Over the past twenty years economists have developed econometric approaches for estimating the impacts of climate change on agriculture by accounting for farmer adaptation implicitly. These reduced-form approaches are simple to implement but provide little insights into impact mechanisms, limiting their usefulness for adaptation policy. Recently, conflicting estimates for US agriculture have led to research with greater emphasis on mechanisms including renewed interest in statistical crop yield models. Findings suggest US agriculture will be mainly and severely affected by an increased frequency of high temperatures with crop yield suggested as a major driver.

This dissertation is comprised of three essays highlighting methodological aspects in this literature. It contributes to the ongoing debate and shows the preeminent role of extreme temperature is overestimated while the role of soil moisture is seriously underestimated. This stems from issues related to weather data quality, the presence of time-varying omitted weather variables, as well as from modeling assumptions that inadvertently underestimate farmers' ability to adapt to seasonal aspects of climate change. My work illustrates how econometric models of climate change impacts on crop production can be improved by structuring them to admit some basic principles of agronomic science.

The first essay shows that nonlinear temperature effects on corn yields are not robust to alternative weather datasets. The leading econometric studies in the current literature are based on a weather dataset that involves considerable interpolation. I introduce the use of a new dataset to agricultural climate change research that has been carefully developed with scientific methods to represent weather variation with one-hour and 14 kilometer accuracy. Detrimental effects of extreme temperature crucially hinge upon the recorded frequency at the highest temperatures. My research suggests that measurement error in short amounts of time spent at extreme temperature levels has disproportionate effects on estimated parameters associated with the right tail of the temperature distribution. My alternative dataset suggests detrimental temperature effects of climate change over the next 50-100 years will be half as much as in leading econometric studies in the current literature.

The second essay relaxes the prevalent assumption in the literature that weather is additive. This has been the practice in most empirical models. Weather regressors are typically aggregated over the months that include the growing season. Using a simple model I show that this assumption imposes implausible characteristics on the technology. I test this assumption empirically using a crop yield model for US corn that accounts for differences in intra-day temperature variation in different stages of the growing season. Results strongly reject additivity and suggest that weather shocks such as extreme temperatures are particularly detrimental toward the middle of the season around flowering time, which corrects a disagreement of empirical yield models with the natural sciences. I discuss how this assumption tends to underestimate the range of adaptation possibilities available to farmers, thus overstating projected climate change impacts on the sector.

The third essay introduces an improved measure of water availability for crops that accounts for time variation of soil moisture rather than season-long rainfall totals, as has been common practice in the literature. Leading studies in the literature are based on season-long rainfall. My alternative dataset based on scientific models that track soil moisture variation during the growing season includes variables that are more relevant for tracking crop development. Results show that models in the literature attribute too much variation in yields to temperature variation because rainfall variables are a crude and inaccurate measure of the moisture that determined crop growth. Consequently, I find that third of damages to corn yields previously attributed to extreme temperature are explained by drought, which is far more consistent with agronomic science. This highlights the potential adaptive role for water management in addressing climate change, unlike the literature now suggests.

The fourth essay proposes a general structural framework for analyzing the mechanisms of climate change impacts on the sector. An empirical example incorporates some of the flexibilities highlighted in the previous essay to assess how farmer adaptation can reduce projected impacts on corn yields substantially. Global warming increases the length of the growing season in northern states. This gives farmers the flexibility to change planting dates that can reduce exposure of crops during the most sensitive flowering stage of the crop growth cycle. These research results identify another important type of farmer adaptation that can reduce vulnerability to climate change, which has been overlooked in the literature but which becomes evident only by incorporating the principles of agronomic science into econometric modeling of climate change impact analysis.