Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains
Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOP...
| Autores principales: | , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/155531 |
| _version_ | 1855527769574735872 |
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| author | Ahmed, Zia Uddin Krupnik, Timothy J. Timsina, Jagadish Saiful Islam Hossain, M. Khaled Alanuzzaman Kurishi, A.S.M. Shah-Al Emran Harun-Ar-Rashid McDonald, Andrew Gathala, Mahesh K. |
| author_browse | Ahmed, Zia Uddin Alanuzzaman Kurishi, A.S.M. Gathala, Mahesh K. Harun-Ar-Rashid Hossain, M. Khaled Krupnik, Timothy J. McDonald, Andrew Saiful Islam Shah-Al Emran Timsina, Jagadish |
| author_facet | Ahmed, Zia Uddin Krupnik, Timothy J. Timsina, Jagadish Saiful Islam Hossain, M. Khaled Alanuzzaman Kurishi, A.S.M. Shah-Al Emran Harun-Ar-Rashid McDonald, Andrew Gathala, Mahesh K. |
| author_sort | Ahmed, Zia Uddin |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP. |
| format | Journal Article |
| id | CGSpace155531 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1555312025-10-26T12:52:53Z Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains Ahmed, Zia Uddin Krupnik, Timothy J. Timsina, Jagadish Saiful Islam Hossain, M. Khaled Alanuzzaman Kurishi, A.S.M. Shah-Al Emran Harun-Ar-Rashid McDonald, Andrew Gathala, Mahesh K. machine learning yields maize nutrient management soil fertility Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrient management, maximizing profitability, ensuring food security, and promoting environmental sustainability. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An additive main effect and multiplicative interaction (AMMI) model was used to explain maize yield variability with nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine learning (AutoML) frameworks were subsequently used to predict attainable yield relative nutrient-limited yield (RY) and to rank variables that control RY. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP. 2024-09 2024-10-23T14:59:16Z 2024-10-23T14:59:16Z Journal Article https://hdl.handle.net/10568/155531 en Open Access application/pdf Elsevier Ahmed, Z. U., Krupnik, T. J., Timsina, J., Islam, S., Hossain, K., Kurishi, A. S. M. A., Emran, S.-A., Harun-Ar-Rashid, M., McDonald, A. J., & Gathala, M. K. (2024). Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains. Artificial Intelligence in Agriculture, 13, 100–116. https://doi.org/10.1016/j.aiia.2024.08.001 |
| spellingShingle | machine learning yields maize nutrient management soil fertility Ahmed, Zia Uddin Krupnik, Timothy J. Timsina, Jagadish Saiful Islam Hossain, M. Khaled Alanuzzaman Kurishi, A.S.M. Shah-Al Emran Harun-Ar-Rashid McDonald, Andrew Gathala, Mahesh K. Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains |
| title | Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains |
| title_full | Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains |
| title_fullStr | Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains |
| title_full_unstemmed | Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains |
| title_short | Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains |
| title_sort | prediction of spatial heterogeneity in nutrient limited sub tropical maize yield implications for precision management in the eastern indo gangetic plains |
| topic | machine learning yields maize nutrient management soil fertility |
| url | https://hdl.handle.net/10568/155531 |
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