) for walnut, wild cherry and
poplar. In the Netherlands, the receipt of grants was conditional on an appropriate
planting density, given by the Ministerie van Landbouw, Natuur en Voedselkwaliteit
(2004), and thinning and pruning regimes were applied using the management rules
in France. The management for the arable systems reflected local practice.
Biophysical modelling
The radiation, temperature, rainfall, soil depth and texture data for each land unit
were used as inputs in a daily time-step bio-physical model of tree and crop
production, based on competition for light and water (Yield-SAFE) (van der Werf et
213
al., 2005) and implemented in Microsoft Excel© by Burgess et al. (2004a) to predict
annual tree and crop yields.
The parameters used in Yield-SAFE to describe the growth of each tree and crop
species were determined from published material and calibrations. An initial
calibration for “potential” monoculture yields (Ittersum and Rabbinge, 1997) was
undertaken against datasets of tree volume and crop yields under high yielding
conditions in the Atlantic and Mediterranean zones, assuming within the model that
light and temperature but not water, limited growth (Burgess et al. 2004a). Then at
each landscape test site and assuming light, temperature, and water limited growth
within the model, the values of three parameters (harvest index, water use efficiency
and a management factor) were adjusted within acceptable boundaries so that output
from the model over the duration of the tree component matched an “actual”
monoculture tree and crop yield (Ittersum and Rabbinge, 1997). The tree and crop
management defined previously for the monocultures and “reference” soil depth and
texture were also used. The monoculture management and actual and reference
values were determined for each landscape test site during workshops held in each
country (Palma & Reisner, 2004; Reisner, 2004; Herzog, 2004).
In Spain, the actual timber volumes for oak and stone pine in all the landscape test
sites in year 60 were assumed to be 0.22 m3 and 0.26 m3 tree-1 respectively,
indicating slow growth. In France, wild cherry (1.04-1.06 m3 tree-1) and walnut (1.04
m3 tree-1) for the same rotation were comparatively fast-growing trees. Poplar was
the fastest growing tree with actual yields of 1.46-1.51 m3 tree-1 after 20 years. In
Spain, actual yields for wheat were comparatively low (1.62-3.71 t ha-1) compared to
those in France (6.5-8.0 t ha-1) and the Netherlands (7.8 t ha-1). Actual sunflower
yields were lower in Spain (0.60-1.09 t ha-1) than in France (2.3-2.5 t ha-1). Actual
yields for oilseed (3.2-4.0 t ha-1) and grain maize (7.5-8.0 t ha-1) were assumed only
for France and an actual yield for fodder maize (12 t ha-1) assumed only for the
Netherlands.
Using the parameter set developed for actual yields and soils at each landscape test
site, tree and crop yields for each land unit were predicted for monoculture forestry
and arable systems and two silvoarable systems of 50 or 113 trees ha-1. From the
biophysical yields, it was possible to estimate a land equivalent ratio (LER) for each
system. LERs were initially defined for mixed cropping systems (Mead and Willey,
1980) and have been adapted for agroforestry systems (Ong 1996, Dupraz, 1998).
The LER is “the ratio of the area under sole cropping to the area under the
agroforestry system, at the same level of management that gives an equal amount of
yield” (Ong, 1996) and is expressed as:
LER =
Tree silvoarable yield
Crop silvoarable yield
+
Tree monoculture yield Crop monoculture yield
Equation 1
Where more than one crop occurred in the rotation, a weighted ratio for each crop
was used, depending on its proportion in the rotation.
Plot-scale economic modelling
The predicted annual yields of trees and crops were used as inputs for a plot- and
farm-scale cost-benefit economic model called “Farm-SAFE” (Graves et al., 2005).
214
In arable systems, profitability is typically compared on an annual and per unit area
basis by adding the revenue generated (R) to the variable costs associated with
generating that revenue (V) to give a gross margin (Gross margin = R − V ) (Nix,
1999; Ministry of Agriculture, Fisheries and Food, 1983). However, in tree-based
systems, “assignable fixed costs” such as labour and machinery (A) are commonly
included and can be derived per unit area. Therefore the arable, forestry, and
silvoarable systems were compared using their net margin (Net margin = R − V − A )
(Willis et al. 1993; Burgess et al. 2000, Graves et al., in press). As the benefits and
costs associated with tree-based systems occur over many years, discounted cost
benefit analysis was used to define the “present” value of future costs and benefits
from the arable, forestry and silvoarable systems using the approach defined by
Faustmann (1849). The net “present” value (NPV; units: € ha-1) was expressed as:
t =T
NPV = ∑
t =0
( Rt − Vt − At )
(1 + i ) t
Equation 2
Where: NPV was the net present value of the arable, forestry or silvoarable
enterprise (€ ha-1), Rt was the revenue from the enterprise (including subsidies) in
year t (€ ha-1), Vt was the variable costs in year t (€ ha-1), At was the assignable fixed
costs in year t (€ ha-1), T was the time horizon (years), and i was the discount rate
(discount rate = 4%).
In order to compare systems with different rotation lengths, an infinite net present
value was calculated. This was the net present value defined over an infinite
rotation, in which each replication had a rotation of n years. The infinite NPV was
defined as:
Infinite NPV =NPV
(1 + i ) n
(1 + i ) n − 1
Equation 3
The infinite net present value was also expressed as an equivalent annual value
(EAV) using the following formula:
EAV = infinite NPV × i
Equation 4
Assessing the feasibility of a given system involves determining how it modifies flows
of farm resources. This is achieved by multiplying plot-scale flows of money, land,
and labour by their area on the farm and aggregating the results, then substituting a
given system with another system, and assessing the effect on farm resources with
and without the substituted system. A maximum of four arable, four forestry and four
silvoarable systems could be used to represent a single farm in the “Farm-SAFE”
economic model. Economic feasibility was determined using the infinite NPV of the
farm ( iNPVfarm; units: € farm-1). This combined the NPV of the different systems and
the NPV of “farm fixed costs” (Ft: units: € farm-1) over the same period of time and
was defined as:
t =T
 l =4
Ft
iNPV farm =  ∑ (NPVa a a + NPV f a f + NPVs a s ) − ∑
t
t = 0 (1 + i )
 l =1
 (1 + i ) n

