Pesticide residues has become a serious environmental concern due to the intensification of agricultural production, its universal use and the risk of contamination. It is an issue that is inserted in the dichotomy between the search for increased food production, waste generation and environmental preservation. There are two main objectives for this work. First, the descriptive analysis of the reverse logistics (RL) of empty pesticide containers (EPCs) as it is practiced under the Campo Limpo system (Brazil) so that the factors enabling its success are identified. Second, the proposal of strategies to improve the collection system. Technical visits and a system dynamics approach were combined, using the Behaviour Over Time graph and a causal loop model to describe the problem and propose strategies. Results indicate three key areas of the Campo Limpo system that can be improved: producer awareness, alternative means of returning containers and data transparency. Small and medium producers are more penalised in the return process due to the lack of information, difficulty in accessing disposal centres and the cost of transportation.

RESUMO A crescente demanda por alimentos é impulsionada pelo crescimento populacional e econômico. Uma estratégia para atender a essa expansão da demanda e reduzir a pressão sobre a produção de alimentos é minimizar o desperdício de alimentos. O objetivo deste trabalho é avaliar se a combinação de práticas operacionais e gerenciais nas etapas da logística e comercialização de frutas e vegetais estão associadas aos menores níveis de desperdício no setor atacadista. Foram analisados cinco produtos do grupo de frutas e hortaliças: alface, batata, laranja, mamão e tomate, vendidos por atacadistas da Central de Abastecimento de Alimentos (CEASA). Foram utilizadas a análise de componentes principais e as regras de associação para reconhecer a inter-relação das práticas que promovem a redução de desperdícios. O desperdício autorreferido de mamão e batata foi de 5,8%, para alface 22,5%, tomate 3,3% e laranja 2,2%. Foram identificadas treze práticas e comportamentos que explicam 100% da variância, e que estão associadas ao componente tecnológico e ao componente de comercialização. Com base nas regras da associação, a elevada frequência de oito práticas, como a prestação de serviços de apoio ao cliente e a utilização de câmaras frigoríficas, está relacionada com a redução do desperdício de frutas e vegetais.

Predicting road freight prices is a challenging task influenced by multiple factors. Understanding which variables have the greatest impact is essential for building more accurate models, and consequently for enhancing the competitiveness of Brazilian soybeans in the global market. This study aims to evaluate the influence of different exogenous variables on soybean freight prices and to analyze how this influence varies across different distance ranges. To achieve this, a combination of machine learning techniques was applied to a comprehensive dataset containing information on freight costs, regional characteristics, production, fuel prices, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, directly reflecting operational expenses such as fuel consumption and labor costs. Additionally, macroeconomic factors such as the exchange rate and export volume play a crucial role, highlighting the global context of Brazil’s soybean exports. Stratified analysis by distance ranges reveals distinct patterns; short-distance freight is predominantly related to domestic markets, while medium- and long-distance freight are strongly linked to export logistics.

The road system is the main mode used for the transportation of agricultural cargo, and in some cases, it is the only option for handling this type of product. This dependence means that the implementation of tools to support the management of logistical costs can reduce the financial impact with the transport felt by the economic agents operating in the soybean chain. This study contributed to a better understanding of the variables that make up the cost of road freight, generating a system of road freight prediction from a multiple linear regression model using the selection of variables Stepwise, Forward, and Backward elimination. This being said, this research intends to evaluate whether the behavior of soybean road freight is influenced by the variables that make up the productive, economic, and infrastructure dimensions in price formation. The regression models had an explanatory power of 87.20%. In the infrastructure dimension, the most impact variable in soybean road freight was the distance traveled; in the economic dimension, the variables of inflation and fuel price stood out; while in the productive dimension, the main contribution was the volume of production. A more assertive predictability of logistical costs and better understanding of the dynamics of freight price formation helps industry agents in planning and decision-making. Another contribution of this study is that it can be used as a practical tool for predicting soybean road freight on several transportation routes.