Browsing by Author "Mueller, Nathan, committee member"
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Item Open Access Analysis of wheat spike characteristics using image analysis, machine learning, and genomics(Colorado State University. Libraries, 2022) Hammers, Mikayla, author; Mason, Esten, advisor; Ben-Hur, Asa, committee member; Mueller, Nathan, committee member; Rhodes, Davina, committee memberUnderstanding genetics regulating yield component and spike traits can contribute to the development of new wheat cultivars. The flowering pathway in wheat is not entirely known, but spike architecture and its relationship with yield component traits could provide valuable information for crop improvement. Spikelets spike-1 (SPS) has previously been positively associated with kernel number spike (KNS) and negatively correlated with thousand kernel weight, meaning a further understanding of SPS could help unlock full yield potential. While genomics research has improved efficiency over time with the development of techniques such as genotyping by sequencing (GBS), phenotyping remains a labor and time intensive process, limiting the amount of phenomic data available for research. Recently, there has been more interest in generating high-throughput methods for collecting and analyzing phenotypic data. Imaging is a cheap and easily reproducible way to collect data at a specific maturity point or over time, and is a promising candidate for implementing deep learning algorithms to extract traits of interest. For this study, a population of 594 soft red winter wheat (SRWW) inbred lines were evaluated for wheat spike characteristics over two years. Images of wheat spikes were taken in a controlled environment and used to train deep learning algorithms to count SPS. A total of 12,717 images were prepared for analysis and used to train, test, and validate a basic classification and regression convolutional neural network (CNN), as well as a VGG16 and VGG19 regression model. Classification had a low accuracy and did not allow for an assessment of error margins. Regression models were more accurate. Of the regression models, VGG16 had the lowest mean absolute error (MAE) (MAE = 1.09) and mean squared error (MSE) (MSE = 2.08), and the highest coefficient of determination (R2) (R2 = 0.53) meaning it had the best fit of all models. The basic CNN was the next well fit model (MAE = 1.27, MSE = 2.61, r = 0.48) followed by the VGG19 (MAE = 1.32, MSE = 2.98, r = 0.45). With an average error of just above one spikelet, it is possible that counting methods could provide enough data with an accuracy high enough for use in statistical analyses such as genome wide association studies (GWAS), or genomic selection (GS). A GWAS was used to identify markers associated with SPS and yield component traits, while demonstrating the use of genomic selection (GS) for prediction and screening of individuals across multiple breeding programs. The GWAS results indicated similar markers and genotypic regions underpinning both KNS and SPS on chromosome 6A and spike length and SPS on chromosome 7A. It was observed that favorable alleles at each locus were associated with higher KNS and SPS on chromosome 6A and longer wheat spikes with higher SPS on chromosome 7A. Significant markers on 7A were observed in the region near WAPO1, the causal gene for SPS on the long arm of chromosome 7A, indicating they could be associated with that gene. GS results showed promise for whole genome selection, with the lowest prediction accuracy observed for heading date (rgs = 0.30) and the highest for spike area (rgs = 0.62). SPS showed prediction accuracies ranging from 0.33 to 0.42, high enough to aid in the selection process. These results indicate that knowledge of the flowering pathway and wheat spike architecture and how it relates to yield components could be beneficial for making selections and increasing grain yield.Item Open Access Assessing drought sensitivity across the shortgrass steppe biome(Colorado State University. Libraries, 2024) Hedberg, Sydney Leigh, author; Knapp, Alan K., advisor; Dao, Phuong D., advisor; Mueller, Nathan, committee memberNet primary productivity (NPP) of grassland ecosystems is dependent on many biotic and abiotic factors. However, water availability is generally considered the primary determinant of NPP, as well as being key for defining grassland community structure, and thus it is imperative to understand how grasslands respond to drought in a climate where droughts are expected to become more frequent and severe. There is a well-documented negative relationship, described by the Huxman-Smith model, between drought sensitivity and mean annual precipitation (MAP) at spatial scales that span multiple biomes. In other words, drier ecosystems are usually more sensitive to drought than more mesic ecosystems. While this cross-biome pattern has been independently confirmed with a variety of research approaches, there is limited research that has explored how patterns of drought sensitivity vary with MAP within a single biome where the dominant species do not vary. My goal was to determine if this negative relationship is evident within a regionally extensive grassland biome generally dominated by a single grass species (Bouteloua gracilis or blue gramma). I characterized the spatial pattern and relationship between drought sensitivity and MAP across the shortgrass steppe biome of the North American Great Plains using satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data (from 2000-2022) as proxies for vegetation productivity. Gridded annual precipitation data were obtained at a comparable spatial scale. I found a negative relationship between drought sensitivity and MAP within the shortgrass steppe biome, indicating that the Huxman-Smith model is also supported within a single biome. Thus, my results suggest