<![CDATA[Rice is a staple food and a crucial element of Nepal’s agrarian economy; however, its yield is significantly affected by climatic factors such as rainfall and temperature, as well as agricultural practices like pesticide use. Understanding these dynamics is essential for sustaining productivity in the face of climate change. This study employs an Autoregressive Distributed Lag (ARDL) model to analyze 33 years of time-series data (1990–2022), focusing on key variables including rice yield, temperature, rainfall, and pesticide use, all derived from secondary data sources. Diagnostic tests confirmed normality (????=0.06), absence of serial correlation (????=0.58), and homoscedasticity (????=0.68), with stability validated through CUSUM and CUSUMSQ tests. The results indicate that temperature has a significant positive long-term impact on rice yield (????=2181.48, ????<0.05), suggesting that moderate warming can enhance productivity. Rainfall exerts a marginal positive effect (????=5.10, ????=0.05), while pesticide use shows a strong correlation with yield (????=17.70, ????<0.01). The Granger Causality Test identifies temperature (????=7.76, ????<0.01) and pesticide use (????=11.25, ????<0.01) as critical predictors of rice yield. These findings demonstrate that while temperature and pesticide use significantly affect rice yield, the impact of rainfall is diminished due to effective irrigation systems. Nevertheless, the heavy reliance on pesticides raises sustainability concerns, underscoring the necessity for integrated pest management and environmental safeguards. This study advocates for the adoption of climate-smart agricultural practices, enhancement of irrigation infrastructure, and promotion of sustainable pesticide management, offering actionable insights for policymakers to devise adaptive strategies that bolster resilience and productivity in Nepal’s rice sector.]]>
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