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

Author Contributions

Fuyou Tian contributed to the research experiments and manuscript preparation. Bingfang Wu contributed to conceptual designing this research and was responsible for the research. Hongwei Zeng, Miao Zhang, Gary R Watmough and Yurui Li gave useful comments which improved the paper. All of the co-authors helped revise and polish the manuscript.

Declaration of Competing Interest

The authors declare that there is no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank the World Bank, FAO, IIASA for providing data for this research. This study was supported financially by the National Key Research and Development Program (Grant No. 2016YFA0600304), the National Natural Science Foundation of China (Grant No. 41861144019), and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19030201).

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