Spatial variability modeling of soil fertility for improved nutrient management in Northwest Ethiopia

Abstract

In the study, two geostatistical methods-Ordinary Kriging (OK) and Least Distance Weighting (IDW)-were utilized to predict the spatial variability and distribution of soil properties for improved nutrient management in the Fogera plain, Northwest Ethiopia. In an area of 5646 ha, 60 composite soil samples were collected at a 0-20-cm soil depth and analyzed for soil pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av.P), exchangeable calcium (Ca 2+ ), potassium (K + ), sodium (Na + ), and magnesium (Mg 2+ ), electrical conductivity (EC), and cation exchange capacity (CEC) using standard analytical procedures. The data were then incorporated into a GIS database and semi-variogram, and geostatistical analyses were performed with ArcGIS software version 10.5. Descriptive statistical treatments were applied using IBM Statistical Packages for Social Sciences (SPSS) software version 24. The performance of interpolation methods was assessed using the mean error (ME), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R 2 ) extracted from cross-validation of predicted maps. Analysis of different semi-variogram models depicts different degrees of spatial dependence. An exponential model is detected for the soil pH, CEC, OC, and TN; a spherical one for EC and Ca 2+ ; a moderate one for soil K + ; and a weak spatial dependence for soil av. P. This result demonstrates a high spatial continuity and dependence between adjacent soil samples. The inverse distance weighting (IDW) and ordinary kriging (OK) models well described the variation of all soil fertility parameters except organic carbon (OC) and available phosphorus (av. P), which had low NSE (≤ 50%) for both IDW and OK methods. Consequently, the generated maps revealed that the spatial variability of soil properties was adequate to predict the values of soil fertility indicators in the non-sampled locations within the study area and similar regions. The geostatistical-based soil fertility maps will be helpful for farmers, researchers, and policymakers to improve soil management methods, optimize fertilization strategies, and enhance crop productivity. By means of the methodological approach applied, we have succeeded in demonstrating the strong spatial dependence of the studied soils; however, a particular attention to the implementation of site-specific soil management practices in the Fogera plain is essential.

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

“…However, the map is insufficient to offer operational soil information to devise soil intervention strategies and increase crop production. The soil fertility of the country is also low (Tiruneh, Alemayehu, Allouche, et al., 2021, Tiruneh, Alemayehu, Meshesha, et al., 2021). Furthermore, efficient soil information is critical for increased agricultural production and ecological sustainability (Denton et al., 2017).…”