The utilisation of digital soil mapping has gained significant traction in the scientific community to construct statistical models that elucidate the intricate connections between environmental factors and soil characteristics. A comprehensive understanding of the inherent spatial variability of soil physical and chemical properties is imperative to enhance the precision and effectiveness of site-specific soil nutrient management strategies. In this research endeavour, we explored the multifaceted domain of spatial variability of a wide range of soil physical and chemical properties, including pH levels, organic carbon content, and available nitrogen, phosphorus, potassium, and sulphur across diverse geographical locations within the Baramulla district. Soil samples were carefully collected from a depth of 0-15 cm, employing a randomized sampling technique with the aid of GPS technology, ensuring precise geospatial coordinates were recorded for each of the 120 sampling locations. The statistical analysis conducted on soil properties revealed that the pH levels exhibit a relatively low coefficient of variation (CV), measuring less than 15%. On the other hand, organic carbon, nitrogen, phosphorus, potassium, and sulphur display a substantial coefficient of variation, surpassing 20%. Although there has been a rise in the use of probabilistic and statistical analysis, there are still several obstacles to overcome when it comes to integrating the spatial variability inherent in soil parameters into prediction analysis. The geostatistical interpolation technique has successfully revealed a moderate spatial variability in the levels of pH, organic carbon, phosphorus, potassium, and sulphur. Additionally, it has indicated a weaker spatial variability in the levels of nitrogen. The soil variables were subjected to fitting models such as Exponential (N and K), Spherical (OC), and Gaussian (pH, P2O5, Sulphur) to analyse their semivariograms. This study demonstrates the versatility of the framework in analysing soil parameters throughout a wide range of variability, from low to high. These cartographic representations enable agricultural practitioners to evaluate the prevailing soil conditions on their farms, facilitating streamlined and optimised decision-making processes. This, in turn, contributes to the preservation of productivity sustainability while ensuring enhanced operational efficiency.

Soil mapping, Geostatistics, Interpolation, Semivariogram, site-specific soil nutrient management

The geostatistical interpolation technique effectively determined that the exponential, spherical, and Gaussian models exhibited optimal conformity with the semivariograms, contingent upon the specific soil chemical variable. In a broader sense, these models demonstrated a relatively weak to moderate degree of spatial dependency across all variables. The utilisation of kriging maps for soil chemical properties has proven to be highly effective in elucidating the spatial distribution patterns of soil properties in areas where no samples were taken, solely relying on the available sampled data. The assessment of spatial heterogeneity in soil physical and chemical attributes is an essential step in implementing targeted soil and crop management strategies. The soil property maps, along with their corresponding spatial structures, have successfully delineated the priority management zones that should be addressed in the future to enhance soil quality. These maps can also be utilised to develop more effective sampling designs for making informed management decisions.

Ayman Javed, Shahid Ahmad Hakeem, Aamir Hassan Mir, Javid Ahmad Bhat, Haziq Shabir, Zahoor Ahmad Baba and Roheela Ahmad (2023). Evaluating the Spatial Variability of Soil Physicochemical Characteristics in an Indian Lesser-Himalayan Region. Biological Forum – An International Journal, 15(11): 298-305.