Analysis of Wheat-Yield Prediction Using Machine Learning Models under Climate Change Scenarios

Nida Iqbal, Muhammad Umair Shahzad, El-Sayed M. Sherif, Muhammad Usman Tariq, Javed Rashid, Tuan-Vinh Le () and Anwar Ghani ()
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Nida Iqbal: Department of Mathematics, Faculty of Science, University of Okara, Okara 56130, Pakistan
Muhammad Umair Shahzad: Department of Mathematics, Faculty of Science, University of Okara, Okara 56130, Pakistan
El-Sayed M. Sherif: Mechanical Engineering Department, College of Engineering, King Saud University, Al-Riyadh 11421, Saudi Arabia
Muhammad Usman Tariq: Marketing, Operations and Information System, Abu Dhabi University, Abu Dhabi 971, United Arab Emirates
Javed Rashid: Department of IT Services, University of Okara, Okara 56130, Pakistan
Tuan-Vinh Le: Bachelor’s Program of Artificial Intelligence and Information Security, Fu Jen Catholic University, New Taipei City 242062, Taiwan
Anwar Ghani: Department of Computer Science, International Islamic University, Islamabad 44000, Pakistan

Sustainability, 2024, vol. 16, issue 16, 1-26

Abstract: Climate change has emerged as one of the most significant challenges in modern agriculture, with potential implications for global food security. The impact of changing climatic conditions on crop yield, particularly for staple crops like wheat, has raised concerns about future food production. By integrating historical climate data, GCM (CMIP3) projections, and wheat-yield records, our analysis aims to provide significant insights into how climate change may affect wheat output. This research uses advanced machine learning models to explore the intricate relationship between climate change and wheat-yield prediction. Machine learning models used include multiple linear regression (MLR), boosted tree, random forest, ensemble models, and several types of ANNs: ANN (multi-layer perceptron), ANN (probabilistic neural network), ANN (generalized feed-forward), and ANN (linear regression). The model was evaluated and validated against yield and weather data from three Punjab, Pakistan, regions (1991–2021). The calibrated yield response model used downscaled global climate model (GCM) outputs for the SRA2, B1, and A1B average collective CO 2 emissions scenarios to anticipate yield changes through 2052. Results showed that maximum temperature (R = 0.116) was the primary climate factor affecting wheat yield in Punjab, preceding the T m i n (R = 0.114), while rainfall had a negligible impact (R = 0.000). The ensemble model (R = 0.988, nRMSE= 8.0%, MAE = 0.090) demonstrated outstanding yield performance, outperforming Random Forest Regression (R = 0.909, nRMSE = 18%, MAE = 0.182), ANN(MLP) (R = 0.902, MAE = 0.238, nRMSE = 17.0%), and boosting tree (R = 0.902, nRMSE = 20%, MAE = 0.198). ANN(PNN) performed inadequately. The ensemble model and RF showed better yield results with R 2 = 0.953, 0.791. The expected yield is 5.5% lower than the greatest average yield reported at the site in 2052. The study predicts that site-specific wheat output will experience a significant loss due to climate change. This decrease, which is anticipated to be 5.5% lower than the highest yield ever recorded, points to a potential future loss in wheat output that might worsen food insecurity. Additionally, our findings highlighted that ensemble approaches leveraging multiple model strengths could offer more accurate and reliable predictions under varying climate scenarios. This suggests a significant potential for integrating machine learning in developing climate-resilient agricultural practices, paving the way for future sustainable food security solutions.

Keywords: wheat yield; machine learning; deep learning; climate change; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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