Browsing by Author "Mooney, Daniel, committee member"
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Item Open Access Benchmarking and analysis of current pre-slaughter management factors and their influence on welfare and meat quality outcomes in fed beef cattle(Colorado State University. Libraries, 2023) Davis, Melissa, author; Edwards-Callaway, Lily, advisor; Nair, Mahesh, committee member; Hess, Ann, committee member; Mooney, Daniel, committee memberSeveral factors related to pre-slaughter management of fed beef cattle and their impacts on welfare and meat quality have been identified and discussed thoroughly in previous literature. However, a full catalog of these factors and indicators used to evaluate their impacts on cattle welfare is missing. Additionally, benchmarked data for these factors and welfare and meat quality outcomes, and an analysis of their relationships on a large scale is underrepresented in current literature. The objectives of the first chapter of this dissertation were to catalog pre-slaughter management factors, identify indicators used to evaluate their impacts, and ultimately gain a further understanding of the relationships between pre-slaughter management factors and cattle welfare. This review included an in-depth analysis of 69 studies from across the globe that identified factors related to transportation and handling using behavioral and physiological indicators to measure welfare that were the most researched throughout the studies. The discussion of this review also identified pre-slaughter factors that require benchmarking and/or more research on their potential impacts on cattle welfare. Thus, the objectives of the second chapter in this dissertation was to benchmark pre-slaughter management factors at a collection of commercial fed cattle processing facilities. This data collection took place at five commercial processing facilities in the West, Midwest, and Southwest regions of the United States from March 2021 to July 2022. Data were collected on a total of n = 637 slaughter lots representing n = 87,220 head of cattle. Transportation factors such as distance travelled and the time cattle waited on the truck to unload after arriving at the facility, space allowance in lairage for cattle, lairage duration and cattle mobility was recorded. Environmental factors were later recorded from an online weather service, and cattle characteristics and several meat quality factors including bruising, quality grading, carcass weight and dark cutting were obtained from plant records. Descriptive statistics were calculated for both the lot and individual animal level depending on the variable. Cattle travelled on average, 155.8 ± 209.6 km (Mean ± SD) to the processing facility from the feedlot, waited 30.3 ± 39.7 minutes to unload at the plant and were held in lairage for 200.7 ± 195.0 minutes. The mean lairage density was 3.1 ± 2.0 m2/animal, and a majority of cattle (91.8%, n = 77,645) were scored as having normal mobility. Carcass bruising prevalence was 69.7% (n = 57,099), and of those that were bruised, 65.2% (n = 39,856) had multiple bruises. Having this baseline benchmarking data outlines not only areas that need further improvement, but also areas in this sector that the industry has already improved upon. This benchmarking data also identified the need for additional analysis on the relationships between these factors and outcomes. Therefore, the objective in the final chapter of this dissertation was to assess the effects of these factors on select welfare and meat quality outcomes in fed beef cattle. Using the same data set and methods as in the second chapter, any slaughter lots with no response variables or < 75% of predictor variables present were excluded. A total of n = 619 slaughter lots representing 84,508 head of cattle were used for further analysis. Descriptive statistics for this subset of data and linear and logistic regression models were performed to assess relationships. Statistical significance was determined at P < 0.05. Predictor variables of interest included plant, breed, sex class, operation shift at the plant, distance travelled, truck waiting time to unload, lairage duration and space allowance, THI, and wind speed. Outcome variables of interest included mobility, bruising, dark cutting, quality grades, and hot carcass weights. All outcomes of interest were associated with several pre-slaughter factors of interest, particularly plant and cattle breed. Increased odds of impaired mobility were associated with increased distance travelled (1.001, 1.000 – 1.001; OR, CI) and truck waiting time (1.003, 1.001 – 1.004; OR, CI). Increased odds of carcass bruising were associated with decreases in distance travelled (0.997, 0.996 – 0.998; OR, CI), but increases in space allowance in lairage (1.035; 1.017 – 