Visualizing the Impact of Climate Change on Agricultural Yields: A Data-Driven Approach

Climate change poses significant challenges to global agricultural productivity, with rising temperatures, fluctuating precipitation patterns, and increasing CO2 emissions disrupting traditional farming practices. This paper explores the impact of these climate variables on agricultural output through a comprehensive analysis of historical data spanning from 1991 to 2024. Using the Kaggle dataset on climate change and agriculture, we conducted a data-driven investigation into the correlations between key climatefactors and crop yields across multiple regions. The study employs exploratory data analysis (EDA) and machine learning models to predict future agricultural outcomes based on historical trends. We identify critical climate variables, such as temperatureincreases and CO2 concentration, that exhibit significant correlations with changes in crop productivity. Furthermore, our results provide insights into the effectiveness of various adaptation strategies implemented by countries to mitigate the adverse effects of climate change. Our findings highlight the growing vulnerability of agriculture to climate disruptions, emphasizing the needfor targeted policy interventions and innovative agricultural practices to ensure future food security. This research underscores the importance of data-driven approaches in understanding and forecasting the longterm effects of climate change on global agriculture.