that while changes in the dominant vegetation may contribute to the patterns observed between MAP and drought sensitivity at large spatial scales that include multiple biomes, gradients in MAP within a biome can also drive this negative relationship. As a result, directional changes in annual precipitation amounts have the potential to alter drought sensitivity directly, even if the dominant plant species do not change.Item Open Access Ecovoltaics and grassland responses to solar energy co-location(Colorado State University. Libraries, 2024) Sturchio, Matthew Anders, author; Knapp, Alan K., advisor; Ocheltree, Troy, committee member; Schipanski, Meagan, committee member; Mueller, Nathan, committee memberThe mitigation of climate change requires a transition to renewable sources of energy, and of all available options solar photovoltaic (PV) energy generation has the greatest potential to reduce CO2 emissions by the year 2030. Even so, ground mounted PV is land use intensive, and ideal locations for solar development often overlap with sensitive natural ecosystems and highly productive agricultural land. A scalable approach with potential to alleviate the land use tension created by solar development is the co-location of PV arrays and grassland ecosystems. While this approach has many positive implications for land sparing, the ecological consequences of PV presence above grassland ecosystems are not well understood. In this dissertation I discuss how the unique microenvironments created by PV arrays alter patterns of productivity, physiological response, and forage quality in a semi-arid grassland in Colorado, USA. I also outline a new approach to PV development, Ecovoltaics, that is informed by several fundamental ecological concepts. An Ecovoltaic approach to solar development co-prioritizes energy generation and ecosystem services by intentional design and management through all aspects of array development. With this work, I hope to inform a more sustainable future for solar energy.Item Open Access Environmental assessment of northern Colorado dairy systems: whole-farm predictions for past, future, and beneficial management practices(Colorado State University. Libraries, 2022) Loudenback, Andrea J., author; Dillon, Jasmine A., advisor; Archibeque, Shawn, committee member; Cramer, Catie, committee member; Mueller, Nathan, committee memberThe Northern Great PlainsāÆregion is projected to experience rising average daily temperatures, greater precipitation variability,āÆand increased overall weather variability over the next 75 years.āÆTheseāÆchanges haveāÆpotentially negative implications forāÆColorado dairy systems.āÆTheāÆobjective of this study wasāÆto (1) evaluate implications of climate change on resource use and environmental footprints of Colorado dairies through the 21st century using the Integrated Farms Systems Model (IFSM) and (2) identify and evaluate Beneficial Management Practices (BMPs) to assess the Colorado dairy industry's ability to remain sustainable and productive through 2100. The Integrated Farm System Model (IFSM) was used toāÆestimate the carbon (CF), blue water (WF), reactive nitrogen (RnF), and energy (EF) footprints ofāÆthree dairy operations: 1100-head conventional (1100C), 1100-head organic (1100OR), and 2000-head conventional (2000C). The IFSM is a whole-farm, process-based model that simulates major biophysical processes, environmental impacts, and economics of beef, dairy, and crop farms over many years of weather. Model inputs were obtained from the literature, publicly available USDA databases, and expert input. Each farm was simulated over three time periods: historic (1990-2015), mid-century (2040-2065), and late century (2075-2100). Eight general climate models (GCMs) and two representative concentration pathway scenarios (RCP 4.5 and 8.5) were used to evaluate potential climate impacts to resource use and environmental footprints of the farms. After baseline footprints were obtained, BMPs were modeled to assess the impacts on each farm's environmental footprints over each time. BMPs included 1) covered manure basin on all three farms 2) covered manure basin with flare on the 1100C farm 3) spring and fall cycle calving and milking on the 1100OR and 4) decrease in dietary crude protein from the NRC recommendation of 16% to 14% and supplementation with amino acids on the 2000C farm. The results of this study indicate that BMPs have the potential to reduce environmental footprints on dairy farms in Colorado under future climate changes. On average, manure management BMPs reduced RnF and CFs over time by 11and 5%, respectively. Reducing CP to 14% reduced ammonia emissions on the 2000C farm by up to 10% over time, however, it resulted in an increase to total CF and WF, likely from changes in upstream processes from the baseline. Spring cycle milking and calving on the 1100OR farm reduced the WF, EF, and RnF over time by 6, 3, and 5% on average, respectively. Fall cycle milking and calving increased these footprints compared to the baseline and other BMPs. Both seasonal milking BMPs increased CFs. A significant finding of the study was that WFs were predicted to decrease over time on the 1100OR and 2000C farms, both of which were producing homegrown feed. Colorado is predicted to have significant water scarcity issues in the later part of the century, and these results show that the decrease in water availability will limit the dairy industries abilities to meet its production needs. Predicted footprint values for baseline and BMP scenarios were compared to studies that evaluated regional and national dairy production using IFSM, as well as life cycle assessment (LCA) findings that averaged US dairy production from many farms. Overall, this study predicts that BMPs can be effective in reducing environmental footprints of Colorado dairy farms, which may