The impacts of climate change on Indian agriculture are well documented. However, there is a dearth of research addressing the inter-regional diversities in the impacts. Furthermore, existing studies are mostly restricted to the impacts on mean agricultural yield, overlooking the impacts on yield variability. This study investigates climatic impacts on rice yield in Odisha, a coastal Indian state, employing district-level panel data from 1995 to 2017. This study uses the Feasible Generalized Least Squares (FGLS) and quantile regression approaches to estimate how climatic impacts are realized on mean yield, yield variability, and conditional quantiles of yield distribution. The use of the agro-climatic zone level dummies empowers the study to account for local weather and soil conditions. The results reveal substantial heterogeneity in the climatic impacts across agro-climatic zones. A marginal increase in the maximum temperature reduces rice yield by 279–325 kg/ha across different agro-climatic zones, largely constituting the state’s coastal region, but increases yield by 340-766 kg/ha across the zones lying in the western region. Conversely, an increase in minimum temperature increases rice yield by around 211 kg/ha in the coastal region, but reduces by 333–744 kg/ha in the western region. In addition, an increase in precipitation reduces yield by 0.318 kg/ha in some parts of the coastal region, but increases yield by 0.268-0.881 kg/ha across other regions. Similar heterogeneity is also identified across conditional quantiles of yield distribution. The heterogeneous impacts call for strategic policy intervention specific to zones and quantiles.

The escalating adversities of climate change increasingly jeopardise agriculture in coastal Indian states like Odisha. The significance of the agriculture sector for the state necessitates effectively mitigating the adverse climatic impacts. Strengthening the resilience of agriculture has been widely acknowledged as one of the most effective strategies for mitigating negative climatic impacts. Framing and implementing essential resilience-enhancing measures depends on a comprehensive preliminary assessment of existing resilience. This study estimates agricultural resilience to climate change in Odisha by constructing district-level composite indicators. The Principal Component Analysis and Analytic Hierarchy Process are employed to assign weights to a multidimensional set of indicators and aggregate them into composite indicators. In addition, the Cluster Analysis is employed to identify heterogeneity among the districts in terms of their agricultural resilience. The study finds that the coastal districts in the state have the lowest agricultural resilience, which may be attributed to the higher vulnerability of these districts to a number of climatic risks. The composite indicators further highlight the need for region-specific interventions. Similarly, the interplay of multiple social and environmental factors is found to influence resilience, underscoring crucial implications for public decision-making.