Precision agriculture aims at sustainably optimizing the management of cultivated fields by addressing the spatial variability found in crops and their environment. Spatial variability can be evaluated using spatial cluster analysis, which partitions data into homogeneous groups, considering the geographical location of features and their spatial relationships. Spatial clustering methods evaluate the degree of spatial autocorrelation between features and quantify the statistical significance of identified clusters. Clustering of orchard data calls for an approach which is based on modeling point data, i.e. individual trees, which can be related to site-specific measurements. We present and evaluate a spatial clustering method using the Getis-Ord G(i)* statistic to the analysis of tree-based data in an experimental orchard. We examine the robustness of this method for the analysis of "hot-spots" (clusters of high data values) and "cold-spots" (clusters of low data values) in orchards and compare it to the k-means clustering algorithm, a widely-used aspatial method. We then present a novel approach which accounts for the spatial structure of data in a multivariate cluster analysis by combining the spatial Getis-Ord G(i)* statistic with k-means multivariate clustering. The combined method improved results by both discriminating among features values as well as representing their spatial structure and therefore represents a superior technique for identifying homogenous spatial clusters in orchards. This approach can be used as a tool for precision management of orchards by partitioning trees into management zones. (C) 2015 Elsevier B.V. All rights reserved.