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Spatial disaggregation of agricultural production data using maximum entropy

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  • Richard Howitt
  • Arnaud Reynaud

Abstract

We develop a dynamic data-consistent method to estimate agricultural land use choices at a disaggregate (district) level, using more aggregate (regional-level) data. The disaggregation procedure consists of two steps. First, we estimate a dynamic model of land use at the regional level, then we disaggregate outcomes of the aggregate model using maximum entropy (ME). The ME disaggregation procedure is applied to a sample of California data including six districts and eight crops. The disaggregation procedure results in the recovery of district-level cropping areas with an average prediction error of 16.2 per cent. Copyright 2003, Oxford University Press.

Suggested Citation

  • Richard Howitt & Arnaud Reynaud, 2003. "Spatial disaggregation of agricultural production data using maximum entropy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(3), pages 359-387, September.
    • Handle: RePEc:oup:erevae:v:30:y:2003:i:3:p:359-387
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    Citations

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    Cited by:

    1. António Xavier & Maria Belem Freitas & Maria do Socorro Rosário & Rui Fragoso, 2016. "Disaggregating Statistical Data at Field Level: An Entropy Approach," CEFAGE-UE Working Papers 2016_06, University of Evora, CEFAGE-UE (Portugal).
    2. Wade, Tara & Kurkalova, Lyubov & Secchi, Silvia, 2016. "Modeling Field-Level Conservation Tillage Adoption with Aggregate Choice Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(2), May.
    3. KURKALOVA, Lyubov A. & WADE, Tara R., 2013. "Aggregated Choice Data And Logit Models: Application To Environmental Benign Practices Of Conservation Tillage By Farmers In The State Of Iowa," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 13(2), pages 119-128.
    4. Rui Fragoso & Maria Leonor da Silva Carvalho, 2013. "Estimation of cost allocation coefficients at the farm level using an entropy approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 1893-1906, September.
    5. 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.
    6. Xavier, Antonio & Martins, Maria de Belem Costa Freitas & Fragoso, Rui Manuel de Sousa, 2011. "Recovery of Incomplete Data of Statistical Livestock Number Applying an Entropy Approach," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 115790, European Association of Agricultural Economists.
    7. António Xavier & Rui Fragoso & Maria Belém Costa Freitas & Maria Socorro Rosário, 2019. "An Approach Using Entropy and Supervised Classifications to Disaggregate Agricultural Data at a Local Level," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 763-779, December.
    8. António Xavier & Rui Fragoso & Maria De Belém Costa Freitas & Maria Do Socorro Rosário & Florentino Valente, 2018. "A Minimum Cross-Entropy Approach to Disaggregate Agricultural Data at the Field Level," Land, MDPI, vol. 7(2), pages 1-16, May.
    9. Qiuqiong Huang & Richard Howitt & Scott Rozelle, 2012. "Estimating production technology for policy analysis: trading off precision and heterogeneity," Journal of Productivity Analysis, Springer, vol. 38(2), pages 219-233, October.
    10. Kempen, Markus & Heckelei, Thomas & Britz, Wolfgang & Adrian, Leip & Koeble, Renate, 2005. "A Statistical Approach for Spatial Disaggregation of Crop Production in the EU," 89th Seminar, February 2-5, 2005, Parma, Italy 234612, European Association of Agricultural Economists.
    11. Raja Chakir, 2009. "Spatial Downscaling of Agricultural Land-Use Data: An Econometric Approach Using Cross Entropy," Land Economics, University of Wisconsin Press, vol. 85(2), pages 238-251.
    12. Lips, Markus, 2014. "Disproportionate joint cost allocation at individual-farm level using maximum entropy," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182851, European Association of Agricultural Economists.
    13. Wade, Tara & Kurkalova, Lyubov A. & Secchi, Silvia, 2012. "Using the logit model with aggregated choice data in estimation of Iowa corn farmers’ conservation tillage subsidies," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124974, Agricultural and Applied Economics Association.
    14. Heckelei, Thomas & Mittelhammer, Ronald C. & Jansson, Torbjorn, 2008. "A Bayesian Alternative To Generalized Cross Entropy Solutions For Underdetermined Econometric Models," Discussion Papers 56973, University of Bonn, Institute for Food and Resource Economics.
    15. Tran, Dat Q. & Kurkalova, Lyubov A., 2017. "Testing for complementarity between the use of continuous no-till and cover crops: an application of Entropy approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259149, Agricultural and Applied Economics Association.
    16. Reynaud, Arnaud, 2009. "Adaptation à court et à long terme de l'agriculture au risque de sécheresse : une approche par couplage de modèles biophysiques et économiques," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement (RAEStud), Institut National de la Recherche Agronomique (INRA), vol. 90(2).
    17. Cook, Larry & Harslett, Philip, 2015. "An introduction to entropy estimation of parameters in economic models," Conference papers 332651, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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