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In order to achieve more efficient agricultural production systems, studies relating to the patterns of influence factors on commercial blocks of outstanding performance can be performed to assist management practices. The performance is considered to be the difference between the yield of a given block and the average yield of the homogeneous group that it belongs to. The methods available to identify these outstanding blocks are usually subjective. The aim of this study was to propose an objective and repeatable approach to identify outstanding performance blocks. The proposed approach consisted of performance determination, using regression trees, and the classification of these blocks by k-means clustering. This approach was illustrated using a sugarcane model. The main factors influencing the tonnes of cane per hectare (TCH) and total recoverable sugar (TRS) yields were found to be crop age and water availability during ripening, respectively. These were used to create potential yield groups, and blocks with high and low performance were identified. The proposed approach was found to be valid in the identification of outstanding sugarcane blocks, and it can be applied to different crops or in the context of precision agriculture.

Identification of commercial blocks of outstanding performance of sugarcane using data mining