{"id":{"repo_id":"colostate","oai_identifier":"oai:mountainscholar.org:10217/239301"},"canonical_url":"https://search.dev.ndltd.org/etd/colostate/oai:mountainscholar.org:10217/239301","repository":{"repo_id":"colostate","name":"Colorado State University","base_url":"https://api.mountainscholar.org/server/oai/request"},"display":{"title":"Management and benchmarking strategies to improve financial health status of U.S. beef operators","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Krehbiel, Bethany Cornwell, author","Rhoades, Ryan D., advisor","Ahola, Jason K., advisor","Blackburn, Harvey D., committee member","Mooney, Daniel, committee member"],"institution":"Colorado State University. 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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. 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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."],"dc:format.medium":["born digital","doctoral dissertations"],"dc:identifier":["Krehbiel_colostate_0053A_18583.pdf"],"dc:identifier.uri":["https://hdl.handle.net/10217/239301","https://doi.org/10.25675/3.02354"],"dc:language":["English"],"dc:language.iso":["eng"],"dc:publisher":["Colorado State University. Libraries"],"dc:rights":["Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. 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