Effect of Production Costs on the Price per Ton of Sugarcane: The Case of Brazil

Abstract

The costs of agricultural inputs added to those of labor represent almost a third of the total cost of Brazilian sugarcane production. This study analyzes the behavior of the price per ton of sugarcane in Brazil, relating it to the main production costs of this cultivation. Twelve price indicators from January 2015 to December 2020 were evaluated. First, the data were adjusted to a multiple linear regression model to identify the significant variables on variation in the price per ton of sugarcane. Then, the Monte Carlo simulation was used to measure the level of certainty of occurrence of these variables, and forecasts were obtained from the adjustment of ARIMA models. The results showed the influence of the costs of diesel oil, two agricultural pesticides, and daily laborers on the price of sugarcane, besides an increasing trend of its, providing relevant short-term projections for decision-making about investments in the agribusiness sector.

Forecasting costs and risk of corn and soybean crops through Monte Carlo simulation

“…In the present article, 50,000 iterations were performed, which is the maximum number of iterations provided by the software and is a significant number for the problem of interest. Similar values were used in the studies by Oliveira et al (2022) and Silva et al (2019) [5,34].…”

“…On the other hand, the application of Monte Carlo simulation employs a stochastic approach in which the involved variables are modeled as probability distributions. Instead of providing fixed estimates, Monte Carlo simulation considers the uncertainties associated with each variable and generates multiple simulations to obtain a probabilistic distribution of possible outcomes, considering market variability and uncertainties [5,34,[73][74][75][76].…”

“…To analyze the probability level of cost variation and gross net revenue, Monte Carlo simulation (MCS) was used to provide projections through a stochastic approach [5,[24][25]34].…”

“…In early 2022, for instance, the prices of corn and soybeans were elevated due to strong global demand [3], rising fertilizer costs [4,5], and the drought in South Ameri-ca caused by the La Niña phenomenon. Another impactful factor for the sector was the war in Ukraine, which contributed to the increase in production costs for all commodities [5].…”

“…Pitrova et al (2020) [10] and Amorim et al (2020) [11] mentioned that computational simulation has proven to be an appropriate support tool for decision-making on rural properties. Meade et al (2016) [2] re-ported that such factors include logistics, land, and storage, while Oliveira et al (2022) [5] include fertilizers, seeds, fuel, pesticides, among others. In this regard, information about the relationship of these variables is essential to maintain and/or improve the agricultural performance of the sector, providing projections for short, medium, and long-term decision-making [5,11].…”