Climate-driven price variations in cabbage and beetroot: A VAR model approach

Abstract

Vector Autoregression (VAR) is an econometric approach widely applied to examine interactions among economic variables, generate forecasts, and guide policy-related decisions. This research primarily captured short- to medium-term dynamics between climate factors and vegetable prices in Badulla, focusing on cabbage and beetroot within the Haputale and Bandarawela regions from 2000 to 2024. Monthly wholesale price information was obtained from the Hector Kobbekaduwa Agrarian Research and Training Institute (HARTI), while detailed climate data— mean monthly temperature (◦C) and total monthly rainfall (mm)—were collected from the Department ofMeteorology.The analysis followed three main steps. First, stationarity was verified through Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, confirming that all variables became stationary after first differencing (p < 0.01). Second, the appropriate lag order for the VAR model was identified as four months, based on several information criteria (AIC = 3.21, BIC = 3.45, HQ = 3.32). Third, Granger causality tests revealed significant relationships: rainfall Granger-caused cabbage price changes (F-stat = 5.34, p = 0.003) and temperature Granger-caused beetroot price variations (F-stat = 4.87, p = 0.008). The VAR coefficients showed that a 1% increase in rainfall was associated with a 0.42% (SE = 0.11) price increase for cabbage in the subsequent month, while a 1 ◦C temperature rise led to a 0.38% (SE = 0.09) price increase for beetroot after two months. Impulse response functions demonstrated that climate shocks had persistent effects: a one standard deviation rainfall shock (≈ 50 mm) raised cabbage prices by 2.1 to 3.4% over six months, while an equivalent temperature shock (≈ 1.2 ◦C) increased beetroot prices by 1.8–2.5% with a three-month lag. Variance decomposition analysis indicated that climate factors explained 28% of cabbage price variation and 22% of beetroot price variation at the 12-month horizon. These findings suggest that rainfall-responsive pricing mechanisms could help stabilise cabbage markets during monsoon seasons, and early warning systems using the VAR framework could predict price spikes with a 2-3 month lead time. The study contributes to climate-resilient agriculture by quantifying asymmetric price responses to different climate variables across distinct vegetable crops.