Rainfall–Wheat Yield Dynamics in Sindh: A VAR Time Series Approach

Abstract

Agriculture in Sindh, Pakistan, is highly sensitive to climatic variability, particularly rainfall fluctuations that directly affect wheat productivity. This study analyzes the dynamic relationship between annual rainfall and wheat yield using time series data from 1991 to 2024. The Augmented Dickey–Fuller (ADF) test confirmed non‑stationarity at levels, which was corrected through first differencing. The optimal lag length was selected as 2 based on the Akaike Information Criterion (AIC). A Vector Autoregression (VAR) model was estimated, followed by Impulse Response Functions (IRFs), Forecast Error Variance Decomposition (FEVD), and short‑term forecasting. Results reveal that rainfall shocks exert significant short‑term effects on wheat yield, while adjustments occur over time. FEVD analysis further indicates that rainfall variability explains a considerable proportion of yield fluctuations. The forecasting exercise (2025–2029) highlights a modest decline in yield immediately after rainfall shocks, followed by gradual stabilization, consistent with IRF patterns. These findings underscore the importance of climate‑resilient agricultural strategies and provide empirical evidence for policy interventions aimed at stabilizing wheat production under changing climatic conditions.

Keywords: Wheat Yield, Rainfall Variability, Vector Autoregression (VAR), Impulse Response Functions (Irfs), Climate Resilience.