Jowar Price Forecasting Using Different Time Series Models in Ballari District, Karnataka, India - , Jahnavi. A. P; Amaresh,; , Vasudeva Naik. K.; , Satish Kumar M (Archives of Current Research International, 2025)
As a staple food grain and fodder source, its price trends directly impact both farmers’ incomes and regional food security. Given the increasing volatility in agricultural markets due to climatic, policy and global economic influences, accurate price forecasting has become essential for informed decision making by farmers, traders and policymakers. Sorghum (Jowar) plays a critical role as a staple food and fodder crop in semi-arid regions of India, especially in Karnataka. This study focuses on forecasting the wholesale prices of Jowar in the Ballari market using monthly data spanning 2002 to 2024. To capture seasonality and complex patterns in the data, different time series models such as ARIMA, SARIMA, BATS, and TBATS were evaluated. Model accuracy was evaluated by using RMSE and MAPE metrics. Among the models, the BATS model exhibited superior forecasting accuracy with the lowest RMSE (87.1809) and MAPE (5.0855) values when compared to other fitted models. BATS model appears to have adequately captured the patterns in the time series data, as indicated by the p-value. This suggests that the model's residuals are approximately white noise, which is a good indication of model fit. Descriptive statistics and seasonal indices highlighted July and August as peak pricing months, aligning with demand-supply dynamics. The findings underscore the value of advanced forecasting methods in supporting informed decision-making for farmers, traders, and policymakers in the region. Seasonal indices confirmed peak prices during July and August, aligning with observed demand-supply dynamics. These insights offer valuable guidance for farmers, traders, and policymakers to make informed decisions related to production, marketing, and pricing strategies. As agricultural markets grow increasingly volatile, integrating advanced statistical forecasting tools becomes not only beneficial but essential for ensuring food security and economic stability in the region.