The understanding of the spatial variation of soil chemical properties is critical in agriculture and the environment. To assess the spatial variability of soil chemical properties in the Fogera plain, Ethiopia, we used Inverse Distance Weighting (IDW), pair-wise comparisons, descriptive analysis, and principal component analysis (PCA). In 2019, soil samples were collected at topsoil (a soil depth of 0–20 cm) from three representative land-uses (cropland, plantation forestland, and grazing lands) using a grid-sampling design. The variance analysis for soil pH, available phosphorus (avP), organic carbon (OC), total nitrogen (TN), electrical conductivity (EC), exchangeable potassium (exchK), exchangeable calcium (exchCa), and cation exchange capacity (CEC) revealed significant differences among the land-uses. The highest mean values of pH (8.9), avP (32.99 ppm), OC (4.82%), TN (0.39%), EC (2.28 dS m−1), and exchK (2.89 cmol (+) kg-1) were determined under grazing land. The lowest pH (6.2), OC (2.3%), TN (0.15%), and EC (0.11 dS m−1) were recorded in cultivated land. The PCA result revealed that the land-use change was responsible for most soil chemical properties, accounting for 93.32%. Soil maps can help identify the nutrient status, update management options, and increase productivity and profit. The expansion of cultivated lands resulted in a significant decrease in soil organic matter. Thus, soil management strategies should be tailored to replenish the soil nutrient content while maintaining agricultural productivity in the Fogera plain.

The production and productivity of malt barley are limited using disease-susceptible and low-yielding varieties. Study was focused on identifying and selecting the best performed and adapted malt barley variety/varieties for yield and yield-related traits. We evaluated six improved malt barley varieties using a randomized complete block design with three replications. The study was conducted for 2 years (2019 and 2020 cropping season) at Lay Gayint district. The combined analysis showed highly significant differences ( P < 0.01 ) among varieties, years, and their interactions in all traits. The highest yield (31.54 qt·ha−1) was obtained from variety Holker. The correlation coefficient analysis showed a significant and very strong positive association of grain yield with number of effective tillers ( r = 0.953 ∗ ∗ ), spike length ( r = 0.973 ∗ ∗ ), and strong positive association with thousand seed weight ( r = 0.739 ∗ ∗ ) with a medium positive association with seed per spike ( 0.554 ∗ ∗ ). In principal component analysis, PC1 was dominated by traits that had a greater effect on yield. A variety of Holker could be recommended in the study areas and other similar agro-ecologies. Farmers lost a lot of quintals of yield by lack of new technologies, by addressing more adapted improved production technology increased average yield.

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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.

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.