Using explainable machine learning techniques to unpack farm-level management x climate interactions
Optimizing the management of maize production systems, including the milpa (intercropping of maize with beans and other species), is crucial for improving on-farm productivity and ultimately reducing food insecurity. This presentation showcases the results of a study aimed at identifying determinant...
| Autores principales: | , , , , , , , |
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| Formato: | Ponencia |
| Lenguaje: | Inglés |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/134910 |
| _version_ | 1855534024030683136 |
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| author | Ramírez Villegas, Julián Armando Jaimes, Diana Gonzalez Rodriguez, Carlos Eduardo Llanos, Lizeth Jimenez, Daniel Gardeazabal, Andrea Estrada, Oscar Nuñez, Daniel |
| author_browse | Estrada, Oscar Gardeazabal, Andrea Gonzalez Rodriguez, Carlos Eduardo Jaimes, Diana Jimenez, Daniel Llanos, Lizeth Nuñez, Daniel Ramírez Villegas, Julián Armando |
| author_facet | Ramírez Villegas, Julián Armando Jaimes, Diana Gonzalez Rodriguez, Carlos Eduardo Llanos, Lizeth Jimenez, Daniel Gardeazabal, Andrea Estrada, Oscar Nuñez, Daniel |
| author_sort | Ramírez Villegas, Julián Armando |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Optimizing the management of maize production systems, including the milpa (intercropping of maize with beans and other species), is crucial for improving on-farm productivity and ultimately reducing food insecurity. This presentation showcases the results of a study aimed at identifying determinants of maize yield in Guatemala using agronomic and climate data. The study employs interpretability techniques in machine learning to explain the interactions between climatic factors and crop management in productivity. The study follows a three-step approach: (1) an Extract, Transform, Load (ETL) process of data, involving feature engineering and data standardization and cleaning; (2) identification of algorithms, metrics, and algorithmic tuning; and (3) delving into interpretability using techniques such as SHAP (SHapley Additive exPlanations), partial dependence plots (PDP), accumulated local effects (ALE) plots, and Friedman's H-statistic to evaluate interactions between features |
| format | Ponencia |
| id | CGSpace134910 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | CGSpace1349102025-11-06T13:12:21Z Using explainable machine learning techniques to unpack farm-level management x climate interactions Ramírez Villegas, Julián Armando Jaimes, Diana Gonzalez Rodriguez, Carlos Eduardo Llanos, Lizeth Jimenez, Daniel Gardeazabal, Andrea Estrada, Oscar Nuñez, Daniel agronomic practices adaptación al cambio climático machine learning adaptation climate agronomy agronomía weather prácticas agronómicas tiempo estadística como ciencia Optimizing the management of maize production systems, including the milpa (intercropping of maize with beans and other species), is crucial for improving on-farm productivity and ultimately reducing food insecurity. This presentation showcases the results of a study aimed at identifying determinants of maize yield in Guatemala using agronomic and climate data. The study employs interpretability techniques in machine learning to explain the interactions between climatic factors and crop management in productivity. The study follows a three-step approach: (1) an Extract, Transform, Load (ETL) process of data, involving feature engineering and data standardization and cleaning; (2) identification of algorithms, metrics, and algorithmic tuning; and (3) delving into interpretability using techniques such as SHAP (SHapley Additive exPlanations), partial dependence plots (PDP), accumulated local effects (ALE) plots, and Friedman's H-statistic to evaluate interactions between features 2023-11-27 2023-12-01T10:39:40Z 2023-12-01T10:39:40Z Presentation https://hdl.handle.net/10568/134910 en Open Access application/pdf Ramirez Villegas, J.; Jaimes, D.; Gonzalez, C.; Llanos, L.; Jimenez, D.; Gardeazabal, A.; Estrada, O.; Nuñez, D. (2023) Using explainable machine learning techniques to unpack farm-level management x climate interactions. Presentation prepared from impact to solutions, data, data science and machine learning for climate adaptation at Wageningen University & Research. 26-28 November 2023. 14 sl. |
| spellingShingle | agronomic practices adaptación al cambio climático machine learning adaptation climate agronomy agronomía weather prácticas agronómicas tiempo estadística como ciencia Ramírez Villegas, Julián Armando Jaimes, Diana Gonzalez Rodriguez, Carlos Eduardo Llanos, Lizeth Jimenez, Daniel Gardeazabal, Andrea Estrada, Oscar Nuñez, Daniel Using explainable machine learning techniques to unpack farm-level management x climate interactions |
| title | Using explainable machine learning techniques to unpack farm-level management x climate interactions |
| title_full | Using explainable machine learning techniques to unpack farm-level management x climate interactions |
| title_fullStr | Using explainable machine learning techniques to unpack farm-level management x climate interactions |
| title_full_unstemmed | Using explainable machine learning techniques to unpack farm-level management x climate interactions |
| title_short | Using explainable machine learning techniques to unpack farm-level management x climate interactions |
| title_sort | using explainable machine learning techniques to unpack farm level management x climate interactions |
| topic | agronomic practices adaptación al cambio climático machine learning adaptation climate agronomy agronomía weather prácticas agronómicas tiempo estadística como ciencia |
| url | https://hdl.handle.net/10568/134910 |
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