PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia
This first pass implementation of the AgWise Fertilizer Recommendation Module for rice in Cambodia assembles fertilizer response trial dataset, calibrates both mechanistic and machine learning models, and generates preliminary maps of indigenous nutrient supply along with prototype fertilizer recomm...
| Autores principales: | , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
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International Rice Research Institute
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179040 |
| _version_ | 1855517924742135808 |
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| author | Mabilangan, Abigail Mkuhlani, Siyabusa Saito, Kazuki |
| author_browse | Mabilangan, Abigail Mkuhlani, Siyabusa Saito, Kazuki |
| author_facet | Mabilangan, Abigail Mkuhlani, Siyabusa Saito, Kazuki |
| author_sort | Mabilangan, Abigail |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This first pass implementation of the AgWise Fertilizer Recommendation Module for rice in Cambodia assembles fertilizer response trial dataset, calibrates both mechanistic and machine learning models, and generates preliminary maps of indigenous nutrient supply along with prototype fertilizer recommendations. A total of 1,909 georeferenced fertilizer response trials across 17 provinces were harmonized with soil, climate, and terrain covariates following the AgWise framework. Analyses reveal higher and more stable yields in the dry season than in the wet season; strong yield responses to nitrogen, a narrow -optimum for phosphorus, and potassium responses that peak at intermediate rates. Reverse and forward -QUEFTS, together with -gradient boosting and random forest models, reproduce observed yields with moderate to good predictive skill and provide -apparent soil N, P, and K supply estimates that can be mapped across the rice domain. These maps show uniformly low P, moderate but heterogeneous N, and high, highly variable K, providing clear evidence for a transition from current fertilizer recommendation toward site- and season--specific nutrient management.- These are explicitly treated as preliminary, pending refinement through incorporation of additional datasets and model re calibration. |
| format | Informe técnico |
| id | CGSpace179040 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Rice Research Institute |
| publisherStr | International Rice Research Institute |
| record_format | dspace |
| spelling | CGSpace1790402025-12-20T02:03:23Z PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia Mabilangan, Abigail Mkuhlani, Siyabusa Saito, Kazuki machine learning fertilizer technology frameworks innovation adoption nutrient management crop management agronomic practices sustainable agriculture This first pass implementation of the AgWise Fertilizer Recommendation Module for rice in Cambodia assembles fertilizer response trial dataset, calibrates both mechanistic and machine learning models, and generates preliminary maps of indigenous nutrient supply along with prototype fertilizer recommendations. A total of 1,909 georeferenced fertilizer response trials across 17 provinces were harmonized with soil, climate, and terrain covariates following the AgWise framework. Analyses reveal higher and more stable yields in the dry season than in the wet season; strong yield responses to nitrogen, a narrow -optimum for phosphorus, and potassium responses that peak at intermediate rates. Reverse and forward -QUEFTS, together with -gradient boosting and random forest models, reproduce observed yields with moderate to good predictive skill and provide -apparent soil N, P, and K supply estimates that can be mapped across the rice domain. These maps show uniformly low P, moderate but heterogeneous N, and high, highly variable K, providing clear evidence for a transition from current fertilizer recommendation toward site- and season--specific nutrient management.- These are explicitly treated as preliminary, pending refinement through incorporation of additional datasets and model re calibration. 2025-11-28 2025-12-19T04:21:41Z 2025-12-19T04:21:41Z Report https://hdl.handle.net/10568/179040 en Open Access application/pdf International Rice Research Institute Mabilangan, Abigail, Siyabusa Mkuhlani, and Kazuki Saito (2025). PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia. Los Baños, Philippines: International Rice Research Institute. 18 p. |
| spellingShingle | machine learning fertilizer technology frameworks innovation adoption nutrient management crop management agronomic practices sustainable agriculture Mabilangan, Abigail Mkuhlani, Siyabusa Saito, Kazuki PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia |
| title | PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia |
| title_full | PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia |
| title_fullStr | PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia |
| title_full_unstemmed | PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia |
| title_short | PROGRESS REPORT: Developing Data‑Driven Agronomic Recommendations for Site‑Specific Nutrient Management on Rice in Cambodia |
| title_sort | progress report developing data driven agronomic recommendations for site specific nutrient management on rice in cambodia |
| topic | machine learning fertilizer technology frameworks innovation adoption nutrient management crop management agronomic practices sustainable agriculture |
| url | https://hdl.handle.net/10568/179040 |
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