article · Agrosystems Geosciences & Environment
Managing agricultural soil demands accurate insight into fertility. In Ethiopia, researchers evaluated soil fertility properties across a 560-hectare area by collecting 82 surface samples at depths between 0 and 20 centimetres. The analysis examined pH, Olsen extractable phosphorus, and organic carbon, using inverse distance weighting and ordinary kriging to model spatial patterns. Olsen extractable phosphorus demonstrated high variability across the site, ranging between 2.68 and 42 milligrams per kilogram. Conversely, soil pH showed low variability, ranging from 4.84 to 6.81, representing moderately acidic conditions. Soil organic carbon ranged from 0.81 to 3.17 percent. Inverse distance weighting proved more accurate than ordinary kriging for predicting spatial variability. Semivariogram analysis confirmed strong spatial dependence for pH and organic carbon, and moderate spatial autocorrelation for phosphorus. These maps provide baseline data to refine local soil management alternatives.
Understanding how essential soil nutrients and acidity vary across farm landscapes allows for targeted interventions rather than uniform treatments. By accurately mapping variations in phosphorus, carbon, and acidity, land managers can tailor fertiliser application and soil restoration efforts. This targeted approach supports sustainable agriculture by helping to raise crop yields while avoiding the environmental risks linked to over-fertilisation.
This work demonstrates an applied mapping approach that could inform precision farming services, agricultural extension programmes, and fertiliser blending initiatives. Because the methodology was tested directly across a 560-hectare tract, it sits at an applied stage of development. Translating the findings into a commercial advisory tool would require embedding these spatial interpolation models into digital agronomy software used by farm consultants and local agricultural planners.
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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 ( R 2 = 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.
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DOI: 10.1002/agg2.20357
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