BAYESIAN SPATIOTEMPORAL MODELING OF CLIMATE-INDUCED AGRICULTURAL YIELD VARIABILITY IN PAKISTAN UNDER DATA SCARCITY CONDITIONS

Abstract

Climate variability has emerged as a major challenge to agricultural productivity in developing countries, particularly in Pakistan, where agricultural systems remain highly dependent on climatic conditions and are often characterized by limited and incomplete datasets. This study investigated climate-induced agricultural yield variability in Pakistan using a Bayesian spatiotemporal modeling framework under data scarcity conditions. The study integrated climatic indicators, including temperature, precipitation, drought severity, evapotranspiration, and vegetation health indices, to examine their spatial and temporal effects on agricultural productivity across different agro-ecological regions of Pakistan. Secondary data were obtained from meteorological databases, satellite-derived remote sensing sources, and agricultural statistics covering multiple districts and time periods. A Bayesian hierarchical spatiotemporal model was employed to address uncertainty, missing observations, spatial dependence, and temporal variability simultaneously. The findings revealed that rising temperatures and drought intensity significantly reduced agricultural yields, whereas rainfall and vegetation health indicators positively influenced crop productivity. Significant spatial and temporal dependencies were also identified, indicating substantial regional heterogeneity in climate-agriculture relationships. The Bayesian framework demonstrated strong predictive accuracy and robustness under incomplete data conditions, outperforming conventional statistical approaches in handling uncertainty and heterogeneous datasets. The study contributes methodologically by advancing probabilistic climate-agriculture modeling and practically by providing evidence-based insights for climate adaptation, agricultural forecasting, and food security planning in Pakistan. The findings support the adoption of Bayesian and geospatial analytical approaches for sustainable agricultural management in climate-vulnerable and data-constrained environments.