Variability modeling and mapping of soil properties for improved management in Ethiopia

Abstract

Abstract Managing soils for improved agricultural production requires information on soil fertility status. Our objective was to map for better soil management in Ethiopia and determine their spatial correlation at a separation distance of 29 m. We collected 82 soil samples (0–20 cm depth) at 560 ha of land and determined pH, Olsen extractable phosphorus (Olsen‐P), and organic carbon (OC). We then interpolated between sample points (ordinary kriging‐OK and distance weighting‐IDW [inverse distance weighting]) to evaluate spatial dependence. Olsen‐P ranged from 2.68–42 mg/kg and exhibited high variability with a coefficient of variation (CV) ≥35%. Conversely, soil pH showed low variability (CV ≤ 15%) and ranging from 4.84 to 6.81. Soil OC content varied from 0.81% to 3.17%. The IDW (R2 = 0.86; RMSE = 0.019) outperformed the OK. The semivariogram results indicate a strong dependence for pH and OC for spherical, exponential, and Gaussian models, while moderately spatially auto correlated for Olsen‐P for all models. The IDW and OK predict the spatial variability of the pH (moderately acidic), Olsen‐P (low), and OC (very low) contents. The soil maps may help to improve soil management alternatives, increase crop productivity, and secure environmental quality.