Abstract Regression discontinuity designs (RDD) are increasingly being employed in agricultural and environmental economics to identify causal effects. Here, we showcase recent applications, identify best practices, discuss commonly invoked identifying assumptions and show how these can be tested. We discuss basic empirical issues and more advanced topics, including how to exploit the availability of panel data, models to explain heterogeneous treatment effects and extrapolation of local estimates. Moreover, we show how agricultural economists can leverage RDD in combination with remote sensing and environmental modelling. Finally, we highlight three areas of emerging opportunities and draw conclusions for research and policy.