# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 1 Introduction  
Worldwide, corn and soybeans stand out among the main commodities of agribusiness. For example, in Brazil, over the last decade, they have been the most produced and exported agricultural commodities, contributing to the Brazilian trade surplus with export revenues exceeding USD 53.889 billion in 2021 [1]. The competitiveness of these crops in international markets is correlated to long-term production and transportation costs, as well as export policies and fluctuations in exchange rates, among other aspects [2, 3]. In early 2022, for instance, the prices of corn and soybean crops increased due to higher global demand [3], rising fertilizer costs [4, 5], and the drought in South America caused by the La Nifna phenomenon. The war in Ukraine also contributed to the increase in production costs for all commodities [5]. Although [PERSON] et al. [2] have extensively researched production costs and competitiveness of soybean and corn exports in the main producing countries, such as Argentina, Brazil, and the United States, there is a lack of application of predictive models that integrate the variability of inputs and production costs and the market volatility. The use of a Monte Carlo Simulation may offer a more robust and dynamic analysis of fluctuations in production costs, thus addressing a gap present in current research. According to several predictions, the high prices of soybeans and corn were expected to continue to become higher during several periods [6]. Given such a scenario of increased costs, forecasts of production costs are particularly important for decision-makers in agribusiness [7; 8; 9]. According to [PERSON] et al. [10] and [PERSON] et al. [11], computational simulation has proven to be an appropriate support tool for decision-making in this context. [PERSON] et al. [2] highlighted the costs related to logistics, land, and storage, while [PERSON] et al. [5] considered also the costs of fertilizers, seeds, fuel, and pesticides, among others. [PERSON] et al. [5] recognized the importance of inputs such as fertilizers and seeds in computing an accurate production cost of agricultural products. However, they have not comprehensively explored the complex interactions between these factors and their impact on cost predictability under different economic scenarios. There, is, indeed, a gap in terms of studies that offer an integrated approach that, in addition to identifying these interactions, can quantify them using probabilistic simulation models. Furthermore, the analysis of the relationship among these variables is fundamental to understanding and improving the performance of agribusinesses, namely, providing predictions for short-, medium-, and long-term decision-making [5; 11]. The literature offers a significant number of studies in different countries focused on the production costs of corn and soybeans [2; 12; 13; 14], as well as others about economic feasibility and risk analysis through case studies [15; 16] and some on the analysis of soybean and corn prices [17; 18]. [PERSON] [17] studied corn price variability and its implications for changes in land use and forest loss in Ghana, highlighting economic factors crucial to agricultural sustainability. However, there is still a lack of research that connects these price variabilities to predictive cost models in the main producers, such as Brazil. Several researchers have explored the competitiveness of the main soybean and corn producers in the international market, highlighting factors such as government policies, production costs, economic challenges, production systems, and the importance of improving agricultural infrastructure. Although they provide relevant information about current conditions and comparisons between countries, little work exists on guidelines that can directly assist producers in making strategic decisions, which limits the practical application of research for agricultural planning. Thus, there is a gap and a need for models focused on the expected costs and their trends for the near future.