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Understanding farmer decisions on land use conversions (LUC) in rural areas has significant importance to understand and predict the patterns of land use changes in China. Many methods have been developed to search for the influencing factors on land use changes at farm household level. However, these methods have difficulty in evaluating the intertwined influences between factors and achieving farmer decision rules on LUC. Taking three regions located in Jiangsu Province as the study areas, the present work proposed a data mining method, classification and regression tree (CART), to simulate farmer decisions on LUC at farm household level. The accuracy of the simulated LUC by CART was above 85.00%, indicating that the proposed method could be used to simulate farmer decisions. The simulation results also showed that farmer decision rules on LUC presented regional characteristics. In Jiangsu Province, 20 rules were inferred for LUC using 10 factors which were related to the household resources, land market, and so on. These factors were ranked in the decreased importance on LUC as labor transfer, land market, location, resources of household, and characteristics of household. In Rudong County, the which were related factors with LUC included land market, labor transfer, and household resource, while in Changshu County, the additional factor of location was involved.
Comprender las decisiones de los agricultores sobre la reconversión del uso de parcelas (LUC) en áreas rurales tiene una importancia significativa para comprender y predecir los patrones en los cambios en el uso de parcelas en China. Se han desarrollado muchos métodos para buscar los factores que influyen en los cambios del uso de parcelas a nivel agrícola familiar. Sin embargo, estos métodos presentan dificultades para evaluar las influencias entrecruzadas entre los factores y las reglas conseguidas para la decisión de los agricultores sobre la LUC. Tomando como áreas de estudio tres regiones de la provincia de Jiangsu, este trabajo propone un método de minería de datos, clasificación y árboles de regresión (CART) para simular las decisiones de los agricultores sobre el LUC a nivel familiar. Se consiguió una precisión
