Impact of Climate Change on Crop Productivity using Machine Learning Models
A Mansi Parajapati
M.Tech Student, Department of Computer Science and Engineering All Saints College of Technology, Bhopal, India
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV) prajapatimansi428@gmail.com
B Prof. Sarwesh Site
Associate Professor, Department of Computer Science and Engineering All Saints College of Technology, Bhopal, India
Affiliated to Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV)
er.sarwesh@gmail.com
Abstract
Climate change has emerged as one of the most significant global challenges, directly affecting agricultural productivity and food security. Rising temperatures, irregular rainfall patterns, and fluctuations in humidity are altering crop growth cycles and reducing yield stability. In developing countries such as India, where agriculture depends largely on monsoon rainfall and traditional cultivation practices, predicting crop productivity under variable climatic conditions becomes essential for strategic planning and climate-resilient farming. This research investigates the impact of climate change on crop productivity using Machine Learning (ML) models, leveraging multi-year historical data that includes climatic parameters (temperature, rainfall, humidity, and solar radiation), soil characteristics (nitrogen, phosphorus, potassium, pH), and crop yield records.
Multiple ML algorithms—including Linear Regression, Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN)—were developed and evaluated. To improve predictive capability, a Hybrid Ensemble Model combining Random Forest, XGBoost, and ANN was proposed. Data preprocessing involved handling missing data, feature scaling, correlation filtering, and creating derived indices such as Growing Degree Days (GDD) and Rainfall Anomaly Index (RAI). The models were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Ensemble Model outperformed all baseline and advanced models, achieving R² = 0.94, indicating a high correlation between predicted and actual crop yields.
Feature importance analysis revealed that rainfall and soil nitrogen are the dominant predictors, followed by temperature and humidity. The study also highlights regional disparities, showing that arid and coastal zones are more vulnerable to climatic variability. The findings confirm that ML models can accurately forecast crop yields and help farmers and policymakers adopt climate-smart agricultural strategies. The developed framework can serve as a decision-support system for resource optimization, early warning, and sustainable agricultural planning.
Keywords:
Climate Change, Crop Productivity, Machine Learning, Ensemble Model, XGBoost, Random Forest, Predictive Analytics, Sustainable Agriculture, Climate Smart Farming, Crop Yield Forecasting