n
 (1 + i ) − 1
Equation 5
215
Where: l was one of four possible land units, NPVa, NPVf, and NPVs were the net
present values (€ ha-1) of arable, forestry and silvoarable enterprises in each unit l;
a a , a f , and, a s were the area (ha) of arable, forestry, and silvoarable systems in each
unit l, Ft was the farm fixed cost in year t (€ farm-1), T was the time horizon (years), i
was the discount rate and n was the duration of the rotation (years).
Parameterisation and use of Farm-SAFE
The financial data for arable, forestry and silvoarable systems were collected on
electronic templates for each landscape test site, using local and national statistics,
and expert opinion.
Arable and crop component finance
The revenue (crop value and associated subsidy), and the variable and assignable
fixed costs for each arable system are described fully by Graves et al. (2005b).
However, for clarity some key values are described. The assumed value of the
arable crops ranged from 85 € t-1 for grain maize to 280 € t-1 for sunflower; the
assumed value of wheat grain ranged from 102 to 142 € t-1. Assumed variable costs
tended to be lowest in Spain (45-189 € ha-1) and highest in the Netherlands (457-479
€ ha-1), and assignable fixed costs such as machinery and labour followed a similar
pattern. For the crop component of the silvoarable system, the variable and
assignable fixed costs were applied according to the proportion of intercrop area in
the system which was constant. Also, as intercrop yields decrease over time due to
tree growth, it was assumed that cropping would only continue for as long as the
intercrop net margin (calculated on a five year moving average to remove the effect
of yield failure caused by poor weather) was profitable, after which it was assumed
the intercrop area would be fallow.
Forestry and tree component finance
The financial data for forestry and the tree component of the silvoarable system
comprised the revenue from timber and subsidies, and the costs of woodland
establishment and management. These are summarised below, but explained fully
in Graves et al. (2005a). The revenue from timber was calculated using relationships
between the standing value of the tree and the average tree volume for each species
in each country. In Spain, the value of oak (17 € m-3) and pine (8-19 € m-3) was low.
By contrast, in France, the value of walnut (40-1300 € m-3), wild cherry (10-380 € m3
), and poplar (7-55 € m-3) was relatively high; thinned timber, given a different per
cubic metre price to clear-felled timber, was also relatively valuable. In the
Netherlands, the perceived value of walnut (18-41 € m-3) was much lower than in
France, but the value of poplar (19-97 € m-3) was slightly higher.
The costs associated with the forestry system and the tree component of the
silvoarable systems were based on numerous sources. Costs varied between
countries, tree species and regions and regarding the tree component of the
silvoarable system, were not assumed to be proportional to the number of trees or
the area of the tree component (except in the Netherlands), as was the assumption
for the crop component. The cost of ground preparation was anticipated to be
highest in Spain and the Netherlands and lowest in France. This was due to difficult
soil conditions in Spain, where it was anticipated that tree pits would need to be
prepared, requiring use of specialised machinery (including labour) at a contract rate
216
of 31 € hr-1. In the Netherlands, it was anticipated that labour and machinery would
be provided by external enterprises at a cost of 22 € hr-1. In France, however, it was
anticipated that the farmer would undertake the majority of operations at a cost of €
7.8 hr-1. The cost of planting materials was greatest for walnut (6 € tree-1) and poplar
(4 € tree-1) in France and walnut (5 € tree-1) in the Netherlands. Oak (0.36 € ha-1)
and pinus (0.76 € ha-1) in Spain were relatively inexpensive. Tree protection
materials, such as spiral guards or fencing, were highest for walnut and cherry in
France (1.5 € tree-1) and lowest for walnut and poplar in the Netherlands (0.29 € tree1
). The time required for planting and protecting the trees was highest in Spain (2.7
min tree-1), than France (1.0-2.0 min tree-1), and lowest in the Netherlands (0.8 min
tree-1). In France (15 € hr-1) and the Netherlands (22 € hr-1), it was anticipated that
planting and protection would be carried out by externally contracted enterprises; in
Spain it was anticipated that this would be done using locally available labour (7.8 €
hr-1). The full establishment cost of forestry systems was greatest in the Netherlands
(3420 € ha-1 for walnut; 1940 € ha-1 for poplar) and lowest in Spain (770 € ha-1) for
oak systems at 400 trees ha-1. The full establishment cost for forestry systems of
cherry (1510 € ha-1), walnut (1633 € ha-1), and poplar (1260 € ha-1) in France and
high density oak (1470 € ha-1) and pine (1786 € ha-1) in Spain were between these
extremes. The full establishment cost of the tree component in the silvoarable
systems was lower for each species. For the 113 trees ha-1 systems, these ranged
from 1200 € ha-1 for walnut in the Netherlands to 233 € ha-1 for oak in Spain; for the
50 trees ha-1 systems they ranged from 710 € ha-1 in the Netherlands to 120 € ha-1
for oak in Spain.
Significant maintenance costs included weeding, sward establishment, pruning and
thinning. In Spain, it was anticipated that management would be minimal because of
the low financial value of the oak and pine timber. The main costs in the forestry
system were associated with weeding in the initial three years and establishing a
grass sward in year 12. For the tree component of the silvoarable system, the only
cost-bearing maintenance operation was assumed to be weeding in the initial five
years. Both these operations were assumed to be externally contracted at a rate of
31 € h-1. Pruning and thinning were assumed to be free of cost as an established
system exists whereby harvested oak and pine timber is given in lieu of payment to
those who undertake the work. By contrast, management was much more intensive
in France and in the Netherlands. In France, the control of undergrowth between
trees was a significant cost for about the first quarter of a forestry rotation and for the
duration of arable cropping in a silvoarable system. Other significant costs included
pruning and an annual land tax that varied marginally between regions. In the
Netherlands, the costs of establishing a grass sward in the first year (417 € ha-1
grass) and subsequent maintenance (136 € ha-1 grass a-1) were high. Pruning,
especially for walnut, and thinning were also significant costs. In addition, it was
assumed that an opportunity cost (a nitrate levy of 408 € ha-1a-1) was incurred when
arable land was converted to forest, because the land could no longer be used to
accept slurry manure. This was also applied on a pro-rata basis to the tree-strips in
the silvoarable system.
Pre-2005 grant regime
The relative profitability of forestry, arable and agroforestry systems on farms in the
European Union is significantly affected by the grant regime. In the pre-2005 grant
scenario, it was assumed that direct payments on the arable system and crop
217
component of the silvoarable system would be dependent on the crop species and
the portion of arable land in the system. These were greatest for maize (400 € ha-1)
in the Netherlands and least for wheat (129 € ha-1) in Spain, but also varied with crop
species and in France, with region. The pre-2005 payments on forestry and tree
component of the silvoarable systems were established from local and national
statistics and expert opinion. In Spain, farmers received a planting grant (849-1593 €
ha-1) dependent on tree species, a compensation payment (225-325 € ha-1 a-1) for 20
years depending on location and previous land-use and a maintenance grant (180288 € ha-1 a-1) for five years, subject to appropriate management of the trees (Graves
et al., 2005). In France, in Poitou Charentes and Centre, planting grants covered
50% of tree costs in the first four years and compensation payments (240-300 € ha-1
a-1) were available for walnut and cherry for ten years and for poplar for seven years.
In Franche Comté, there were no grants or payments, due to existing and substantial
areas of forest. In the Netherlands, a planting grant of 95% of costs was available up
to a maximum of 1500 € ha-1, a compensation payment of 100 € ha-1 a-1 for five years
and a maintenance payment of 545 € ha-1 a-1 for 18 years. For the tree component of
the silvoarable system, all tree payments were forfeited in Spain and the
Netherlands. In the Poitou Charentes and Centre regions of France, establishment
grants were available at 50% of the tree costs in the first four years, but no tree
payments were available in Franche Comté.
Post-2005 grant regime
In the post-2005 grant scenario, the changes anticipated for the Common Agriculture
Policy were implemented. For the arable crop, the changes meant that the area
payments could be fully decoupled from crop type, resulting in a single farm payment
for as long as the land was cropped. The per hectare value of these payments were
calculated to be lowest in Spain (116-330 € ha-1) and highest in France (329-353 €
ha-1) and the Netherlands (353-586 € ha-1). In the post-2005 scenario for forestry,
existing levels of payments applied, where they were in accordance with the rural
development strategy of the European Union (2004). In France, there was therefore
no change, but in Spain and the Netherlands, planting payments at each site were
changed to 50% of tree costs in the first four years. The compensation payments and
maintenance grants were reduced to 500 € ha-1 a-1 with a maximum duration of 10
years, unless they were already below these levels. In that case existing values were
used.
Since the effect of these changes on silvoarable systems is still unclear, two extreme
scenarios were developed for the post-2005 situation (Erreur ! Source du renvoi
introuvable.). In scenario 1, the single farm payment was assumed for the
percentage of crop area in the system with no tree payments. In scenario 2, the
single farm payment was assumed for the whole system with 50% of the tree costs in
the first four years covered by a planting grant.
(Table 3)
Farm-scale data
Only a brief description of the approach and data used in the farm-scale modelling is
provided here. A more detailed description can be found in Graves et al. (2005a).
218
Economic feasibility was assessed by multiplying the one-hectare results for each
land unit by their area and adding farm fixed costs from the FADN and ROSACE for
the hypothetical farms at each site (Equation 5). The quality of the land units was
ranked assuming that higher average yields meant better land. Expert opinion was
then used to determine which tree species and which crop rotation would be most
suitable for each land unit. The infinite net present value of the farm was used to
evaluate the economic effect of planting 10% of the farm with forestry or silvoarable
systems in comparison with the status quo arable farm under the pre-2005 and post2005 grant regimes. Planting was assumed in year 1 and holm oak, stone pine, wild
cherry and walnut were “harvested” to provide revenue in year 60. A rotation of 20
years was assumed for poplar, and by re-planting in years 21 and 41, three full
rotations of poplar were completed in 60 years. It was assumed for poplar that the
tree related grants in year 21 and 41 would be the same as for year 1.
RESULTS AND DISCUSSION
Biophysical production in arable and forestry systems
The predicted yield of the monoculture arable crops within a specific year on the 42
land units ranged from 0.2 t ha-1 for sunflower in Spain to 15.9 t ha-1 for maize in the
Netherlands (Erreur ! Source du renvoi introuvable.). Although the greatest
absolute variation in yield was associated with high yielding crops in the Netherlands
and France, the relative variation in yields was greatest in Spain. For the forestry
systems, the mean timber volume per tree ranged from 0.25 m3 for stone pine after
60 years, to 1.34 m3 for poplar after 20 years. The maximum recorded tree size was
for poplar (1.59 m3) in France and the minimum for oak (0.23 m3) in Spain. The
standard deviation suggested that absolute variation was greatest for wild cherry in
France and poplar in the Netherlands. The coefficient of variation showed that the
relative variation was greatest for wild cherry, oak and poplar in the Netherlands.
(Table 4)
Within each landscape test site, crop yield within a land unit could potentially vary
with soil depth, soil type and radiation level. For each crop, except wheat in Spain,
there was a significant positive correlation between predicted annual crop yields and
soil depth (Erreur ! Source du renvoi introuvable.). The standard error of the
estimate showed that in absolute terms, variation was greatest for wheat in Spain
and France. However, in relative terms, the variation was greatest for wheat in
Spain. Predicted timber yields were also positively correlated with soil depth for
cherry, poplar and oak (Erreur ! Source du renvoi introuvable.). However, this
correlation was only significant (P=0.05) in the case of wild cherry.
In each country, analysis of variance (analysis not summarised here) showed that
there were significant differences (P=0.05) in soil texture and predicted crop yields,
except in the case of oilseed in France. However, there were no significant
difference in soil texture and predicted timber yields in any of the countries.
(Table 5)
219
Biophysical production in silvoarable systems
The biophysical outputs from Yield-SAFE for the silvoarable systems (50 and 113
trees ha-1) showed a general decline in crop yields as the trees became larger and
competed more effectively for light and water. Typical relations for four land units are
shown in (Figure 2). Oak ((Figure 2)a) and stone pine (which showed similar growth
over time to oak and is therefore not shown) grew slowly throughout the whole
rotation. Hence relatively high crop yields were sustained for most of the tree
rotation. The initial rate of timber formation by wild cherry ((Figure 2)b) was slow
compared with walnut ((Figure 2)c) and poplar ((Figure 2)d), and crop yield reduction
in the walnut and poplar systems was predicted to occur earlier than in the wild
cherry systems.
The model was also used to predict difference in crop and tree yield at two tree
densities (50 and 113 trees ha-1). As expected relative crop yields were greatest in
the 50 tree ha-1 system and relative timber yields (m3 ha-1) were greatest in the 113
tree ha-1 system (Erreur ! Source du renvoi introuvable.).
(Figure 1)
(Figure 2)
In Spain, the relative yields of autumn-planted species, such as wheat, tended to be
greater than for spring-planted crops, such as sunflower (Erreur ! Source du renvoi
introuvable.a). As oak and stone pine are evergreen species, it was assumed that
this was due to greater competition experienced by the spring-planted crop for water.
In France, the difference in the relative yield of the autumn- (i.e. wheat and oilseed)
and spring-planted (sunflower and grain maize) crops was larger than in Spain
(Erreur ! Source du renvoi introuvable.b). This was probably due to reduced
competition for light, because the tree species planted in France were deciduous and
hence had no leaves for a large proportion of the growing period of the autumnplanted crops, whereas in Spain as the trees were evergreen and competition for
light was similar for both the spring and autumn-planted crops. Under poplar in the
Netherlands (Erreur ! Source du renvoi introuvable.c), similar effects regarding the
difference between autumn-planted wheat and spring-planted forage maize were
evident. These patterns were similar in both the low density and high density
systems, but the relative yields of the crops were higher at 50 trees ha-1 than at 113
trees ha-1.
(Figure 3)
Land equivalent ratios
The predicted land equivalent ratios for timber (including thinnings) and crop yield
(assuming a full rotation) of the silvoarable systems at both 113 and 50 trees ha-1,
with a few exceptions, were between 1 and 1.4. Hence the Yield-SAFE model
predicted that, under typical management, integrating crops and trees on the same
land was more productive than growing them separately. The relationship between
relative tree and crop yield suggested that the land equivalent ratio formed a convex
arc with maximum values obtained when the trees and crops had similar relative
yields and minimum values where either the tree or crop component was dominant
(Erreur ! Source du renvoi introuvable.). At each landscape test site, the land
220
equivalent ratio at 113 trees ha-1 (Erreur ! Source du renvoi introuvable.a) was
greater than that at 50 trees ha-1 (Erreur ! Source du renvoi introuvable.b),
suggesting that in biophysical terms, 50 trees ha-1 was sub-optimal and more efficient
use of resources in silvoarable systems could be achieved above this density.
The highest land equivalent ratios at both tree densities were associated with poplar,
walnut and cherry systems in France ((Figure 4)
(Figure 5)a and (Figure 4)
(Figure 5)b). Oak and pine in Spain at both densities were associated with much
lower land equivalent ratios. The reason for this is not clear; it may be that predicted
growth of oak and pine was so slow that they were unable to make use of available
resources at the densities used in the silvoarable systems. Alternatively, it may be
that the crops competed more strongly for water than other trees of the same
species. In either case, production benefits from oak and pine-based silvoarable
systems in Spain appear to be limited unless tree densities can be increased without
detriment to the relative yield of either component.
(Figure 4)
(Figure 5)
Plot-scale economic results
The annual time-series production data developed using Yield-SAFE and economic
data for crop grants and crop revenue and costs, tree grants and tree revenue and
costs for landscape test site were modelled in Farm-SAFE (Graves et al., 2005b).
The economic performance of the arable, forestry and the silvoarable systems (113
trees ha-1 only) was compared using the equivalent annual value (EAV) (discount
rate = 4%). The effects of zero grants, the pre-2005 grants and the post-2005 grants
were also examined. As intercrop yields decreased over time due to tree growth, the
crop rotation was optimised by ending intercrop production when the five-year
moving-average of the intercrop net margin was zero.
Profitability with no grants
The equivalent annual values (at a discount rate of 4%) of the forestry systems with
oak and stone pine in Spain, poplar and walnut in the Netherlands, and cherry in
France were negative (Erreur ! Source du renvoi introuvable.). Only walnut, due
to the high value of the timber, and poplar, due to the short rotation, in France was