1.053; OR, CI). Cattle that experienced increases in lairage duration were associated with decreased hot carcass weights (P < 0.0367) and increased odds of cark cutting (1.034, 1.001 – 1.068; OR, CI). Additionally, cattle that were slaughtered during the first shift of operation at the plant were associated with decreased odds of being bruised (0.806, 0.772 – 0.842; OR, CI), being classified as a dark cutter (0.416, 0.336 - 0.514; OR, CI), and having a poorer quality grade (0.777, 0.657 - 0.920; OR, CI). Results from these studies identify areas where further and more detailed research is needed to fill knowledge gaps and fully understand these relationships. This research also has the potential to aid in informed decision-making regarding cattle management during the pre-slaughter period and further educate the industry on sustainable management practices.Item Embargo Climate change, soil carbon sequestration, and agricultural technology adoption: the case of deeper root system corn adoption and diffusion in the U.S. Corn Belt(Colorado State University. Libraries, 2024) Al Maamari, Aaisha, author; Graff, Gregory, advisor; Mooney, Daniel, committee member; Hill, Alexandra, committee member; McKay, John, committee memberTraditional agricultural practices and land use changes have resulted in greenhouse gas emissions back into the atmosphere, which negatively contributes to climate change. Traditional practices also reduce soil organic matter including soil carbon which is essential for soil health, maintenance of soil biological processes, and environmental sustainability. Currently, a range of agricultural conservation practices has come to be recommended and incentivized for soil carbon sequestration by either increasing carbon inputs and/or reducing carbon losses. These include practices such as reduced tillage, cover cropping, and crop rotations. Although private carbon markets have taken the initiative to provide incentive payments for carbon sequestration in agriculture, the adoption of SCS practices can be hindered by different socioeconomic, farm operational, and environmental constraints. In addition to currently recommended soil management practices, new crop genetic innovations, including perennial grain crops and annual crops, such as corn, with larger root systems or deep root traits are emerging as additional examples of SCS frontier technologies. The first chapter of this dissertation utilizes a joint adoption model to hypothetically examine the impact of socioeconomic, environmental, and farm variables on the probability of adopting either or both of currently recommended SCS practices and these novel genetic innovations, such as deeper root corn varieties. Moreover, deeper root system traits are expected to maintain yields under drought conditions. Deeper root system hybrids are also expected to be an effective agricultural technology for maintaining soil health as they can reduce soil erosion and increase soil organic matter. Existing drought tolerant (DT) corn varieties that have been commercially marketed for more than 10 years exhibit some of these same characteristics. Therefore, the adoption of existing DT hybrids is likely a good indication of the potential for the adoption of hybrids with further enhanced root systems. In the second chapter, we use state-level and field-level data for corn planted in the United States Corn Belt to examine the influence of climate change, soil characteristics, and production practices in the decision to adopt DT varieties. Seed industry data indicates that 44 percent of Corn Belt planted corn acres were allocated to a DT variety in 2021 and 58 percent were planted to DT in 2022. Results suggest that exposure to recent years' drought is a significant determinant of the adoption of DT corn. DT corn is more likely to be adopted in non-irrigated fields. We find that western Corn Belt states are more likely to increase the share of DT corn acres compared to eastern and central Corn Belt states, associated with lower precipitation values and higher drought severity. Thus, deeper root varieties are likely to be more attractive to farmers in western more arid regions of the Corn Belt, where associated soil carbon benefits of deeper root varieties are likely to be more limited. Anticipating that enhanced deeper root corn hybrids with public benefits may come to be treated as a conservation practice included under incentive payment programs, and understanding that soil carbon potential is heterogeneous, in the third chapter we consider the question of spatial targeting of payments. We develop three per acre payment scenarios under the benefit optimization approach to estimate and compare the metric tons of carbon inset under an optimal cropland acre enrolled in a carbon incentive program. The study uses cross section data for counties in