What does this research mean for the field?

Ensemble methods and deep learning architectures, particularly a hybrid CNN-LSTM model, substantially outperform traditional statistical models in predicting crop yields, with precipitation and maximum temperature being the most influential climatic predictors. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to assess how key climatic variables influence crop yield outcomes by leveraging machine learning techniques.

May 8, 2026Open Access

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

Key Points

This study aims to assess how key climatic variables influence crop yield outcomes by leveraging machine learning techniques.
Utilized multi-decade observational data for analysis
Employed various machine learning algorithms including Linear Regression, SVM, RF, GB, LSTM, and CNN-LSTM
Evaluated model performance based on R² score and Root Mean Square Error (RMSE)
CNN-LSTM hybrid model achieved an R² score of 0.95 and RMSE of 0.14 t/ha
Precipitation and maximum temperature emerged as the most influential predictors
Machine learning methods significantly outperformed traditional statistical models in yield prediction

Abstract

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.

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

Key Points

Abstract

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