Geostatistics and multivariate analysis to determine experimental blocks for sugarcane

Abstract The objective of this work was to define experimental blocks for sugarcane experiments using geostatistical techniques, principal component analysis, and clustering techniques applied to soil properties. For this, data of soil chemical properties from a sugarcane experiment were used. Geostatistical techniques were applied to identify the spatial variability of these properties and to estimate the values for non-sampled locations through kriging. The principal components analysis was used for dimensional reduction, and, with the new variables obtained, the cluster analysis was performed using the k-means method to determine the experimental blocks with two to five replicates. Of the 12 analyzed variables, 10 showed spatial dependence. The principal component analysis allowed reducing the dimensionality of the data to two variables, which explained 82.27% of total variance. The obtained blocks presented irregular polygonal shapes, with different formats and sizes, and some of them showed discontinuities. The proposed methodology has the potential to identify more uniform areas in terms of soil chemical properties to allocate experimental blocks for sugarcane.