Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We investigated using digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC) of soils in Nigeria using three published Pedotransfer functions (PTFs). Soil information, particularly water storage capacity, is of utmost importance for assessing and managing land resources for sustainable land management. We used digital soil mapping (DSM) and digital soil functional mapping (DSFM) procedures to predict available water capacity (AWC) of soils in Nigeria based on three published Pedotransfer functions (PTFs). We followed the specifications of the GlobalSoilMap.net project to produce predictions at a grid resolution of 100 m using regression tree models applied to a compiled soil point database together with auxiliary environmental predictors. Mean AWC (cm cm-1) estimates for Nigeria using methods published by Hodnett and Tomasella (PTF-HT), Zacharias and Wessolek (PTF-ZW), and Minasny and Hartemink (PTF-MH) PTFs were 0.08, 0.21, and 0.12 cm cm-1 for the 0- to 5-cm depth interval and 0.16, 0.08, and 0.08 for the cumulative depth (0–200 cm). The AWC estimates from the PTFs and from the literature for a number of discrete points and locations generally compared well. Comparison of AWC estimated from predicted soil properties (AWPp) against those estimated directly from profile observations (AWPd) for a number of discrete point locations showed a significant relationship only for PTF-HT (R2 = 0.24, p <0.05, for the 0–5 cm depth interval) and PTF-ZW (R2 = 0.25, p <0.05, for the cumulative depth). Soil properties predictions using remote sensing environmental covariates alone yielded similar results compared to predictions using a more extensive environmental covariate datasets. Overall, the process adopted for estimating AWC in this study shows promising results, but field measurements are still needed for validation and fine tuning of the process.
- moisture retention characteristics
- depth functions
- carbon storage
Ugbaje, S. U., & Reuter, H. I. (2013). Functional digital soil mapping for the prediction of available water capacity in Nigeria using legacy data. Vadose Zone Journal, 12(4). https://doi.org/10.2136/vzj2013.07.0140