Climate Change and Future Food Security: Predicting the Extent of Cropland Gain or Degradation

TL;DR

This study uses machine learning and climate projections to predict future cropland productivity changes, highlighting regional risks and opportunities for global food security under climate change.

Contribution

It introduces a machine learning framework to estimate future cropland productivity based on climate projections, providing regional risk assessments and insights for international collaboration.

Findings

Vietnam and Thailand face 10-14% rice production decline by 2026.

The Philippines is projected to increase rice output by 11%.

The study emphasizes the need for proactive international cooperation.

Abstract

Agriculture is crucial in sustaining human life and civilization that relies heavily on natural resources. This industry faces new challenges, such as climate change, a growing global population, and new models for managing food security and water resources. Through a machine learning framework, we estimate the future productivity of croplands based on CMIP5 climate projections on moderate carbon emission scenario. We demonstrate that Vietnam and Thailand are at risk with a 10\% and 14\% drop in rice production, respectively, whereas the Philippines is expected to increase its output by 11\% by 2026 compared with 2018. We urge proactive international collaboration between regions facing crop land gain and degradation to mitigate the climate change and population growth impacts reducing our society's vulnerability. Our study provides critical information on the effects of climate change and human activities on land productivity and uses that may assist such collaboration.