Multivariate assessment of soil quality across different land use types in the hilly terrain of the subtropics of India

The unsustainable conversion of forest areas into agricultural land poses a serious danger to the soil eminence of Arunachal Pradesh's environmentally delicate hilly topography. Understanding the impacts of this land-use change is crucial for preventing further degradation. This study aimed to develop soil quality indices (SQIs) for different land use types: natural forest (NF), current jhum cultivation (JC), fallow jhum land (FJC), and pineapple cultivation (PA). Samples of soil were taken at a depth of 0 to 15 cm and examined for 22 potential soil quality indicators, with 19 showing significant (P < 0.05) influence from land use, constituting the total dataset (TDS). Principal component analysis (PCA) was employed on TDS to identify the minimum data set (MDS), comprising dehydrogenase activity, diethylenetriaminpentahacetic acid (DTPA)-extractable iron, and bulk density, contributing 73%, 19%, and 8% to the overall SQI, respectively. Subsequently, different SQIs were estimated using linear/nonlinear and additive/weighted scoring functions. The results revealed substantial alterations in SQIs among the land use types, through NF exhibiting the highest soil quality. Notably, the nonlinear SQIs exhibited greater sensitivity to land use conversion compared to their linear counterparts, indicating their potential as a more robust tool for assessing soil quality changes. This study concludes that the transformation of land use in the hilly regions of subtropics of Arunachal Pradesh has led to the deterioration of soil quality. The proposed indexing framework, leveraging the sensitivity and clarity of nonlinear SQIs, can effectively evaluate and compare soil quality across different land use scenarios, thereby informing sustainable land management strategies.