Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach
Despite the United States’s major role as a rice exporter with significant economic and environmental impacts, most remote sensing and machine learning research on rice yield prediction has focused on Asian production regions. Moreover, while rice cultivation generates substantial methane...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10969758/ |
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| author | Jameson Augustin Munisamy Gopinath Berna Karali Yuhan Rao |
| author_facet | Jameson Augustin Munisamy Gopinath Berna Karali Yuhan Rao |
| author_sort | Jameson Augustin |
| collection | DOAJ |
| description | Despite the United States’s major role as a rice exporter with significant economic and environmental impacts, most remote sensing and machine learning research on rice yield prediction has focused on Asian production regions. Moreover, while rice cultivation generates substantial methane emissions, few studies have explored the trade-offs between productivity and environmental impact. This study leverages remote sensing and machine learning techniques to predict U.S. county-level rice yields and methane emissions from 2008 to 2022 across 67 counties in six major rice-producing states. We use eight different machine learning models for predictions. XGBoost and EBM emerge as top performers, accurately predicting yields and emissions, individually, without overfitting. A key finding reveals that these models excel at out-of-season forecasts, accurately predicting yields as early as April-June of the growing season. Feature importance analysis highlights soil properties, particularly pH and texture at various depths, as critical predictors for both yield and emissions. Most importantly, this study advances an integrated economic-environmental modeling in agriculture by analyzing yield-emissions trade-offs through the Non-dominated Sorting Genetic Algorithm II (NSGA-II), revealing an unexpected synergy where practices that improve economic productivity also reduce environmental impact, as higher yields correlate with lower methane emissions. |
| format | Article |
| id | doaj-art-c3d5bb86d0764f0590731a21e8222040 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c3d5bb86d0764f0590731a21e82220402025-08-20T03:53:27ZengIEEEIEEE Access2169-35362025-01-0113700187004310.1109/ACCESS.2025.356239710969758Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning ApproachJameson Augustin0https://orcid.org/0000-0001-6055-779XMunisamy Gopinath1https://orcid.org/0000-0002-6249-7548Berna Karali2https://orcid.org/0000-0001-7969-7506Yuhan Rao3Department of Agricultural and Applied Economics, University of Georgia, Athens, GA, USADepartment of Agricultural and Applied Economics, University of Georgia, Athens, GA, USADepartment of Agricultural and Applied Economics, University of Georgia, Athens, GA, USANorth Carolina Institute for Climate Studies, Asheville, NC, USADespite the United States’s major role as a rice exporter with significant economic and environmental impacts, most remote sensing and machine learning research on rice yield prediction has focused on Asian production regions. Moreover, while rice cultivation generates substantial methane emissions, few studies have explored the trade-offs between productivity and environmental impact. This study leverages remote sensing and machine learning techniques to predict U.S. county-level rice yields and methane emissions from 2008 to 2022 across 67 counties in six major rice-producing states. We use eight different machine learning models for predictions. XGBoost and EBM emerge as top performers, accurately predicting yields and emissions, individually, without overfitting. A key finding reveals that these models excel at out-of-season forecasts, accurately predicting yields as early as April-June of the growing season. Feature importance analysis highlights soil properties, particularly pH and texture at various depths, as critical predictors for both yield and emissions. Most importantly, this study advances an integrated economic-environmental modeling in agriculture by analyzing yield-emissions trade-offs through the Non-dominated Sorting Genetic Algorithm II (NSGA-II), revealing an unexpected synergy where practices that improve economic productivity also reduce environmental impact, as higher yields correlate with lower methane emissions.https://ieeexplore.ieee.org/document/10969758/Machine learningmethane emissionsremote sensingrice yield predictionsustainable agriculture |
| spellingShingle | Jameson Augustin Munisamy Gopinath Berna Karali Yuhan Rao Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach IEEE Access Machine learning methane emissions remote sensing rice yield prediction sustainable agriculture |
| title | Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach |
| title_full | Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach |
| title_fullStr | Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach |
| title_full_unstemmed | Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach |
| title_short | Joint Prediction of U.S. Rice Yields and Methane Emissions: A Machine Learning Approach |
| title_sort | joint prediction of u s rice yields and methane emissions a machine learning approach |
| topic | Machine learning methane emissions remote sensing rice yield prediction sustainable agriculture |
| url | https://ieeexplore.ieee.org/document/10969758/ |
| work_keys_str_mv | AT jamesonaugustin jointpredictionofusriceyieldsandmethaneemissionsamachinelearningapproach AT munisamygopinath jointpredictionofusriceyieldsandmethaneemissionsamachinelearningapproach AT bernakarali jointpredictionofusriceyieldsandmethaneemissionsamachinelearningapproach AT yuhanrao jointpredictionofusriceyieldsandmethaneemissionsamachinelearningapproach |