Forecasting Production of Potato for a Sustainable Future: Global Market Analysis

This study goes into the essential challenge of estimating potato output in order to ensure sustainable agricultural practices while also providing vital insights into global market patterns. The potato production data series compares the accuracy of two popular forecasting models, ARIMA (AutoRegressive Integrated Moving Average) and ETS (Error-Trend-Seasonality), in predicting potato production. The study assesses the efficacy of these models with a particular focus on their relevance to the agricultural markets of India, China, and the USA, three major potato-producing countries. This research builds ARIMA and ETS models and thoroughly assesses their forecasting performance using historical production data series from these important nations. The results show that the ETS model, especially when considering the chosen countries, consistently performs better in predicting potato production for the testing data set than the ARIMA model. According to the models, China and India will keep contributing more to the potato market, solidifying their positions as key players. It is anticipated that the US economy will plateau and stabilize. For the anticipated year 2027, the expected potato output for China, India, and the USA is 100,417, 61,882, and 18,229 thousand tonnes, respectively. Nonetheless, the increasing diversity of confidence intervals in extended forecasts illustrates the intricacy of agricultural productivity and the numerous factors that could impact outcomes. We believe that this research significantly advances sustainable farming methods by offering a thorough analysis of worldwide potato production projections. It also improves our comprehension of the dynamics of the potato market, providing insightful information that can guide decision-making at different levels. In the conclusions, we stated that the studies not only have consequences for the potato sector, but they also highlight how crucial it is to use cutting-edge forecasting methods in order to promote sustainable food production and guarantee future food security.