The present study was carried out to assess inter and intra field spatial variability based on Apparent Electrical Conductivity (ECa) data at different depths of soil. Further, Principal Component Analysis (PCA) and Cluster Analysis (CA) were carried out to study the distinct soil variations based on field-scale ECa measurements. PCA results of score plot were observed to verify pattern matching between measured soil spatial variability representatives i.e., ECa, crop yield and variable input nutrient rates. The paper further reports delineation of management zones (MZs) using ECa and crop yield data and PCA, hierarchical clustering and fuzzy c-means (FCM) clustering algorithms besides finding predictions for un-sampled spatial surfaces using univariate geo-statistical technique. Moreover, for determining optimal number of zones, clustering performance was measured using Fuzzy Performance Index (FPI) and Normalized Classification Entropy (NCE) indices. The results revealed that soil ECa, nutrient rate and crop yield information could be quantified and aggregated using CA that characterize spatial variability among soil and crop productivity
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