Efficiency of Geostatistical Approach for Mapping and Modeling Soil Site-Specific Management Zones for Sustainable Agriculture Management in Drylands

Abstract

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO3), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties.