基于随机森林的农地购买/租赁预测模型

Ho-Jung Jeong, Youngjune Kim, soktay lim
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引用次数: 0

摘要

本研究通过使用机器学习工具“随机森林:监督机器学习算法”进行分析,为理解韩国的农田购买和租赁决策提供指导。采用农户经济调查方法预测农地征用与农户经济特征之间的关系。我们的主要发现有两方面。首先,农地购买决策与转移性收入、存货和固定资产价值以及农民拥有的农地价值呈正相关。其次,农田租金决定也与上一年支付的租金、大田作物收入、库存和农业资产以及转移收入呈正相关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Predictive Model for Farmland Purchase/Rent Using Random Forests
This study contributes to guidance for understanding farmland purchase and rent decisions in Korea via an analysis using a machine learning tool, Random Forests: A Supervised Machine Learning Algorithm. Farm Household Economy Survey is employed to predict the relationship between farmland acquisition and farm household economic characteristics. Our main findings are two folds. First, a farmland purchase decision is positively related to transfer incomes, the value of inventory & fixed assets, and the value of farmland that farmers owned. Second, a farmland rent decision is also positively associated with a rent paid in a prior year, revenue from field crops, inventory and agricultural assets, and transfer incomes.
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