profitable. The low profitability of forestry in the Netherlands was partly due to the
opportunity cost of slurry manure management, as the application allowance was
assumed to be zero for forest land. The equivalent annual values (4% discount rate)
of the arable system were positive in Alcala la Real, Cardenosa El Espinar,
Fontiveros, Olmedo and St Maria del Paramo in Spain, in Poitou Charentes and
Centre in France and at all sites in the Netherlands, but negative in Torrijos, Ocaña
and St Maria del Campo and at most sites in Franche Comté (i.e. at Dampierre and
Vitrey). Positive values were associated with sites of high productivity. In Franche
Comté, relatively high assignable fixed costs explained the negative values.
221
The equivalent annual values (4% discount rate) of the silvoarable systems in Spain
were marginally below those for the arable system. By contrast, in France, values for
the silvoarable systems with walnut, with poplar in Centre, and with wild cherry in
Poitou Charentes and Franche Comté were higher than those for both arable
agriculture and forestry. In the Netherlands, the values for the silvoarable system
with poplar were marginally greater than the arable system, but the value for the
silvoarable system with walnut was negative because of the long tree rotation and
low value given to walnut timber.
(Figure 6)
The long-term cash value of pre-2005 and post-2005 grant regimes
Under the pre-2005 grant regime, the actual cash value (discount rate = 0%) of forest
payments for the duration of the tree rotation was greatest in the Netherlands and
lowest in France (Erreur ! Source du renvoi introuvable.). The assumed levels of
arable compensation payments were marginally greater in the Netherlands than in
France, and both were much greater than in Spain.
Within Spain, support for silvoarable agroforestry was lower than for forestry and the
arable system because of ineligibility for tree grants and reduction of the arable
compensation payments by twice the proportion of the canopy area of the trees. In
France, in Poitou Charentes and Centre, arable payments were at least five-times
the value of forestry payments and the value of silvoarable payments was marginally
less than that for arable systems. In Champlitte, Dampierre and Vitrey in Franche
Comté, there were no forestry payments and hence the greatest level of support was
for arable systems. For poplar sites, payments for all systems were relatively low
because of the 20- rather than the 60-year rotation. Support for walnut and poplar
forestry in the Netherlands was identical because they were both temporary,
production-based systems. Since arable payments were dependent on the length of
the tree rotation, they were greater for walnut (Bentelo) rather than for poplar
(Balkbrug and Scherpenzeel). In each case, the support for silvoarable systems was
less than for forestry and arable systems, as no payments were received for the tree
component.
The actual cash value of each system in the post-2005 payment scenario and the
change, relative to the pre-2005 scenario was determined (Table 6). The greatest
relative change was predicted for Spain, where forestry payments were greatly
reduced, due to compensation being limited to 10 years, while for arable and
particularly for silvoarable systems, payments were predicted to increase. The
predicted value of the new single farm payment at Alcala la Real, and St Maria del
Paramo and St Maria del Campo was greater than pre-2005 area payments, as
support for non-arable activities on typical farms in these areas was assumed to be
re-allocated on an area basis. The large relative increase of the cash value of
payments in the silvoarable systems demonstrated the disadvantage of the system
under pre-2005 regime. In France, there was no change for forestry, and only
marginal changes for arable systems due to modulation under the single farm
payment. For silvoarable systems, scenario 1 was similar to the pre-2005 regime but
marginal benefits were evident under scenario 2. In the Netherlands, the major
change was due to the reduction in the compensation payments associated with
forestry from 18 to 10 years.
222
(Table 6)
Equivalent annual value under pre-2005 grant regime
In the pre-2005 grant regime (Erreur ! Source du renvoi introuvable.), the
equivalent annual values (4% discount rate) of forestry in Spain was generally higher
than those for arable systems, except where crop yields were high. Because of the
low level of government support, the equivalent annual value of the silvoarable
systems was generally lower than for forestry and arable systems. In France, the
equivalent annual value for the arable systems, tended to be greater than that for
silvoarable agroforestry with wild cherry, which was much greater than that for wild
cherry forestry. Hence, silvoarable agroforestry offered the most profitable means of
establishing cherry trees at these sites. The predicted equivalent annual values of
the silvoarable systems with poplar and walnut systems in France were higher than
that for both forestry and arable systems. In the Netherlands, the conventional
arable systems were the most profitable, followed by silvoarable agroforestry. Thus
in the Netherlands, silvoarable systems also appeared to provide a more profitable
means of establishing trees in the landscape.
(Figure 7)
Equivalent annual value under post-2005 grant regime
In the post-2005 (Erreur ! Source du renvoi introuvable.), compared to the pre2005 (Erreur ! Source du renvoi introuvable.), grant regime in Spain, the
equivalent annual value of forestry was predicted to be reduced whilst it was
predicted to increase for arable and silvoarable systems despite modulation (Figure
7). In France, the values for the equivalent annual value were generally similar to
those under the pre-2005 regime. For silvoarable systems, the pessimistic scenario,
scenario 1, resulted in marginal reductions, while the optimistic scenario, scenario 2,
resulted in marginal increases. In the Netherlands, a substantial decrease in the
equivalent annual value of forestry was predicted, whilst the change in the equivalent
annual value of arable systems was marginal. For silvoarable agroforestry, little
change was predicted for scenario 1, but a small and consistent increase was
predicted for scenario 2.
The net effect of the above changes was most significant in Spain. Under the pre2005 scenario, forestry systems were consistently more profitable than silvoarable
systems. Under the post-2005 scenario, silvoarable agroforestry was predicted to be
more profitable than forestry in almost 50% of cases, although both systems were
predicted to remain less profitable than arable agriculture. At sites in France and the
Netherlands, the ranking of the systems in the post-2005 and pre-2005 regimes were
similar.
(Figure 8)
Farm-scale feasibility
Under the pre-2005 grant-regime in Spain, it was not profitable to re-plant arable land
with a silvoarable system. This was due to low timber volume and value, the lack of
tree grants and the loss of arable area payments by twice the canopy area of the tree
component. By contrast, establishing forestry on arable land was predicted generally
223
to increase farm profitability (Figure 9). In France, establishing silvoarable
agroforestry was predicted to increase farm profitability when it involved walnut or
poplar, and decrease it if it included wild cherry. In each case, silvoarable systems
improved farm profitability relative to forestry on the same area of land. In the
Netherlands, both forestry and silvoarable systems reduced farm profitability.
Under the post-2005 grant regime in Spain, replanting arable land with silvoarable
systems continued to result in reduced farm profitability. Replanting arable land with
forestry was predicted to increase farm profitability at five sites (Torrijos, Ocaña,
Almonacid de Zorita, Olmedo and St Maria del Campo), and decrease it at the other
four. In France, farm profitability increased following the establishment of silvoarable
systems with walnut and poplar, and decreased with silvoarable systems using wild
cherry; in both cases silvoarable systems were more profitable than forestry. In the
Netherlands, there was no advantage to introducing silvoarable systems or forestry in
comparison with the status quo.
An analysis of the frequency with which silvoarable systems increased profitability
relative to the status quo (Erreur ! Source du renvoi introuvable.) showed that in
Spain there were no cases where farm profitability was improved by establishing
silvoarable systems. Instead government support favoured the establishment of
forestry, and this was attractive in about 80% of cases under the pre-2005 grant
regime. The post-2005 regime was predicted to reduce the relative profitability of
forestry, but forestry still remained financially attractive on about 50% of the selected
farms. In France, under the pre-2005 grant regime, silvoarable systems were
predicted to increase farm profitability in approximately 50% of cases. This
frequency remained similar under scenario 1 of the post-2005 grant regime, and
increased to 80% under scenario 2. The proportion of farms where forestry was
attractive (20%) was less than for silvoarable systems and was the same for both the
pre-2005 or post 2005 regimes. In the Netherlands (not shown), the introduction of
forestry and silvoarable systems always reduced farm profitability, under the pre2005 and post-2005 payment scenarios.
(Figure 9)
In Spain the use of silvoarable systems was preferable to forestry in 12% of cases
under the pre-2005 grant regime and 50% of cases in scenarios 1 and 2 of the post2005 grant regime. In France and the Netherlands, farm profitability was always
increased with the use of silvoarable rather than forestry systems. Hence, in Spain,
forestry generally provided the most cost effective method of establishing trees under
the pre-2005 regime, an advantage predicted to disappear under post-2005 regime.
In France and the Netherlands, silvoarable systems with walnut, wild cherry, and
poplar provided the most profitable means of establishing trees on farms irrespective
of grant regime.
(Figure 10)
SUMMARY AND RECOMMENDATIONS
Using a geographical information system, a statistical analysis of climatic,
topographic and land classification data was used to select 19 landscape test sites in
Spain, France and the Netherlands. Within each site, land use, soil depth and
texture, and elevation were digitised. Daily weather data were generated for each
224
site using a weather generator. Proportional differences in solar radiation and soil
water holding capacity were calculated and used in cluster analysis to divide the
arable land at each site into between one and four land units. A biophysical model
called “Yield-SAFE” was developed and calibrated for potential yields of a range of
tree and crop species. Typical forestry and arable systems and associated
management regimes were determined for each land unit and Yield-SAFE was
calibrated for actual tree and crop yields at each site. The calibrated model was then
used to calculate daily values of tree and crop yields for a forestry, arable and
agroforestry system at each land unit according to changes in solar radiation, soil
depth and texture. Financial data for forestry, arable, and silvoarable production at
each site were collected and four grant scenarios were described (no grants, a pre2005 scenario, and two possible post-2005 scenarios). The financial data was
combined with the physical values in an economic model called “Farm-SAFE”, and
the equivalent annual value (discount rate = 4%) at a plot-scale and the infinite net
present value at a farm-scale were used to examine the profitability of different
systems.
The Yield-SAFE biophysical model predicted lower timber yields and crop yields per
hectare for silvoarable systems compared to the forestry and arable systems
respectively (Erreur ! Source du renvoi introuvable.). However, the total
productivity of the silvoarable system, as determined by a land equivalent ratio, was
predicted to be between 100 and 140% of that for the monoculture systems (Erreur !
Source du renvoi introuvable. and (Figure 4)
(Figure 5)). High land equivalent ratios were achieved with a tree stand density of
113 rather than 50 trees ha-1, suggesting that the high density system made fuller
use of the available light and water resources. The highest ratios were obtained by
integrating deciduous trees and autumn-planted crops, which were complementary in
terms of light use (Erreur ! Source du renvoi introuvable.). The lowest ratios were
obtained from evergreen tree species in Spain, where productivity was appeared to
be constrained by the slow growth of the trees and low soil water availability (Erreur !
Source du renvoi introuvable.).
At a plot scale, the economic performance of the systems was compared in a zero
grant scenario (Erreur ! Source du renvoi introuvable.). In Spain, arable systems
were marginally more profitable than silvoarable systems with oak or stone pine,
which in turn were more profitable than forestry systems with the same species. By
contrast in France, silvoarable systems with walnut in each of three regions, poplar in
one region, and wild cherry in two regions were more profitable than arable and
forestry systems. In the Netherlands, silvoarable systems with poplar, but not walnut,
were predicted to be more profitable than the described arable system. However,
both the poplar and walnut silvoarable systems were more profitable than forestry.
Under pre-2005 grants (Erreur ! Source du renvoi introuvable.), support for
silvoarable systems in Spain and the Netherlands was substantially lower than for
arable and forestry systems. Hence, the profitability of silvoarable systems was
always less than for arable or forestry systems. In France, support for silvoarable
systems was marginally lower than for arable systems but significantly higher than for
forestry systems. Hence it was predicted that silvoarable systems with poplar and
walnut could be more profitable (at a 4% discount rate) than both forestry and arable
225
systems. Silvoarable systems with cherry although more profitable than forestry were
predicted to be less profitable than arable systems. In the Netherlands, silvoarable
systems were more profitable than forestry, but less profitable than arable systems.
Under two possible post-2005 grant regime (Erreur ! Source du renvoi
introuvable.), the relative value of support for forestry in Spain was predicted to
decrease, whilst for silvoarable and arable systems it was predicted to increase. In
France and the Netherlands the relative value of support for silvoarable systems
compared to arable and forestry systems remained similar to the pre-2005 regime for
scenario 1, and increased marginally for scenario 2. Hence the profitability of
silvoarable systems in Spain increased and frequently exceeded the profitability of
forestry systems, but remained marginally less profitable than arable systems. In
France and the Netherlands, little relative change in profitability between the systems
was predicted.
At a farm-scale and under both pre-2005 and post-2005 grants in France (Erreur !
Source du renvoi introuvable.), planting arable land with silvoarable systems of
walnut and poplar increased farm profitability, while silvoarable systems with cherry
reduced farm profitability. In Spain and the Netherlands, silvoarable systems
consistently reduced farm profitability in comparison with the arable status quo.
However, in both France and the Netherlands, silvoarable systems were a more costeffective way of establishing trees on the farm than forestry (Erreur ! Source du
renvoi introuvable.). In Spain, under pre-2005 grants, silvoarable systems were a
less cost-effective means of establishing trees than forestry. However, under post2005 grants, silvoarable systems were predicted to be a most profitable means of
establishing trees in half the examined cases.
A number of recommendations regarding further research can be made. Predictions
are subject to uncertainty and this could be examined using sensitivity analysis or
stochastic modelling. Certain baseline data could also be re-examined. The
recorded value of walnut timber in the Netherlands and France differed greatly, even
though both countries are part of a free-trade zone. This strongly influenced the
relative profitability of walnut systems in these countries. The assumption regarding
prohibition of slurry manure application in the Netherlands in forests also had an
important effect. If this is a true opportunity cost, the establishment of productive
forests on farms is unlikely to be attractive, unless the opportunity cost is removed or
payment schemes can account for it. Assumptions regarding beating-up, tree
management and the extent of payments could also be re-assessed for Spain. Tree
mortality is likely to be high due to difficult conditions and should be accounted for;
the assumptions regarding pruning and thinning costs in Spain may be valid for
traditional management of widely spaced trees in open woodlands (dehesas), but
invalid for forestry and silvoarable systems, even if these are established within areas
where dehesas predominate. Finally, the assumptions and value of post-2005 grants
should be re-assessed when the changes are implemented.
CONCLUSION
The process used to model plot- and farm-scale economics of arable, silvoarable and
forestry systems in three European countries has been described. This integrated
the use of geographical information systems with a biophysical model of tree, crop
226
and integrated tree and crop growth, and an economic model developed during the
SAFE project.
Under the economic conditions envisaged in the analysis, the most financially
attractive silvoarable systems tended to have a land equivalent ratio that was
substantially above one. Conditions that most favoured a high land equivalent ratio
appeared to be the use of relatively high tree-densities to make full use of available
resources, the use of deciduous trees and autumn-planted crops to make
complementary use of light, and a high soil water availability to ensure that extra
biomass production could be sustained. Conversely, it appeared that low ratios were
associated with low tree density, evergreen trees, spring-planted crops, and low soil
water availability.
Silvoarable agroforestry was most financially attractive where both components of
the system were profitable as a monoculture since an unprofitable or relatively
unprofitable component tended to reduce the profitability of the mixed system. In
addition, the profitability of silvoarable agroforestry tended to be maximised if the
profitability of the forestry and agricultural system were similar. Under the two
proposed post-2005 grant regimes, it is predicted that silvoarable systems with
walnut and poplar in France could provide a profitable alternative to arable or forestry
systems. In Spain, it appeared that holm oak and stone pine could be integrated into
