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- CLIMATIC VARIABILITY AND CHANGE IN THE MIDWESTERN UNITED STATES : IMPLICATIONS FOR NITROGEN LEACHING IN AGRICULTURAL SYSTEMS
- Baule, William James
- Electronic Theses & Dissertations
How has the background climate of the Midwestern United States changed over recent decades and how has this affected nitrate leaching? These are the core questions addressed in this dissertation, through three self-contained studies focused on different aspects of the climate-agriculture interface in the Midwestern United States. In Chapter 2, statistical methods are used to quantify the solar radiation biases present in a widely used reanalysis-based hydrometeorological dataset over space,...
Show moreHow has the background climate of the Midwestern United States changed over recent decades and how has this affected nitrate leaching? These are the core questions addressed in this dissertation, through three self-contained studies focused on different aspects of the climate-agriculture interface in the Midwestern United States. In Chapter 2, statistical methods are used to quantify the solar radiation biases present in a widely used reanalysis-based hydrometeorological dataset over space, implement statistical bias correction and interpolation to address the spatial nature of this bias, and quantify the impacts of the solar radiation bias and proposed correction on simulated maize yields and water stress. Correction of reanalysis solar radiation alone brought simulated yield and water usage more in line with simulations forced with in-situ solar radiation. Chapter 3 examines changes in precipitation, utilizing a unique approach to station screening during the period 1951-2019 over a region encompassing the Great Lakes and broader Midwestern regions, of the United States. A multiple tier procedure was utilized to identify high quality input data series from the Global Historical Climatology Network-Daily dataset. Temporal and spatial trends were analyzed for a broad range of related annual and seasonal indicators ranging from accumulated totals and frequency of threshold events to event duration and potential linkages with total precipitable water. Our analyses confirm the results of previous studies while providing unique insights to data quality and seasonality. The trends of the indicators in our study exhibited more cohesive spatial patterns and temporal similarities when compared with studies with different quality control criteria, illustrating the importance of quality control of observations in climatic studies and highlighting the complexity of the changing character of precipitation. In Chapter 4, System Approach to Land Use Sustainability, a process-based crop model was applied with gridded soil and meteorological data using a yield stability zone concept to simulate corn and soybean production in 14 Midwestern states at the sub-field scale during the 1989-2019 period. Five zones based on multi-year yield stability were simulated for each field at 30m x 30m resolution, with zones being relative to each individual field. Outputs were evaluated using a nitrogen balance approach to establish zone-specific statistical distributions of nitrate leaching across the 14 states, specifically highlighting periods with changing and highly variable precipitation. Results indicate that low stable, unstable hill tops, and unstable slope zones are associated with an outsized contribution to overall nitrate leaching and that unstable zones exhibit variable year-to-year response to weather tied to their position in the landscape. Spatial analysis of the results suggests leaching is tied to precipitation variability, water stress, and total precipitation amount. In aggregate, the chapters presented here highlight the interconnectedness of the soil-plant-atmosphere continuum to changes in hydrologic regime and sensitivity to the biases in the data used to conduct analyses, run models, and from which conclusions are drawn. The study findings shed light on the potential for improved management of agricultural fields and illustrate how process-based crop models can be useful for designing management practices to reduce environmental pollution and increase profits to producers.