Ecological Indicators (Apr 2021)
Indexing soil properties through constructing minimum datasets for soil quality assessment of surface and profile soils of intermontane valley (Barak, North East India)
Abstract
Soil quality (SQ) assessment considering surface and profile soils holds great significance in sustainable soil health management. We assessed the effect of land-use systems (agriculture, forest and plantation) on SQ using three SQ indices (SQI) models namely additive (SQIa), weighted additive (SQIw) and nemoro (SQIn) from total (TDS) and minimum (MDS) datasets in the intermontane valley soils of North East India (Barak Valley). Surface samples were taken from 13 to 24 cm depth and profile samples were taken up to 102–167 cm depth from 67 profiles selected by multi-layer thematic overlay analysis of agro-physical parameters with Survey of India (SOI) topo-sheets at 1:50 K scale as base map. The estimated SQIs averaged over models and land-uses were nearly two-fold higher for surface (0.751 ± 0.03) and profile (0.722 ± 0.02) soils from TDS than the MDS (surface: 0.398 ± 0.09; profile: 0.333 ± 0.06) approach. The SQIn model underestimated the SQI values by 28–35% less than SQIa and SQIw for both datasets. The effect of land-uses on SQIs values was mostly non-significant (p > 0.05) for both surface and profile soils. We mapped (at 1: 50 K scale) spatial variability in SQ grades as poor, medium, good and very good in quality and validated by error matrix and Kappa analysis against independent variable (NDVI map derived from Landsat 8). The SQ maps developed from TDS were superior to MDS in Kappa agreement. Among the models, SQIw followed by SQIa from TDS had better Kappa agreement in representing spatial distribution of SQ across soil depth and land-use. We propose the SQIw model as a better option to evaluate soil quality of surface and profile soils with a satisfactory level of accuracy and reliability since it is independent of qualitative assumptions and superior in Kappa agreement for spatial variability mapping. The information generated can be harnessed for managing soil health in intermontane valleys and other such similar agro-ecological regions.