arable systems without significantly reducing arable production for many years.
Since these trees are of ecological and landscape importance, rather than productive
importance, additional support in the form of an agri-environment payment would be
justified. A moderate annual amount would be sufficient to overcome income losses
caused by yield reductions and encourage establishment for non-productive benefits.
In the Netherlands, the low value of timber and an assumed opportunity cost of losing
arable land for slurry manure application made silvoarable and forestry systems
relatively unattractive compared with arable systems.
ACKNOWLEDGEMENTS
This research was carried out as part of the SAFE (Silvoarable Agroforestry for
Europe) collaborative research project. SAFE is funded by the EU under its Quality
of Life programme, contract number QLK5-CT-2001-00560, and the support is
gratefully acknowledged. We also acknowledge and are thankful for the involvement
of Terry Thomas, Bob Bunce, Yvonne Reisner, Klaas Metselaar and Martina Mayus
at key stages in the project.
REFERENCES
Assemblée Permanente des Chambres d’Agriculture (2005). Réseau d’observation
des systèmes d’exploitation.
http://paris.apca.chambagri.fr/apca/default.htm
(Accessed 5 May 2005)
Agricultural Economics Research Institute (2005). Database of agricultural statistics
for the Netherlands. http://www.lei.dlo.nl/uk/ (Accessed 5 May 2005).
Boulet-Gercourt, B. (1997) Le merisier. Institut pour le Développement Forestier,
2ème édition. 128 pp.
Burgess, P.J., Graves, A., Metselaar, K., Stappers, R., Keesman, K., Palma, J.,
Mayus, M. & van der Werf, W. (2005). Parameterisation of the Yield-SAFE model
227
and its use to determine yields at the Landscape Test Sites. Unpublished report.
Silsoe, Bedfordshire: Cranfield University. 53 pp. http://montpellier.inra.fr/safe/
(Accessed 5 May 2005)
Burgess, P.J., Graves, A.R., Metselaar, K., Stappers, R., Keesman, K., Palma, J,
Mayus, M., & van der Werf, W. (2004a). Description of the Plot-SAFE Version 0.3.
Unpublished document. 15 September 2004. Silsoe, Bedfordshire: Cranfield
University. 52 pp. http://montpellier.inra.fr/safe/ (Accessed 5 May 2005)
Burgess, P.J., Seymour, I., Incoll, L.D., Corry, D.T., Hart, B. & Beaton, A. (2000).
The application of silvoarable agroforestry in the UK. Aspects of Applied Biology
62:269-276.
Burgess. P.J., Incoll, L.D., Corry, D.T., Beaton, A.. & Hart, B.J. (2004b). Poplar
(Populus spp) growth and crop yields in a silvoarable experiment at three lowland
sites in England. Agroforestry Systems 63: 157-169.
Centre Régional de la Propriété Forestière (1997). Boiser une Terre Agricole. 28 pp.
Commission of the European Union (2004). Proposal for a Council Regulation on
support for rural development by the European Agricultural Fund for Rural
Development (EAFRD). European Union Report No 2004.0161 (CNS). 68 pp.
Conrad O (2002). DiGeM – Software for Digital Terrain Analysis. Accessed 4 April
2005. http://www.geogr.uni-goettingen.de/pg/saga/digem/index.html
Dupraz C, Lagacherie M, Liagre F, Boutland A (1995).
Perspectives de
diversification des exploitation agricoles de la région Midi-Pyrénées par
l’agroforesterie. Rapport de fin d’études commandité par le Conseil Régional MidiPyrénées. Institute National de la Recherche Agronomique, Montpellier. Contract
AIR3 CT92-0134. 253 pp
Dupraz C., 1998. Adequate design of control treatments in long term agroforestry
experiments with multiple objectives. Agroforestry Systems, 43(1/3): 35-48.
Dupraz C., Newman S., 1997. Temperate agroforestry : the European way. In : A. M.
Gordon and S.M. Newman (editors), Temperate Agroforestry Systems, CAB
International, Wallingford, UK, 181-236.
European Commission (2005). FADN Public Database. Accessed 4 April 2005.
http://europa.eu.int/comm/agriculture/rica/dwh/index_en.cfm
Faustmann, M. (1849). Berechnung des Wertes Waldboden sowie noch nicht
haubare Holzbestände für die Waldwirfschaft besitzen, Allgemeine Forst und JagdZeitung, 25, 411-455.
Global Data Systems (2005). Database of historical climate data compiled by Global
Data Systems for the United States Department of Agriculture World Weather Board
from
World
Meteorological
Organisation
climate
reporting
systems.
http://hydrolab.arsusda.gov/nicks/nicks.htm (Accessed 5 May 2005).
Graves AR, Matthews RB and Waldie K. (2004). Low external input technologies for
livelihood improvement in subsistence agriculture. Advances in Agronomy 82: 473555
Graves, A.R., Burgess, P.J., Liagre, F., Dupraz, C. & Terreaux, J.-P. (2003). The
development of a model of arable, silvoarable and forestry economics. Paper
prepared for submission to Agroforestry Systems. Silsoe, Bedfordshire : Cranfield
University. 30 pp. http://montpellier.inra.fr/safe/ (Accessed 5 May 2005)
228
Graves, A.R., Burgess, P.J., Liagre, F., Terreaux, J.P., & Dupraz, C. (2005a).
silvoarable economics. Paper accepted by Agroforestry Systems.
Graves, A.R., Burgess, P.J., Palma, J.H.N., Herzog, F., Moreno, G., Bertomeu, M.,
Dupraz, C. and Liagre, F. (2005b). Report on plot-economics of European
silvoarable systems in target regions and the economic feasibility of silvoarable in
target regions report. Silsoe, Bedfordshire: Cranfield University 11 March 2005. 42
pp. http://montpellier.inra.fr/safe/ (Accessed 5 May 2005)
Herzog, F. (2004). Working visit report on upscaling for nine landscape test sites in
Spain. Workshop at Plasencia, Spain 5-8 July 2004. Unpublished report Zurich :
Agroscope FAL Reckenholz. http://montpellier.inra.fr/safe/ (Accessed 5 May 2005)
Institut pour le Développement Forestier (1997). Les noyer à bois. 3ème édition,
Février 1997. 132 pp.
Mead R and Willey RW (1980). The concept of a "Land Equivalent Ratio" and
advantages in yields from intercropping. Expl. Agric. 16: 217-228
Mercer DE, Miller RP, Nair PKR and Latt CR (1998) Socioeconomic research in
agroforestry: progress, prospects, priorities. Agroforestry Systems 38: 177-193
Ministerie van Landbouw, Natuur en Voedselkwaliteit (2004). Subsidieregeling
Agrarisch Natuurbeheer.
LASER vestging Roermond, Az Roermond, The
Netherlands. 51 pp.
Ministry of Agriculture, Fisheries and Food (1983). Definitions of terms used in
agricultural business management. Alnwick, Northumberland: MAFF Publication
Booklet 2269. 39 pp.
Montero G and Cañella I (2000). Selvicultura de Pinus pinea L. Estado actual de los
conociminetos en España. In: Simposio del pino piñonero (Pinus pinea l.). Valladolid,
pp 21-38
Mücher, C.A., Bunce, R.G.H., Jongman, R.H.G., Klijn, J.A., Kooment, A.J.M.
Metzger, M.J. and Wascher, D.M. (2003). Identification and characterisation of
environments and landscapes in Europe.
Alterra-rapport 832.
Wageningen
University, 119 pp.
Mücher, CA (2000) PELCOM project. Final report submitted to the European
Commission. Contract No ENV4-CT96-0315. 299 pp
Nair PKR (1985) Classification of agroforestry systems. Agroforestry Systems 3: 97128
Nix, J. (2001). Farm Management Pocketbook. Ashford Kent: Wye College Press.
244 pp.
Ong, C.K. (1996). A framework for quantifying the various effects of tree-crop
interactions. In: Tree-Crop Interactions A Physiological Approach 1-23 Eds. C.K.
Ong and P. Huxley. Wallingford: CAB International.
Palma, J. & Reisner, Y. (2004). Work visit report on the upscaling of the seven
landscape test sites in France. Unpublished report. Zurich: Agroscope FAL
Reckenholz 15 pp. http://montpellier.inra.fr/safe/ (Accessed 5 May 2005)
229
Pulido, F.J. Campos, P and Montero, G. (2003). La gestión forestal de las dehesas.
Historia, Ecológía, Selvicultura y economía. IPROCOR-Junta de Extremadura.
Merida, Spain.
Reisner Y (2004). Work visit report: upscaling for three landscape test sites in the
Netherlands. 24-28 May 2004. Unpublished report. Zurich: Agroscope FAL
Reckenholz. 9 pp. http://montpellier.inra.fr/safe/ (Accessed 5 May 2005)
Sinclair FL (1999) A general classification of agroforestry practice. Agroforestry
Systems, 46: 161-180.
Souleres, G. (1992) les milieux de la populiculture, Institut pour le Développement
Forestier, 310 pp.
Thomas TH (1991) A spreadsheet approach to the economic modelling of
agroforestry systems. Forest Ecology and Management 45: 207-235
Thomas TH and Willis RW (1997) Linking bio-economics to biophysical agroforestry
models. Agroforestry Forum 8(2): 40-42
United States Department of Agriculture (2005). CLIGEN Weather Generator.
United States Department of Agriculture Agricultural Research Service and Unites
States Forest Service http://horizon.nserl.purdue.edu/Cligen/ (Accessed 5 May
2005).
Van der Werf, W., Keesman, K., Burgess, P.J., Graves, A.R., Pilbeam, D., Incoll,
L.D., Metselaar, K., Mayus, M., Stappers, R., Palma, J., Dupraz, C. & van Keulen, H.
(2005) Yield-SAFE, a parameter sparse model for yield predictions, including
uncertainty analysis, in European agro-forestry systems. Paper prepared for
submission to Ecological Engineering.
Van Genuchten, M. Th., (1980). A closed-form equation for predicting the hydraulic
conductivity of unsaturated soils, Soil Science Society of America Journal, 44, 892898.
Van Ittersum, MK and Rabbinge, R (1997). Concepts in production ecology for
analysis and quantification of agricultural input-output combinations. Field Crops
Research 52(3) 197-208
Willis, R.W., Thomas, T.H., and J. van Slycken, (1993) Poplar agroforestry: a reevaluation of its economic potential on arable land in the United Kingdom. Forest
Ecology and Management, 57, 85-97.
Wösten, J.H.M., Lilly, A., Nemes, A., & Le Bas, C. (1999). Development and use of a
database of hydraulic properties of European soils. Geoderma 90: 169-185.
Yagüe, S. (1994). Producción y selvicultura del pino piñonero (Pinus pinea L.) en la
provincia de Avila. Montes 36: 45-51.
230
TABLES
Table 2. Summary of the latitude, longitude, altitude, mean air temperature, annual
solar radiation receipt and annual rainfall at each site
Country and
Site name
Latitude
Longitude
Altitude
Mean
Solar
Annual
temp
radiation
rainfall
(m)
(°C)
(MJ m-2)
(mm)
region
Spain
Andalucia
Alcala la real
37.36N
3.88W
1000
15.3
5490
355
Castilla La
Torrijos
39.89N
4.39W
500
15.5
5560
348
Mancha
Ocaña
39.94N
3.44W
700
14.7
5780
316
Almonacid de Zorita
40.23N
2.61W
900
12.6
6610
404
Cardenosa El Espinar
40.78N
4.53W
1000
12.0
5700
404
Fontiveros
40.86N
5.00W
900
12.0
6170
393
Olmedo
41.28N
4.80W
750
12.5
5480
410
St Maria del Campo
42.11N
3.91W
800
9.1
5630
530
St Maria del Paramo
42.44N
5.69W
800
10.2
6600
519
Poitou Charentes
Champdeniers
46.41N
0.02E
200
11.0
4740
648
Centre
Chateauroux
46.92N
1.65E
150
11.0
4750
587
Fussy
47.18N
2.47E
200
10.6
4800
626
Sancerre
47.30N
2.72E
400
10.7
4590
724
Champlitte
47.64N
5.58E
300
8.5
4940
773
Dampierre
47.61N
5.82E
300
10.0
5090
1072
Vitrey
47.81N
5.78E
400
9.5
4900
1084
Balkbrug
52.57N
6.34E
0
8.9
4830
818
Bentelo
52.22N
6.67E
0
8.8
3690
729
Scherpenzeel
52.57N
6.34E
0
9.0
3710
801
Castilla y Leon
France
Franche Comté
The Netherlands
Table 3. The total utilised agricultural area for each hypothetical farm and description
of the 42 different land units, and the selected tree and crop species
231
Site
Area
Land
Area
Radiation
Soil
Soil
of
unit
of
(%)
type
depth
farm
land
(ha)
unit
Tree
Crop rotation
(cm)
(ha)
Spain
Alcala la real
Torrijos
73
63
LU1
58
97
M
140
Oak
w/w/f
LU2
15
86
M
50
Oak
w/w/f
LU1
10
101
M
140
Oak
w/f
LU2
56
100
M
140
Oak
w/w/f
Ocaña
66
LU1
66
100
M
140
Oak
w/w/f
Almonacid de
66
LU1
59
97
M
140
Oak
w/f
LU2
7
83
F
140
Oak
s/s/s/s/s/w/f
LU1
23
93
M
140
Oak
w/w/w/f
LU2
35
101
F
140
Oak
w/w/w/f
LU1
49
99
C
140
Oak
w/w/w/w/f
LU2
9
98
C
140
Pine
w/w/w/w/f
LU1
5
100
C
140
Pine
w/s/f
LU2
34
100
M
140
Oak
w/s/f
LU3
18
99
C
140
Oak
w/s/f
LU1
44
99
C
140
Pine
w/w/w/f
LU2
14
99
M
140
Oak
w/w/w/w/w/f
LU1
4
100
M
140
Oak
w/w/w/s/f
LU2
34
100
M
140
Oak
w/w/w/s/f
LU3
21
101
M
140
Oak
w/w/w/s/f
LU1
67
100
F
80
Cherry
w/w/s/w/o/s
LU2
27
100
M
120
Walnut
w/w/s/w/o/s
LU1
32
102
F
80
Walnut
w/w/o/w/o/s
LU3
86
102
M
120
Walnut
w/w/o
Zorita
Cardenosa El
58
Espinar
Fontiveros
Olmedo
St Maria del
58
57
58
Campo
St Maria del
59
Paramo
France
Champdeniers
Chateauroux
94
152
232
Fussy
80
Sancerre
Champlitte
Dampierre
Vitrey
98
130
130
120
LU2
23
102
F
40
Cherry
w/w/o/w/o/s
LU4
11
100
F
40
Cherry
w/w/o/w/o/s
LU1
10
101
F
40
Cherry
w/o
LU2
43
103
M
80
Poplar
w/w/o
LU3
27
102
F
120
Cherry
w/o
LU1
37
103
F
40
Cherry
o/w/s/w/w/w/o
LU3
44
101
Vf
120
Cherry
o/w/s/w/w/w/o
LU4
7
100
C
80
Cherry
o/w/s/w
LU2
10
102
Vf
140
Poplar
o/w/s/w/w/w/o
LU1
68
103
M
140
Cherry
w/w/o
LU2
62
103
M-f
35
Walnut
w/w/w/w/w/gm
LU1
64
98
M
140
Cherry
w/w/gm
LU2
43
97
F
35
Cherry
w/w/w/gm
LU3
23
95
Mf
60
Poplar
w/gm
LU1
46
103
M
60
Cherry
w/w/o
LU2
74
103
Mf
60
Poplar
w/w/gm
The Netherlands
Balkbrugg
40
LU1
40
100
C
140
Poplar
fm
Bentelo
40
LU1
40
100
C
140
Walnut
w/w/fm
Scherpenzeel
10
LU1
10
100
C
140
Poplar
fm
Note: Soil type: C: coarse; M: Medium; M-f: Medium-fine, F: Fine; V-f: Very fine
Crop type: w: wheat; f: fallow; o: oilseed; s: sunflower; gm: grain maize; fm: forage maize
Table 4 Four post-2005 grant scenarios assumed for silvoarable agroforestry
Arable payment
Tree payment
Scenario 1
Percentage crop area in system
None
Scenario 2
Total area of system
Fifty percent costs in years 1-4
233
Table 5 Summary and description of yields for crops and trees in France, Spain and
the Netherlands
Country
Spain
France
the Netherlands
Arable crop
No of
value
s
France
the Netherlands
Standard
deviation
Range
Coefficien
t of
variation
(t ha-1)
(t ha-1)
(t ha-1)
(%)
Sunflower
120
0.8
0.4
0.2-1.7
52
Wheat
697
2.5
1.0
0.6-5.8
40
61
6.3
1.2
2.9-9.8
20
Oilseed
260
3.2
0.4
1.9-4.3
13
Sunflower
106
1.7
0.4
0.7-2.6
26
Wheat
613
5.5
1.5
0.9-10.5
27
Forage maize
80
11.5
1.7
8.0-15.9
15
Wheat
20
7.9
1.2
5.9-11.1
16
Grain maize
(m3 ha-1)
Tree species
Spain
Mean
(m3 ha-1)
(m3 ha-1)
(%)
Oak (60)
16
0.33
0.050
0.23-0.43
15
Pine (60)
3
0.25
0.005
0.25-0.26
2
Cherry (60)
12
0.88
0.151
0.71-1.15
17
Poplar (60)
4
1.34
0.143
1.26-1.59
11
Walnut (60)
4
1.01
0.008
1.00-1.02
1
Poplar (20)
2
1.28
0.215
1.06-1.49
17
Walnut (60)
1
0.71
n/a
0.71
n/a
Note: values in brackets show length of rotation
234
Table 6 Relationship between a) crop yield and b) timber volume and soil depth for
selected crop and tree species in Spain and France
Country
Crop or tree
species
No of
pairs
Linear regression of crop yield (t ha-1)
or timber volume (m3 ha-1) against depth (d;
m)
Correlation
coefficient
Significan
t
(P=0.05)
Spain
Wheat
697
2.34 (± 1.02) + 0.19 d
0.02
No
France
Wheat
613
3.69 (± 1.23) + 2.15 d
0.57
Yes
Grain
maize
61
4.90 (± 0.92) + 1.82 d
0.67
Yes
Sunflower
106
1.06 (± 0.39) + 0.72 d
0.49
Yes
Oilseed
260
2.95 (± 0.42) + 0.32 d
0.26
Yes
Spain
Oak
16
0.17 (± 0.04) + 0.12 d
0.53
No
France
Cherry
12
0.64 (± 0.10) + 0.29 d
0.75
Yes
Walnut
4
1.02 (± 0.01) + 0.0028 d
-0.12
No
Poplar
4
0.98 (± 0.04) + 0.42 d
0.97
No
235
Table 7 The predicted value of government support (€ ha-1), over a full tree-rotation
(60 years for oak, pine walnut and cherry; 20 years for poplar), for forestry, arable and
silvoarable systems in the pre-2005 grant regime, and the predicted change in that
support in a post-2005 grant regime (scenario 1 and scenario 2)
Pre-2005 government support
Land unit
Rotation
(a)
Forestry
Arable
Alcala 1
60
6860
Alcala 2
60
Torrijos 1
Predicted net change in support with the
post-2005 grant regime
Silvoarable
Forestry
Arable
Silvoarable
scenario 1
Silvoarable
scenario 2
5170
2010
-2940
8030
10010
11408
6860
5170
2690
-2940
8030
9320
10728
60
9380
3870
1410
-4190
210
820
1256
Torrijos 2
60
9380
5170
1920
-4180
270
1790
2378
Ocaña 1
60
9380
5170
1770
-4190
350
2120
2864
Almonacid 1
60
9380
3870
1380
-4190
600
2010
2712
Almonacid 2
60
9370
8770
4080
-4180
-1030
2980
3886
Cardenosa 1
60
8860
5810
2900
-3380
-590
1850
2538
Cardenosa 2
60
8860
5810
2670
-3390
-590
2080
2768
Fontiveros 1
60
8850
6200
2940
-3380
950
3570
4430
Olmedo 2
60
8860
5160
2260
-3390
600
2990
3718
Olmedo 3
60
8860
6100
2520
-3380
-340
2720
3458
Campo 2
60
8860
6460
2610
-3380
1990
4160
6058
Paramo 1
60
8860
6760
3080
-3390
2500
5350
6402
Paramo 2
60
8860
6760
3080
-3390
2500
5350
6402
Paramo 3
60
8860
6760
3060
-3390
2500
5370
6422
Fontiveros 2
60
8000
6200
2060
-2960
950
4450
5335
Olmedo 1
60
8010
6100
1780
-2970
-340
3470
4223
Campo 1
60
8010
5810
1050
-2970
1790
2640
4263
Champdeniers
1
60
4440
21180
16130
0
0
-390
Fussy 3
60
-30
-430
Spain
France
3840
21090
19590
0
1564
1865
236
Sancerre 3
60
3840
20860
19380
0
-70
-460
1805
Fussy 1
60
3840
21090
19590
0
-30
-420
1865
Chateauroux
2
60
3840
21000
19510
0
-50
-440
Chateauroux
4
60
-50
-440
Sancerre 4
60
3850
20940
19450
0
-150
-530
1735
Sancerre 1
60
3840
20860
19380
0
-70
-460
1805
Champlitte 1
60
0
20080
11880
0
-100
-60
2169
Dampierre 1
60
0
21040
15320
0
-1300
-1550
868
Vitrey 1
60
0
19840
14450
0
-60
-50
2759
Dampierre 2
60
0
20940
12700
0
-1200
-730
856
Champdeniers
2
60
4270
21180
16440
0
0
-700
Chateauroux
3
60
150
-570
Chateauroux
1
60
-50
-750
Champlitte 2
60
0
Sancerre 2
20
Fussy 2
3840
3670
3680
21000
20800
21000
19510
19630
19820
1835
0
1835
1567
0
2027
0
1837
19880
3920
0
100
20
3449
2720
6940
6850
0
-10
-540
610
20
2720
6960
6870
0
60
-480
680
Dampierre 3
20
0
7080
3870
0
-500
-280
609
Vitrey 2
20
0
6600
3610
0
-10
-10
877
The Netherlands
Bentelo
60
11810
23000
5230
-1980
-1820
-410
2811
Balkbrug
20
11810
8000
3640
-3310
0
0
1026
Sherpenzeel
20
11810
8000
4370
-3640
0
0
1096
Note: Negative changes are shown in brackets
CAPTIONS FOR FIGURES
Figure 20 Predicted effects of tree species in a silvoarable system planted at a) 113
trees ha-1 and b) 50 trees ha-1 on the yield of the tree and the crop components relative
to a monoculture (error bars show confidence intervals for mean values)
237
Figure 21 Predicted relative crop yields and the timber volume for (a) an oak, b) a wild
cherry, c) a walnut and a d) poplar silvoarable agroforestry systems (50 and 113 trees
ha-1) for selected land units
Figure 22 Effect of crop species on the relative crop yield over a complete tree
rotation, in (a) Spain and (b) France, under all tree species and in (c) the Netherlands
under poplar, at 113 trees ha-1 and 50 trees ha-1 (error bars show the maximum and
minimum values in each group)
Figure 23 Predicted land equivalent ratio in France, Spain and the Netherlands
Figure 24 Predicted land equivalent ratios for poplar, cherry walnut, oak and pine
Figure 25 Equivalent annual value (discount rate of 4%) without grants of the arable,
forestry and silvoarable (113 trees ha-1) system in a) Spain, b) France and c) the
Netherlands
Figure 26 Equivalent annual value (4% discount rate) of the arable, forestry and
silvoarable (113 trees ha-1) system in a) Spain and b) France and c) the Netherlands,
assuming the pre-2005 grant regime.
Figure 27 Equivalent annual value (4% discount rate) of a forestry, arable, and
silvoarable (113 tree ha-1) system in a) Spain and b) France and c) the Netherlands,
assuming the 2005 grant scenario 1 (error bars show the equivalent annual value for
scenario 2)
Figure 28 Proportion of farms where the farm net present value was improved
compared with the status quo by the introduction of silvoarable systems or forestry
