How Do Climatic Parameters Influence Tomato Price Forecasting in Punjab?

Using monthly wholesale price data from January 2004 to September 2024 with climatic parameters, we built the autoregressive integrated moving average model and the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model to examine the impact of climatic parameters, specifically temperature and rainfall, on enhancing the accuracy of tomato price forecasting in Punjab. We find that minimum temperature and lagged rainfall significantly influence tomato prices and reflect their impact on production and supply dynamics. The SARIMAX [1,1,2],[0,0,3][12] model demonstrated the lowest root mean square error and mean absolute percentage error among all the models we tested, signifying superior performance. The study highlights the importance of integrating weather-based forecasting into agricultural planning to improve market efficiency and enhance farmer resilience. JEL Classification: C53, Q02, Q11, Q12