Advancing climate resilient agriculture in the U.S. Great Plains: modeling climate dynamics and impacts on crop production



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Climate variability has historically been a major driver of food production across the globe. Projections of climate demonstrate changes in the variability of temperature and precipitation, threatening the security of food production in areas such as the U.S Great Plains, one of the most agriculturally significant regions in the world. In this region over pumping of groundwater from the Ogallala Aquifer, which has been critical to sustain crop productivity in semiarid regions, has led to declines in the water table. Large-scale irrigated agriculture along with changes of land cover and land use may produce significant interactions with regional climate in the future. These critical issues motivated three objectives for this dissertation: 1) identify the historical behavior of drought, a major agricultural climate driver, 2) dynamically model the agricultural water management impacts on regional climate change, and 3) quantify changes in the resiliency of wheat production to environmental changes in the Great Plains.

In this dissertation, multiple long-term surface climate, regional satellite, and crop phenology and production datasets were integrated and investigated in simulations and analysis. Statistical and dynamic climate modeling were the main methodologies utilized in this study. Regional climate analysis indicated that winter and summer growing season temperatures and drought intensities have significantly increased in the U.S. Great Plains in recent decades. There were 9−12 identified seasonal subregions of homogeneous drought variability, and several subregions demonstrated wetting trends since the early twentieth century. Summer and winter drought became more and less complex across space and time, respectively, indicating changes in resource management may be necessary to mitigate impacts in the future. Regional climate model simulations with new irrigation modules developed indicated that irrigation significantly alters the ambient moisture and temperature profiles at the surface and the mid-levels of the atmosphere. Precipitation increased over irrigated grid cells that had a reduction in the number of acres under irrigation over the last thirty years. Choice of land surface model parameterizations and modeling scale was a significant source of uncertainty in several climate responses, suggesting that future research should carefully examine these options during initial experimental design. Statistical modeling of winter wheat yields demonstrated that at the regional level historical changes in climate since the early 1970s have negatively impacted yield trends while changes in phenology have partially offset some of the negative impacts from climate change. Furthermore, recently developed winter wheat varieties have higher sensitivities to both spring heat and cold stress. Newer varieties achieved their optimal yield response under higher precipitation regimes, indicating recent varieties were less resilient to weather-related impacts. Both changes in phenology and climate sensitivities helped explain the observed increase in yield variance in recent decades at the regional and variety levels. Overall, this dissertation explored diverse areas related to climate resiliency in agriculture, leading to new insights into relationships between major climate drivers and crop production while introducing new tools that provide pertinent information to a wide audience, including agronomists, breeders, and earth system modelers.



Climate change, Drought, Downscaling, Winter wheat, Empirical orthogonal functions, Ogallala Aquifer

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Doctor of Philosophy


Department of Agronomy

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Xiaomao Lin