Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer.

Soybean is one of the main sources of protein directly and indirectly in human nutrition, and it is highly dependent on logistics to connect country growers and international markets. Although recent studies deal with the impact of logistics on international trade, this impact in agricultural commodities is still an open research question. Moreover, these studies usually do not consider the influence of all components of the logistics on trade. This paper, therefore, aims at identifying the role of logistics performance in soybean exports among Argentina, Brazil, the US and their trading partners from 2012 to 2018. Using an extended gravity model, we examine whether the indicators of the World Bank Logistics Performance Index (LPI), adopted as a proxy of logistics efficiency, are an important determinant of bilateral soybean trade facilitation. The results lead to the conclusion that it is necessary to analyze the LPI throughout its indicators because they may affect trade differently. The novelty of this article is to provide an analysis of the impact of different logistics aspects on commodity trade, more specifically in the soybean case. Finally, regarding the model results, logistics infrastructure has a positive and significant correlation with soybean trade as supposed in most of the literature.

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.

Resumo: Brasil e Estados Unidos foram responsáveis por dois terços da produção mundial da soja durante a safra de 2016/17, equivalente a cerca de 348,1 milhões de toneladas. A soja é o principal produto na corrente de comércio brasileira, visto que sua participação na balança comercial foi de 33%, com o volume exportado de 68 milhões de toneladas, equivalente a US$ 25,7 bilhões. Assim, faz-se necessário analisar e estimar a relação entre área plantada, produtividade e produção, visando tomadas de decisões que possam afetar o suprimento interno e externo desse cereal. Nesse contexto, propõe-se nesse trabalho, uma rede neural artificial para estimar a produção futura da soja brasileira. Utilizou-se o software Matlab R2017b e a Neural Network tool box para elaboração, treinamento, validação e testes da rede. Os dados foram coletados das séries históricas de 41 anos de área plantada, produtividade e produção, fornecidas pela Companhia Nacional de Abastecimento. Os resultados apontaram uma produção de 108,1 milhões de toneladas, para a safra 2017/2018, ou seja, uma pequena queda de 5% em relação à safra 2016/17 que foi de 114,1 milhões de toneladas.Palavras-chave: agronegócio, previsões em series temporais, sistemas inteligentes, Abstract: Brazil and the United States accounted for two-thirds of world soybean production during the 2016/17 crop, equivalent to about 348.1 million tonnes. Soybeans are the main product in the Brazilian trade chain, since its share in the trade balance was 33%, with the volume exported of 68 million tons, equivalent to US$ 25.7 billion. Thus, it is necessary to analyze and estimate the relationship between harvested area, yield and production, aiming at decisions that may affect the internal and external supply of this cereal. In this context, we propose an artificial neural network to estimate the future production of Brazilian soybean. The software Matlab R2017b and the Neural Network tool box were used for the elaboration, training, validation and testing of the network. The data were collected from the historical series of 41 years of harvested area, yield and production, provided by Companhia Nacional de Abastecimento. The results showed a production of 108.1 million tons, for the 2017/2018 harvest, a small decrease of 5% compared to the 2016/17 harvest of 114.1 million tons.

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.

Brazil's agricultural economy is growing and increasing productivity. Therefore, it has required transportation systems with high load capacity and lower transportation costs. However, with the drought in the Southeast region of Brazil, the waterway Tietê-Paraná closed since May 2014 generating a loss of more than 30 million last year. Thus, this study investigates the impacts on direct, indirect and hidden costs resulting from this change of route for soy transport. The methodology consists of an exploratory, descriptive and bibliographic research that seeks to raise the main costs. The results show that failing to ensure the production of soybeans by the Tiete-Parana

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.

Agricultural products are an important part of the Brazilian economy. In soybean production, the country is the second largest producer with 114.0 million tons in the 2016/2017 harvest. Mato Grosso state is the largest Brazilian producer with 30.5 million tons and the port of Santos is mainly requested by being the largest port in Latin America. However, the poor infrastructure of the transport road causes bottlenecks when dispatching soybean through the major ports. Artificial Neural Networks (ANN) are used worldwide in logistics; therefore, we propose to design, train and simulate an ANN on MatLab©software to forecast the demand of soybean produced in Mato Grosso and exported through the port of Santos. The value of 9.0 million tons was predicted for 2017 as an increase of about 26.5% compared with the 2016 movement of 7.1 million tons. In addition, it was noticed that 5.9 million tons were moved only in the first five months (Jan-May) of transactions in 2017.