Detecting the linkage between arable land use and poverty using machine learning methods at global perspective

Abstract

Eradicating extreme poverty is one of the UN's primary sustainable development goals (SDG). Arable land is related to eradicating poverty (SDG1) and hunger (SDG2). However, the linkage between arable land use and poverty reduction is ambiguous and has seldom been investigated globally. Six indicators of agricultural inputs, crop intensification and extensification were used to explore the relationship between arable land use and poverty. Non-parametric machine learning methods were used to analyze the linkage between agriculture and poverty at the global scale, including the classification and regression tree (CART) and random forest models. We found that the yield gap, fertilizer consumption and potential cropland ratio in protected areas correlated with poverty. Developing countries usually had a ratio of actual to potential yield less than 0.33 and fertilizer consumption less than 7.31 kg/ha. Overall, crop extensification, intensification and agricultural inputs were related to poverty at the global level.

Keywords

• Arable land use
• Poverty
• Machine learning
• Yield gap
• Random forest