the United States Corn Belt, a region with the largest number of productive cropland acres and higher potential carbon sequestration rates compared to other regions across the United States. Results show that if the carbon incentive program is designed to target the adoption of SCS practices that result in high, medium, and low SCS rates respectively, then we can expect that about 32, 24, and 19 million metric tons of carbon can be sequestered in Corn Belt croplands annually.Item Open Access Corn grower change for climate change: ex-ante economic analysis of adoption of enhanced root traits(Colorado State University. Libraries, 2019) Giraud, Angelique, author; Graff, Gregory, advisor; Mooney, Daniel, committee member; McKay, John, committee memberSustainable agriculture technologies of enhanced root corn possess the potential to offset more than half of the greenhouse gas emissions of the transportation sector if completely diffused. Weather variability resulting from climate change is predicted to decrease agricultural productivity. Enhanced corn root traits aim to mitigate and adapt to climate change by improving drought tolerance and soil quality and increasing carbon sequestration rates. Encouraging adoption is challenging among heterogeneous corn growers in an enormous market. Previous research on farmer preferences around four categories of benefits stemming from adoption of corn with enhanced root traits frames the motivation to detail profit margins influencing business decisions utilizing a linear programming model. Several scenarios of changes to cost, revenue, carbon sequestration, and water scarcity are analyzed to provide guidance for policy. Results indicate that a corn grower will not choose enhanced root corn when the only benefit is carbon sequestration with cost and revenue as sole drivers of the decision to adopt. As water scarcity progresses, drought tolerance becomes increasingly valuable, substantially shifting production decisions in favor of adoption of corn with enhanced root traits.Item Open Access Cover crops for ecological management of U.S. agricultural systems: quantifying ecosystem services across multiple scales(Colorado State University. Libraries, 2023) Eash, Lisa, author; Fonte, Steven J., advisor; Schipanski, Meagan E., committee member; Trivedi, Pankaj, committee member; Mooney, Daniel, committee memberManaging agricultural systems to provide multiple ecosystem services (ES) beyond food provisioning has gained considerable attention in recent years. The integration of cover crops (CC) into U.S. cropping systems presents an opportunity to support multifunctional agricultural systems, which alleviate negative environmental impacts of agriculture, mitigate greenhouse gas (GHG) emissions and support sustained crop production. However, CC impacts on these ES are variable and depend on management and site characteristics, contributing to uncertainty surrounding to what extent CC can improve ES. Reducing this uncertainty is critical to both identify appropriate environmental and management conditions for CC adoption and improve the estimated potential for CC to improve multifunctionality of U.S. cropping systems. This dissertation aims to quantify CC impacts on ES at multiple scales, exploring benefits to the soil microbiome, at the farm level, and nationally. Throughout this assessment I explore how these effects are influenced by climate and soil characteristics and how management can be leveraged to optimize the provision of ES. Chapter two estimates the potential for widespread adoption of CC to increase soil organic carbon (C) stocks and mitigate GHG emissions in the U.S. Analysis using current U.S. crop management data and a biogeochemical model revealed that the mitigation potential over a 20 year period is lower than previous estimates due to regional variability, decreasing rates of C accrual over time, and limited CC integration. Changes in N2O emissions did not offset C sequestration but introduced large uncertainty surrounding total national mitigation potential. Soil C gains due to CC offer important co-benefits to U.S. cropping systems, but the contribution of CC to achieving U.S. emissions targets will likely be lower than previously anticipated. Our spatially-explicit analysis also highlights regions where adoption of CC can have greater relative contributions to GHG mitigation. I then quantify a larger suite of ES in dryland wheat systems of the semi-arid western U.S., a particularly challenging context for CC due to lower potential productivity and associated economic trade-offs. I used two existing field trials to monitor CC impacts on soil health, cash crop productivity, and economics over a period of six years. No-till, CC planting window, and the sale of CC biomass as forage were also explored as strategies to optimize ES