Variability of soil physicochemical properties at different agroecological zones of Himalayan region: Sikkim, India

Quantification of soil properties and identification of soil water holding capacity (WHC) are essential for proper cropping and irrigation planning. The traditional approach of calculating WHC involves the application of pedo-transfer functions (PTFs), which are unavailable in many developing countries especially in the Himalayan region. Therefore, this study evaluates the effect of elevation on the physical and chemical properties of the surface soil in a Himalayan river basin in India. Furthermore, regression models were generated for calculating maximum WHC at different agroecological zones (AEZs). A total of 129 soil samples were randomly collected from three AEZs (subtropical, temperate and trans-himalayan). Laboratory analysis was done to identify the pH, organic carbon (OC), particle size distribution, particle density, bulk density (BD), soil water (air dry) (SW), total porosity (TP) and maximum WHC at the three AEZs. Pearson correlation coefficients were derived among the soil attributes. Also, stepwise (forward) multiple linear regression (MLR) models were generated for the predictability of WHC. The results illustrate a statistically significant variation in soil attributes for pH, OC, sand, silt, BD and WHC at all three AEZs. Furthermore, WHC exhibits significant correlation with BD, TP and SW through all three AEZs. Stepwise multiple linear regression analysis shows high predictability of maximum WHC while using appropriate soil attributes; with an adjusted coefficient of determination of 0.87, 0.82 and 0.78 for subtropical, temperate and trans-himalayan AEZs, respectively. Based on the results, it is concluded that soil properties vary significantly through AEZs in the Himalayan region. Also, several properties are inter-correlated at the AEZs and simple MLR models are recommended while estimating maximum WHC. The outputs of this study can be used as a guide to decide crop suitability and develop MLR models for the calculation of WHC in the areas where PTFs are not readily available.