Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia
Maize is essential for food security and income in Colombia, but its production faces challenges such as drought, waterlogging, heat stress, and inadequate agronomic practices. To improve production in the face of climate variability, it is crucial to optimize agronomic practices. This study analyze...
| Main Authors: | , , , , |
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| Format: | Informe técnico |
| Language: | Inglés |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/163489 |
| _version_ | 1855542481797513216 |
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| author | Jaimes, Diana Estrada, Oscar Gonzalez, Arturo Llanos Herrera, Lizeth Ramirez Villegas, Julian |
| author_browse | Estrada, Oscar Gonzalez, Arturo Jaimes, Diana Llanos Herrera, Lizeth Ramirez Villegas, Julian |
| author_facet | Jaimes, Diana Estrada, Oscar Gonzalez, Arturo Llanos Herrera, Lizeth Ramirez Villegas, Julian |
| author_sort | Jaimes, Diana |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Maize is essential for food security and income in Colombia, but its production faces challenges such as drought, waterlogging, heat stress, and inadequate agronomic practices. To improve production in the face of climate variability, it is crucial to optimize agronomic practices. This study analyzes maize yield in response to agronomy and climate using machine learning algorithms. The approach employed addresses the explainability of machine learning algorithms, including data extraction, transformation, and loading (ETL), algorithm selection and tuning, and techniques to deepen interpretability. The approach seeks to explain the effects of independent predictor variables and their interactions on maize yield. The case study in Colombia uses 5 years of farm-level data from Colombia’s key maize producing regions. The dataset includes data on yield, agronomic management, terrain, and climate. The results provide findings and recommendations based on the models and data for the department of Córdoba. The use of explainability techniques makes machine learning models in agronomy more transparent, thus improving trust and applicability of data-driven recommendations. |
| format | Informe técnico |
| id | CGSpace163489 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1634892025-11-05T12:36:02Z Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia Jaimes, Diana Estrada, Oscar Gonzalez, Arturo Llanos Herrera, Lizeth Ramirez Villegas, Julian climate change adaptation machine learning maize data analysis modelling Maize is essential for food security and income in Colombia, but its production faces challenges such as drought, waterlogging, heat stress, and inadequate agronomic practices. To improve production in the face of climate variability, it is crucial to optimize agronomic practices. This study analyzes maize yield in response to agronomy and climate using machine learning algorithms. The approach employed addresses the explainability of machine learning algorithms, including data extraction, transformation, and loading (ETL), algorithm selection and tuning, and techniques to deepen interpretability. The approach seeks to explain the effects of independent predictor variables and their interactions on maize yield. The case study in Colombia uses 5 years of farm-level data from Colombia’s key maize producing regions. The dataset includes data on yield, agronomic management, terrain, and climate. The results provide findings and recommendations based on the models and data for the department of Córdoba. The use of explainability techniques makes machine learning models in agronomy more transparent, thus improving trust and applicability of data-driven recommendations. 2024-12-04 2024-12-15T09:44:01Z 2024-12-15T09:44:01Z Report https://hdl.handle.net/10568/163489 en Open Access application/pdf Jaimes, D.; Estrada, O.; Gonzalez, A.; Llanos Herrera, L.; Ramirez Villegas, J. (2024) Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia. CGIAR Initiative on Excellence in Agronomy Technical Report. 17 p. |
| spellingShingle | climate change adaptation machine learning maize data analysis modelling Jaimes, Diana Estrada, Oscar Gonzalez, Arturo Llanos Herrera, Lizeth Ramirez Villegas, Julian Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia |
| title | Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia |
| title_full | Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia |
| title_fullStr | Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia |
| title_full_unstemmed | Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia |
| title_short | Application of explainable AI techniques to site-specific and climate-smart management of maize systems in Colombia |
| title_sort | application of explainable ai techniques to site specific and climate smart management of maize systems in colombia |
| topic | climate change adaptation machine learning maize data analysis modelling |
| url | https://hdl.handle.net/10568/163489 |
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