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Impact of Climate Variability on Crop Yields

Journal: Journal Administration Integration Test (JAIT)

Authors

Department of Environmental Science, University of California, Berkeley, CA, USA

Department of Science, University of Texas, Texas, USA

School of Computer Science, Stanford University, Stanford, CA, USA

Corresponding author

Publication Information

Article type: Review

Source: Journal Administration Integration Test · 2026 · Volume 4 · Issue 3 · Pages 23–43

Publication history: Received 1 October 2026 · Revised 3 October 2026 · Accepted 5 October 2026 · Published 7 October 2026

DOI: 10.58175/testj

Highlights

  • Climate variability presents significant challenges
  • Machine Learning framework using Random Forest and LSTM neural networks to predict crop yield fluctuations

Abstract

Climate variability presents significant challenges to global food security. This study proposes a Machine Learning framework using Random Forest and LSTM neural networks to predict crop yield fluctuations under varying climate scenarios. Using historical weather data and crop yields from 2000 to 2025 across 50 regions, our models achieved an R-squared value of 0.89, outperforming traditional statistical regression models by 14%. These findings highlight the potential of AI-driven forecasting in agricultural planning.

Keywords

Climate Change Machine Learning Crop Yield Prediction

Graphical Abstract

Graphical abstract of Impact of Climate Variability on Crop Yields

CRediT Contributions

Conceptualization: JS. Data curation: JS. Formal analysis: JS. Visualization: JP. Writing – original draft: JP. Writing – review & editing: JP.

References

1 reference