Random Forest Analysis of Exogenous Variables Impacting Rice Production in the Philippines

Vicente E. Montano, Maria Teresa S. Bulao
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Abstract

This research examines the relationship of rice production as the endogenous variable in a production function theory that considers key exogenous factors such as fertilizer consumption, irrigation water use, agricultural machinery, poverty rate, and agricultural land area. The study reveals the interdependencies shaping rice production in the Philippines. Applying the Cobb- Douglas function enhanced through the random forest regression algorithm establishes fertilizer consumption's focal role, focusing its essential impact on rice yields. Proper allocation of irrigation, access to agriculture machinery, poverty alleviation, and effective land use appear as significant contributors to overall production, defining 98% of the variability in rice production in random forests in both the in-sample and out-of-sample results. The findings emphasize the necessity for holistic strategies in agricultural planning, aiming for targeted interventions in fertilizer management, irrigation infrastructure, mechanized farming, poverty alleviation, and land-use.
影响菲律宾水稻生产的外生变量的随机森林分析
本研究探讨了水稻生产作为生产函数理论中的内生变量与化肥消耗、灌溉用水、农业机械、贫困率和农业用地面积等关键外生因素之间的关系。研究揭示了影响菲律宾水稻生产的相互依存关系。通过随机森林回归算法增强柯布-道格拉斯函数的应用,确定了化肥消费的核心作用,并重点关注其对水稻产量的重要影响。灌溉的合理分配、农业机械的使用、扶贫和土地的有效利用似乎对总体产量有重要贡献,在样本内和样本外的结果中,随机森林定义了水稻产量变异的 98%。研究结果强调了在农业规划中采取整体战略的必要性,目的是在肥料管理、灌溉基础设施、机械化耕作、扶贫和土地利用方面采取有针对性的干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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