Systematic Review of Methods for Specialty Coffee Price Estimation

Despite being the initial and fundamental actor in specialty coffee's global production and marketing chain, coffee growers receive a disproportionately small share of the product's final value. This situation, aggravated by the adverse effects of climate change, market volatility, and increased production costs, exposes them to a precarious economic situation. There are many ways to help these producers, such as estimating production costs and selling prices, estimating climate, improving infrastructure, supporting the creation of cooperatives, and promoting fair trade. This review aims to identify several methods employed across different countries for estimating the price of specialty coffee. For this purpose, a systematic review methodology is conducted. This methodology involves identifying the need for the review, selecting and analyzing primary studies, and disseminating findings. Several studies employ mathematical models based on regression analysis, time series models, and artificial neural network-based models for coffee price estimation. In addition, it was identified that the most used evaluation metrics for the models mentioned above were R2, Akaike Information Criterion (AIC), and Average Mean Squared Error (MSE), respectively. Findings also reveal that these models often used data from online auctions, supermarkets, and the stock market, emphasizing the Cup of Excellence (CoE) auction contest. It was also identified that the main analyzed variables for the estimation were date or year, price of coffee, country of origin and destination, variety, ranking in the auction, and altitude. The study emphasizes the need for new methods and variables to estimate specialty coffee prices and their potential positive impact on the industry. These methods must adjust parameters flexibly as the models are prone to change over time.