Assessing Policy and Environmental Impacts on Agricultural Yield Trends Using Bayesian Spline and Interrupted Time Series Regression Models

Agriculture is a vital sector in Sri Lanka, yet it has been increasingly challenged by environmental disturbances and policy shifts. This study analyzes the impact of two significant disruptions—the 2017 drought and the 2021 fertilizer ban—on paddy yield trends. To address the complexities of these influences, Bayesian spline regression was employed to model non-linear and seasonal yield patterns, and interrupted time series regression was used to capture abrupt changes in response to external shocks. Uncertainty and the causal effects of these interventions were quantified through posterior distributions, estimated using the brms package and the NUTS algorithm. The results offer nuanced insights into how environmental and policy factors shape agricultural productivity, with implications for researchers and policymakers concerned with sustainable agriculture and climate adaptation. Overall, this study underscores the value of Bayesian modeling in assessing multifaceted, real-world issues that span agriculture, environment, and policy.