Machine learning-based cotton yield forecasting under climate change for precision agriculture
The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, particularly for key agricultural commodities, raises concerns about future yields. This...
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| Vydáno v: | Smart agricultural technology Ročník 12; s. 101117 |
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| Médium: | Journal Article |
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Elsevier B.V
01.12.2025
Elsevier |
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| ISSN: | 2772-3755, 2772-3755 |
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| Abstract | The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, particularly for key agricultural commodities, raises concerns about future yields. This study examines the potential effects of climate change on cotton production by integrating historical climate data, Global Climate Models (GCMs, CMIP3) projections, and cotton yield data. This study employs a diverse range of machine learning (ML) methods, including multiple regression, k-nearest neighbors (KNN), boosted tree algorithms, and various types of artificial neural networks (ANNs), to investigate the intricate relationship between climate factors and cotton yields. The models are developed and tested using data on climate and crop yields collected from three regions in Punjab, Pakistan, spanning the years 1991 to 2020. To estimate future yield outcomes, climate projections from General Circulation Models (GCMs) are downscaled under the SRA1B, A2, and B1 carbon emission scenarios, enabling forecasts extending to the year 2050. Results show that rainfall has a negligible impact on cotton yield (R = 0.0002), whereas maximum temperature (R = -0.183) is identified as the primary climatic factor influencing yield, followed by minimum temperature (R = 0.248). Among the models, the generalized feedforward (GFF) demonstrated the best performance (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), outperforming probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. In contrast, linear regression (LR) and multiple regression models performed less effectively. The reliability of GFF and KNN in providing yield estimates (R2 = 0.892, 0.861) supports their potential for accurate predictions. The study forecasts a 4.5% decline in cotton yield by 2050 compared to the highest recorded yield for the region, highlighting the impact of climate change on cotton production and its potential threat to food security. Nevertheless, the adaptive capabilities of the ANN (GFF) models across various climate scenarios present promising tools for integrating ML into climate-resilient agricultural practices, contributing to sustainable agrarian security and mitigating the adverse effects of climate change on food supply.
•Identified key climate factors influencing cotton yield, mainly temperature, and rainfall.•Applied ANN, KNN, regression, and ensemble models to simulate yield–climate response.•Forecasted 30-year cotton yield trends using GCM data to support climate adaptation.•Performed comparative analysis with state-of-the-art based on performance parameters. |
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| AbstractList | The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, particularly for key agricultural commodities, raises concerns about future yields. This study examines the potential effects of climate change on cotton production by integrating historical climate data, Global Climate Models (GCMs, CMIP3) projections, and cotton yield data. This study employs a diverse range of machine learning (ML) methods, including multiple regression, k-nearest neighbors (KNN), boosted tree algorithms, and various types of artificial neural networks (ANNs), to investigate the intricate relationship between climate factors and cotton yields. The models are developed and tested using data on climate and crop yields collected from three regions in Punjab, Pakistan, spanning the years 1991 to 2020. To estimate future yield outcomes, climate projections from General Circulation Models (GCMs) are downscaled under the SRA1B, A2, and B1 carbon emission scenarios, enabling forecasts extending to the year 2050. Results show that rainfall has a negligible impact on cotton yield (R = 0.0002), whereas maximum temperature (R = -0.183) is identified as the primary climatic factor influencing yield, followed by minimum temperature (R = 0.248). Among the models, the generalized feedforward (GFF) demonstrated the best performance (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), outperforming probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. In contrast, linear regression (LR) and multiple regression models performed less effectively. The reliability of GFF and KNN in providing yield estimates (R2 = 0.892, 0.861) supports their potential for accurate predictions. The study forecasts a 4.5% decline in cotton yield by 2050 compared to the highest recorded yield for the