(Spain: n =17; France: n = 14)
Figure 29 Frequency with which silvoarable systems outperformed forestry (Spain: n
= 17; France: n = 14; the Netherlands: n = 3)
FIGURES
Relative yield
a) Relative yields for 113 trees ha-1
b) Relative yields for 50 trees ha-1
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
Cherry Walnut Poplar
Oak
Pine
Relative tree yield
Cherry Walnut Poplar
Oak
Pine
Relative crop yield
Figure 1
238
1.0
0.8
0.6
0.4
0.2
0.0
0
20
40
60
2.0
Timber volume (m 3/tree)
Relative crop yield
a) Land unit 2, St Maria del Campo, Spain (oak; wheat/wheat/wheat/wheat/wheat/fallow)
1.5
1.0
0.5
0.0
0
20
40
60
1.0
0.8
0.6
0.4
0.2
0.0
0
20
40
60
2.0
Relative crop yield
b) Land unit 1, Champdeniers, France (wild cherry; wheat/wheat/s/wheat/oilseed/sunflower)
1.5
1.0
0.5
0.0
0
20
40
60
40
60
Relative crop yield
c) Land unit 2, Champdeniers, France (walnut; wheat/wheat/s/wheat/oilseed/sunflower)
1.0
0.8
0.6
0.4
0.2
0.0
0
10
20
30
40
50
2.0
1.5
1.0
0.5
0.0
0
60
20
d) Land unit 1, Sherpenzeel, the Netherlands (poplar; forage maize)
Relative crop yield
1.0
0.8
0.6
0.4
0.2
0.0
0
20
Tim e from tree planting (a)
Arable
50 trees/ha
2.0
1.0
0.0
0
20
113 trees/ha
50 trees/ha
113 trees/ha
Forestry
Figure 2
239
Figure 2
Relative crop yield
a) Spain
b) France
c) the Netherlands
1.0
1.0
1.0
0.8
0.8
0.8
0.6
0.6
0.6
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
Wheat
Sunflower
Wheat
Oilseed Sunflower
113 trees
50 trees
Grain
maize
Wheat
Forage
maize
Figure 3
240
a) 113 trees per hectare
b) 50 trees per hectare
1.4
1.2
Relative tree yield
1.4
France
1.0
Spain
1.2
Netherlands
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Relative crop yield
Relative crop yield
Figure 4
241
a) 113 trees per hectare
b) 50 trees per hectare
1.4
1.4
Relative tree yield
1.2
1.0
Poplar
Cherry
1.2
Oak
Pine
1.0
Walnut
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Relative crop yield
Relative crop yield
Figure 5
242
Equivalent annual value (€ ha-1 a -1)
600
600
400
400
200
200
0
0
-200
-200
Cherry
Walnut
Poplar
200
b) France
Scherpenzeel_1
400
Balkbrugg_1
Campo_1
Olmedo_1
Fontiveros_2
Paramo_3
Paramo_2
Paramo_1
Campo_2
Olmedo_3
Olmedo_2
Fontiveros_1
Cardenosa_2
Cardenosa_1
Almonacid_2
Almonacid_1
Ocaña_1
Torrijos_2
Torrijos_1
Alcala_2
Alcala_1
600
Bentelo_1
Vitrey_2
Oak
Dampierre_3
Sancerre_2
Fussy_2
Champlitte_2
Chateauroux_3
Chateauroux_1
Champdeniers_2
Vitrey_1
Dampierre_2
Dampierre_1
Champlitte_1
Sancerre_4
Sancerre_3
Sancerre_1
Fussy_3
Fussy_1
Chateauroux_4
Chateauroux_2
Champdeniers_1
Equivalent annual value (€ ha-1 a-1)
a) Spain
Forestry
Arable
Silvoarable
0
-200
Pine
c) the Netherlands
Walnut Poplar
Figure 6
243
Cherry
Walnut
-1
a -1)
800
600
400
200
0
-200
-400
-600
-800
Scherpenzeel_1
200
Bentelo_1
-1
a -1)
400
Balkbrugg_1
b) France
Campo_1
Olmedo_1
Fontiveros_2
Paramo_3
Paramo_2
Paramo_1
Campo_2
Olmedo_3
Olmedo_2
Fontiveros_1
Cardenosa_2
Cardenosa_1
Almonacid_2
Almonacid_1
Ocaña_1
Torrijos_2
Torrijos_1
Alcala_2
Alcala_1
Equivalent annual value (€ ha
Oak
Vitrey_2
Dampierre_3
Sancerre_2
Fussy_2
Champlitte_2
Chateauroux_3
Chateauroux_1
Champdeniers_2
Vitrey_1
Dampierre_2
Dampierre_1
Champlitte_1
Sancerre_4
Sancerre_3
Sancerre_1
Fussy_3
Fussy_1
Chateauroux_4
Chateauroux_2
Champdeniers_1
Equivalent annual value (€ ha
a) Spain
Forestry
Arable
Silvoarable
0
-200
Pine
c) the Netherlands
800
600
400
200
0
-200
-400
-600
-800
WalnutPoplar
Poplar
Figure 7
244
Proportion of cases where
introducing silvoarable
agroforestry or forestry
increased farm profitability (%)
100
Spain
Pre-2005
France
Cherry
Spain
Walnut
Silvoarable agroforestry
France
Post-2005 scenario 1
800
600
400
200
0
-200
-400
-600
-800
b) France
Spain
Campo_1
200
0
Poplar
Walnut
Scherpenzeel_1
Oak
Olmedo_1
Fontiveros_2
Paramo_3
Paramo_2
Paramo_1
Campo_2
Olmedo_3
Olmedo_2
Fontiveros_1
Cardenosa_2
Cardenosa_1
Almonacid_2
Almonacid_1
Ocaña_1
Torrijos_2
Torrijos_1
Alcala_2
Alcala_1
Equivalent annual value (€ ha-1 a-1)
400
Balkbrugg_1
Bentelo_1
Vitrey_2
Dampierre_3
Sancerre_2
Fussy_2
Champlitte_2
Chateauroux_3
Chateauroux_1
Champdeniers_2
Vitrey_1
Dampierre_2
Dampierre_1
Champlitte_1
Sancerre_4
Sancerre_3
Sancerre_1
Fussy_3
Fussy_1
Chateauroux_4
Chateauroux_2
Champdeniers_1
Equivalent annual value (€ ha-1 a -1)
a) Spain
Forestry
Arable
Silvoarable
-200
Pine
c) the Netherlands
800
600
400
200
0
-200
-400
-600
-800
Poplar
Figure 8
Forestry
80
60
40
20
0
France
Post-2005 scenario 2
Country and grant regime
Figure 9
245
80
60
40
20
2004 grant scenario
2005 grant scenario 1.1
Netherlands
France
Spain
Netherlands
France
Spain
Netherlands
France
0
Spain
Proportion of cases where
agroforestry was more profitable
than forestry (%)
100
2005 grant scenario 2.2
Figure 10
246
ANNEX 10. Yield-SAFE: a parameter-sparse processbased dynamic model for predicting resource capture,
growth and production in agroforestry systems
Wopke van der Werf1, Karel Keesman2, Paul Burgess3, Anil Graves3, David
Pilbeam4, L.D. Incoll4, Klaas Metselaar1,2, Martina Mayus1,7, Roel Stappers1,2,
Herman van Keulen5, João Palma6 & Christian Dupraz7
1
Wageningen University, Group Crop & Weed Ecology, P.O. Box 430, 6700 AK Wageningen, The
Netherlands; 2Wageningen University, Systems & Control Group, P.O. Box 43, 6700 AA, Wageningen,
The Netherlands; 3Cranfield University, Silsoe, MK45 4DT, Bedfordshire, United Kingdom; 4School of
Biology, University of Leeds, Leeds LS2 9JT United Kingdom; 5Wageningen University, Group Plant
Production Systems, P.O. Box 430, 6700 AK Wageningen, The Netherlands; 6Agroscope FAL
Reckenholz. Swiss Federal Research Station for Agroecology and Agriculture, Reckenholzstrasse
191, CH-8046 Zurich, Switzerland; 7Institut National de Recherche Agronomique, UMR Systèmes de
Cultures Méditerranéens et Tropicaux, 2, Place Viala, 34060 Montpellier, France
Dr Wopke van der Werf, Wageningen University, Department of Plant Sciences,
Group Crop & Weed Ecology, P.O. Box 430, 6700 AK, Wageningen, The
Netherlands
Email: [email protected]
Tel.: +31 317 484 765
Fax: +31 317 484 892
247
ABSTRACT
1. Silvoarable agroforestry (SAF) is the cultivation of trees and arable crops on the
same parcel of land. SAF may contribute to modern diversified land use objectives in
Europe, such as enhanced biodiversity and productivity, reduced leaching of
nitrogen, protection against flooding and erosion, and attractiveness of the
landscape. Long term yield predictions are needed to assess long term economic
profitability of SAF.
2. A model for growth, resource sharing and productivity in agroforestry systems was
developed to act as a tool in forecasts of yield, economic optimization of farming
enterprises, and exploration of policy options for land use in Europe. The model is
called Yield-SAFE; from “YIeld Estimator for Long term Design of Silvoarable
AgroForestry in Europe”. The model was developed with as few equations and
parameters as possible to allow model parameterization under constrained
availability of data from long term experiments.
3. The model consists of seven state equations expressing the temporal dynamics of:
(1) tree biomass; (2) tree leaf area; (3) number of shoots per tree; (4) crop biomass;
(5) crop leaf area index; (6) soil water content, and (7) heat sum. The main outputs of
the model are the growth dynamics and final yields of trees and crops. Daily inputs
are temperature, radiation and precipitation. Planting densities, initial biomasses of
tree and crop species, and soil parameters must be specified.
4. A parameterization of Yield-SAFE is generated, using published yield tables for
tree growth and output from the comprehensive crop simulation model STICS.
Analysis of tree and crop growth data from two poplar agroforestry stands in the
United Kingdom demonstrates the validity of the modelling concept and calibration
248
philosophy of Yield-SAFE. A sensitivity analysis is presented to elucidate which
biological parameters most influence short and long term productivity and land
equivalent ratio.
5. The conceptual model, elaborated in Yield-SAFE, in combination with the outlined
procedure for model calibration, offers a valid tool for exploratory land use studies.
INTRODUCTION
Silvoarable agroforestry is the mixed cultivation of arable crops and trees on a single
parcel of land. Interest in the introduction of trees in arable systems in Europe is
increasing to diversify the farm landscape, promote biodiversity, enhance
productivity, and benefit from the function of trees as windbreaks or as protection
against nitrogen leaching, flooding and erosion. In recent years, European agricultural
policy has sought to reduce arable surpluses and increase the number of trees planted
on farms (Burgess et al., 2000). Unlike monoculture forestry, silvoarable agroforestry
can provide an annual income. This is obtained from an arable intercrop which is grown
for the initial or full duration of the tree rotation, depending in part on the tree density. In
tropical countries, there are economic benefits from timber and non-timber tree
products on arable land and the production of annual intercrops in plots planted with
trees (Graves et al., 2004). Such diversification contributes to economic resilience to
external fluctuations in markets. Tree-crop complementarity, leading to higher biomass
production than in equivalent areas of arable or forestry monocultures (Droppelmann et
al., 2000) lays a basis for higher economic returns.
To express the benefits of mixed cropping systems various characteristics have been
proposed (Vandermeer, 1989). In the current analysis a choice has been made for the
use of the Land Equivalent Ratio LER), first proposed by Mead and Willey (1980). LER
249
is defined as: ratio of the area needed under sole cropping to the area of
intercropping at the same management level to obtain a particular yield. LER is
calculated as the sum of the fractions of the yields of the intercrops relative to their
sole crop yields:
LER =
I
I1
I
+ 2 + ... + n
M1 M 2
Mn
[1]
where
I = yield of crop when intercropped
M = yield of crop as a monoculture
1 = one crop; 2 = another crop; n = nth crop
In agroforestry systems, which are characterized by differences in growing period of
the component plant species of the mixture, many approaches are possible to
calculate an integrated value of LER over multi-year periods. In this paper we
calculate LER in two ways. The first method integrates productivity over the whole
rotation, calculating LER as the sum of (1) average value of relative crop yield,
compared to monocrop crop yields, and (2) cumulative timber production compared
to the monoculture (Equation 2):
LER rotation =
Sum of silvoarable crop yields
Silvoarable timber volume
+
Sum of monoculture crop yields Monoculture timber volume
[2]
The second method of calculating LER produces an estimate for each year i in the
tree rotation. This estimate is calculated as the sum of (1) the relative crop yield in
year i (compared with monocrop crop yield in the same year) and (2) cumulative
250
timber production from year 1 through i (compared with monoculture tree growth from
year 1 through i) (Equation 3).
LER i =
Silvoarable crop yield i
Silvoarable timber volumei
+
Monoculture crop yield i Monoculture timber volumei
[3]
Two of the key factors in determining adoption and maintenance of silvoarable systems
are their profitability relative to alternative enterprises and their feasibility, in terms of
the use of farm resources (Burgess et al., 2000; Graves et al., 2005b). The profitability
of silvoarable systems, relative to pure arable agriculture and forestry, can be
determined by comparing their net present value (NPV), calculated from cost-benefit
analysis by discounting and aggregating future benefits and costs (Graves et al.,
2005a). The feasibility of the system, within a specific farm depends, among others, on
the availability of and requirements for labour or finance. Fundamental to both
assessments, is the need for biophysical data on yields of crops and trees in silvoarable
as well as in arable and forestry systems. As empirical data on silvoarable systems are
scarce, an alternative method is necessary to generate long-term time series of yields
based on interactions of trees and crops in mixed systems. Such a method is the use of
dynamic computer simulations that predict the effect of climate, tree and crop species,
soil type and management choices on tree and crop production, economics and the
environment.
The need for a minimal modelling approach. Key issues in the analysis of dynamic
simulation models are stability, sensitivity of the output to parameter values,
uncertainty propagation and identifiability. Identifiability analysis attempts to answer
the question: can we estimate a unique value for specific parameter, given sufficient
data? In general, identifiability decreases with increasing complexity of a model,
because of the potential interactions between parameters. If, for a complex model,
251
poorly identifiable parameters are estimated from experimental data, errors in
parameter estimates may become very large. As a consequence, uncertainty in
model predictions will become large. Hence, from the viewpoint of restricting
uncertainty in model predictions, a minimal modelling approach, allowing estimation
of a maximum set of identifiable parameters, is preferred (Young, 1984; Ljung, 1987).
The need for a minimal modelling approach is high for agroforestry systems, because
of the lack of quantitative long term data on the productivity of those systems.
Currently available biophysical models for agroforestry systems, such as WaNulCAS
(Van Noordwijk & Lusiana, 1999) and HyPAR (Mobbs et al. 1999) are highly complex
and rich in parameters, and the above-mentioned drawbacks of complex models
apply. As an alternative approach, a very parameter sparse, yet process-based
model is proposed and presented here. The conceptual and algorithmic simplicity of
this model, called YIELD-SAFE7, allow the application of powerful mathematical
methods for parameter estimation, and the analysis of uncertainties in model
predictions. The model can be easily adapted to different crops and environmental
conditions by adjusting parameter values and input functions (Graves et al., this
volume), and its code is compact enough to be included in agro-environmental
modelling environments that aim at levels of aggregation above the field level
(Rabbinge & van Latesteijn, 1992; van Ittersum & Donatelli, 2001).
The ultimate goal of the YIELD-SAFE model is to predict dynamically site-specific
long-term tree and crop yields under competitive conditions on the basis of historical
or generated weather data, i.e. solar radiation, temperature and precipitation and
relevant soil physical characteristics. Growth of trees and crops can essentially be
7
YIeld Estimator for Long term Design of Silvoarable AgroForestry in Europe
252
described as the conversion of primary resources, i.e. light, water and inorganic ions
into useful organic material, and can therefore be described in terms of the
availability of these resources and their utilization efficiency (Monteith, 1990). The
objective of the current version of the model is to describe conditions where
availability of plant nutrients is not a limiting factor for crop production, hence light
and temperature as yield-determining factors and water as (possible) yield-limiting
factor (van Ittersum and Rabbinge, 1997) are taken into account.
The objectives of this paper are:
-
To describe and justify the conceptual background and equations of YieldSAFE;
-
to provide the first calibration and validation of Yield-SAFE, using published
yield tables for poplar stands and two experimental data sets pertaining to the
growth of an agroforestry system with poplars and arable crops at two sites in
the United Kingdom.
-
To provide a sensitivity analysis of Yield-SAFE.
MATERIALS & METHODS
MODEL DESCRIPTION
The objective of the YIELD-SAFE model is to describe the dynamics of competitive
resource acquisition and the associated growth of the constituent components in an
agroforestry stand with the minimum number of equations. Such an equation- and
parameter-sparse approach is chosen because it provides the best chance that
robust parameter values can be identified from experiments. Dynamic equations for
the following state variables were identified as essential:
253
(1) biomass of tree
(2) leaf area of tree
(3) number of shoots of tree
(4) biomass of crop
(5) leaf area of crop
(6) heat sum
(7) available soil water
Biomasses of tree and crop are used to derive temporally-integrated timber volumes
and crop yields. Leaf area of tree and crop are essential because they govern
radiation capture, and thus the capacity for dry matter production and the associated
water loss through transpiration. The number of shoots per tree is required because it
governs the potential leaf area within a given year. By contrast the intra-annual leaf
area dynamics (at the time scale of days to months) are primarily governed by the
growth of leaf area per shoot. Available soil water is included to account for
differential growth conditions across Europe with respect to the degree of water
limitation, due to variation in precipitation, soil depth and water holding properties of
soils. Finally, heat sum is integrated each season to define phenological
development of the crops. Nutrient dynamics are not included, because of lack of
information from existing agroforestry trials necessary to determine pertinent
parameters. The model can be readily extended to include nutrient dynamics, e.g. by
quantifying the minimum nutrient uptake required to produce calculated water-limited
yields (cf. van Keulen & Wolf, 1988).
254
Equations and associated parameters were developed as follows:
Potential tree growth
The potential growth rate of the woody biomass of the tree (Bt) is described as:
dBt I f t ε t
=
ρ
dt
[4]
where
Bt is the woody biomass of the tree (g dry matter per tree)
I is the global radiation, incoming to the forestry or agroforestry stand (MJ per m2 per
day)
f t is the proportion of incoming radiation (I) intercepted by the trees
εt is the radiation use efficiency of the trees (g woody dry matter per MJ intercepted
global radiation), and
ρ is the tree density (number of trees per m2 silvoarable area)
The variable t (italicized) is time (d), while the subscript t (in roman type) indicates
parameters and variables for the tree.
The fraction of radiation intercepted by the trees in the agroforestry system is
calculated as:
f t = 1 − e − kt Lt
[5]
where
kt is the radiation extinction coefficient of the tree leaf canopy
255
Lt is the leaf area index of the tree stand (m2 tree leaf area per m2 silvoarable stand)
Water limited effective tree growth
Under water-limiting conditions, and accounting for biomass losses due to
maintenance or attrition such as branch senescence and storm damage, Equation
[5] is modified into:
dBt I f t ε t wt
=
− aBt
dt
ρ
[6]
where wt expresses the relative effect of soil water potential on the tree growth rate.
This factor is calculated as:
pF ≤ pFc :
wt = 1

