Farm-scale soil spatial variability at a mountain research centre in Northwestern Himalayas.

The appraisal of spatial variability of soil properties is crucial for gaining a comprehensive understanding of the intricate relationships between soil properties and for establishing effective management practices for soil resource utilization. Despite extensive research on regional soil variability, farm-scale assessments in temperate mountainous agro-ecosystems remain scarce, limiting precision soil management strategies. This study aimed to evaluate spatial variability and generate spatial distribution maps. The study area at 75.0916°E and 33.7237°N was the Research Farm of Mountain Research Centre for Field Crops (MRCFC) - Khudwani, characterized by temperate climate conditions. Soil samples were randomly collected from eighty-nine (89) sites across the whole research farm at a depth of 0-15 cm using a global positioning system (GPS). Then, the samples were processed and tested for various physical and chemical properties. Descriptive analysis and data transformation were performed using the SPSS package. The Shapiro-Wilk test assessed the normality of parameters. ArcGIS 10.8 software was used to conduct geostatistical analysis. The physical properties revealed a predominant silty clay loam texture. The soil bulk density, particle density, and porosity distribution were 2.05 to 2.76 g cm⁻³, 38.32-56.93%, and 1.12 to 1.38 g cm⁻³, respectively. The chemical properties analysis revealed soil pH (5.30-8), EC (0.01-0.18 dS m - 1), and SOC (0.52-1.21%) and concentrations of soil available nutrients (AN 187.80-455.83 kg ha - 1, AP 12.04-33.07 kg ha - 1, AK 77.57-195.87 kg ha - 1, AS 8.30-15.90 mg kg - 1, Ex. Ca 7.10-12.40 cmol(+)kg⁻¹, Ex. Mg 1.80-3.90 cmol(+)kg⁻¹, AMn 3.31-27.2 mg kg - 1, AFe 4.64-19.8 mg kg - 1, ACu 0.69-1.88 mg kg - 1 and AZn 0.27-1.64 mg kg - 1). CV values indicated very high variation for EC, K, Cu; high variation for OC, P, Fe, Mn, and Zn; medium for pH, N, S, Ex. Ca and Ex. Mg, while physical parameters indicated low variability in the examined soil. Geostatistical analysis revealed strong (bulk density, porosity, pH, EC, OC, P, K, Ex. Ca, Fe, Mn, Cu, and Zn), moderate (particle density, N and S), and weak (Ex. Mg) spatial dependence for soil parameters with the best-fit models being Spherical for EC, OC, N, P, S, Ex. Ca, Ex. Mg, Cu, and Zn, Gaussian for bulk density, particle density, porosity, pH, K, and Mn, while Exponential for Fe. These findings indicate that historical land-use patterns and long-term fertilization practices have led to significant spatial heterogeneity, underscoring the need for location-specific nutrient management. The generated spatial distribution maps offer practical tools for optimizing experimental site selection and guiding precision agriculture strategies at the research station. The significant variability in soil properties and spatial distribution primarily arises from factors such as land use type, fertilization practices, and historical management. The spatial distribution maps of soil properties could be used for location-specific nutrient management strategies and for identifying the optimum locations for setting up specialized experiments on the research farm.