| Autor: |
Kwabena Abrefa Nketia, Stephen Boahen Asabere, Daniela Sauer |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Data in Brief, Vol 42, Iss , Pp 108192- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2352-3409 |
| DOI: |
10.1016/j.dib.2022.108192 |
| Popis: |
In this article, we present the space-time variability of soil moisture (SM) and soil water storage (SWS) from key agricultural benchmark soil types measured across the Guinea savannah zone of Ghana (n ≈ 2,000 measurements) in a single cropping season (Nketia et al., 2022). From 36 locations, SM measurements were obtained with a PR2/60 moisture probe calibrated for a 0–100 cm soil depth interval (at six depths). We further introduce a new pedotransfer model that was developed in deriving the SWS for the same depth interval of 0–100 cm. Assessing information on the space-time variability of SM and SWS is essential for agricultural intensification efforts, especially in semi-arid landscapes of sub-Saharan Africa (SSA), where there is the need and the potential to increase food-crop production. This dataset spans the main topographic units of the Guinea savannah zone and covers dominant vegetation types and land uses of the region, which is similar to most parts of West Africa. The comprehensive dataset and the customized machine learning models can be used to support crop production with respect to water management and optimized agricultural resource allocation in the Guinea savannah landscapes of Ghana and other parts of SSA. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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