Due to the high costs associated with planting a new sugarcane field, sugarcane ratooning is explored to decrease production costs. However, ratoons have successively smaller yields, because of the effect known as sugarcane yield decline, which can impair the profits. The factors underpinning the ratoon yield decline are yet to be established. The objective of this work is to apply decision trees to sugarcane production mill data to evaluate factors related to the sugarcane ratoon yield decline. For this, meteorological and production data from four sugarcane mills were evaluated, comparing the yield obtained with the yield of the following year.

Model validation often is performed with metrics unsuitable for the task. Also, no metric should be used alone as criterion. One alternative is the use of Regression Error Characteristic Curve (REC). The use of REC curves was able to replace the results of single metric evaluation while providing information about trade-offs and model variability. Considering the limitations of plotting several curves, REC curves should be used for final steps of model validation.