Predicting Divestment Decisions in Rice Farms Using Machine Learning Approach: A Mixed Method Study - Niño, Norian; Mellezar, Donavel; Lacuesta, Shaina; Dosano, Dion Mark; Pono, Joemarie (International Journal of Sustainability and Advanced Integrated Research, 2026)
This study examines the external factors influencing divestment decisions among smallhold rice farmers in Tacurong City, utilizing a mixed-method approach. By combining qualitative analysis of respondents' narratives with quantitative data, the research identifies key factors driving divestment. The findings highlight that persistent crop failures, rising production costs, low rice prices, and delayed or insufficient government support are central to farmers’ decisions to sell their land. Family needs, particularly for food and education, often outweigh the tradition of rice farming. High input costs, such as for fertilizers, seeds, and labor, coupled with low rice prices, lead to debt accumulation and unsustainable farming practices. Although government subsidies provide some relief, delays and inadequacies force farmers to depend on high-interest loans or alternative sources of income. Despite the economic challenges, many farmers prioritize immediate survival over maintaining farming traditions. The study emphasizes that without effective interventions, including price stabilization, improved financial aid systems, and stronger community support, further divestment from rice farming is likely. This trend threatens the livelihoods of farmers and local food security. The mixed-method approach enabled a comprehensive understanding of the complex economic and social factors shaping divestment decisions, providing critical insights for addressing the challenges faced by farmers in Tacurong City.
Keywords: Divestment Decisions; Agricultural Sustainability; Knowledge Management; Business Resilience; Machine Learning; Food Security