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.

The adoption of digital agricultural technologies is becoming increasingly important for enhancing agricultural productivity, improving resource-use efficiency, and supporting the digital transformation of the agricultural sector. However, empirical evidence on the factors influencing farmers’ adoption of such technologies remains limited in Uzbekistan and other developing agricultural economies. This study examines the factors associated with the adoption of digital agricultural technologies among agricultural producers in Uzbekistan by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework to include Trust and Perceived Risk. Primary data were collected from agricultural producers using a structured questionnaire. A total of 373 responses were obtained, of which 280 valid observations were retained after data screening. The proposed model was assessed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Behavioral Intention is significantly associated with actual technology use, while Facilitating Conditions and Habit also contribute to Use Behavior. Among the factors explaining Behavioral Intention, Perceived Risk was the only statistically significant predictor; however, contrary to theoretical expectations, the relationship was positive rather than negative. The remaining UTAUT2 constructs and Trust did not have statistically significant independent effects on Behavioral Intention. These findings suggest that farmers’ decisions regarding the adoption of digital agricultural technologies in Uzbekistan may be influenced not only by perceived technological benefits but also by uncertainty, perceptions of risk, enabling conditions, and previous experience. The unexpected positive relationship between Perceived Risk and Behavioral Intention should, however, be interpreted with caution and warrants further investigation. The study provides empirical evidence from an underrepresented agricultural context and demonstrates the relevance of incorporating contextual factors such as Trust and Perceived Risk into technology acceptance models. The findings also highlight the importance of strengthening digital infrastructure, providing practical support, and reducing technology-related uncertainty to facilitate the digital transformation of agriculture in Uzbekistan.