Delineation of Irrigation Management Zones Using Geographical Weighted Principal Component Analysis and Possibilistic Fuzzy C-Means Clustering Approach

Abstract

Delineation of irrigation management zones (IMZs) depend on spatial variability of soil hydro-physical properties like soil texture, bulk density (BD), field capacity (FC), permanent wilting point (PWP) and available water content (AWC). This work presents a method for delineation of irrigation zones under such constraints. A total of 67 geo-referenced soil profiles were collected from the study area covering an area of 4206 ha. The spatial variability and correlations of hydro-physical properties were firstly characterized using geostatistics and principal component analysis. Their spatial variability was analyzed and geostatistical analysis showed that Gaussian, spherical and circular models were the best-fit models. Then, IMZs were delineated by geographical weighted principal component analysis (GWPCA) and possibilistic fuzzy C-means (PFCM) clustering algorithm. Optimum clusters were identified using fuzzy performance index (FPI) and normalized classification entropy (NCE). The study area was divided into two IMZs by PFCM clustering, and soil hydro-physical properties had high uniformity in each subzone. The IMZs can provide the basis for decision making of precision irrigation practices. The IMZ-based crop water requirement reduces the application quantity of water significantly at a large extent and maximizes crop production.