Forecasting storage capacity using exponential smoothing method

Abstract

Purpose: Analysis of the production and storage capacity of soybeans and corn in Mato Grosso (Brazil) and its projections until the year 2022. Methodology/Approach: Data regarding Mato Grosso’s soy and corn production, area planted, and storage and yield capacity were organized for the years 1995 to 2018. Exponential smoothing models were then used to preview the grain storage balance for the period between 2019 and 2022. Findings: Grain production is expected to grow 11.9%, while the increase in storage capacity is expected to be 9.7%. Therefore, the results showed that the gap between production and storage may increase by 118%, between the harvests of 2017/18 and 2021/22. Moreover, the deficit in storage capacity will represent 53% of the total harvest in 2021/22. Research Limitation/implication: Interferences in the analyzed variables, such as economic, environmental, or technological events, may affect the forecasts’ outcomes. Originality/Value of paper: The estimation combines exponential smoothing models with a non-parametric resampling technique.