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An Approach Using Entropy and Supervised Classificationsto Disaggregate Agricultural Data at a Local Level

Xavier, A., R. Fragoso, M.B.C. Freitas, M.S. Rosário (forthcoming), "An Approach Using Entropy and Supervised Classificationsto Disaggregate Agricultural Data at a Local Level", Journal of Quantitative Economics.
Resumo:

Changes in the Common Agricultural Policy (CAP) had several consequences onland-use and on the environment. This calls for detailed disaggregated agriculturaldata with precise geographical references. To tackle such problems data disaggrega-tion processes are needed and a series of studies are being carried out at internationallevel, which still have not taken the utmost advantage of remote sensing technolo-gies by combining them with mathematical programming methods, namely entropy.Therefore, the objective of this article was to provide an approach to disaggregateagricultural data at the local level, taking advantage of the existent up-to-date satelliteimagery and an entropy approach for manage different sets of data. The results werecompared with other approaches and showed to be coherent, and may be improvedfurther with the inclusion of other information.