reduce the environmental impacts of the state's dairy industry. However, farms should be wary of one size fits all solutions and need to assess their goals, productivity needs, and feasibility before implementing changes to management practices.Item Embargo Isolation, interpretation, and implications of physical soil organic matter fractions in soil systems(Colorado State University. Libraries, 2024) Leuthold, Samuel J., author; Cotrufo, M. Francesca, advisor; Lavallee, Jocelyn M., advisor; Mueller, Nathan, committee member; Schipanski, Meagan, committee memberSoil organic matter (SOM) is crucial to sustained ecosystem function, due to its role in regulating nutrient cycling, carbon (C) storage, and soil structure relevant to both food production and climate regulation. Since the early 1990s, physical fractionation methods have been used to separate bulk SOM into discrete components. The central aim of these methodologies is to simplify the complex heterogeneity of the bulk SOM pool by isolating fractions with more homogenous chemistries, formation pathways, and mechanisms of persistence. By understanding the relative distribution of C and nitrogen (N) among these various fractions, we gain appreciable insight into the mechanisms underlying fundamental soil biogeochemical processes. Despite their historic use, however, significant questions remain regarding the means of proper isolation and interpretation. This dissertation looks to these questions directly, reviewing and then interrogating the methods by which fractions separated before applying those fractionation schemes to answer key questions relating SOM to ecosystem function. The first section reviews the history and current state of physical fractionation methodologies, before using a triangulation of experimental evidence, including chemical, isotopic, and spectral indicators, to identify the best practices for laboratory use. These chapters advance our current understanding of SOM biogeochemistry by drawing an explicit link between the conceptual definitions of SOM fractions and the various procedural definitions that have been used historically. Across a range of soils representative of agricultural land in the United States, we show that fractionation methods that separate particulate organic matter (POM) fraction by density isolate fractions more in line with the conceptual definition of POM than the more frequently used size separation. This work aims to unify understanding across the field of soil biogeochemistry and allows for more robust analyses and modeling efforts. The subsequent chapters use this approach to investigate fundamental questions around SOM stability and persistence. The mineral associated organic matter (MAOM) fraction has long been understood to be relatively stable, with slower turnover times and a more homogenous composition as compared to POM. Its accumulation has thus been discussed as a target for climate change mitigation. We leveraged a unique long-term experimental site with archived samples stretching back over 60 years to test this assumption, aiming to identify a dynamic fraction of MAOM by comparing the SOM composition of plots that had not received organic inputs over the course of the experiment against plots that had received regular inputs for six decades. Our spectral and isotopic analyses showed that a dynamic fraction of the MAOM existed and was primarily composed of plant derived compounds. As the exchangeable MAOM pool was exhausted due to a lack of fresh C inputs, we found that the composition of the MAOM pool became more strongly dominated by microbial byproducts. This work represents useful evidence towards a holistic understanding of the dynamic nature of SOM, and forces reimagining of long-held paradigmatic views. One challenge in the current SOM biogeochemistry landscape is that often questions exist downstream of methodologies, such that the fractions that can be isolated drive the research that is conducted. By first identifying robust methodologies, in the second half of this dissertation we were able to ask specific questions about the link between SOM dynamics and ecosystem function. To this end, we pursued three different lines of inquiry: a field study in which the objective was to link the fractional distribution of C and N to yield stability in agricultural systems, a field study that seeks to understand the persistence dynamics of SOM over a decadal scale in grassland systems, and a laboratory incubation that aims to discern the relative contributions of POM and MAOM in regard to plant available N. The first field study used samples from 9 working farms across the Central United States to better understand how SOM might moderate the spatiotemporal stability of crop yields at the field scale. Yield instability is a major cause of economic and environmental distress in row crop systems, and regional studies have suggested that increasing SOM may be able to mitigate variation in yield across time and space. The chapter presented here is the first study that attempts to identify a mechanistic link between SOM fractions and yield stability. In disagreement with regional and county scale studies, we found that SOM abundance was not linked to increased yield stability in cropping systems. Rather, unstable yield zones had significantly higher SOM content than stable zones, particularly in regard to the POM fraction. This work indicates that at the subfield scale, interactions between climate, topography, and management may be driving spatial patterns of both yield stability and SOM accumulation. This is a key insight, implying that some of the relationships between SOM and agronomic outcomes are scale dependent, and highlighting the need for field scale work to maintain relevance to growers. The second field study produced novel insights, tracing isotopically enriched litter and