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Solving the Crop Allocation Problemusing Hard and Soft Constraints∗

Published online by Cambridge University Press:  18 April 2013

Mahuna Akplogan ,
Simon de Givry ,
Jean-Philippe Métivier ,
Gauthier Quesnel ,
Alexandre Joannon  and
Frédérick Garcia
Mahuna Akplogan
Affiliation:
INRA, UR 875 Biométrie et Intelligence Artificielle, 31326 Toulouse. makploga@toulouse.inra.fr, degivry@toulouse.inra.fr, gquesnel@toulouse.inra.fr, fgarcia@toulouse.inra.fr
Simon de Givry
Affiliation:
INRA, UR 875 Biométrie et Intelligence Artificielle, 31326 Toulouse. makploga@toulouse.inra.fr, degivry@toulouse.inra.fr, gquesnel@toulouse.inra.fr, fgarcia@toulouse.inra.fr
Jean-Philippe Métivier
Affiliation:
GREYC-CNRS, UMR 6072, Université de Caen, 14032 Caen; jean-philippe.metivier@unicaen.fr
Gauthier Quesnel
Affiliation:
INRA, UR 875 Biométrie et Intelligence Artificielle, 31326 Toulouse. makploga@toulouse.inra.fr, degivry@toulouse.inra.fr, gquesnel@toulouse.inra.fr, fgarcia@toulouse.inra.fr
Alexandre Joannon
Affiliation:
INRA, UR 980 SAD-Paysage, 35042 Rennes; joannon@rennes.inra.fr
Frédérick Garcia
Affiliation:
INRA, UR 875 Biométrie et Intelligence Artificielle, 31326 Toulouse. makploga@toulouse.inra.fr, degivry@toulouse.inra.fr, gquesnel@toulouse.inra.fr, fgarcia@toulouse.inra.fr
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Abstract

Application tools for the crop allocation problem (CAP) are required for agricultural advisors to design more efficient farming systems. Despite the extensive treatment of this issue by agronomists in the past, few methods tackle the crop allocation problem considering both the spatial and the temporal aspects of the CAP. In this paper, we precisely propose an original formulation addressing the crop allocation planning problem while taking farmers’ management choices into account. These choices are naturally represented by hard and soft constraints in the Weighted CSP formalism. We illustrate our proposition by solving a medium–size virtual farm using either a WCSP solver (toulbar2) or an ILP solver (NumberJack/SCIP). This preliminary work foreshadows the development of a decision–aid tool for supporting farmers in their crop allocation strategies.

Keywords

Weighted constraint satisfaction probleminteger linear programmingcrop allocation problem
Type
Research Article
Information
RAIRO - Operations Research , Volume 47 , Issue 2: ConstraintProgramming , April 2013 , pp. 151 - 172
Copyright
© EDP Sciences, ROADEF, SMAI, 2013

