Clean Technologies and Environmental Policy ( IF 4.2 ) Pub Date : 2021-05-15 , DOI: 10.1007/s10098-021-02105-z Fabio Vogelaar Carlucci 1 , Stella Vannucci Lemos 1 , Alexandre Pereira Salgado Junior 1 , Perla Calil Pongeluppe Wadhy Rebehy 1
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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 (× 105/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.
Graphic abstract
中文翻译：
提高巴西和澳大利亚甘蔗厂农业绩效的环境、田间和杂质因素
本研究旨在确定提高巴西和澳大利亚甘蔗厂农业绩效的解释因素。巴西和澳大利亚与制糖业的相关性推动了这项研究的发展，该研究基于两国负责提高甘蔗生产效率的最重要因素。因此，本研究旨在评估以下假设：有一些解释变量对糖能部门的农业效率负有重大责任。作为一个具体目标，它提出了一个 DEA（数据包络分析）模型，旨在优化按种植面积的总可回收糖 (TRS) 的生产，同时最大限度地减少矿物质和植物杂质。样本由来自 32 个甘蔗厂的 82 个观测值组成。使用两阶段 DEA 进行了农业效率研究，其中根据所提出模型中的效率水平评估工厂。然后，进行多元线性回归分析，以确定在效率方面对工厂性能影响最大的变量。结果揭示了提高甘蔗生产中农业绩效的六个相关变量：降雨量（毫米每周）、切碎的甘蔗交付量（%）、交付时间（h）、螟虫（%）、空气湿度（%）和棒材原酒（×10 进行了多元线性回归分析，以确定在效率方面对工厂绩效影响最大的变量。结果揭示了提高甘蔗生产中农业绩效的六个相关变量：降雨量（毫米每周）、切碎的甘蔗交付量（%）、交付时间（h）、螟虫（%）、空气湿度（%）和棒材原酒（×10 进行了多元线性回归分析，以确定在效率方面对工厂绩效影响最大的变量。结果揭示了提高甘蔗生产中农业绩效的六个相关变量：降雨量（毫米每周）、切碎的甘蔗交付量（%）、交付时间（h）、螟虫（%）、空气湿度（%）和棒材原酒（×105 /毫升）。最后，对巴西和澳大利亚糖能部门专家的半结构化访谈确定了数据库中没有的五个其他相关补充因素：遗传多样性、农业种植活动、土壤气候因素、甘蔗田更新和灌溉系统. 本研究的结果分为环境、产量和杂质三个维度，提供量化和更好地理解已确定的解释因素和农业生产效率方面的绩效，提供基本信息，使管理人员能够做出决策并优先考虑对提高种植面积的农业生产力贡献更大的方面。
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