Estimating sugarcane productivity: an approach using fuzzy logic

Abstract

Brazil is a benchmark in sugarcane production, with the state of São Paulo standing out as the largest Brazilian producer. However, for sugarcane suppliers and mills to sustain this activity, there is a need to improve productivity per hectare and reduce production costs. In this regard, this study aimed to propose fuzzy systems to estimate sugarcane productivity based on planted area (Area) and total cost of soil tillage (TCST) for raw material suppliers and mills. To this end, two fuzzy inference systems were constructed for the output variable (productivity) from two input variables (Area and TCST), considering five membership functions (very low, low, medium, high, and very high). Additionally, a survey on 42 sugarcane suppliers and 31 mills in the state of São Paulo was used for model construction. The results showed that the relationship between Area and TCST reflects on the productivity of sugarcane suppliers and mills in distinct ways. For suppliers, an increase in productivity is observed when there is an almost negative relationship between both input variables. For mills, productivity rises when these variables fluctuate in the same direction. Therefore, the proposed method is viable and provides relevant information for conjecturing survival strategies for agents in the sugarcane energy sector.