S B Singh

Ginger contributes substantially to the agricultural sector in Meghalaya, yet comprehensive forecasting analyses that integrate both traditional and volatility-sensitive time series approaches to study its price movements are still scarce. In this study, we explore common time series yet very powerful forecast models, namely the autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and ARIMA with exogenous inputs (ARIMAX), along with autoregressive conditional heteroscedasticity (ARCH) and generalized autoregressive conditional heteroscedasticity (GARCH) models, to forecast the monthly price of ginger in Meghalaya. During the estimation process, attention was given to the intrinsic forecasting strengths and limitations of these models, as well as to essential time series diagnostics, including stationarity, parsimony and overfitting. The discussion in this study is based on the forecast results obtained from a real-time monthly price dataset spanning 10 years. While fitting the models to the dataset, special care was taken to select the most parsimonious model. To evaluate forecast accuracy and compare the performance of the different models applied to the time series of monthly ginger prices, we used seven forecast performance measures: ME (mean error), RMSE (root mean square error), MAE (mean absolute error), MAPE (mean absolute percentage error), MASE (mean absolute scaled error), AIC (Akaike information criterion) and BIC (Bayesian information criterion). The GARCH (1,1) model outperformed the others, yielding the lowest MAE (1,212.28), MASE (0.2817) and a high persistence in volatility (β₁ = 0.99772). The average price during the study period was ₹4400.47. The forecasts indicated a decline in prices from ₹11910 in March 2024 to ₹10938 in July 2024.

The marketing effectiveness of tomato and cabbage in the Ukhrul District of Manipur was evaluated using Acharya’s modified marketing efficiency approach, with a focus on identifying the key factors influencing marketing efficiency. The economic viability of tomato and cabbage production in the state was also analyzed. The per quintal production costs of tomatoes and cabbage were found to be Rs. 219.36 and Rs. 249.31 respectively, with benefit-cost ratios of 5.76 and 2.30 respectively. Due to the perishable nature of tomatoes and cabbage, combined with limited processing and storage infrastructure, insufficient knowledge among stakeholders and the presence of numerous intermediaries, significant price fluctuations were observed, which negatively impacted both the farmers and consumers. Most tomatoes were sold through Channel 1 (Producer-Retailer-Consumer), while cabbage was mainly marketed via Channel 3 (Producer-Wholesaler-Retailer-Consumer). Marketing costs and margins varied with the length of the distribution channel; more extended channels led to higher costs and wider price spreads. As the number of intermediaries increased, both the consumer price and marketing margin rose, reducing overall marketing efficiency. Factors such as transportation, market price, marketing margin and volume significantly influenced efficiency. The highest efficiency was found in the direct channel from farmers to retailers to consumers. Developing a dedicated farmers' market model with essential infrastructure, such as storage, weighing systems, water, electricity and shelter, could streamline the supply chain, reduce costs and improve overall market performance by minimizing intermediary roles.