region, highlighting the impact of climate change on cotton production and its potential threat to food security. Nevertheless, the adaptive capabilities of the ANN (GFF) models across various climate scenarios present promising tools for integrating ML into climate-resilient agricultural practices, contributing to sustainable agrarian security and mitigating the adverse effects of climate change on food supply. The escalating threat of climate change presents a significant challenge to modern agriculture, with serious consequences for global food security. The impact of changing climate variables on crop productivity, particularly for key agricultural commodities, raises concerns about future yields. This study examines the potential effects of climate change on cotton production by integrating historical climate data, Global Climate Models (GCMs, CMIP3) projections, and cotton yield data. This study employs a diverse range of machine learning (ML) methods, including multiple regression, k-nearest neighbors (KNN), boosted tree algorithms, and various types of artificial neural networks (ANNs), to investigate the intricate relationship between climate factors and cotton yields. The models are developed and tested using data on climate and crop yields collected from three regions in Punjab, Pakistan, spanning the years 1991 to 2020. To estimate future yield outcomes, climate projections from General Circulation Models (GCMs) are downscaled under the SRA1B, A2, and B1 carbon emission scenarios, enabling forecasts extending to the year 2050. Results show that rainfall has a negligible impact on cotton yield (R = 0.0002), whereas maximum temperature (R = -0.183) is identified as the primary climatic factor influencing yield, followed by minimum temperature (R = 0.248). Among the models, the generalized feedforward (GFF) demonstrated the best performance (R = 0.960, MSE = 0.110, NMSE = 0.187, MAE = 0.269), outperforming probabilistic neural network (PNN), KNN, multilayer perceptron (MLP), and boosted trees. In contrast, linear regression (LR) and multiple regression models performed less effectively. The reliability of GFF and KNN in providing yield estimates (R2 = 0.892, 0.861) supports their potential for accurate predictions. The study forecasts a 4.5% decline in cotton yield by 2050 compared to the highest recorded yield for the region, highlighting the impact of climate change on cotton production and its potential threat to food security. Nevertheless, the adaptive capabilities of the ANN (GFF) models across various climate scenarios present promising tools for integrating ML into climate-resilient agricultural practices, contributing to sustainable agrarian security and mitigating the adverse effects of climate change on food supply. •Identified key climate factors influencing cotton yield, mainly temperature, and rainfall.•Applied ANN, KNN, regression, and ensemble models to simulate yield–climate response.•Forecasted 30-year cotton yield trends using GCM data to support climate adaptation.•Performed comparative analysis with state-of-the-art based on performance parameters. |
| ArticleNumber | 101117 |
| Author | Mansoor, Sheikh Tahir, Sana Rashid, Javed Khashan, Osama A. Ghani, Anwar Ahmad, Rashid Shahzad, Muhammad Umair |
| Author_xml | – sequence: 1 givenname: Muhammad Umair surname: Shahzad fullname: Shahzad, Muhammad Umair email: mushahzad@uo.edu.pk organization: Department Mathematics, University of Okara, Okara, 56310, Punjab, Pakistan – sequence: 2 givenname: Sana surname: Tahir fullname: Tahir, Sana email: tsana2945@gmail.com organization: Department Mathematics, University of Okara, Okara, 56310, Punjab, Pakistan – sequence: 3 givenname: Javed orcidid: 0000-0003-3416-9720 surname: Rashid fullname: Rashid, Javed email: RanaJavedRashid@gmail.com organization: Department IT Services, University of Okara, Okara, 56310, Punjab, Pakistan – sequence: 4 givenname: Osama A. orcidid: 0000-0003-1965-1869 surname: Khashan fullname: Khashan, Osama A. email: okhashan@ra.ac.ae organization: Research and Innovation Centers, Rabdan Academy, Abu Dhabi, 114646, United Arab Emirates – sequence: 5 givenname: Rashid surname: Ahmad fullname: Ahmad, Rashid email: rmahmad@su.edu.om organization: Faculty of Computing and Information Technology, Sohar University, Sohar, 311, Sultanate of Oman – sequence: 6 givenname: Sheikh surname: Mansoor fullname: Mansoor, Sheikh email: mansoorshafi@jejunu.ac.kr organization: Department of Plant Resources and Environment, Jeju National University, Jeju-si 63243, South Korea – sequence: 7 givenname: Anwar orcidid: 0000-0001-7474-0405 surname: Ghani fullname: Ghani, Anwar email: anwar.ghani@iiu.edu.pk, anwar.ghani@jejunu.ac.kr organization: Big Data Research Center, Department of Computer Engineering, Jeju National University, Jeju-si 63243, South Korea |
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| Keywords | Deep learning Smart agriculture Climate change Cotton yield Machine learning Prediction |
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