A Minimum Cross-Entropy Approach to Disaggregate Agricultural Data at the Field Level

António Xavier, Rui Fragoso, Maria de Belém Costa Freitas (), Maria Do Socorro Rosário and Florentino Valente
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António Xavier: CEFAGE-UE (Center for Advanced Studies in Management and Economics), Management Department, Universidade de Évora, N° 2, Apt. 95, 7002-554 Évora, Portugal
Rui Fragoso: CEFAGE-UE (Center for Advanced Studies in Management and Economics), Management Department, Universidade de Évora, N° 2, Apt. 95, 7002-554 Évora, Portugal
Maria Do Socorro Rosário: Direção de Serviços de Estatística, GPP (Gabinete de Planeamento e Políticas), Praça do Comércio, 1149-010 Lisboa, Portugal
Florentino Valente: Direção Regional de Agricultura e Pescas do Algarve, Patacão, 8001-904 Faro, Portugal

Land, 2018, vol. 7, issue 2, 1-16

Abstract: Agricultural policies have impacts on land use, the economy, and the environment and their analysis requires disaggregated data at the local level with geographical references. Thus, this study proposes a model for disaggregating agricultural data, which develops a supervised classification of satellite images by using a survey and empirical knowledge. To ensure the consistency with multiple sources of information, a minimum cross-entropy process was used. The proposed model was applied using two supervised classification algorithms and a more informative set of biophysical information. The results were validated and analyzed by considering various sources of information, showing that an entropy approach combined with supervised classifications may provide a reliable data disaggregation.

Keywords: data disaggregation; supervised classifications; classification algorithms; minimum cross-entropy; land uses; Algarve; empirical validation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:7:y:2018:i:2:p:62-:d:145362

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