To account for the effects of heterogeneity in micro-econometric models has been major concern in labor economics, empirical industrial organization or trade economics for at least two decades. The micro-econometric agricultural production choice models found in the literature largely ignore the impacts of unobserved heterogeneity. This can partly be explained by the dimension of these models which deals with a large set of choices, e.g., acreage choices, input demands and yield supplies. We propose a random parameter framework to account for the unobserved heterogeneity in micro-econometric agricultural production choices models. This approach allows accounting for unobserved farms’ and farmers’ heterogeneity in a fairly flexible way. We estimate a system of yield supply and acreage choice equations with a panel set of French crop growers. Our results show that heterogeneity significantly matters in our empirical application and that ignoring the heterogeneity of farmers’ choice processes can have important impacts on simulation outcomes. Due to the dimension of the estimation problem and the functional form of the considered production choice model, the Simulated Maximum Likelihood approach usually considered in the applied econometrics literature in such context is empirically intractable. We show that specific versions of the Stochastic Expectation-Maximization algorithms proposed in the statistics literature are easily implemented for estimating random parameter agricultural production models.