Soil respiration signals in response to sustainable soil management practices enhance soil organic carbon stocks
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866913397868593152 |
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| author | Guevara, Mario |
| author_facet | Guevara, Mario |
| contents | Development of a spatial-temporal and data-driven model of soil respiration at the global scale based on soil temperature, yearly soil moisture, and soil organic carbon (C) estimates. Prediction of soil respiration on an annual basis (1991-2018) with relatively high accuracy (NSE 0.69, CCC 0.82). Lower soil respiration trends, higher soil respiration magnitudes, and higher soil organic C stocks across areas experiencing the presence of sustainable soil management practices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05737 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soil respiration signals in response to sustainable soil management practices enhance soil organic carbon stocks Guevara, Mario Machine Learning Development of a spatial-temporal and data-driven model of soil respiration at the global scale based on soil temperature, yearly soil moisture, and soil organic carbon (C) estimates. Prediction of soil respiration on an annual basis (1991-2018) with relatively high accuracy (NSE 0.69, CCC 0.82). Lower soil respiration trends, higher soil respiration magnitudes, and higher soil organic C stocks across areas experiencing the presence of sustainable soil management practices. |
| title | Soil respiration signals in response to sustainable soil management practices enhance soil organic carbon stocks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.05737 |