# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 4 Conclusions  
The model proposed here has shown accuracy in estimating future production prices of corn and soybeans. This makes it a useful tool to support pricing strategies under different production scenarios for these commodities, particularly in periods of significant market volatility and uncertainty. The model proposed forecasts prices more accurately, as it considers the variation in the costs of inputs that most significantly influence the costs of corn and soybean crops. The model shows that production costs in the 2023/24 and 2024/25 harvests will depend mostly on the costs of labor, soybean seed, and fungicides, which may increase in this period. Several strategies and actions can be used to reduce these costs (e.g., changes in the cultivation areas, investment in mechanization, and digitalization of production processes). Furthermore, the results suggest that the profitability of soybean is higher than corn. Understanding the factors that influence the costs of corn and soybeans inputs, as well as the behavior of price variability, is crucial for producers to make informed decisions on their crops. By doing so, it becomes possible to ensure the sustainability of agricultural production in competitive global markets. Big Data applications can be particularly important in estimating future prices in so-called smart agriculture and they will be important to assist decision-makers and will influence the entire food supply chain [69]. More broadly, digital transformation, with its speed and scope, is revolutionizing agribusiness, modernizing production methods and profoundly changing the cost structure and risk management associated with crops [70]. However, these innovations also introduce new challenges, such as the need to manage additional costs and adapt to increasingly complex production dynamics [69]. In the context of the digital economy, crop cost risks are now influenced by new factors and variations. The increasing dependence on digital technologies implies costs for acquiring, maintaining, and updating software and hardware, making transactions more vulnerable to technological failures and obsolescence [69]. Furthermore, the volatility of costs has also increased, with machine learning techniques offering promises of more accurate specification, although these depend on the quality of the data and models used [70]. Looking ahead, digital transformation will continue to redefine the costs and risks associated with agricultural production. Farmers and businesses need to constantly adapt to new technologies to mitigate risks and take advantage of emerging opportunities [70]. However, this evolution can also accentuate technological inequalities, benefiting large producers to the detriment of smaller ones, which may face higher costs and challenges to compete [71]. Furthermore, digitalization can bring both sustainability benefits and new environmental risks, such as increased carbon footprint and challenges associated with cybersecurity, especially with the increasing interconnection of devices through the Internet of Things (IoT) [72; 73]. This work stands out by applying MCS to model different cost scenarios, taking into account market volatility and the correlation between key variables. In this way, we not only address a gap identified in the literature but also offer a practical tool for economic and financial planning in agribusiness. This approach enables a more detailed and accurate analysis of the risks associated with production costs, offering managers a clearer view of potential cost variations, which is crucial for both operational and strategic decisions in such an uncertain environment. However, it is important to recognize some limitations of this study. Firstly, the simulation model used is based on historical data and the precision of input parameter estimates, which can restrict the accuracy of forecasts in highly uncertain scenarios or in cases of abrupt changes in the market.