Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields
Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme cli...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2022-10-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/11/1791 |
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| author | Qing Sun Yi Zhang Xianghong Che Sining Chen Qing Ying Xiaohui Zheng Aixia Feng |
| author_facet | Qing Sun Yi Zhang Xianghong Che Sining Chen Qing Ying Xiaohui Zheng Aixia Feng |
| author_sort | Qing Sun |
| collection | DOAJ |
| description | Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme climate events on soybean, which brings large uncertainties. This study proposed an approach of hybrid models to constrain such uncertainties by coupling the GEPIC model and extreme climate indicators using machine learning. Subsequently, the key extreme climate indicators for the globe and main soybean producing countries are explored, and future soybean yield changes and variability are analyzed using the proposed hybrid model. The results show the coupled GEPIC and Random Forest (GEPIC+RF) model (R: 0.812, RMSD: 0.716 t/ha and rRMSD: 36.62%) significantly eliminated uncertainties and underestimation of climate extremes from the GEPIC model (R: 0.138, RMSD: 1.401 t/ha and rRMSD: 71.57%) compared to the other five hybrid models (R: 0.365–0.612, RMSD: 0.928–1.021 and rRMSD: 47.48–52.24%) during the historical period. For global soybean yield and those in Brazil and Argentina, low-temperature-related indices are the main restriction factors, whereas drought is the constraining factor in the USA and China, and combined drought–heat disaster in India. The GEPIC model would overestimate soybean yields by 13.40–27.23%. The GEPIC+RF model reduced uncertainty by 28.45–41.83% for the period of 2040–2099. Our results imply that extreme climate events will possibly cause more losses in soybean in the future than we have expected, which would help policymakers prepare for future agriculture risk and food security under climate change. |
| first_indexed | 2024-03-09T19:22:01Z |
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| id | doaj.art-79a0763d403d428d89aeacd49f398ba7 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T19:22:01Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj.art-79a0763d403d428d89aeacd49f398ba72023-11-24T03:16:56ZengMDPI AGAgriculture2077-04722022-10-011211179110.3390/agriculture12111791Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean YieldsQing Sun0Yi Zhang1Xianghong Che2Sining Chen3Qing Ying4Xiaohui Zheng5Aixia Feng6State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaChinese Academy of Surveying & Mapping, Beijing 100830, ChinaTianjin Climate Center, Tianjin 300074, ChinaEarth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20737, USAChina Meteorological Administration Training Center, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaSoybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme climate events on soybean, which brings large uncertainties. This study proposed an approach of hybrid models to constrain such uncertainties by coupling the GEPIC model and extreme climate indicators using machine learning. Subsequently, the key extreme climate indicators for the globe and main soybean producing countries are explored, and future soybean yield changes and variability are analyzed using the proposed hybrid model. The results show the coupled GEPIC and Random Forest (GEPIC+RF) model (R: 0.812, RMSD: 0.716 t/ha and rRMSD: 36.62%) significantly eliminated uncertainties and underestimation of climate extremes from the GEPIC model (R: 0.138, RMSD: 1.401 t/ha and rRMSD: 71.57%) compared to the other five hybrid models (R: 0.365–0.612, RMSD: 0.928–1.021 and rRMSD: 47.48–52.24%) during the historical period. For global soybean yield and those in Brazil and Argentina, low-temperature-related indices are the main restriction factors, whereas drought is the constraining factor in the USA and China, and combined drought–heat disaster in India. The GEPIC model would overestimate soybean yields by 13.40–27.23%. The GEPIC+RF model reduced uncertainty by 28.45–41.83% for the period of 2040–2099. Our results imply that extreme climate events will possibly cause more losses in soybean in the future than we have expected, which would help policymakers prepare for future agriculture risk and food security under climate change.https://www.mdpi.com/2077-0472/12/11/1791soybean yieldextreme climate eventsmachine learningcrop modeluncertainty |
| spellingShingle | Qing Sun Yi Zhang Xianghong Che Sining Chen Qing Ying Xiaohui Zheng Aixia Feng Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields Agriculture soybean yield extreme climate events machine learning crop model uncertainty |
| title | Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields |
| title_full | Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields |
| title_fullStr | Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields |
| title_full_unstemmed | Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields |
| title_short | Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields |
| title_sort | coupling process based crop model and extreme climate indicators with machine learning can improve the predictions and reduce uncertainties of global soybean yields |
| topic | soybean yield extreme climate events machine learning crop model uncertainty |
| url | https://www.mdpi.com/2077-0472/12/11/1791 |
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