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Soil is an ecologically indispensable component for preserving and nourishment of various life processes that are directly or indirectly associated such as food and water security, biodiversity protection, climate change mitigation. Soil is considered a major concerned on the worldwide environmental policy agenda. The present study assessed the soil physico-chemical parameters i.e. pH, electrical conductivity, bulk density, soil moisture, soil organic carbon, total nitrogen, available phosphorous, available potassium, available Sulphur and micronutrients (Copper, Manganese, iron and zinc). The soil samples were collected through composite sampling from different land use patterns i.e. forest land, agricultural land, settlement land, and waste land. Analysis of variance (ANOVA), and principal component analysis (PCA) were applied to assess the correlation in soil quality parameters. Based on the statistical analysis, electrical conductivity, bulk density, pH, soil organic matter, available phosphorous, available potassium and essential micronutrients were identified as the more representative indicators of the soil of different land use pattern of Takoli Gad watershed. Pearson correlation shown a strong correlation between available phosphorous and zinc (r=0.731), available phosphorous and copper (r= 0.814), and zinc and copper (r= 0.803). ANOVA, (p < 0.05) indicates significant result for all the physicochemical parameters. Principal component analysis revealed that the first three PCs explain 61.32% of variance. According to the factor loading, the first PC, which explains 40.275% of the total variance, has negative correlation with Zinc (Zn), Mn (Manganese) and Cu (Copper). The descriptive study of soil quality parameters suggests that the soil in Takoli Gad watershed is suitable for cultivation. The present study revealed the importance of multivariate statistical approaches for analyzing and understanding complicated datasets to comprehend variations in soil quality and implement efficient watershed management strategies. Keywords: Analysis of variance, principal component analysis, soil, multivariate, watershed
Chhillar et al. (Sat,) studied this question.