Modeling the Impact of Climate Risk on Agricultural Productivity and Food Prices

Abstract

Climate change has emerged as one of the most critical global challenges, with profound implications for agricultural productivity and food prices. The complexity and interconnectivity of climatic variables, such as temperature fluctuations, irregular precipitation patterns, and increased frequency of extreme weather events, necessitate sophisticated modeling approaches to capture their influence on agriculture. This study aims to model the impact of climate risk on agricultural productivity and food prices using a hybrid econometric-climate simulation framework. By integrating climate projections with agricultural yield models and price forecasting mechanisms, this research presents a comprehensive understanding of how climate risk translates into economic disruptions in the agri-food sector. Through historical data analysis, regional case studies, and machine learning-assisted regression models, the study provides empirical evidence of climate-induced yield variability and subsequent market volatility. The experimental results demonstrate that climate variability can significantly suppress crop yields, especially in vulnerable regions, which leads to sharp increases in food prices, disproportionately affecting low-income populations. The findings underscore the urgency of adaptive strategies and policy responses aimed at climate resilience and food security.