Utilising soil diagnostic features and environmental covariates to estimate nutrient content in Ethiopian soils

Smallholder farmers in Ethiopia generally do not have access to soil testing services for nutrient management planning decisions; as soil analysis is too costly for most farmers. Fertiliser advice is generally accessible via blanket recommendations at a national scale. Hence, an alternative approach is needed to estimate soil nutrient content across the diverse landscapes of Ethiopia. In this study, we propose using diagnostic features to estimate soil nutrient content, which could contribute to the development of fertiliser recommendations. To achieve this the following objectives were defined: (i) to estimate soil nutrient content as influenced by soil diagnostic features; and (ii) to elucidate the influence of environmental covariates and diagnostic features on the estimation of soil nutrient levels in the Ethiopian context. Data from 550 soil profiles, distributed across Ethiopia, were collected from a range of published sources, collated and harmonised. The data were cleaned, and 496 soil profiles were prepared for modelling. To identify which diagnostic characteristics were present across these soils we applied a presence/absence scoring method to identify dominant diagnostic features. Multiple linear regression analyses were used to predict soil chemical properties from the diagnostic features and diagnostic features along with environmental covariates. The performance of the models was evaluated by applying a 10-fold cross-validation using mean error (ME), Lin's concordance correlation coefficient (LCCC), root mean square error (RMSE) and model efficiency coefficient (MEC). The MEC values for pH, TN, and CEC derived from a combination of diagnostic features and environmental covariates were 0.38, 0.33, and 0.38. The corresponding RMSE values were 0.78, 0.07 %, and 13 cmol kg⁻¹. Additionally, the LCCC values for pH, TN, and CEC were 0.62, 0.58, and 0.62, respectively. The cross-validation results for soil chemical properties showed that the model's performance improved when environmental covariates were added. Precipitation, temperature, geology and land cover were the most important environmental covariates for estimating nutrient content, along with diagnostic features of Ethiopian soils. In conclusion, the diagnostic approach offers a useful starting point for estimating soil nutrient content. However, the variation in nutrient content across the six diagnostic features was not adequately quantified, and the model's predictive performance remains insufficient for practical application at the local scale. Further expansion of the dataset is required to fully exploit the potential of these models for underpinning nutrient management decisions across Ethiopia and in other regions where access to soil test information is limited.