Climate Change Impact on Agricultural Land Suitability: An Interpretable
  Machine Learning-Based Eurasia Case Study
Valeriy Shevchenko Daria Taniushkina Aleksander Lukashevich Aleksandr Bulkin Roland Grinis Kirill Kovalev Veronika Narozhnaia Nazar Sotiriadi Alexander Krenke Yury Maximov
The United Nations has identified improving food security and reducing hunger
as essential components of its sustainable development goals. As of 2021,
approximately 828 million people worldwide are experiencing hunger and
malnutrition, with numerous fatalities reported. Climate change significantly
impacts agricultural land suitability, potentially leading to severe food
shortages and subsequent social and political conflicts. To address this
pressing issue, we have developed a machine learning-based approach to predict
the risk of substantial land suitability degradation and changes in irrigation
patterns. Our study focuses on Central Eurasia, a region burdened with economic
and social challenges.
  This study represents a pioneering effort in utilizing machine learning
methods to assess the impact of climate change on agricultural land suitability
under various carbon emissions scenarios. Through comprehensive feature
importance analysis, we unveil specific climate and terrain characteristics
that exert influence on land suitability. Our approach achieves remarkable
accuracy, offering policymakers invaluable insights to facilitate informed
decisions aimed at averting a humanitarian crisis, including strategies such as
the provision of additional water and fertilizers. This research underscores
the tremendous potential of machine learning in addressing global challenges,
with a particular emphasis on mitigating hunger and malnutrition.