Spatial variations in soil physicochemical characteristics and quality indicators under different land use practices in the tropical deciduous forests of Chhattisgarh, Central India
Description
HYPOTHESIS Converting natural forest to agriculture and degraded land reduces soil fertility, and the decline varies with soil depth and location. The study tested whether six land-use types (Sal forest, mixed forest, open forest, plantation, agriculture, degraded open land) differ in soil quality, and whether land use, depth, or forest range best explains this variation. DATA DESCRIPTION 756 composite samples came from 252 geo-referenced plots across seven forest ranges (Aitmanagar, Chaitma, Jatga, Katghora, Kendai, Pali, Pasan), Katghora Forest Division, Chhattisgarh. Design: 6 land-use types × 7 ranges × 6 replicate plots, each sampled at three depths (0–10, 10–20, 20–30 cm). Samples were analysed for pH, EC, organic carbon (Walkley–Black), available N, P, K, S, B, and DTPA-extractable Zn, Cu, Fe, Mn by standard lab methods. The workbook holds a 756-row long-format table (one row per sample-depth), a 252-row wide-format table (one row per plot, depths as columns), plus descriptive statistics, correlation/PERMANOVA output, and an OC depth-trend summary. KEY FINDINGS OC and N followed one gradient: Mixed ≈ Sal forest > Open forest > Plantation > Agriculture > Degraded land; surface OC and N were 53–65% lower in degraded land than forest. OC fell 45–60% from 0–10 to 20–30 cm in every land use, so depth drives carbon loss regardless of land-use type. PERMANOVA confirmed land use, depth, and range each significantly structure soil chemistry (p=0.001); range explained the most variance (R²=0.157), ahead of depth (0.090) and land use (0.081). Mixed-effects models showed land use strongly affects pH, OC, N, P, K, Zn, Mn (p≤0.006) but not EC, S, B, Cu, Fe; OC had the strongest response (R²m=0.926). PCA (34.4% variance) separated forest from farmed/degraded plots along an OC–N–Cu axis; a PCA-weighted Soil Quality Index ranked Plantation (0.390) and Sal forest (0.388) highest, agriculture/degraded land lowest. INTERPRETATION AND USE The data quantify how forest conversion erodes topsoil fertility and show range-level variation matters as much as land-use type, so restoration should be range-specific. Use the long-format sheet for depth- or nutrient-specific modelling, the wide-format sheet for plot-level multivariate work (PCA, clustering, SQI). Findings support prioritising afforestation and organic-matter restoration in agricultural/degraded plots, especially at the surface where losses are greatest.