Due to its agricultural potential, land extensions, and favorable climate, Brazil is one of the largest producers and exporters of various agricultural products. A significant part of this production is placed in Mato Grosso, the primary national producer of several agricultural commodities. The soybean complex alone produced more than 33 million tons of soybean for the 2019/2020 harvest, representing 27% of national production. The economic potential that the soybean commodity represents is linked to the increase in demand for inputs, planted area, production, and productivity. Given these factors, the present study aims to analyze how the largest municipalities of soybean production behave, and the degree of interaction and positive associations between the economic potential promoted by soybean production and the economic/social development and environmental impacts in the Mato Grosso State, Brazil. The methodology was to categorize the thirty largest soybean producing municipalities, using the factor analysis method for selected indicators. The interpretation is made through the adoption of the Driver-Pressure-State-Impact-Response (DPSIR) framework. The results indicated that the groups formed are not homogeneous in terms of socio-economic and environmental development. The three factors that formed, were interpreted using the DPSIR are characterized by the significant influence of the population, reflect on its development, how economic activities are other and not just agriculture. The second also belongs to the driver in the DPSRI framework group. It is associated with the soybean production indicator, implying larger planting areas, generating jobs focused on agricultural activities. The interpretation is made through the adoption of the Driver-Pressure-State-Impact-Response (DPSIR) framework. The results indicated that the groups formed are not homogeneous in terms of socio-economic and environmental development. The significant influence of the population characterizes the three found factors. The first reflects on the region’s development and how other economic activities (not just agriculture) are carried on. The second also belongs to the driver in the DPSRI framework group, and it is associated with the soybean production indicator, generating jobs focused on agricultural activities. The third group, formed by municipalities in the Amazon region, with environmental factors associated with large geographical areas, extensive native forests, and more significant carbon sequestration, considers the DPSRI framework’s impacts. Showing that there are behavior patterns and taking this into account is the optimal way to use the predictors appropriately. Municipalities are expected to be more reactive to some changes than to others to achieve a good level of development.

This paper aims to identify and analyze the factors that influence the decision of Mato Grosso’s farmers to produce soybean using the Analytic Hierarchy Process (AHP). We found evidence that decision-making of soybean production is related to rural production aspects such as climate, financing, cost of inputs, and soil quality rather than marketing and logistics. The novelty of this paper is the empirical analysis of the decision-making in agricultural production using AHP. The decision model was created and tested considering 21 farmers and 19 experts linked to the soybean production. Three different scenarios were considered: farmers' view, experts' view, and combined view. Our findings indicate that farmers and experts agree with rural aspects are predominant in the decision to plant soybean. Moreover, logistics have been used as an important flag of soybean competitiveness on international trade by soybean stakeholders in Brazil. However, our results show that logistics impact in the soybean decision-making process is low. Due to data limitation access, this study focuses only on Mato Grosso. However, this study has an exploratory character and presents empirical results that may help to understand soybean production over the country.

This study aimed to identify how the main variables that are influenced by the anthropic activity resulting from the soybean production in the Mato Grosso Municipalities cluster among themselves. Factor analysis method was used to identify underlying dimensions that can account for the shared variation of observed variables. The factorial analysis proposes to reduce the number of variables by the extraction of independent factors, so that a better explanation of the relationship between the original variables occurs, avoiding correlational problems and reducing the relevance of endogeneity. Three dimensions were identified, each with a different combination of variables. Based on the results from principal components modelling it is fair to state that the impacts of the anthropic activity resulting from soybean production in the Mato Grosso municipalities can be analyzed according to three main domains: production impacts, socioeconomic impacts and demographic impacts. The main contribution of this paper is that it offers a useful framework of analysis for both public and private decision-makers regarding the influence of soybean production on economic, social, environmental, and cultural factors.

O crescimento demográfico e o aumento da demanda por produtos alimentícios e energia, tornou necessária a intensificação da produção agropecuária, visando buscar melhor competitividade global dessa cadeia de suprimentos alimentar, fomentando os demais setores da economia. Diante da pujança desse setor econômico do país, o Estado brasileiro com maior destaque para a produção agropecuária é o Mato Grosso, principal produtor brasileiro de commoditties, tanto de grãos como o setor de carne (pecuária). Nesse sentido, o objetivo deste trabalho foi realizar uma análise para verificar a correlação do PIB agropecuário de cada município com os índices de desenvolvimento socioeconômico IFDM do ano de 2015. Para este propósito, utilizou-se o Statistical Package for the Social Sciences - SPSS® , realizando-se análise de correlação para identificar e medir a relação entre as variáveis. Os resultados mostram correlação positiva entre todos os indicadores. No entanto, foi observada forte correlação entre o PIB Agropecuario e o PIB Per capita, bem como com o IFDM Emprego e Renda.