Modeling Farmers' Crop Choice Using Data Mining Approach: A Revisit
Kamol Ngamsomsuke, Benchaphun Ekasingh · 2005
Ekasingh et al. (2005) introduced a data mining approach to study farmers’ crop choice in the watershed areas of Mae Rim, Mae Kuang and Mae Ping Part II, Thailand. The authors found a set of socioeconomic conditions determining the farmers’ crop choice decision. This study used the same approach to model farmers’ crop choice in other two watershed areas in Thailand i.e., Chan and San watersheds to test the applicability of the approach in different settings. The study yielded very similar results to the earlier study. Soil condition whether expressed as a more unique concept (i.e. land unit: LU) or a more general one (i.e. soil series), growing season, cash investment and gross margin were found to play important roles in farmers’ decision to grow a crop. In addition, this study also showed that land characteristics such as paddy, lowland, upland or slope area also determined farmers’ crop selection. The model’s predictability was 85-87 percent but lower than the previous study (96 percent). Nevertheless these findings confirmed that the methodology used was appropriate and the results were robust. The resulted farmers’ crop choice model was one of the key inputs for integrated water resource management and its decision support system.