Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya
Digital Soil Mapping (DSM) enhances digital twin models by supplying detailed, spatially explicit soil data crucial for accurate virtual representations. With high-resolution maps of soil properties (e.g., texture, moisture, organic matter), DSM allows twins to simulate soil processes precisely and...
| Autores principales: | , , , , , , , , , |
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| Formato: | Brief |
| Lenguaje: | Inglés |
| Publicado: |
International Livestock Research Institute
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/159654 |
| _version_ | 1855529588682129408 |
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| author | Paliwal, Ambica Cherotich, Fredah Leitner, Sonja Pearce, F. Rufino, M. Quinton, J. Dhulipala, Ram Salavati, M. Gluecks, Ilona V. Whitbread, Anthony M. |
| author_browse | Cherotich, Fredah Dhulipala, Ram Gluecks, Ilona V. Leitner, Sonja Paliwal, Ambica Pearce, F. Quinton, J. Rufino, M. Salavati, M. Whitbread, Anthony M. |
| author_facet | Paliwal, Ambica Cherotich, Fredah Leitner, Sonja Pearce, F. Rufino, M. Quinton, J. Dhulipala, Ram Salavati, M. Gluecks, Ilona V. Whitbread, Anthony M. |
| author_sort | Paliwal, Ambica |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Digital Soil Mapping (DSM) enhances digital twin models by supplying detailed, spatially explicit soil data crucial for accurate virtual representations. With high-resolution maps of soil properties (e.g., texture, moisture, organic matter), DSM allows twins to simulate soil processes precisely and incorporate real-time updates for applications like precision agriculture. This integration supports predictive modeling, letting digital twins assess potential impacts of various scenarios on soil health and productivity. By informing sustainable land management and resilience planning, DSM-powered digital twins offer actionable insights for forage species selection, and conservation practices, enabling better resource management and environmental adaptation decisions. |
| format | Brief |
| id | CGSpace159654 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Livestock Research Institute |
| publisherStr | International Livestock Research Institute |
| record_format | dspace |
| spelling | CGSpace1596542025-01-27T15:00:52Z Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya Paliwal, Ambica Cherotich, Fredah Leitner, Sonja Pearce, F. Rufino, M. Quinton, J. Dhulipala, Ram Salavati, M. Gluecks, Ilona V. Whitbread, Anthony M. machine learning research soil wildlife Digital Soil Mapping (DSM) enhances digital twin models by supplying detailed, spatially explicit soil data crucial for accurate virtual representations. With high-resolution maps of soil properties (e.g., texture, moisture, organic matter), DSM allows twins to simulate soil processes precisely and incorporate real-time updates for applications like precision agriculture. This integration supports predictive modeling, letting digital twins assess potential impacts of various scenarios on soil health and productivity. By informing sustainable land management and resilience planning, DSM-powered digital twins offer actionable insights for forage species selection, and conservation practices, enabling better resource management and environmental adaptation decisions. 2024 2024-11-13T12:53:57Z 2024-11-13T12:53:57Z Brief https://hdl.handle.net/10568/159654 en Open Access application/pdf International Livestock Research Institute Paliwal, A., Cherotich, F., Leitner, S., Pearce, F., Rufino, M., Quinton, J., Dhulipala, R., Salavati, M., Gluecks, I. and Whitbread, A. 2024. Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya. Nairobi, Kenya: ILRI. |
| spellingShingle | machine learning research soil wildlife Paliwal, Ambica Cherotich, Fredah Leitner, Sonja Pearce, F. Rufino, M. Quinton, J. Dhulipala, Ram Salavati, M. Gluecks, Ilona V. Whitbread, Anthony M. Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya |
| title | Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya |
| title_full | Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya |
| title_fullStr | Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya |
| title_full_unstemmed | Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya |
| title_short | Machine learning- based gridded soil mapping for Kapiti research station and wildlife conservancy, Kenya |
| title_sort | machine learning based gridded soil mapping for kapiti research station and wildlife conservancy kenya |
| topic | machine learning research soil wildlife |
| url | https://hdl.handle.net/10568/159654 |
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