pFPWP − pF

pFc < pF ≤ pFPWP : wt =
pFPWP − pFc

pF > pFPWP :
wt = 0
[7]
where pF is the soil water tension, defined as the negative log of the water potential
in cm water. Hence as long as pF is below the critical value (pFc), there is no
reduction, when pF is between the critical value and the permanent wilting point
( pFPWP ), the degree of reduction is proportional to the difference between current pF
and pFPWP as scaled by the difference between pFc and pFPWP , while the reduction is
100% when pF is greater than pFPWP (Fig. 1).
The product term aBt ensures that in due course, the growth rate of the tree will slow
down until, ultimately, the tree will reach a maximum biomass. Outside the growing
season, the rate of change of tree biomass is set to 0.
Water use by the tree
256
The amount of water that is used by the trees per unit area is calculated by
multiplying the water-limited growth rate per tree by the tree density (ρ) and a
transpiration coefficient, γ t :
Wt = γ t ρ
dBt
dt
[8]
where
Wt is the tree water use (m3 water per m2 silvoarable area per day)
γ t is the transpiration coefficient of the trees (m3 water per g woody dry matter)
Leaf area of the tree
The rate of increase in leaf area index of a tree leaf canopy ( Lt ) is calculated as:
dLt
A −A
= ρN m
τ
dt
[9]
where
Lt is the leaf area index of the tree (m2 tree leaf area per m2 silvoarable area)
ρ is the density of trees (number of trees per m2 silvoarable area)
N is the number of shoots on a single tree (see below)
Am is the maximum leaf area per shoot on a tree (m2)
A is the current leaf area per shoot on a tree (m2; see below)
τ is the time constant of the leaf unfolding process (day) as driven by re-allocation of
reserve carbohydrates in the spring (Versteeg and van Keulen, 1986)
257
The rationale for Equation 9 is that early leaf growth in trees is not an autonomous
positive feedback process as in crop plants, governed by incident radiation
interception, but a translocation and conversion process from reserve carbohydrates,
stored at the end of the preceding season, to new leaf biomass. Hence, the dynamics
are fundamentally different. The state variable N, the number of shoots on a tree,
expresses the “memory” of the tree with respect to preceding year’s number of
branches and storage of reserve carbohydrates. Leaves start to unfold at time tb, the
date of bud burst and all leaf canopy is shed at the day of leaf fall (tf).
Number of shoots per tree
The number of shoots per tree is calculated on the basis of a saturating curvilinear
Monod function of tree biomass, according to:
N = Nm
Bt
Bt + K N
[10]
where
Nm is the maximum number of shoots on a mature tree
KN is the biomass of a single tree at which the number of shoots is half the maximum
As KN is difficult to estimate from data, an expression for the growth of N was derived
from which the parameter KN is eliminated.
From Equation 10 we derive:
K N = Bt
Nm − N
N
[11]
After differentiation of Equation [10] and substitution of Equation [11] one obtains:
258
dN dBt N 
N 
=
1 −

dt
dt Bt  N m 
[12]
with unknown initial condition N(t0) where t0 is the planting date of the trees. In
practice, N(t0) is easier to estimate from experimental data than KN, hence this
reformulation of the model. Furthermore,
Equation 12 allows a straightforward
adjustment of the tree growth in case of pruning.
Pruning and thinning
When pruning takes place, biomass and number of shoots are reduced by
appropriate factors πB and πN, which can, in principle, be different. Thinning is
effectuated by reducing tree density ρ by a thinning factor πρ.
Potential crop growth
Within each cropping season, crop biomass starts at an initial value of Bc(te) where te
is the date of crop emergence. The subsequent potential growth rate of the crop is
described as:
dBc
= I c f cε c
dt
[13]
where
Bc is the above-ground biomass of the crop (g dry matter per m2 silvoarable area)
Ic is the radiation, transmitted by the trees (MJ per m2 silvoarable area)
fc is the proportion of Ic intercepted by the crop
259
εc is the radiation use efficiency of the crop (g above-ground dry matter per MJ
intercepted global radiation)
The radiation transmitted through the trees is calculated as:
I c = (1 − f t ) I
[14]
where
f t is the proportion of incoming global radiation intercepted by the tree crowns
I is global radiation, incoming to the agroforestry stand (MJ per m2 per day)
The fraction of radiation intercepted by the crop ( f c ) is calculated as:
L
− kc c 

f c = C 1 − e C 


[15]
where
C is the proportion of the total area that is cropped (m2 cropped area per m2
silvoarable area)
kc is the radiation extinction coefficient of the crop
Lc is the leaf area index of the crop (m2 crop leaf area per silvoarable area)
Water-limited crop growth
Under water limiting conditions, Equation [13] is modified into:
dBc
= I c f cε c wc
dt
[16]
260
where wc expresses the reduction in crop growth rate, relative to the potential growth
rate. This is calculated in the same way as the value for wt (Equation 7, Figure 1), but
with crop specific parameter values for pFc and pFPWP.
Water use by the crop
Water use by the crop is calculated by multiplying the water-limited growth rate by a
transpiration coefficient, γ c :
[17]
Wc = γ c
dBc
dt
where
wc is the crop water uptake (m3 water per m2 silvoarable area per day)
γ c is the transpiration coefficient of the crop (m3 water per g above-ground dry
matter. The value of γc can vary with crop type and the water vapour pressure deficit
of air (VPD) (Loomis & Connor, 1992), but otherwise the value is relatively constant
(Monteith, 1990).
Leaf area of the crop
Change in leaf area index of the crop ( Lc ; m2) is calculated as:
dLc
dB
=σP c
dt
dt
[18]
where
σ is the specific leaf area of the crop (m2 leaf area per g leaf dry matter), and
261
P is the partitioning coefficient to leaves for the crop; i.e. the proportion of the daily
increase in above-ground dry matter that is invested in growth of new leaves,
Leaf area starts at an initial value of Lc(te) where te is the date of emergence.
Leaf area growth is set to zero when the heat sum at harvest (Sh) is attained (see
below).
Heat sum
The increase in cumulative temperature (heat sum) is calculated as:
dS
= max [ 0, T − Tb ]
dt
[19]
where
S is the heat sum since crop emergence (°C d)
T is daily average temperature (°C)
Tb is the base temperature for phenological development (°C)
The function max [ g] takes the maximum value of the arguments
Partitioning of dry matter to leaves in the crop
Partitioning of dry matter to leaves decreases linearly with crop development stage,
according to:
 S ≤ S1 :
P = P0

S2 − S

 S1 < S ≤ S 2 : P = P0
S 2 − S1

 S > S 2 :
P=0
[20]
262
where
P0 is the proportion of above-ground biomass initially partitioned to leaves
S1 is heat sum where partitioning of dry matter to leaves starts to decline
S2 is heat sum where the partitioning coefficient becomes zero.
Soil water dynamics
The model assumes a homogeneous soil of depth D (m) and volumetric water
content θ, which is described by:
dθ 1
= ( R + Wirr + Fgw − Wc − Wt − Eact )
dt D
[21]
where
θ is soil volumetric moisture content (m3 per m3)
R is precipitation (m3 per m2 silvoarable area per day)
Wirr is irrigation (m3 per m2 silvoarable area per day)
Fgw is drainage of soil water below the potential rooting zone (m3 per m2 silvoarable
area per day)
Eact is actual soil evaporation (m3 per m2 silvoarable area per day)
Soil moisture characteristics are often described in terms of soil moisture tension, ψ,
i.e. the force with which the soil matrix holds the water. For ease of notation, the
tension is then expressed in terms of pF, where pF = log10(ψ), with ψ is expressed in
263
cm water tension. The relation between ψ and θ is given by the van Genuchten
(1980) equation:


