Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach
This study aimed to create a model to identify land suitable for growing sunn hemp (Crotalaria juncea) and pigeon pea (Cajanus cajan) in the Okavango River basin of the Kavango East region of Namibia. Advanced tree-based ensemble learning models, including Random Forest, Extra Trees, Gradient Boosti...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
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Elsevier
2024
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| Acceso en línea: | https://hdl.handle.net/10568/168138 |
| _version_ | 1855522267732115456 |
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| author | Negussie, Kaleb Gizaw Gebrekidan, Bisrat Haile Wyss, Daniel Kappas, M. |
| author_browse | Gebrekidan, Bisrat Haile Kappas, M. Negussie, Kaleb Gizaw Wyss, Daniel |
| author_facet | Negussie, Kaleb Gizaw Gebrekidan, Bisrat Haile Wyss, Daniel Kappas, M. |
| author_sort | Negussie, Kaleb Gizaw |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This study aimed to create a model to identify land suitable for growing sunn hemp (Crotalaria juncea) and pigeon pea (Cajanus cajan) in the Okavango River basin of the Kavango East region of Namibia. Advanced tree-based ensemble learning models, including Random Forest, Extra Trees, Gradient Boosting, XGBoost and multivariate regression analysis , were employed to enhance analytical accuracy. The Random Forest and XGboost models exhibited outstanding performance, as evidenced by their respective accuracy values of 0.97 and 0.96. In addition, this study proposed an innovative approach through the integration of subjective and objective analytical methods, which are independent of one another. The subjective component of the analysis employed a Multi-Criteria Decision Making-Analytic Hierarchy Process (MCDM-AHP). On the other hand, the objective component used a data-driven multivariate approach supported by tree-based learning algorithms. Twenty-two variables were considered, encompassing climatic conditions, hydro-geomorphologic features, soil characteristics, vegetation patterns, and socio-economic factors. These variables played a crucial role to identify the most suitable areas for growing the selected leguminous crops. The MCDM-AHP method utilised expert evaluations to rank the importance of variables, identifying water sources, slope, and soil properties as key factors. A suitability mapping analysis revealed that 17.63% of the area was highly suitable and 62.77% moderately suitable, while 10% was less suitable and 9.59% unsuitable for growing these two legumes. According to the data driven methodology, soil fertility and nitrogen content emerged as key determinants for land suitability. This is particularly relevant for nitrogen-fixing leguminous crops such as sunn hemp and pigeon pea, which play a central role in improving soil quality and ensuring food security. |
| format | Journal Article |
| id | CGSpace168138 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1681382025-12-08T09:54:28Z Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach Negussie, Kaleb Gizaw Gebrekidan, Bisrat Haile Wyss, Daniel Kappas, M. decision making learning legumes land suitability This study aimed to create a model to identify land suitable for growing sunn hemp (Crotalaria juncea) and pigeon pea (Cajanus cajan) in the Okavango River basin of the Kavango East region of Namibia. Advanced tree-based ensemble learning models, including Random Forest, Extra Trees, Gradient Boosting, XGBoost and multivariate regression analysis , were employed to enhance analytical accuracy. The Random Forest and XGboost models exhibited outstanding performance, as evidenced by their respective accuracy values of 0.97 and 0.96. In addition, this study proposed an innovative approach through the integration of subjective and objective analytical methods, which are independent of one another. The subjective component of the analysis employed a Multi-Criteria Decision Making-Analytic Hierarchy Process (MCDM-AHP). On the other hand, the objective component used a data-driven multivariate approach supported by tree-based learning algorithms. Twenty-two variables were considered, encompassing climatic conditions, hydro-geomorphologic features, soil characteristics, vegetation patterns, and socio-economic factors. These variables played a crucial role to identify the most suitable areas for growing the selected leguminous crops. The MCDM-AHP method utilised expert evaluations to rank the importance of variables, identifying water sources, slope, and soil properties as key factors. A suitability mapping analysis revealed that 17.63% of the area was highly suitable and 62.77% moderately suitable, while 10% was less suitable and 9.59% unsuitable for growing these two legumes. According to the data driven methodology, soil fertility and nitrogen content emerged as key determinants for land suitability. This is particularly relevant for nitrogen-fixing leguminous crops such as sunn hemp and pigeon pea, which play a central role in improving soil quality and ensuring food security. 2024-12 2024-12-20T14:52:04Z 2024-12-20T14:52:04Z Journal Article https://hdl.handle.net/10568/168138 en Open Access application/pdf Elsevier Negussie, K. G., Gebrekidan, B. H., Wyss, D., & Kappas, M. (2024). Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach. International Journal of Applied Earth Observation and Geoinformation, 135, 104284. https://doi.org/10.1016/j.jag.2024.104284 |
| spellingShingle | decision making learning legumes land suitability Negussie, Kaleb Gizaw Gebrekidan, Bisrat Haile Wyss, Daniel Kappas, M. Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach |
| title | Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach |
| title_full | Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach |
| title_fullStr | Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach |
| title_full_unstemmed | Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach |
| title_short | Assessing land suitability for leguminous crops in the okavango river basin: A multicriteria and machine learning approach |
| title_sort | assessing land suitability for leguminous crops in the okavango river basin a multicriteria and machine learning approach |
| topic | decision making learning legumes land suitability |
| url | https://hdl.handle.net/10568/168138 |
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