This paper illustrates and addresses weak identification in the correlated random coefficient (CRC) model that Suri (2011) uses to study agricultural technology adoption. Using the publicly available dataset, which differs slightly from the version used in Suri (2011), we obtain different point estimates for the paper's main CRC model. To understand why these differences are as large as they are, we recast the CRC model as a more general random coefficient model in which the returns to hybrid adoption are restricted to be linear in comparative advantage. This reveals that the key structural parameter in the CRC model (φ), which governs the relationship between baseline productivity and the returns to adoption, is prone to a weak identification problem. We then propose a procedure to conduct weak-identification robust inference on φ using test inversion. Our weak-identification robust confidence intervals contain the original Suri (2011) point estimates, which suggests that the difference in results may be attributable to a combination of minor data differences and weak identification.