provision and economic viability. Chapters three and four demonstrate that the integration of CC amidst water limitations can benefit erosion control and soil structure, but also present significant productivity and economic trade-offs. The integration of fall-planted CC, no-till management, and the use of CC for forage provided the greatest potential for maximizing ES benefits in an economically viable manner. In Chapter five, I conducted a greenhouse study to examine the impact of CC type and functional diversity on microbial community composition and associated ES. Plant functional types (Poaceae, Brassicaceae, and Fabaceae) were associated with distinct increases in ES proxies, which appear to be mediated by shifts in microbial community composition. Specifically, Fabaceae (legume) CC enhanced the presence of copiotrophic microbes, which were associated with improvements in soil structure and high enzyme activity, a proxy for nutrient cycling. Poaceae and Brassicaceae led to improvements in microbial diversity. Ecosystem service benefits and microbial community shifts were conserved in diverse CC mixtures, contributing to increased multifunctionality. Across studies and scales, CC were observed to support a number of ES that address environmental concerns resulting from modern intensive agricultural practices. However, slight benefits and substantial productivity trade-offs in water-limited systems may limit the extent to which CC can mitigate GHG emissions and restore soil C reserves nationally. Management choices, such as CC composition and diversity, no-till management, and the sale of a portion of CC biomass as forage, can be leveraged to optimize the provision of ES in an economically viable manner. Overall, CC effectively contribute to multifunctional agroecosystems whose ES extend beyond food provisioning.Item Open Access Evaluation of the All Heifer, No Cow beef production system to improve beef production efficiency(Colorado State University. Libraries, 2019) Harrison, Meredith Ann, author; Ahola, Jason, advisor; Seidel, George, committee member; Mooney, Daniel, committee member; Archibeque, Shawn, committee memberTo view the abstract, please see the full text of the document.Item Open Access Management and benchmarking strategies to improve financial health status of U.S. beef operators(Colorado State University. Libraries, 2024) Krehbiel, Bethany Cornwell, author; Rhoades, Ryan D., advisor; Ahola, Jason K., advisor; Blackburn, Harvey D., committee member; Mooney, Daniel, committee memberThe objective of this dissertation was to obtain, analyze, and summarize historical Standardized Performance Analysis (SPA) benchmark information and subsequently determine significant Key Performance Indicators (KPI) influencing beef producer's Unit Cost of Production (UCOP). Using the KPI's, a Ranch Health Index (RHI) was developed to assist producers in simply analyzing their financial health while analyzing beef production and financial relationships. Lastly, producer information using the significant KPI's incorporated into the RHI was analyzed for sensitivity to explore potential leverage points to enhance overall financial health. The SPA Beef cattle production performance and financial data was obtained from the SPA program conducted by Texas A&M AgriLife Extension which has records from three states: Oklahoma, Texas, and New Mexico. The dataset contained 25 years of beef financial and production metrics from 1992 – 2016. Three models (linear regression, random forest, and step-wise) were used to assess the SPA data for KPI. Upon further analyses, six variables were considered most impactful to predict Unit Cost of Production: Financial Grazing per CWT, Financial Raised/Purchased Feed per CWT, Livestock Cost Basis per CWT, Weaning Pay Weight per CWT, Pounds Weaned, and Number of Adjusted Exposed Females. The RHI was developed from the six variables using a Random Forest machine learning model and their corresponding importance factors as weights in the model. The model selected was tested and showed concordance with all the SPA variables predicting UCOP. Therefore, the RHI results showed utility in usefulness to assess financial health. Subsequently, three producers with 5 consecutive years of data were tested for sensitivity at ± 5% and ± 10% from the original value to determine sensitivity of each KPI variable. Finally, the models were investigated for maximum and minimum RHI values. Results showed changes in RHI up to $13,000 when accounting for all KPI improvements at 10% sensitivity. In conclusion, knowledge of the SPA data and ultimately the RHI provides information to cattle producers on what may be the most indicative variables for enhanced profits. In addition, this research has provided a simple and effective way for producers to analyze their beef operation.