Graphic abstract
This study aims to identify explanatory factors to increase the agricultural performance of Brazilian and Australian sugarcane mills. The relevance of Brazil and Australia for the sugar industry motivated the development this study based on the most important factors in both countries responsible for increasing the efficiency in sugarcane production. Thus, this study is designed to assess the hypothesis that there are a few explanatory variables that are deeply responsible for the agricultural efficiency in the sugar-energy sector. As a specific objective, it proposes a DEA (Data Envelopment Analysis) model that seeks to optimize the production of Total Recoverable Sugar (TRS) by planted area, and simultaneously, minimizes mineral and vegetable impurities. The sample consists of 82 observations from 32 sugarcane mills. An agricultural efficiency study was performed using the two-stage DEA, in which the evaluated mills according to the level of efficiency in the proposed model. Then, a Multiple Linear Regression Analysis was performed to identify the variables with the greatest influence on the performance of the mills in terms of efficiency. The results revealed six relevant variables for increasing the agricultural performance in the production of sugarcane: rainfall (mm weekly), chopped cane delivery (%), delivery time (h), borer (%), air humidity (%), and rods in raw wine (× 10
5
/mL). Finally, semi-structured interviews with Brazilian and Australian experts in the sugar-energy sector allowed the identification of five other relevant complementary factors that were unavailable in the database: genetic variety, agricultural cultivation activities, edaphoclimatic factors, renewal of sugarcane fields and irrigation system. The results of this study were grouped into the dimensions of environment, yield, and impurities, providing quantification and better understanding of the identified explanatory factors and the agricultural performance in terms of production efficiency, offering fundamental information that enables managers to make decisions and prioritize the aspects that contribute more significantly to the increase in agricultural productivity of the planted area.