Inferences about the potential effects of climate change on crop yields have been derived using models that are based on plant physiology (agronomic models) as well as statistical models.  We combine a large and unique geo-referenced dataset of farm-level maize yield outcomes and planting dates form USDA with daily fine-scale 800m weather data for the state of Iowa for the years 2002-2010. Agronomic models are used to derive simulated maize yields, which are included in a statistical model that accounts for temperature, precipitation, and solar radiation akin to Schlenker and Roberts (PNAS, 2009). This exercise allows us to assess the degree to which temperature effects identified in earlier statistical work are due to processes captured in physiological models, like reduced time to maturity, and how much are due to factors such as heat stress and lack of water.