<p>Assessing the impact of climate change on agricultural productivity is crucial for targeted adaptation and policy planning. In this context, the Ricardian model is often used to estimate the impact of climate change on economic measures on farms. The model regresses agricultural land values (or other proxies for agricultural productivity) on historical climate and control variables using cross-sectional data. Despite its wide utilisation, the model has frequently been criticised for its proneness to omitted variable bias. In this study, we replicate the results of eight published Ricardian studies and assess how robust their estimates are to omitted variable bias, using tests developed by Oster in J Bus Econ Stat 37(2):187–204, 2019 and Diegert, Masten &amp; Poirier in arXiv, 2023. We find that the majority of the climate coefficients tested in this study are likely relatively sensitive to omitted variable bias. Yet, these test results must be interpreted in light of the model setup, especially the included control variables, as they are the benchmark for evaluating sensitivity to omitted variable bias. This is also key for the general use of the tests. Additionally, it appears that other model choices, such as the decision on the dependent variable or sample delimitation, affect the coefficients’ robustness. We suggest that future research on climate change impacts on agriculture using the Ricardian approach should be complemented with the presented coefficient stability tests to support the validity of their results.</p>