1
θ = θ PWP + (θ s − θ PWP ) 
n 
1 + (αψ ) 
m
[22]
where
θ s is soil water content at saturation (m3 per m3)
θ PWP is soil water content at permanent wilting point (the lower limit of plant-available
water in m3 per m3)
α , m and n are soil-type specific parameters
ψ is soil water tension in cm water.
Precipitation and irrigation are introduced as forcing functions. Drainage flow to
groundwater is dependent on the pF of the soil according to:
pF < pFFC : Fgw = δ K s
pF ≥ pFFC : Fgw = 0
[23]
where
pFFC is the pF value at field capacity, usually set to 2.3
Ks is soil hydraulic conductivity at saturation (m per day)
The factor δ is given by:
δ = 10
−
pFFC
2
[24]
264
Where in practice, if soil water data is available, the factor 2 will be estimated
between 1 and 4 depending on the water distribution in the soil, which depends on
many factors, but especially on soil characteristics.
Evaporation from the soil surface (Eact) is calculated as:
Eact = η I s ws
[25]
where
η is heat of vaporization (m3 water per MJ)
Is is the radiation incident on the soil (MJ per m2 per day)
ws is a factor accounting for the reduction in soil evaporation due to drying of the soil,
and is calculated in the same way as the reduction factor for the tree (Equation 7;
Fig. 1)
Radiation incident on the soil (Is) is calculated as:
I s = I f t ( Cf c + (1 − C ) )
[26]
Model implementation
The model has been implemented as a set of difference equations on several
computer platforms including MatLab (Stappers et al., 2003) and Microsoft© Excel
(Burgess et al., 2004b). These references give further implementation details that are
omitted here for clarity.
265
POPLAR VALIDATION DATA
Two agroforestry experiments with poplar (Populus species) were carried out in the
United Kingdom. Full initial details of the experiments are provided by Burgess et al.
(2004a), but the key features are summarised here for clarity. The cooler and most
northerly site is at the Leeds University Farms at Bramham near Tadcaster in West
Yorkshire (53°53’ N, 1°15’ W); the warmest site is at Silsoe in Bedfordshire (52°0’ N,
0°26’ W) in eastern England. Soils at the Leeds and Silsoe sites are sandy clay loam
over limestone and clay over clay, respectively. At both sites the main experiment
covered 2.5 ha and comprised three replicate blocks that included each combination
of four poplar hybrids and three or four arable treatments.
Poplars were planted as unrooted sets in spring 1992 at a rectangular spacing of 10
m between tree rows (in a North-South orientation) and 6.4 m between trees within
the rows. Part of the alleys between the tree rows were cropped yearly in the middle
8 m (leaving a 2 m uncultivated strip for the tree row), while another part of the alleys
was left uncropped and weed free in subsequent years in order to obtain estimates of
the yield of poplar in an agroforestry situation compared to a pure poplar stand at the
same density. An area of the same field at least 15 m from the trees was used as an
arable monocrop area. Starting in 1992, the rotation at Leeds comprised spring
barley (Hordeum vulgare L.), peas (Pisum sativum L.), two crops of winter wheat
(Triticum aestivum L.), winter barley, spring mustard (Brassica alba L.), winter wheat,
winter barley, two winter wheat crops, winter barley and winter oilseed rape (Brassica
napus L.). At Silsoe, following poor crop yields in the initial three years, there were
three winter wheat crops followed winter beans (Vicia faba L.), spring wheat, winter
wheat, fallow, winter barley and spring beans. Crop management was the same for
intercrop and monocrop. The poplar cultivars were Beaupré, Gibecq, Trichobel and
266
Robusta. From 1992 to 2000 the trees were regularly pruned, by removing the lower
whorls of branches, in order to maintain a canopy depth equal to about half the tree
height (Burgess et al., 2003).
Measurements
From 1992 onwards, the height of each tree in each arable treatment was measured
after leaf fall. The diameters of the same trees were measured at breast height (1.3
m above the ground) each winter from 1994 onwards at Bramham and from 1995
onwards at Silsoe. Timber volume was estimated by first assuming the trunk is a
perfect cylinder, with a volume calculated from height and diameter, and then
multiplying this calculated volume by a form factor to account for taper of the trunk
(Burgess et al., 2004a). The form factor was derived from poplar yield tables, given in
Christie (1994).
Each year, grain, bean or pea yield within each poplar-hybrid x arable-treatment plot
was determined by harvesting with a plot combine. Corresponding measurements
were also taken within the monocropped control area.
MODEL CALIBRATION FOR POPLAR AND INTERCROPS
For the calibration of Yield-SAFE the following approach was used. First, the
potential growth of monoculture stands of tree and crop species were fitted under
specific climatic conditions in Europe, using yield tables for trees (e.g. Thomas et al.
1998) and validated model calculations for crops (Brisson et al., 2003). Potential
growth is determined foremost by temperature (which drives developmental and
phenological processes) and radiation (which drives photosynthesis) but is
unaffected by water and nutrients as these are assumed to be non-limiting under the
potential growth assumption (van Ittersum & Rabbinge, 1997). Second, given actual
267
monoculture yields of tree and crops as “reference” yields for a specific experimental
site the model was fine-tuned by adjusting – within physiologically meaningful bounds
- the transpiration coefficient (γ) and harvest index (HI) and by introducing – if
necessary – a management factor (between 0 and 1) that reduces the radiation use
efficiency (ε). Hence, yield in agroforestry stands is predicted from the resulting
model, which is – as described - calibrated to represent site-specific monoculture
behaviour of trees and crops as affected by temperature and radiation driven growth
potential in combination with site specific limitations due to water and nutrients, soil
properties, and the local effects of weeds, pest, diseases and management
shortcomings.
The calibration of model parameters for the potential growth of poplar trees was
conducted using published yield tables for unthinned poplar (monoculture) stands
with 8 x 8 spacing and a site class of 58 (Thomas et al., 1998). Because timber
growth is expressed in terms of timber volume, it was necessary to convert the
biomass yield into a timber volume. The timber volume of a tree (Vt; m3 tree-1) was
derived from:
Vt =
HI timber Bt
ρ timber
[27]
where HItimber is the proportion of the total woody-biomass partitioned to timber, and
ρ timber is the density of the timber (g m-3).
On the basis of practical identifiability analysis we decided to estimate the initial
number of shoots, N(t0), and the radiation use efficiency, εt. Other parameter values
were fixed at biologically plausible parameter values, based on literature (see
results). Attempts to estimate additional parameters led to unreliable results and did
268
not improve the fit. A least-squares optimization algorithm was used to estimate both
N(t0) and εt.
As the crop data available related to harvested crop yield (Yc) rather than crop
biomass, it was necessary to assume a crop harvest index (HIc).
Yc = HIc Bc
[28]
Simulation data from STICS (Brisson et al., 2003), given appropriate parameters for
an Atlantic climate was used to provide potential growth curves for winter wheat.
In particular, the following parameters were adjusted: εc, S0 (heat sum after sowing
when crop emerges), S1, S2, Sh (heat sum at harvest) and harvest index HIc. Again, a
least-squares optimization was performed to identify the parameter values from the
data.
MODEL VALIDATION FOR POPLAR AGROFORESTRY SYSTEMS
Given the calibrated parameters related to potential growth, in a second step only
three parameters: transpiration coefficient ( γ t or γ c ), harvest index (HItimber or HIc) and
a management factor were adjusted to fit actual yields (i.e. locally attained yields; van
Ittersum and Rabbinge, 1997) for the monoculture tree and crop systems at a
specific site, in our case Silsoe (UK). The model was then used to predict tree and
crop growth within a silvoarable system using these site-specific parameters, and
these results were compared with experimental data collected over 12 years.
SENSITIVITY ANALYSIS
269
In this paper the objective of the sensitivity analysis is to investigate how different
biophysical parameters influence the land equivalent ratio (LER). The model
parameters were analyzed by systematically changing their nominal values by adding
±10%. The nominal values were obtained from the calibration of Yield-SAFE using
the procedure described in the previous section. Then, after running the model with
the perturbed parameters, the outputs were stored and the sensitivity was calculated
from
∆y y ( pi , M ) − y ( pi ,m )
=
∆pi
pi , M − pi ,m
[29]
where y(pi,M) and y(pi,m) denote the simulation model output (e.g. LER) when only the
ith parameter is changed while keeping the others fixed at their nominal value. In
order to avoid scale effects the relative sensitivity was calculated and used for
analyses. The relative sensitivity, or elasticity (eLER), of LER for a specific parameter
pi, with nominal values pi and LER , is given by
eLER =
∆LER pi
.
∆pi LER
[30]
This very simple type of sensitivity analysis provided a first indication of those
parameters that dominate the output.
RESULTS
AGROFORESTRY EXPERIMENTS WITH POPLAR
During the first 12 years, the UK field experiments showed that poplar tree growth
was reduced by the presence of arable crops, rather than a bare-fallow, between the
rows of poplars (Fig. 2). The effect on timber volume per tree (or equivalently, per
270
hectare) was approximately minus 30% after 12 years of poplar growth, both in
Silsoe and in Leeds. Growth in Silsoe was marginally greater than in Leeds but the
effect of crop competition on tree growth was similar at the two sites. During the initial
nine years, the mean crop yield in the silvoarable system was 94% of the
monoculture yield on a cropped area basis, and 75% on a total area basis, after
allowing for the 20% of the area that was uncropped (Fig. 3). After the ninth year,
relative crop yields started to decline substantially due to the cessation of pruning
and the development of large tree canopies. A trend of the resulting LER is provided
in Fig. 4, showing initially high values and a decline after nine years. Different ways
of calculating LER, give different results. In Fig. 4, LER was calculated according to
Equation 3, that is by summing relative tree growth in SAF as shown in Fig. 1, and
relative crop growth in SAF (Fig. 2). Initial calculations (results not shown) indicate
that an annual LER, calculated as the sum of annual crop yields (normalized by
comparison with monoculture) and the annual increment in timber volume (also
normalized by comparison with monoculture) maintained stable values of the order
1.3-1.4 for any year in the experimental period.
MODEL CALIBRATION
Calibration, to represent these data, started with the calibration of the potential
growth of a poplar forestry system under Atlantic growing conditions. The calibration
was made on the basis of the development of timber volume for poplar with a site
class of 58, assuming an unthinned stand of 8 m x 8 m (Thomas et al., 1998) and
weather data from Orleans in France. The dynamic model parameters are described
in Table 1, and the estimated model parameters for poplar were εt = 1.409 g MJ-1 and
N(t0) = 0.6225 The timber volume calculated by the model was similar to that
provided by the yield table (Fig. 5)
271
For the potential growth of e.g. winter wheat five parameters were obtained: εc = 1.34
g MJ-1; S0= 57 °Cd; S1= 456 °Cd; S2 = 464; Sh = 1312 °Cd and HIc = 0.51. HIc was
derived directly from the simulation results; the other parameters by calibration.
Figure 5 presents the Yield-SAFE prediction of biomass growth in a monoculture
wheat crop in Wageningen, using 1983/1984 Wageningen weather data, in
comparison with the output from STICS.
The next stage was to calibrate the tree and crop components of the Yield-SAFE
model for the specific conditions of Silsoe. For the tree component, the model was
calibrated by assuming a timber volume per tree at the end of the tree rotation, in this
case, of 30 years. At Silsoe, the increase in timber volume during the first 12 years
matched that of the yield tables provided by Christie (1994) for an 8 m x 8 m poplar
stand with a maximum mean annual increment of 13 m3 ha-1. Hence from the yield
table, a reference timber volume of 2.41 m3 tree-1 was assumed for year 30. Using
the Yield-SAFE model, and meteorological and soils data for Silsoe, the values of the
transpiration coefficient and the harvest index were modified (Table 2) so that the
model predicted a timber yield of 2.41 m3 tree-1 in year 30 (Fig. 7). The tree growth
predicted by Yield-SAFE lags somewhat behind during early tree growth; this may
partly be due to the assumption of a constant harvest index.
A continuous rotation of winter wheat was assumed for the crop component of the
agroforestry system and a reference yield of 8.23 t ha-1 was derived from regional
farm surveys. To obtain such a mean value over 30 years, it was necessary to modify
the transpiration coefficient for the wheat to 0.316 m2 kg-1 (Table 2), which is within
the plausible range for temperate conditions. It was not necessary to modify the
harvest index. Thus, the model was calibrated to a site-specific reference yield using
272
eco-physiologically meaningful values for all the parameters. This is evidence that
the model structure is eco-physiologically appropriate.
MODEL VALIDATION
The calibrated model was then run to calculate growth trajectories and yields (under
water limitation) for crops and trees within a silvoarable system over a 30 year tree
rotation. The predicted relative crop yields for the first twelve years (Fig. 7) generally
matched the experimental results. This match between data and simulation results in
the agroforestry situation provides further evidence for the validity for the modelling
concept and calibration philosophy. Remember that the model was not fitted to any
data from the agroforestry stand, but only to data from pure stands of crops or trees.
Thus, the rather good fit of the model to the yields in an actual agroforestry
experiment provides evidence that it correctly captures the essence of the crop-tree
interactions.
SENSITIVITY ANALYSIS
Using the Yield-SAFE model it was possible to predict the LER over a tree rotation of
30 years, using Equation 2. Assuming a continuous rotation of wheat the predicted
LER, at the end of the tree rotation of after 30 years, was 1.34. Perturbations of plus
or minus 10% in the parameters used for this analysis resulted in values of LER
ranging from 1.30 to 1.39 (Table 3). Thus, LER estimates by Yield-SAFE are
moderately robust to parameter inaccuracies. The parameters kt, εt, N(t0) and Am had
the greatest relative effect on LER (cf. Keesman et al., 2005). These tree parameters
define to a large extent the shading of the tree on the crop.
A sensitivity analysis (Dennis & Schnabel, 1983) was also undertaken to determine
how the elasticity of the LER to specific parameters changed during the tree rotation
and with the light extinction coefficient. For this analysis, LER in a specific year was
273
determined using Equation 3. The default parameter sets, with varying values for the
tree light extinction coefficient are given in Table 4. The results from both datasets
matched the tree and crop growth during the first 12 years of the agroforestry stand,
but resulted in a long term overestimation of tree growth, compared to yield tables of
Christie (1994). No water limitation was taken into account.
As a result of the different choice of nominal kt in the two parameter sets, different
values are obtained for other parameters, notably those that affect the early growth of
the tree: εt and the initial number of shoots, N(t0). The values of εt and N(t0) when kt
was small (0.4) of 1.84 g MJ-1 and 1.32 respectively, were greater than the
corresponding values of 1.09 g MJ-1 and 1.075 when kt was large (0.8).
The elasticity analyses show that the most sensitive parameters were associated with
the tree component of the model (Table 5). The importance of the tree parameters in
determining the complementarity of resource use, as expressed by the value of LER,
is also shown in a mathematical analysis by Keesman et al. (2005). Complementarity
under potential growing conditions is entirely the result of the tree leaf canopy
transmitting light that can be utilized by the crop component in the system. The
maximum number of branches of the tree (Nm) has very low elasticity initially, but
gains in importance as the trees grow. For mature trees, the maximum amount of
shading by trees is determined in part by Nm; hence this parameter influences LER in
a mature stand more than in a young stand.
The crop’s partitioning coefficient to leaves showed large sensitivity during the early
years of the tree rotation. Surprisingly, some crop parameters attained greater
relative importance to LER during the late years (20 and 25) of the tree rotation. For
instance, in year 25, when the maximum number of shoots is (almost) achieved and
274
the shade is severe and the contribution of crop growth to LER small, the crop
parameters light extinction (kc) coefficient and light use efficiency (εc) still become
important. This is because, due to the large leaf area of the tree leaf canopy,
changing the value of kt by a factor 0.002 (0.2 %) has only a small effect on the
amount of light available for the crop. Given the shade condition, a small change (0.2
%) of the value of P, kc and εc (responsible for light interception and light use
efficiency by the crop) has an impact on crop growth and LER. The effect is clearest
at the greater nominal value of tree light interception (kt = 0.8; Table 5).
DISCUSSION
Compared to existing bio-physical agroforestry models (e.g. Mobbs et al., 1999; van
Noordwijk & Lusiana, 2000), the model proposed here is very simple. In support of
this approach the following arguments can be given: a simpler model is often easier
to parameterise and may produce more robust results; it is less work to build; and it is
easier to explain and understand. This results in a shorter learning curve when the
model is used in upscaling studies, and this may favour its inclusion in higher level
studies, e.g. explorations of land use. Of course, a simple model may be
underparameterised and unable to represent real situations using the few equations
that were chosen as essential. We have not encountered data sets in which this is
the case. This model was built with the philosophy that it could be extended when
simulation of realistic situations required further detailing. This might be necessary,
for instance, when agroforestry at different nutrient levels and nutrient limitation is
simulated. However, the current set of parameters can represent many realistic
situations without expanding the set of variables or equations, by simply adjusting
values of parameters to specific conditions. For instance, the effect of nutrient
limitation on growth rates can be captured in the value of the light efficiencies εc and
275
