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Modeling spatio-temporal crop allocation patterns by a stochastic decision tree method, considering agronomic driving factors

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  • Sorel, Luc
  • Viaud, Valérie
  • Durand, Patrick
  • Walter, Christian
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    Abstract

    Evaluating the environmental impacts of agricultural practices increasingly involves the use of spatially distributed simulation models that account for crop allocations across fields as an input factor. Our objective was to develop a model for spatio-temporal allocation of crops to a field pattern that was able to account for agronomic and spatial driving factors including crop production objectives, spatial distribution of the crops around farmsteads, and preferential allocation of crops on soil waterlogging classes. We developed a model based on stochastic decision trees (SDTs) to integrate farm type and field characteristics (area, distance to farmstead, waterlogging, and current crop) in the spatio-temporal allocation process without prior expert knowledge, and we compared the model to a reference model based on first-order Markov chains or transition matrices. A case study comparing both models was performed in the Naizin catchment (Western France), where crop allocation to fields was known for the period 1993-2006. The SDTs built had a general structure similar to transition matrices. SDTs and transition matrices exhibited similar performances in predicting crop transitions in time and in allocating crops to the proper soil waterlogging class. However, SDTs proved to better reproduce the spatial distribution of crops around the farmsteads. SDTs provide an integrated way to analyze and simulate crop allocation processes within a single integrated framework. The ease of constructing decision trees suggests potential couplings of SDT to various landscape-scale ecological models requiring a detailed description of the land use mosaic as input data.

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    Bibliographic Info

    Article provided by Elsevier in its journal Agricultural Systems.

    Volume (Year): 103 (2010)
    Issue (Month): 9 (November)
    Pages: 647-655

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    Handle: RePEc:eee:agisys:v:103:y:2010:i:9:p:647-655

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    Web page: http://www.elsevier.com/locate/agsy

    Related research

    Keywords: Crop rotation Decision tree Land cover change modeling Transition probability matrices Stochastic modeling;

    References

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    1. Heidenberger, Kurt, 1996. "Dynamic project selection and funding under risk: A decision tree based MILP approach," European Journal of Operational Research, Elsevier, vol. 95(2), pages 284-298, December.
    2. Thornton, P. K. & Jones, P. G., 1998. "A conceptual approach to dynamic agricultural land-use modelling," Agricultural Systems, Elsevier, vol. 57(4), pages 505-521, August.
    3. Bockstael, N. & Costanza, R. & Strand, I. & Boynton, W. & Bell, K. & Wainger, L., 1995. "Ecological economic modeling and valuation of ecosystems," Ecological Economics, Elsevier, vol. 14(2), pages 143-159, August.
    4. Sylvain Payraudeau & Hayo M.G. Van Der Werf & Francoise Vertes, 2006. "Evaluation of an operational method for the estimation of emissions of nitrogen compounds for a group of farms," International Journal of Agricultural Resources, Governance and Ecology, Inderscience Enterprises Ltd, vol. 5(2/3), pages 224-246.
    5. F Wu & C J Webster, 1998. "Simulation of land development through the integration of cellular automata and multicriteria evaluation," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 25(1), pages 103-126, January.
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    Cited by:
    1. Moreau, Pierre & Ruiz, Laurent & Vertès, Françoise & Baratte, Christine & Delaby, Luc & Faverdin, Philippe & Gascuel-Odoux, Chantal & Piquemal, Benoit & Ramat, Eric & Salmon-Monviola, Jordy & Durand, 2013. "CASIMOD’N: An agro-hydrological distributed model of catchment-scale nitrogen dynamics integrating farming system decisions," Agricultural Systems, Elsevier, vol. 118(C), pages 41-51.
    2. Aurbacher, Joachim & Dabbert, Stephan, 2011. "Generating crop sequences in land-use models using maximum entropy and Markov chains," Agricultural Systems, Elsevier, vol. 104(6), pages 470-479, July.

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