The delineation of site-specific management zones (MZs) can enable economic use of precision agriculture for more producers. In this process, many variables, including chemical and physical (besides yield data) variables, can be used. After selecting variables, a cluster algorithm like fuzzy c-means is usually applied to define the classes. Selection of variables comprise a difficult issue in cluster analysis because these will often influence cluster determination. The goal of this study was to assess the effectiveness of the variable selection techniques - spatial correlation analysis, principal component analysis (PCA) and multivariate spatial analysis based on Moran's index PCA (MULTISPATI-PCA) - when used with the fuzzy c-means algorithm to generate MZs. The data used in experiments were collected from 2012 to 2014 in two agricultural fields with corn and soybean crops, located in Brazil. The variables selected were used as input for the fuzzy c-means, generating two, three, and four classes. The performance of the three techniques was assessed by applying analysis of variance (ANOVA), variance reduction index, fuzziness performance index, and modified partition entropy index. The delineated MZs were different according to the variable selection approach used along with fuzzy c-means. For the two agricultural fields, it was possible to define two classes with potential yields that showed statistically significant differences. The MULTISPATI-PCA technique resulted in classes with higher internal homogeneity, better performance of the clustering algorithm, the best variance reduction values, ​​and the most viable MZs to be implemented in terms of field operations.