Can soil fertility properties in rice fields in sub-Saharan Africa be predicted by digital soil information? A case study of AfSoilGrids250m
| cg.authorship.types | CGIAR single centre | en |
| cg.contributor.affiliation | Africa Rice Center | en |
| cg.contributor.affiliation | University of Abomey-Calavi | en |
| cg.contributor.affiliation | University of Bonn | en |
| cg.contributor.donor | European Union | en |
| cg.contributor.donor | CGIAR Trust Fund | en |
| cg.contributor.donor | International Fund for Agricultural Development | en |
| cg.contributor.donor | African Development Bank | en |
| cg.contributor.initiative | Excellence in Agronomy | |
| cg.coverage.country | Benin | |
| cg.coverage.iso3166-alpha2 | BJ | |
| cg.coverage.region | Africa | |
| cg.coverage.region | Western Africa | |
| cg.creator.identifier | Jean-Martial Johnson: 0000-0002-2638-8774 | |
| cg.creator.identifier | Kazuki Saito: 0000-0002-8609-2713 | |
| cg.howPublished | Formally Published | en |
| cg.identifier.doi | https://doi.org/10.1016/j.geodrs.2022.e00563 | en |
| cg.isijournal | ISI Journal | en |
| cg.issn | 2352-0094 | en |
| cg.journal | Geoderma Regional | en |
| cg.reviewStatus | Peer Review | en |
| cg.subject.actionArea | Resilient Agrifood Systems | |
| cg.subject.iita | FOOD SECURITY | en |
| cg.subject.iita | SOIL FERTILITY | en |
| cg.subject.iita | SOIL INFORMATION | en |
| cg.subject.impactArea | Poverty reduction, livelihoods and jobs | |
| cg.subject.impactArea | Nutrition, health and food security | |
| cg.subject.sdg | SDG 1 - No poverty | en |
| cg.subject.sdg | SDG 2 - Zero hunger | en |
| cg.volume | 30 | en |
| dc.contributor.author | Djagba, J.F. | en |
| dc.contributor.author | Johnson, J.M. | en |
| dc.contributor.author | Saito, Kazuki | en |
| dc.date.accessioned | 2023-01-18T15:53:55Z | en |
| dc.date.available | 2023-01-18T15:53:55Z | en |
| dc.identifier.uri | https://hdl.handle.net/10568/127428 | |
| dc.title | Can soil fertility properties in rice fields in sub-Saharan Africa be predicted by digital soil information? A case study of AfSoilGrids250m | en |
| dcterms.abstract | Soil information is essential for sustainable agricultural intensification in sub-Saharan Africa (SSA). This is the case for rice production, for which soil fertility is one of the main constraints. Through the Africa Soil Information Service (AfSIS), digital soil information at 250 m resolution (AfSoilGrids250m) is available for SSA. However, it was not validated in a wide range of rice-growing conditions. The objective of this study was to assess the accuracy of AfSoilGrids250m by comparing predicted soil fertility properties including pH H2O, clay and silt contents, total nitrogen (TN) and organic carbon (OC) with wet chemistry (WC) analysis and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) methods. Soil samples were collected from 1002 rice fields in three production systems (irrigated lowland, rainfed lowland, and rainfed upland) in 32 sites and over five agro-ecological zones (AEZ). The coefficient of determination (R2), index of Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBias) were used to assess the predictive performance of AfSoilGrids250m. In comparison with WC and DRIFTS methods, AfSoilGrids250m underestimated the studied soil fertility properties. At the field scale, the prediction accuracy of AfSoilGrids250m for pH H2O, clay and silt contents, total nitrogen (TN), and organic carbon (OC) were poor (R2 < 0.50). The best predictive performances were obtained when data were aggregated by site-production system combination (site x PS) (n = 40). With this aggregation, AfSoilGrids250m achieved satisfactory to good prediction accuracy for TN and OC. The classification of AfSoilGrids250m had a fair to moderate agreement with both WC and DRIFTS classifications for clay content, TN, and OC. We conclude that current digital soil information (AfSoilGrids250m) is useful for assessing and classifying soil fertility properties of rice fields in different production systems at the site scale in SSA, but not as much for predicting them at the farmers' field scale. | en |
| dcterms.accessRights | Limited Access | |
| dcterms.available | 2022-07-26 | |
| dcterms.bibliographicCitation | Djagba, J. F., Johnson, J.-M., & Saito, K. (2022). Can soil fertility properties in rice fields in sub-Saharan Africa be predicted by digital soil information? A case study of AfSoilGrids250m. In Geoderma Regional (Vol. 30, p. e00563). Elsevier BV. https://doi.org/10.1016/j.geodrs.2022.e00563 | en |
| dcterms.extent | e00563 | en |
| dcterms.issued | 2022-09 | |
| dcterms.language | en | |
| dcterms.license | Copyrighted; all rights reserved | |
| dcterms.publisher | Elsevier | en |
| dcterms.replaces | https://hdl.handle.net/10568/128165 | en |
| dcterms.type | Journal Article |
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