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References

D. Allouche, C. Bessiere, P. Boizumault, S. de Givry, P. Gutierrez, S. Loudni, JP. Métivier and T. Schiex, Decomposing Global Cost Functions, in Proc. AAAI-12, Toronto, Canada (2012).
D. Allouche, S. Traoré, I. André, S. de Givry, G. Katsirelos, S. Barbe and T. Schiex, Computational protein design as a cost function network optimization problem, in Proc. CP-12, Quebec City, Canada (2012).
Annetts, J. and Audsley, E., Multiple objective linear programming for environmental farm planning. J. Oper. Res. Soc. 53 (2002) 933–943. Google Scholar
K. Apt and S. Brand, Infinite Qualitative Simulations by Means of Constraint Programming, in Proc. CP-06, Nantes, France (2006) 29–43.
Bachinger, J. and Zander, P., ROTOR, a tool for generating and evaluating crop rotations for organic farming systems. Europ. J. Agron. 26 (2007) 130–143. Google Scholar
N. Beldiceanu, I. Katriel and S. Thiel, Filtering algorithms for the same constraint, in Proc. CPAIOR-04, Nice, France (2004) 65–79.
Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown, I., Conrad, K.F. and Perry, J.N., Simulation scenarios of spatio–temporal arrangement of crops at the landscape scale. Envir. Modell. Soft. 25 (2010) 1881–1889. Google Scholar
Cooper, M., de Givry, S., Sanchez, M., Schiex, T., Zytnicki, M. and Werner, T., Soft arc consistency revisited. Artificial Intell. 174 (2010) 449–478. Google Scholar
Dogliotti, S., Rossing, W.A.H. and van Ittersum, M.K., ROTAT, a tool for systematically generating crop rotations. Eur. J. Agron. 19 (2003) 239–250. Google Scholar
J. Dury, The cropping-plan decision–making: A farm level modelling and simulation approach. PhD thesis, INP Toulouse, France (2011). http://ethesis.inp-toulouse.fr/archive/00001788/01/dury.pdf
Dury, J., Schaller, N., Garcia, F., Reynaud, A. and Bergez, JE., Models to support cropping plan and crop rotation decisions. A review. Agron. Sustain. Develop. 32 567-580, 2012. Google Scholar
El-Nazer, T. and McCarl, B.A., The Choice of Crop Rotation: A Modeling Approach and Case Study. Am. J. Agric. Econ. 68 (1986) 127–136. Google Scholar
S. de Givry, M. Zytnicki, F. Heras and J. Larrosa, Existential arc consistency: Getting closer to full arc consistency in weighted CSPs, in Proc. IJCAI-05, Edinburgh, Scotland (2005).
W.D. Harvey and M.L. Ginsberg, Limited discrepency search, in Proc. IJCAI-95, Montréal, Canada (1995).
E.O. Heady, The Economics of Rotations with Farm and Production Policy Applications. J. Farm Econ. (1948) 645–664.
W.J. van Hoeve, G. Pesant,L.M. Rousseau, On global warming: flow–based soft global constraints. J. Heurist. (2006) 347–373.
S. Irnich and G. Desaulniers, Shortest Path Problems with Resource Constraints, chapter 2, GERAD 25th Anniversary Series. Springer (2005) 33–65.
Itoh, T., Ishii, H. and Nanseki, T., A model of crop planning under uncertainty in agricultural management. Int. J. Prod. Econ. 81-82 (2003) 555–558. Google Scholar
Kein Haneveld, W.K. and Stegeman, A.W., Crop succession requirements in agricultural production planning. Eur. J. Oper. Res. 166 (2005) 406–429. Google Scholar
A. Koster, S. van Hoesel and A. Kolen. Solving frequency assignment problems via tree–decomposition. Tech. Rep. RM/99/011, Universiteit Maastricht, The Netherlands (1999).
J. Lee and K.L. Leung, Towards efficient consistency enforcement for global constraints in weighted constraint satisfaction, in Proc. IJCAI’09, Pasadena, CA (2009) 559–565.
J. Lee and K.L. Leung, A stronger consistency for soft global constraints in weighted constraint satisfaction. in Proc. AAAI’10, Atlanta, GA (2010).
Lee, J. and Leung, K.M., Consistency techniques for flow–based projection–safe global cost functions in weighted constraint satisfaction. JAIR 43 (2012) 257–292. Google Scholar
J. Lee and Y.W. Shum, Modeling Soft Global Constraints as Linear Programs in Weighted Constraint Satisfaction, in Proc. ICTAI-11, Boca Raton, FL (2011) 305–312.
Leteinturier, B., Herman, J., Longueville, F. D., Quintin, L. and Oger, R., Adaptation of a crop sequence indicator based on a land parcel management system. Agric. Ecosyst. Environ. 112 (2006) 324–334. Google Scholar
Marriott, K., Nethercote, N., Rafeh, R., Stuckey, P., De La Banda, M. and Wallace, M., The design of the Zinc modelling language. Constraints 13 (2008) 229–267. Google Scholar
McCarl, B. A., Candler, W.V., Doster, D.H. and Robbins, P.R., Experiences with farmer oriented linear programming for crop planning. Can. J. Agric. Econ./Rev. Can. Agroecon. 25 (1977) 17–30. Google Scholar
P. Meseguer, F. Rossi and T. Schiex, Soft Constraints Processing, on edited by F. Rossi, P. van Beek and T. Walsh. Handbook Constraint Programm, chapter 9. Elsevier (2006).
J.-P. Métivier, P. Boizumault and S. Loudni, Solving nurse rostering problems using soft global constraints, in Proc. CP-09, Lisbon, Portugal (2009) 73–87.
G. Pesant, A regular language membership constraint for finite sequences of variables, in Proc. CP-04, Toronto, Canada (2004) 482–495.
T. Petit and E. Poder, The Soft Cumulative Constraint. CoRR (2009).
J.-C. Régin, Generalized arc consistency for global cardinality constraint, in Proc. AAAI’96, Portland, OR (1996) 209–215.
Sánchez, M., de Givry, S. and Schiex, T., Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques. Constraints 13 (2008) 130–154. Google Scholar
Sarker, R. and Ray, T., An improved evolutionary algorithm for solving multi-objective crop planning models. Comput. Electr. Agric. 68 (2009) 191–199. Google Scholar
Stone, N., Buick, R., Roach, J., Scheckler, R. and Rupani, R., The planning problem in agriculture: farm–level crop rotation planning as an example. AI Appl. 6 (1992) 59–75. Google Scholar