Soil variability mapping and delineation of site-specific management zones using fuzzy clustering analysis in a Mid-Himalayan Watershed, India

B. N. Shashikumar (), Suresh Kumar (), K. Justin George () and Abhishek Kumar Singh ()
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B. N. Shashikumar: Indian Institute of Remote Sensing, ISRO
Suresh Kumar: Indian Institute of Remote Sensing, ISRO
K. Justin George: Indian Institute of Remote Sensing, ISRO
Abhishek Kumar Singh: Indian Institute of Remote Sensing, ISRO

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2023, vol. 25, issue 8, No 46, 8539-8559

Abstract: Abstract The rate of soil degradation is increasing in the Himalayan ecosystem and inducing soil nutrient loss due to numerous environmental effects. The site-specific soil management zones (MZs) are necessary for such terrain with variability in spatial soil nutrient distribution. The present study investigates the spatial variability of soil nutrients and delineates the MZs in a mid-Himalayan watershed. Overall, 100 m grid 65 surface soil samples (0–30 cm) were collected from an area of 102 ha mini watershed located in the Tehri Garhwal district of Uttarakhand state in India. The samples were processed and analyzed for soil pH, electrical conductivity (EC), aggregate stability (AS), clay content (Cl), total carbon (TC), total nitrogen (TN), available phosphorous (AP) and available potassium (AK). The soil pH, EC, AS, Cl, TC, AP, AK and TN had mean values of 5.15, 104.57 dS cm−1, 0.80, 17.78%, 2.55%, 32.14 kg ha−1, 163.59 kg ha−1 and 0.24%, respectively. Geostatistical analysis showed a spatial distribution pattern of soil variables with moderate to weak spatial dependency. The principal component analysis (PCA) divulged five principal components (PCs) with eigenvalue > 1 with 69.48% total variance. The fuzzy c means algorithm for the scores of the selected PCs was carried out to establish the optimum number of clusters, i.e., management zones (MZs). The geostatistical analysis, PCA and fuzzy clustering resulted in four optimum soil management zones. The analysis of variance indicated that there is a significant difference between the soil and terrain parameters among the MZs. The soil pH and TN among MZ1 and MZ4, whereas the EC, AK, TC and elevation were significantly different among all the delineated MZs. In addition, the delineated MZs using cluster analysis resulted in within-zone variability and could be used as a guide for farmers to adopt site-specific soil management.

Keywords: Soil spatial variability; Soil management zones; Geostatistics; Principal component analysis; Fuzzy c means cluster analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10668-022-02411-6

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