Enhancing soil health and crop productivity through spatial variability analysis in precision agriculture

Spatial variability analysis is a pivotal component of precision agriculture, particularly in the assessment of soil health. This research focuses on a rapeseed field located in western Iran, underscoring the critical role of spatial variability in evaluating soil quality and agricultural productivity. A comprehensive sampling strategy involved collecting undisturbed soil samples from 245 strategically chosen points within a 50 m x 50 m grid, encompassing both surface and subsurface layers. Key indicators for assessing the soil's physical quality included available water content, saturated hydraulic conductivity (Kfs), bulk density (BD), noncapillary porosity and organic carbon levels. The physical rating index (PRI), a key metric for measuring soil productivity, was derived from the integrated rating values of these parameters. After the growing season, manual harvesting of rapeseed allowed for the quantification of biological yield. To create pedotransfer functions (PTF) for estimating bulk density (BD) and saturated hydraulic conductivity (Kfs), multiple regression analysis was utilized. Findings indicated the existence of a subsurface hardpan attributable to soil compaction, which impedes root development and negatively impacts soil productivity. By evaluating critical soil parameters, specifically, BD (>1.6 Mg m-3) and Kfs (<0.5 cm hr-1), the compacted zones were identified within the field. Geostatistical analysis, particularly kriging interpolation, facilitated the creation of spatial maps for BD, Kfs, and PRI, equipping farmers with targeted insights for deep tillage interventions aimed at enhancing soil health. Ultimately, this study elucidates how spatial variability analysis can inform agricultural practices, optimize crop yields, and reduce input costs.