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Browsing by Author "Blackburn, Harvey D., committee member"

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    Evaluation of population genetic structure in two British Bos taurus breeds across five U.S. climate zones
    (Colorado State University. Libraries, 2017) Krehbiel, Bethany Cornwell, author; Thomas, Milton G., advisor; Blackburn, Harvey D., committee member; Enns, R. Mark, committee member; Speidel, Scott, committee member; Byrne, Patrick, committee member
    The objective of this thesis was to determine the fine-scale genetic diversity in Hereford and Red Angus cattle in relation to climate. Two hundred and twenty-five Hereford cattle and 174 Red Angus prominent AI sires were assigned to five U.S. climate regions (Cool Arid, Cool Humid, Transition Zone, Warm Arid, and Warm Humid). SNP-based methods were used to evaluate genetic diversity in the cattle in each of the U.S. climate zones. The first method utilized neutral SNP and the ADMIXTURE software to determine the genetic structure of the population. The second method used 66 SNP associated with traits potentially influenced by climate (body weight, heat stress, milk yield, heifer conception rate, and early embryonic survival) to determine Hardy-Weinberg Equilibrium and detection of loci under selection in each climate zone for Hereford and Red Angus breeds. Using 14,312 SNP, analyses of Hereford cattle revealed genetic structure that corresponded with climate zone. Additionally, 15 of the 66 SNP violated Hardy-Weinberg Equilibrium and detection of loci under selection (P < 0.05). Analysis of the 15 SNP revealed allele frequencies that were unique to the climate zones. Using 13,960 SNP, the genetic structure analysis of Red Angus sires revealed that there were eight sub-populations present within the breed. Additionally, 23 of the 66 SNP violated Hardy-Weinberg Equilibrium and detection of loci under selection (P < 0.05). Allele frequency analysis of the 23 SNP did not show genetic substructure that corresponded to climate zone. In conclusion, fine-scale evaluation of Hereford cattle revealed a genetic substructure corresponded with climate zone. However, fine-scale genetic substructure was detected in Red Angus sires, but did not correspond to U.S. climate zones. By identifying the genetic diversity in these prominent British beef breeds in relation to climate, management strategies can be formed to utilize the genetic diversity of these breeds to combat climate change.
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    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 member
    The 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.