pyrogenic organic matter (PyOM) through various SOM fractions over the course of a decade, one of the longest tracer experiments that has occurred in grassland ecosystems. We found that after 10 years, the majority of the remaining litter derived C and N inputs were stored in the MAOM fraction, a result well aligned with our hypotheses. Interestingly though, the litter derived MAOM fraction formed rapidly (~ 1 year) and persisted at a relatively similar concentration for the duration of the study. This suggests the potential for divergent persistence mechanisms of POM and MAOM, implying less inter-fraction transfer than previous frameworks have proposed and prompting re-evaluation of the mechanisms of MAOM formation and persistence. In contrast, the applied PyOM remained almost completely in the POM fraction over the 10-year period, reinforcing both the heterogeneity of the bulk SOM pool, and the myriad of persistence mechanisms that stabilize various SOM fractions. Given that PyOM is ubiquitous in soil regardless of burn history and can persist for hundreds of years, this result has critical importance for our understanding of turnover time of the POM fraction, and suggests that we may be underestimating the dynamic nature of POM when PyOM is not accounted for. Finally, in a lab incubation experiment, we took advantage of recent advances in isotopic measurement to prove recent theories around MAOM N accessibility. Whereas POM is often thought of as the fraction that provides nutrients in the short term, our two-week incubation showed that under certain conditions, the majority of plant available N may be derived from the MAOM fraction. This work validates proposed frameworks and is an important step towards understanding coupled C and N management in agroecosystems that could improve N use efficiency and increase producer sustainability. Overall, the work in this dissertation aims to provide a comprehensive overview of how fractions can and should be isolated, and the information gained via this fractionation. By clarifying and advancing methodology to quantify SOM components and the understanding of their contribution to critical soil functions for the sustainability of food production and the mitigation of climate change this dissertation represents a major step forward for the study, modeling and managing of SOM in agricultural systems.Item Open Access The impact of tropical intraseasonal variability on subseasonal-to-seasonal predictability(Colorado State University. Libraries, 2021) Hsiao, Wei-Ting, author; Maloney, Eric, advisor; Barnes, Elizabeth, advisor; Mueller, Nathan, committee memberSubseasonal-to-seasonal (S2S) timescales have been identified as a gap in weather forecast skill at 2 weeks to 2 months lead times. This timescale is set by midlatitude synoptic predictability limits, and sits between the typical weather timescale and the longer annual to interannual periods that may have skill due to knowledge of low-frequency phenomena such as El NiƱo-Southern Oscillation (ENSO). Previous studies have shown that tropical intraseasonal variability serves as an important source of S2S predictability in the midlatitudes based on a linear Rossby wave theory. The theory suggests that consistent weather patterns are excited by tropical divergence and associated teleconnections to the extratropics on S2S timescales that influence predictability. However, those physical processes that provide sources of S2S forecast skill have yet to be fully characterized. This thesis examines aspects of tropical intraseasonal variability that are important for S2S prediction, including how tropical intraseasonal variability has changed with warming over the last century and how the misrepresentation of such variability in a weather forecast model leads to errors in midlatitude precipitation S2S forecasts. In the first part of this thesis, three reanalyses datasets (ERA5, MERRA-2, and ERA 20-C) are examined to quantify the amplitude changes in a dominant mode of intraseasonal tropical variability, the Madden-Julian oscillation (MJO), over the last century. MJO-associated precipitation and vertical velocity amplitude are found to exhibit a complex evolution over the observational record, where the precipitation has larger increases than the vertical velocity. A decrease in the ratio of MJO circulation to precipitation anomaly amplitude is detected over the observational period. Tropical weak temperature gradient theory is used to show that this decrease is consistent with the change in tropical dry static stability that has occurred under climate warming. The weakening MJO circulation per unit precipitation over the past century may have modified associated teleconnections and has implications for S2S prediction in the tropics and midlatitudes. In the second part of the thesis, emphasis is placed on understanding S2S precipitation forecast errors for the western United States (U.S.) in an operational weather model. A set of hindcasts during boreal winter, where the tropics are nudged toward reanalysis, is compared to hindcasts without nudging. The western U.S. precipitation forecasts are found to improve with nudging at 3-4 week lead times. Using a multivariate k-means clustering method, hindcasts are grouped by their initial states and one cluster that exhibits an initially strong Aleutian Low is found to provide better forecast improvement. The improvement originates from the poor representation in the non-nudged hindcasts of the destructive interference between (1) the anomalous Aleutian Low and (2) the teleconnection pattern generated by certain phases of the MJO during non-cold ENSO conditions. These results suggest that improving the simulation of tropical intraseasonal precipitation during the early MJO phases under non-cold ENSO may lead to better 3-4 week precipitation forecasts in the western U.S.