Dynamic Effects of Temperature and Precipitation on Wheat Yields in Uzbekistan: An Autoregressive Distributed Lag (ARDL) Analysis

Climate change increasingly threatens agricultural productivity in climate-sensitive regions, yet evidence on how temperature and precipitation shape wheat yields over both short- and long-term horizons remains limited in Central Asia. This study investigates the dynamic relationship between temperature, precipitation, and wheat yield in Uzbekistan using annual time-series data for 1992–2023. An Autoregressive Distributed Lag (ARDL) bounds-testing approach was applied, with stationarity confirmed via ADF and Phillips–Perron tests and model stability verified through CUSUM and standard diagnostic tests. The results show a statistically significant positive long-run association between temperature and wheat yield, with a 1% increase in temperature linked to a 2.62% yield increase, alongside a negative, one-year-lagged effect of precipitation, indicating that excessive rainfall in the prior season penalizes subsequent yields. The estimated Error Correction Term (−0.234) indicates a comparatively slow adjustment to climatic shocks, equivalent to roughly 4.3 years under this specification. These findings show that temperature and lagged precipitation are statistically associated with wheat yield dynamics in Uzbekistan, though the temperature effect should be interpreted alongside concurrent irrigation expansion and agricultural modernization rather than as an isolated climatic benefit. The results offer an empirical basis for climate-adaptive agricultural planning in Uzbekistan, including drainage management and adjusted sowing calendars.

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