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.

Anais do 61º Congresso da Sociedade Brasileira de Economia, Administração e Sociologia Rural (SOBER) (978-85-5722-931-0) - Caracterização Dos Procedimentos Logísticos E De Comercialização Nas Centrais De Abastecimento De Alimentos: O Caso Das Ceasas De Campinas E Belém

Freight price prediction is a complex problem with multiple determinants. Identifying the most important variables can be used to develop more accurate prediction models and increase the competitiveness of Brazilian soybeans. This study aims to assess the influence of various exogenous variables on the price of soybean road freight and how this influence varies across different distance ranges. A combination of machine learning techniques was employed to evaluate a dataset containing variables related to freight, region, production, fuel, storage, and commercialization. The results indicate that distance is the most significant variable in determining freight costs, aligning with operational expenses such as fuel and labor. Additionally, the exchange rate and export volume emerge as key factors, reflecting the macroeconomic context of Brazilian soybean exports. The stratified analysis highlights the differentiation between short, medium, and long-distance freights, showing that different variables influence the price. Short-distance transportation is mainly geared toward the domestic market, while longer distances are more related to export logistics.