Spatial prediction of soil organic carbon stocks in Ghana using legacy data
Soil organic carbon (SOC) is a major driver of the multiple functions of soils in the delivery of ecosystem services. While the spatial prediction and mapping of SOC stocks are of key importance for land managers to understand the synergies between SOC management and soil health, a spatially-explici...
| Autores principales: | , , , |
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| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2019
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| Materias: | |
| Acceso en línea: | https://www.sciencedirect.com/science/article/pii/S0016706118319074 http://hdl.handle.net/20.500.12123/6430 https://doi.org/10.1016/j.geoderma.2019.114008 |
| _version_ | 1855035673516441600 |
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| author | Owusu, Stephen Yigini, Yusuf Olmedo, Guillermo Federico Omuto, Christian Thine |
| author_browse | Olmedo, Guillermo Federico Omuto, Christian Thine Owusu, Stephen Yigini, Yusuf |
| author_facet | Owusu, Stephen Yigini, Yusuf Olmedo, Guillermo Federico Omuto, Christian Thine |
| author_sort | Owusu, Stephen |
| collection | INTA Digital |
| description | Soil organic carbon (SOC) is a major driver of the multiple functions of soils in the delivery of ecosystem services. While the spatial prediction and mapping of SOC stocks are of key importance for land managers to understand the synergies between SOC management and soil health, a spatially-explicit map of the distribution of SOC stocks in Ghana is non-existent. Therefore, we quantified the spatial distribution of SOC stocks and associated uncertainties to a target depth of 0–30 cm based on regression-kriging modelling to fill this knowledge gap. The mean error (ME) of the predictions is negligible. The mean absolute error (MAE) shows that the model has prediction errors of about 0.48%. The coefficient of determination (R2) shows that the model explains 34% of the variation in model predictions of SOC stocks. The RMSE is 0.63% of the prediction errors. The predicted SOC stocks show significant variation in their spatial distribution throughout the country. Generally, a trend of decreasing SOC stocks from the southwest to the northeast is clearly recognized. SOC stocks are highest in the Semi-Deciduous agro-ecological zone (43.5 Mg C ha−1) and lowest in the Guinea Savannah agro-ecological zone (0.05 Mg C ha−1). About 5.4 Tg of SOC stocks is stored in the top 0–30 cm of the soils in Ghana. This preliminary work at a spatial resolution of 30 arc-seconds (~1 km) has been accomplished within the framework and guidelines of the Global Soil Partnership (GSP). To our knowledge, this is the first time ever in Ghana that a soil property map has been produced along with its uncertainties. Thus, this study represents a significant first step towards revolutionizing future soil property mapping in Ghana. Even though there remains the need to improve on the quality and spatial resolution, the SOC stocks map presented herein is a satisfactory first step to guide future research on soil organic carbon management at both national and global scales. |
| format | info:ar-repo/semantics/artículo |
| id | INTA6430 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA64302019-11-29T13:49:20Z Spatial prediction of soil organic carbon stocks in Ghana using legacy data Owusu, Stephen Yigini, Yusuf Olmedo, Guillermo Federico Omuto, Christian Thine Carbono Orgánico del Suelo Suelo Estimación de las Existencias de Carbono Teledetección Ghana Soil Organic Carbon Soil Carbon Stock Assessments Remote Sensing Soil organic carbon (SOC) is a major driver of the multiple functions of soils in the delivery of ecosystem services. While the spatial prediction and mapping of SOC stocks are of key importance for land managers to understand the synergies between SOC management and soil health, a spatially-explicit map of the distribution of SOC stocks in Ghana is non-existent. Therefore, we quantified the spatial distribution of SOC stocks and associated uncertainties to a target depth of 0–30 cm based on regression-kriging modelling to fill this knowledge gap. The mean error (ME) of the predictions is negligible. The mean absolute error (MAE) shows that the model has prediction errors of about 0.48%. The coefficient of determination (R2) shows that the model explains 34% of the variation in model predictions of SOC stocks. The RMSE is 0.63% of the prediction errors. The predicted SOC stocks show significant variation in their spatial distribution throughout the country. Generally, a trend of decreasing SOC stocks from the southwest to the northeast is clearly recognized. SOC stocks are highest in the Semi-Deciduous agro-ecological zone (43.5 Mg C ha−1) and lowest in the Guinea Savannah agro-ecological zone (0.05 Mg C ha−1). About 5.4 Tg of SOC stocks is stored in the top 0–30 cm of the soils in Ghana. This preliminary work at a spatial resolution of 30 arc-seconds (~1 km) has been accomplished within the framework and guidelines of the Global Soil Partnership (GSP). To our knowledge, this is the first time ever in Ghana that a soil property map has been produced along with its uncertainties. Thus, this study represents a significant first step towards revolutionizing future soil property mapping in Ghana. Even though there remains the need to improve on the quality and spatial resolution, the SOC stocks map presented herein is a satisfactory first step to guide future research on soil organic carbon management at both national and global scales. EEA Mendoza Fil: Owusu, Stephen. Council for Scientific and Industrial Research. Soil Research Institute; Ghana Fil: Yigini, Yusuf. Naciones Unidas. Food and Agriculture Organization (FAO); Italia Fil: Olmedo, Guillermo Federico. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; Argentina Fil: Omuto, Christian Thine. University of Nairobi. Department of Environmental and Biosystems Engineering; Kenia 2019-11-29T13:45:54Z 2019-11-29T13:45:54Z 2019-11 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.sciencedirect.com/science/article/pii/S0016706118319074 http://hdl.handle.net/20.500.12123/6430 0016-7061 1872-6259 https://doi.org/10.1016/j.geoderma.2019.114008 eng info:eu-repo/semantics/restrictedAccess application/pdf Elsevier Geoderma 360 : 114008 (February 2020) |
| spellingShingle | Carbono Orgánico del Suelo Suelo Estimación de las Existencias de Carbono Teledetección Ghana Soil Organic Carbon Soil Carbon Stock Assessments Remote Sensing Owusu, Stephen Yigini, Yusuf Olmedo, Guillermo Federico Omuto, Christian Thine Spatial prediction of soil organic carbon stocks in Ghana using legacy data |
| title | Spatial prediction of soil organic carbon stocks in Ghana using legacy data |
| title_full | Spatial prediction of soil organic carbon stocks in Ghana using legacy data |
| title_fullStr | Spatial prediction of soil organic carbon stocks in Ghana using legacy data |
| title_full_unstemmed | Spatial prediction of soil organic carbon stocks in Ghana using legacy data |
| title_short | Spatial prediction of soil organic carbon stocks in Ghana using legacy data |
| title_sort | spatial prediction of soil organic carbon stocks in ghana using legacy data |
| topic | Carbono Orgánico del Suelo Suelo Estimación de las Existencias de Carbono Teledetección Ghana Soil Organic Carbon Soil Carbon Stock Assessments Remote Sensing |
| url | https://www.sciencedirect.com/science/article/pii/S0016706118319074 http://hdl.handle.net/20.500.12123/6430 https://doi.org/10.1016/j.geoderma.2019.114008 |
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