Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model - Sarah Yasmin; Md. Moniruzzaman (Journal of Agriculture and Food Research, 2024)
Jute holds significant importance as a cash crop in Bangladesh and was once renowned as the country's ''golden fiber.'' Before embarking on a comprehensive strategy, it is crucial to gain insight into the future scenarios of jute in Bangladesh. This study's main objective is to employ the Box-Jenkins method to choose the most suitable Autoregressive Integrated Moving Average (ARIMA) models for forecasting area, production, and yield of jute in Bangladesh for the period from 2023 to 2030. This research relies on historical time series data spanning from 1970 to 2022. After thorough analysis, the most appropriate models were determined: ARIMA (2,0,3) for area, ARIMA (1,0,2) for production, and ARIMA (1,0,3) for yield. These selections were made based on model selection criteria, normalized BIC values, examination of ACF and PACF plots, and confirmation that residuals from these chosen ARIMA models exhibit no serial correlation, as indicated by Ljung-Box Q statistics. The ARIMA model predicts a continuous upward trajectory for jute cultivation, projecting that the jute cultivation area will expand to 7.54 lac hectares by 2023 and 8.30 lac hectares by 2030. This represents an estimated increase of approximately 13.85 % compared to the jute area in 2022. However, projections indicate a decline in both jute production and yield. By 2023, production is forecasted to be 80.48 lac bales with a yield of 11.47 bales per hectare, while by 2030, production is expected to decrease to 74.34 lac bales with a yield of 11.22 bales per hectare, down from 84.58 lac bales and 11.59 bales per hectare respectively in 2022.These findings are valuable for researchers and policymakers as they provide new insights into the future dynamics of jute cultivation in Bangladesh. This information can guide the development of initiatives and plans aimed at enhancing jute area, production, and yield in the country.