| Full text | |
| Author(s): |
Alcília Mena José Sitoe Macarringue
[1]
;
Andréa Leda Ramos de Oliveira
[2]
;
Carlos Tadeu dos Santos Dias
[3]
;
Karina Braga Marsola
[4]
Total Authors: 4
|
| Affiliation: | [1] Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Agrícola (FEAGRI). Laboratório de Logística e Comercialização Agroindustrial (LOGICOM) - Brasil
[2] Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Agrícola (FEAGRI). Laboratório de Logística e Comercialização Agroindustrial (LOGICOM) - Brasil
[3] Universidade de São Paulo (USP). Escola Superior de Agricultura “Luiz de Queiroz” (ESALQ). Departamento de Ciências Exatas - Brasil
[4] Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Agrícola (FEAGRI). Laboratório de Logística e Comercialização Agroindustrial (LOGICOM) - Brasil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | Ciência Rural; v. 54, n. 4 2023-10-30. |
| Abstract | |
ABSTRACT: 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. (AU) | |
| FAPESP's process: | 18/19571-1 - An intelligent platform for forecasting agricultural freight rates using data mining techniques |
| Grantee: | Andréa Leda Ramos de Oliveira |
| Support Opportunities: | Regular Research Grants |