εt. Our philosophy with Yield-SAFE is that the model should keep its present simple
structure until it is unable to represent real situations due to lack of structure or
degrees of freedom. In this sense we follow Peters’ (1991) plea for simple, useful and
predictive models in ecology.
In the current model version, the leaf area of the trees was assumed to spread out
over the whole of the agroforested area, without explicitly accounting for clumping of
tree leaf area in the tree crowns. Reasoning from existing literature on light
distribution in crops (e.g. Goudriaan & van Laar, 1994) indicates that the extinction
coefficient might change at low tree densities as the canopy is more heterogeneous.
Initial use of the model has suggested that it may be necessary to modify the light
extinction coefficient in such situations. An alternative approach is to use detailed
models on light distribution (e.g. Pronk et al., 2002) to estimate parameters for YieldSAFE. Likewise, detailed models for root distribution and activity in agroforestry might
be used to parameterize Yield-SAFE functions for water capture by crops and trees.
During the same project an elaborate model was built for agroforestry system
performance, based on details of resource use processes in agroforestry systems.
This model is called Hi-SAFE to indicate the high level of process detail contained in
it. The applications of Hi-SAFE are more geared towards shorter time scales, and
detailed questions regarding spatial configuration in agroforestry designs, whereas
Yield-SAFE focuses on issues of production and resource use in the longer term. For
both models, parameter estimation is an issue. Yield-SAFE requires long term data
on tree growth for parameter estimation and validation of model results. Such data
are not yet available for agroforestry systems, but they may be come available in the
future as the experiments that have been planted in the 1990s mature and
accumulate timber. It is quite important that minimal data are collected in such
276
experiments to allow estimation of parameters of the model proposed here. In this
respect it would be very helpful if records were taken of leaf area index and/or soil
cover by the crop as well as the trees at different times during the season. Moreover,
allometric relationships for widely-spaced trees are needed. At the present time, for
studies on future land use, there is a pressing need for models that can be built with
the limited information on agroforestry that is now available, as very few agroforestry
systems have yet been planted in Europe. A simple model like Yield-SAFE can play
a pivotal role in land use explorations by predicting production in agroforestry
systems by integrating the vast information on forestry and arable systems, based on
well proven eco-physiological principles, that – as this study shows – hold up as well
in agroforestry as in agriculture and forestry.
ACKNOWLEDGEMENT
The research presented here was carried out as part of the collaborative research
project SAFE: Silvoarable Agroforestry for Europe. Support for SAFE was provided
by the Quality of Life Programme of the European Union (contract number QLK5-CT2001-00560). The silvoarable experiments in the UK were conducted with support
from, what is now, the UK Department for the Environment, Food and Rural Affairs.
REFERENCES
Brisson, N., C. Gary, E. Justes, R. Roche, B. Mary, D. Ripoche, D. Zimmer, J. Sierra,
P. Bertuzzi, P. Burger, F. Bussière, Y.M. Cabidoche, P. Cellier, P. Debaeke, J.P.
Gaudillère, C. Hénault, F. Maraux, B. Seguin, H. Sinoquet, (2003). An overview of
the crop model STICS. European Journal of Agronomy 18, 309-332.
277
Burgess, P.J., I. Seymour, L.D. Incoll, B. Hart & A. Beaton (2000). The application of
silvoarable agroforestry in the UK. Aspects of Applied Biology 62, 269-276.
Burgess, P.J., L.D. Incoll, B.J. Hart., A. Beaton, R.W. Piper, I. Seymour, F.H. Reynolds, C.
Wright, D.J. Pilbeam & A.R. Graves (2003). The impact of silvoarable agroforestry with
poplar on farm profitability and biological diversity. Final report to DEFRA Project Code
AF0105. Silsoe, Bedfordshire: Cranfield University. 63 pp.
Burgess, P.J., L.D. Incoll, D.T. Corry & B.J. Hart (2004a). Poplar (Populus spp) growth and
crop yields in a silvoarable experiment at three lowland sites in England. Agroforestry
Systems 63: 157-169.
Burgess, P.J., K. Metselaar, A.R. Graves, R. Stappers, K. Keesman, M. Mayus and W. van
der Werf (2004b). The SAFE-RESULT equations in Excel. Version 10. Technical report,
21 April 2004, Cranfield University, Silsoe, UK, 26 pp.
Christie, J.M. (1994) Provisional yield tables for poplar in Britain. Forestry Commission
Technical Paper 6. Forestry Commission, Edinburgh, 36 pp.
Dennis J. and Schnabel, R. (1996). Numerical methods for unconstrained optimization and
nonlinear equations. Society for Industrial and Applied Mathematics, Philadelphia, 378
pp.
Droppelmann, K.J., J.E. Ephrath & P.R. Berliner (2000). Tree/crop complementarity
in an arid zone runoff agroforestry system in northern Kenya. Agroforestry
Systems 50, 1-16.
Goudriaan, J. & H.H. van Laar (1994). Modelling potential crop growth processes.
Kluwer Academic Publishers, Dordrecht, The Netherlands, 238 pp.
278
Graves, A.R., R.B. Matthews & K. Waldie (2004). Low external input technologies for
livelihood improvement in subsistence agriculture. Advances in Agronomy 82,
473-555.
Graves, A.R., P.J. Burgess, F. Liagre, J.-P. Terreaux & C. Dupraz (2005a).
silvoarable economics. Agroforestry Systems, in press.
Graves, A.R., P.J. Burgess, J.H.N. Palma, F. Herzog, G. Moreno, M. Bertomeu, C.
Dupraz, F. Liagre, A. Koffeman and J. van den Briel (2005b). The development
and application of bio-economic modelling for silvoarable systems in Europe.
Submitted to this issue of Ecological Engineering
Keesman, K.J., W. van der Werf and H. van Keulen (2005). Mathematical Production
Ecology: Analysis of a Silvo-arable Agro-forestry System. Submitted to Bulletin
Mathematical Biology.
Ljung, L. (1987). System Identification: Theory for the User, 2nd ed. Prentice Hall, New
Jersey, 609 pp.
Loomis, R.S. & D.J. Connor (1992). Crop ecology; productivity and management in
agricultural systems. Cambridge University Press, 538 pp.
Mead, D. R., R.W. Willey (1980). The concept of a ‘land equivalent ratio’ and
advantages in yields from intercropping. Experimental Agriculture 16, 217-228.
Mobbs, D.C., G.J. Lawson, A.D. Friend, N.M.J. Crout, J.R.M. Arah & M.G. Hodnett
(1999). HyPAR Model for Agroforestry Systems. Technical Manual Model
Description for Version 3.0. DFID Forestry Research Programme R5652 Penicuik,
Edinburgh: Institute of Terrestrial Ecology, 113 pp.
279
Monteith, J.L. (1990). Conservative behaviour in the response of crops to water and
light. In: Rabbinge, R., J. Goudriaan, H. van Keulen, F.W.T. Penning de Vries and
H.H. van Laar (Eds.), Theoretical Production Ecology: Reflections and Prospects.
Pudoc, Wageningen, pp. 3-16.
Peters, R.H. (1991). A Critique for Ecology. Cambridge University Press, 384 pp.
Powell, M. J. D. (1964). An efficient method for finding the minimum of a function of
several variables without calculating derivatives. Computer Journal. 7, 155 -162.
Pronk, A.A., J. Goudriaan, E. Stilma and H. Challa (2003). A simple method to
estimate radiation interception by nursery stock conifers: a case study of eastern
white cedar. Netherlands Journal of Agricultural Science 51, 279-295.
Rabbinge, R. and H.C. van Latesteijn (1992). Long-term options for land use in the
European community. Agricultural Systems 40, 195-210.
Stappers, R., K.J. Keesman and W. van der Werf (2003). The SAFE-RESULT
Equations: an Agro-Forestry Model. Technical Report, Oct. 2003, Wageningen
University, The Netherlands, 20 pp.
Thomas, T.H., P. Tabbush, M. Bulfin, T. Bradford, T. Kent, N. O’Dowd, P. Bonduelle,
J.M. Roda, A. Berthelot, D. Coaloa, P.M. Chiarabaglio, J. Bonany, F. Camps, J.
van Slycken, L. Meiresonne & R.M. Willis (1998). Poplars for Farmers. Final
Technical report. Appendix 3-2. AIR3-CT94-1753, European Commission DG12,
Brussels.
Vandermeer, J. (1989). The ecology of intercropping. Cambridge University Press,
Cambridge, MA, 237 pp.
280
Van Genuchten, M.T. (1980). A closed-form equation for predicting the hydraulic
conductivity of unsaturated soils. Soil Science Society of America Journal 44,
892-898.
Van Ittersum, M.K. and R. Rabbinge (1997). Concepts in production ecology for
analysis and quantification of agricultural input-output combinations. Field Crops
Research 52, 197-208.
Van Ittersum, M.K. and M. Donatelli (Eds) (2001). Modelling cropping systems:
science, software and applications. Special Issue: European Journal of Agronomy
18(3-4), pp. 187-393.
Van Keulen, H. & J. Wolf (Eds) (1986). Modelling of agricultural production: weather,
soils and crops. Pudoc, Wageningen, 479 pp.
Van Noordwijk, M. & B. Lusiana (2000). WaNuLCAS version 2.0, Background on a
model of water, nutrient and light capture in agroforestry systems. International
Centre for Research in Agroforestry (ICRAF), Bogor, Indonesia, 186 pp.
Versteeg, M.N. and H. van Keulen (1986). Potential crop production prediction by
some simple calculation methods, as compared with computer simulations.
Agricultural Systems 19, 249-272.
Young, P.C. (1984). Recursive Estimation and Time Series Analysis. Springer
Verlag, Berlin, 300 pp.
281
Table 1. Assumed and estimated tree dynamic model parameters for poplar.
Symbol
Description
Value Units
Assumed parameters
410000 g m-3
ρt
Timber density
kt
Light extinction coefficient
ρ
Tree stand density
0.0156 m-2
Nm
Maximum number of shoots per tree
10000 -
Am
Maximum leaf area per shoot
a
Attrition rate of standing tree biomass
τ
Time constant of leaf area growth
10 d
HItimber
Proportion of woody biomass partitioned to
timber
0.5 -
0.8 -
0.05 m2
0.0001 d-1
Estimated parameters
εt
Radiation use efficiency
N ( t0 )
Initial number of shoots per tree
1.409 g MJ-1
0.6225
282
Table 2. Reference yields and calibrated values for transpiration coefficient and
harvest index for poplar and wheat at Silsoe. The calibrated management factor was
1 for both species.
Specie
s
Time of Reference
Reference
clear fell
yield
at crop yield
clear fell
(year)
Poplar
30
Wheat
-
(m3 tree-1)
(t ha-1 a-1
Calibrated
transpiration
coefficient
Calibrated
harvest
index
(m3 kg-1)
(%)
2.41
-
0.420
48.6
-
8.23
0.316
51.0
283
Table 3: The effect of a ± 10% change in selected parameters in the Yield-SAFE model on the
predicted tree and crop yields, and land equivalent ratios (LER) for a poplar silvoarable
system in year 30 (LER calculated with Equation 3).
Monoculture
Silvoarable
Nominal
Tree
Crop
value of yield
yield
parameter (m3 ha-1) (t ha-1)
Reference
Tree
Crop
yield
yield
(m3 ha-1) (t ha-1 )
Sensitivity
LER
377
247
345
104
1.34
334
na
302
120
1.39
408
na
377
92
1.30
345
na
316
114
1.38
402
na
369
95
1.30
350
na
319
114
1.37
399
na
367
96
1.31
352
na
321
113
1.37
397
na
365
97
1.31
409
na
375
106
1.35
350
na
320
102
1.33
369
na
325
110
1.33
361
na
332
102
1.33
374
na
342
105
1.34
379
na
347
103
1.33
340
na
311
104
1.34
340
na
311
104
1.34
na
237
345
101
1.34
na
262
344
109
1.33
na
237
352
95
1.34
(normalized
main
effects:
∆LER/LER*p/∆p)
Tree parameters
kt
0.8
εt
1.4086
0.05
Am
N(t0)
γt
0.6225
0.00042
pFc
Nm
HItimber
4
10000
0.486
-0.36
-0.28
-0.24
-0.23
-0.08
0.03
-0.02
0.00
Crop parameters
Sh
pFc
1312
2.9
-0.05
284
εc
1.34
S0
HIc
γc
57
0.51
0.00032
na
255
339
110
1.33
na
233
352
93
1.33
na
256
337
114
1.34
na
247
345
104
1.34
na
246
346
103
1.34
na
222
345
94
1.34
na
272
345
114
1.34
na
269
349
110
1.34
na
228
341
98
1.34
-0.02
0.01
0.00
0.00
0.00
na = not applicable
285
Table 4: Parameter setting and initial conditions (after calibration) for a sensitivity
analysis of Yield-SAFE for a poplar agroforestry stand (156 trees ha-1) with
continuous wheat.
286
Parameter
set 1
Parameter
set 2
g MJ-1
1.84
1.4086
-
0.4
0.8
m2
0.05
0.05
Component Symbol Unit
Tree
εt
kt
Am
τ
a
N(t0)
Bt(t0)
Lt(t0)
Nm
Crop
d
10
10
-
0
0.0001
tree-1
1.32
0.6225
g tree-1
m2 tree-1
tree-1
100
100
0
0
8000
10000
tb
Day
year
of
100
100
tf
Day
year
of
265
300
εc
kc
σ
P
T0
S0
S1
S2
Sh
Lc(t0)
Bc(t0)
g MJ-1
1.6
1.6
-
0.7
0.7
m2 g-1
0.02
0.02
-
0.8
0.8
o
0
0
o
Cd
150
150
o
Cd
160
160
2350
2350
2950
2950
C
o
Cd
o
Cd
-
0.1
g m-2
0.1
10
10
ts
Day
year
of
280
280
th
Day
year
of
235
235
287
Table 5. Ranking of elasticities of land equivalent ratios in years 2, 10 and 25 of a
poplar-wheat agroforestry stand to biological parameters of tree and crop, for tree
parameter scenario’s based on an assumed coefficient of light extinction kt of 0.4 and
0.8. Ranking per column, i.e. over all parameters in a given year, with the first rank
(1) for the most sensitive parameter.
Parameter set 1 (kt = 0.4)
Component
Parameter Year 2
Parameter set 2 (kt =
0.8)
Year Year 10 Year 25
Year 10 Year 25 2
Tree
tb
1
1
1
1
2
1
Crop
P
2
8
7
2
8
3
Tree
kt
3
3
2
3
3
6
Tree
Am
4
4
3
4
4
5
Tree
N(t0)
5
5
9
5
5
9
Tree
tf
6
2
5
6
1
7
Tree
Bt(t0)
7
7
11
7
7
11
Tree
εt
8
6
10
8
6
10
Tree
τ
9
10
12
9
9
12
Crop
kc
10
11
6
10
10
2
Crop
εc
11
12
8
11
11
4
Crop
S1
12
16
13
12
14
15
Crop
S2
13
14
14
13
13
13
Crop
Bc(t0)
14
13
15
14
15
14
Crop
Lc(t0)
15
15
16
15
16
16
Crop
σ
16
17
17
16
17
17
288
Tree
Nm
17
9
4
17
12
8
289
Relative growth rate coefficient
(wc )
1
0
0
1
2
3
4
5
pF
Figure 1. Relationship between the reduction factor for the rate of crop growth ( wc )
and the pF of the soil (pFc = 2.9 and pFPWP = 4.2).
290
3
Timber volume per tree (m )
0.4
0.35
Silsoe monoculture
Leeds monoculture
0.3
Silsoe agroforestry
0.25
Leeds agroforestry
0.2
0.15
0.1
0.05
0
1992
1994
1996
1998
2000
2002
2004
Year
Fig. 2: Growth of poplar in agroforestry stand and monoculture Silsoe (UK) and
Leeds (UK), 1992-2003.
291
Relative crop yield
(per total area)
1.0
0.8
0.6
0.4
Silsoe
0.2
Leeds
0.0
1992
1994
1996
1998
2000
2002
Year
Fig. 3: Relative yield of crops in agroforestry stands at Silsoe (UK) and Leeds (UK),
1992-2003. Yield in the intercrop is expressed as a proportion of yield in monocrop.
292
LER
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1992
Silsoe
Leeds
1994
1996
1998
2000
2002
2004
Year
Fig 4. Evolution of the annual land equivalent ratio at Silsoe (UK) and Bramham
(UK), 1992-2003. Annual land Equivalent Ratio is calculated as the sum of crop
yield in any year and the cumulative tree growth up to the same year, both
normalized by their productions in monoculture (Equation 3).
293
Volume (m3 ha-1)
700
600
500
400
300
200
100
0
0
2
4
6
8
10
12
14
16
18
20
Time from tree planting (a)
Figure 5. Potential poplar growth in the Atlantic region, simulated with Yield-SAFE.
294
-1
Biomass (t ha )
30
25
20
15
10
5
0
0
100
200
300
400
Time from planting (d)
Yield-SAFE
STICS
Figure 5. Total crop biomass predictions (wheat) from Yield-SAFE (dashed line)
calibrated to outcomes from the comprehensive crop growth model STICS (drawn
line). Weather data from Wageningen, 1984.
295
Timber volume (m3/tree)
Yield-SAFE forestry
Silsoe forestry measured
2.0
Silsoe forestry predicted YC=13
1.0
0.0
0
10
20
30
Fig. 7: Calibration of Yield-SAFE: Model prediction of tree growth in a poplar agroforestry stand, compared to yield tables (YC 13; Christie, 1994) and tree growth in
the
forestry
treatment
at
Silsoe
(1992-2003).
296
Relative crop yield
1.0
0.8
Arable
Silvoarable
0.6
Silsoe
0.4
Leeds
0.2
0.0
0
10
20
30
Fig. 8: Validation of Yield-SAFE: model prediction of relative yield of continuous
winter wheat, compared with monoculture wheat yield, in a poplar agroforestry stand
(156 trees ha-1), compared to observed relative crop yields in Silsoe and Leeds
agroforestry experiments, 1992-2004 (open symbols).
297
Appendix A. Variables and parameters in Yield-SAFE.
Symbol Units
Meaning
State variables
Bt
g tree-1
Dry mass of the trunk and branches of the tree
Lt
m2 m-2
Leaf area index of trees, i.e. tree leaf area per area
silvoarable system
Nt
-
Number of shoots per tree
Bc
g m-2
Above-ground dry mass of the crop per area of the
silvoarable system
Lc
m2 m-2
Leaf area index of crop, i.e. crop leaf area per area of
silvoarable system
θ
m3 m-3
Volumetric water content of the soil
S
°C d
Heat sum since crop emergence
Tree parameters
εt
g MJ-1
Radiation use efficiency of the trees, i.e. woody biomass
produced per unit intercepted short-wave radiation
kt
-
Light extinction coefficient of the trees
γt
m3 g-1
Transpiration coefficient of the trees, i.e. water transpired
per unit of woody dry matter produced
Am
m2
Maximum leaf area of a single tree shoot
τ
d
Time constant of leaf area growth of a tree shoot
a
d-1
Relative rate of attrition of standing tree biomass
Crop parameters
εc
g MJ-1
Radiation use efficiency of the crop, i.e. above-ground
dry biomass production per unit of intercepted total shortwave radiation
kc
-
Light extinction coefficient of the crop
γc
m3 g-1
Transpiration coefficient of the crop; i.e. water transpired
per unit of above-ground crop dry biomass
σ
m2 g-1
Specific leaf area of crop; i.e. leaf area per mass of dry
matter
298
matter
Sh
°C d
Heat sum at crop harvest
Tb
°C
Base temperature for crop phenological development
P0
-
Initial partitioning factor to leaves
S1
°C d
Heat sum at which partitioning to leaves starts to
decrease
S2
°C d
Heat sum at which partitioning to leaves ceases
Soil parameters
pFPWP
-
Log of soil water tension expressed as cm of water at
permanent wilting point
pFFC
-
Log of soil water tension expressed as cm of water at
field capacity
m, n
-
Shape parameters of the van Genuchten equation
describing the (θ, ψ) function
Ks
m d-1
Soil hydraulic conductivity at field capacity
D
m
Depth of the soil compartment
299
Appendix A. Variables and parameters in Yield-SAFE (continued)
Symbol
Units
Meaning
Intermediate variables
P
g g-1
Partitioning coefficient of above-ground dry matter to
leaves
Ic
MJ m-2
Radiation underneath the tree leaf canopy per area of
silvoarable system
fc
-
Proportion of radiation incident on crop intercepted by
crop
wc
-
Coefficient (0-1) expressing response of crop growth
rate to water shortage
ft
-
Proportion of incident radiation intercepted by trees
wt
-
Coefficient (0-1) expressing response of tree growth
rate to water shortage
Coefficient (0-1) expressing
evaporation to water shortage
ws
response
of
soil
ψ
cm water
Water tension of soil
pF
-
Water tension of soil using a log scale in pF-units:
log10(ψ)
δ
-
Parameter affecting drainage rate below root zone
Physical constants
η
g MJ-1
1/heat of vaporization
Forcing functions
Ι
MJ m-2
Daily total short wave radiation
Τ
°C
Daily mean temperature
R
m3 m-2
Daily precipitation
Management functions
C
m2 m-2
The cropped area expressed as a proportion of the
total silvoarable area
300
ts
DOY
Crop sowing date (for each year in the tree cycle)
ρ
trees m-2
Tree stand density
πt
-
Proportion of trees thinned (time-dependent)
πb
-
Proportion of tree biomass pruned (time-dependent)
πs
-
Proportion of tree shoots pruned (time-dependent)
Initial conditions
Ν(t0)
tree-1
Number of shoots on a newly planted tree
Note: DOY is Day of Year
301

Silvoarable Agroforestry For Europe (SAFE)

Transcription

Documents pareils

Eco-culture INDOOR TREE GROWING KIT by Seracon Montreal, QC

Télécharger notre catalogue

Téléchargez ici - Beau vers l`œil

Ces arbres qui ont une légende

MERCI À NOS COMMANDITAIRES 2014!

84000 Instructions

Compte rendu des Rencontres de la fête libre - Ferme à wiki coop-tic

by Christian Nold - Rennes Emotion Map

Depuis 1987 - Helicopter Services Courchevel