Fuzzy K-Means and Principal Component Analysis for Classifying Soil Properties for Efficient Farm Management and Maintaining Soil Health

Abstract

Soil health indicators can guide soil management-related decisions for sustainable agriculture. Principle component (PC) analysis and the fuzzy k-means technique, also known as continuous classification, are useful for designing site-specific management strategies for varying soil properties within a contiguous area. The objective of this study was to identify appropriate soil health indicators as well as to create contiguous areas for precision management of a large diverse farm from measured soil properties. From the farm, which is sited on Armijo–Harkey soil, 286 loose and intact samples were obtained, representing a depth of 15 cm from the soil surface. Statistical analysis showed that several data were log-normally distributed. PCA analysis showed that the first three PCs explained 73% of the variation with PC1, consisting of factors related to the soil’s physical condition; PC2, containing factors related to chemical properties; and PC3, including factors related to macro- and micro-porosities. Minimizing the fuzziness performance index (FPI) and modified partition entropy (MPE) delineated four management classes. The membership class maps showed that the contrasting management strategies could be developed for the four management zones to achieve yield goals while conserving scarce surface water for irrigation, increasing water use efficiency, and decreasing nitrate leaching in arid and semi-arid irrigated farmlands.