Author : Daniel Woodford Urban
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Page : pages
File Size : 25,29 MB
Release : 2015
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ISBN :
Sustaining crop yield increases to meet the world's rapidly growing food demand will be a vital challenge of the 21st century, and one made more difficult by rising temperatures and more frequent weather extremes related to anthropogenic global warming. While much scientific research has focused on links between climate change and mean yield impacts, far less has addressed the likeliest consequences for interannual yield variability, a key driver of global price volatility and food security. As the supplier of nearly 40% of the world's maize and soybean, the United States is a critical component of this picture, and its extensive historical yield and weather data make it exceptionally well suited to empirically study climatic effects on yields. Centered on the U.S. and especially the Corn Belt, this dissertation investigates the mean and variability impacts of projected average temperature and precipitation changes, extreme precipitation during the planting season, modulation of heat stress by moisture level, and improved transpiration efficiency through elevated CO2. In all of these aspects, this dissertation seeks not only to quantify effects where the existing literature is sparse, but also to improve the skill and sophistication of statistical approaches to these questions. Chapter 1 applies a statistical model to U.S. county-level maize yields since 1950 based on growing season average temperature (T) and precipitation (P). While these seasonal predictors mask monthly and daily weather variability, they nonetheless explain a large amount of variance. The model assumes a quadratic yield response to T and P, and this nonlinearity implies an increase in yield variability for increases in either the mean or variance of either weather variable. To assess the potential impact of climate change in this region, we predict yields under a range of future projected T and P values from a suite of climate models, and find remarkably strong model agreement toward yield mean decrease and variability increase. Chapter 2 examines the effects of heavy precipitation resulting in excess soil moisture during the planting season. Starting with a model similar to that in Chapter 1, it utilizes extreme precipitation and hydrologic model-derived soil moisture indices constructed from daily time series. In so doing, it extends the work of Chapter 1 by examining weather-related variation outside of the growing season, and in capturing variation related to daily-scale events that may not necessarily correlate strongly with seasonal averages. While the county-level impacts of a moderately wet year are small, counties' wettest years can account for 6-8% yield loss, or roughly the equivalent of a one standard deviation temperature increase. Chapter 3 likewise deepens and extends the insights from Chapter 1, by using more targeted measures of evaporative demand and soil moisture supply than seasonal T and P averages. A more detailed dataset allows for vapor pressure deficit (VPD) and precipitation measures in the critical period of 61 to 90 days after planting, and we find that the yield response to high VPD is indeed significantly ameliorated when moisture levels are high and exacerbated when low. We then examine the potential of CO2 to reduce evaporative demand through improved transpiration efficiency, and quantify the yield mean and variability implications under both a high and low emissions scenario. We find that while the demand-reducing effect of CO2 significantly improves mean yields and reduces variability, the damage due to exceptionally high demand in the high-emissions scenario outweighs its larger CO2 benefit, such that the low-emission scenario is clearly preferable by the end of the 21st century.