Impact Of Climatic Variables On Crop Yield Prediction Using Machine Learning Algorithms

The accelerating pace of climate change poses one of the most pressing challenges to global food security, making accurate and timely crop yield prediction an urgent scientific priority. This study investigates the influence of key climatic variables—including maximum and minimum temperature, precipitation, relative humidity, solar radiation, and atmospheric CO₂ concentration—on crop yield outcomes across diverse agricultural regions. Employing a suite of machine learning algorithms, namely Linear Regression, Support Vector Machines (SVM), Random Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM architecture, we develop and evaluate predictive models using multi-decade observational data. Our findings demonstrate that ensemble methods and deep learning architectures substantially outperform traditional statistical models, with the CNN-LSTM hybrid achieving an R² score of 0.95 and a Root Mean Square Error (RMSE) of 0.14 t/ha. Precipitation and maximum temperature were identified as the most influential predictors. The results highlight the transformative potential of machine learning in enabling climate-adaptive agricultural planning and underscore the necessity of integrating climatic intelligence into yield forecasting systems.