Geostatistical and fuzzy clustering approach for delineation of site-specific management zones and yield-limiting factors in irrigated hot arid environment of India.

Abstract

Delineation of management zones (MZs) are needed to manage fields in order to maximize economic return, minimize environmental impact, and improve soil and crop management. The MZs of uniform production potential may offer an effective solution to nutrient management. In this study, a total of 122 geo-referenced representative surface (0–250 mm depth) soil samples were collected from the study area covering an area of 6296 ha. Soil samples were analysed for pH, EC, CaCO3, organic carbon (SOC), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and micronutrients (Fe, Mn, Zn and Cu). Their spatial variability was analyzed and spatial distribution maps were constructed using geostatistical techniques. Geostatistical analysis showed that exponential, rational quadratic, tetraspherical, pentaspherical and circular models were the best-fit models for soil properties and available nutrients. Further, geographical weighted principal component analysis (GWPCA) and possibilistic fuzzy C-means (PFCM) clustering algorithm were carried out to delineate the management zones based on optimum clusters identified using fuzzy performance index (FPI) and normalized classification entropy (NCE). The results revealed that the optimum number of MZs for this study area was four and there was heterogeneity in soil nutrients in four MZs. The study indicated that MZ-based soil test crop response recommendation reduces the application quantity of fertilizer significantly at a large extent. Therefore, the management zone concept can reduce agricultural inputs and environmental pollution, and maximize crop production.