Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

Abstract

This study addresses the critical global issue of food security, particularly under the influence of climate change on agricultural land suitability. The primary objective of our research is to predict the risks associated with land suitability degradation and changes in irrigation patterns, directly impacting food security. This research aligns with the United Nations' sustainable development goals to reduce hunger and malnutrition. Central Eurasia, a region facing unique economic and social challenges, serves as the focal point of our investigation, providing a pertinent example for analyzing the effects of climate change on food security. In our approach, we employ interpretable machine learning techniques to analyze the impact of climate change on agricultural land suitability under different carbon emission scenarios. The developed model demonstrates strong performance, evidenced by an accuracy of 86% and a mean average precision of 72% in a multi-class land suitability classification task. Focusing on the most vulnerable regions in Eastern Europe and Northern Asia, our research provides crucial insights for policymakers. These insights are instrumental for strategic planning, including the allocation of critical resources like water and fertilizers, to prevent humanitarian crises. The results demonstrate that machine learning can be a powerful tool in predicting and managing the impacts of climate change on food security.