Author(s): Shanya Tiwari, Sharad Kumar Jain, Umesh Singh and Kuldeep Rajpoot

Abstract: Tomato prices in Jabalpur exhibit significant seasonal fluctuations and high volatility, necessitating accurate detection of price patterns to inform effective policy and market interventions. Seasonal indices reveal peak prices during June (1.23) & July (1.48), driven by high perishability and monsoon disruptions, and troughs from December (0.91) to February (0.81) due to post-harvest supply. The fitted linear trend model (R² = 0.5304) evidenced a statistically significant upward trajectory, indicating an average increase of ₹10.42/quintal per month (₹125.04/quintal annually), higher-order polynomial models like quadratic (R² = 0.5504) and cubic (R² = 0.5599) yielded only marginal improvements in model fit, and overall, all deterministic trend models failed to adequately capture the pronounced stochastic behaviour inherent in agricultural commodity prices. A log-linear model (adj. R² = 0.7311), estimating a Compound Annual Growth Rate (CAGR) of 15.83%, highlights both long-term price growth and the importance of variance stabilization. Structural breaks, notably in April-May 2020 and April 2022, correspond with COVID-19 related disruptions and market realignments after COVID respectively. Given the superior variance stabilization with logarithmic transformation and the inadequacy of deterministic models, stochastic trend approaches such as ARIMA are recommended for precise forecasting.

DOI: 10.22271/maths.2025.v10.i11a.2192

Pages: 12-20 | Views: 39 | Downloads: 10

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