The beef industry plays a vital role in global agriculture, with carcass quality and consumer preference being key determinants of market success. This thesis examines predictive modeling techniques for estimating the Total Score of beef carcasses, a composite measure representing yield and quality, primarily used by the Nebraska Cattlemen Association. Using data from the Nebraska Cattlemen’s Foundation Retail Value Steer Challenge (2000–2023), the study compares the performance of First Order Multiple Linear Regression (MLR) with three machine learning techniques: K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting Machine (GBM).
The analysis focuses on six key predictors: Hot Carcass Weight, Back Fat Thickness, Ribeye Area, Final Yield Grade, Final Carcass Retail Value, and Year. Two modeling scenarios are evaluated: (1) training on a large dataset (2000–2021) and validating on recent years (2022–2023), and (2) smaller rolling window subsets (five-year training to predict the next two years, iterated across the full dataset). Performance is assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Accuracy Percentage.
Results show that while MLR provides interpretable models, modern machine learning models offer higher predictive accuracy in scenarios with limited and complex data, effectively capturing non-linear relationships and interactions. This research extends previous works that relied solely on MLR by integrating modern machine learning techniques into beef carcass prediction, offering practical recommendations for stakeholders and setting a benchmark for data-driven evaluation methods that can support improved decision-making in beef production.
","abstract_html":"<p>The beef industry plays a vital role in global agriculture, with carcass quality and consumer preference being key determinants of market success. This thesis examines predictive modeling techniques for estimating the Total Score of beef carcasses, a composite measure representing yield and quality, primarily used by the Nebraska Cattlemen Association. Using data from the Nebraska Cattlemen’s Foundation Retail Value Steer Challenge (2000–2023), the study compares the performance of First Order Multiple Linear Regression (MLR) with three machine learning techniques: K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting Machine (GBM).</p> <p>The analysis focuses on six key predictors: Hot Carcass Weight, Back Fat Thickness, Ribeye Area, Final Yield Grade, Final Carcass Retail Value, and Year. Two modeling scenarios are evaluated: (1) training on a large dataset (2000–2021) and validating on recent years (2022–2023), and (2) smaller rolling window subsets (five-year training to predict the next two years, iterated across the full dataset). Performance is assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Accuracy Percentage.</p> <p>Results show that while MLR provides interpretable models, modern machine learning models offer higher predictive accuracy in scenarios with limited and complex data, effectively capturing non-linear relationships and interactions. This research extends previous works that relied solely on MLR by integrating modern machine learning techniques into beef carcass prediction, offering practical recommendations for stakeholders and setting a benchmark for data-driven evaluation methods that can support improved decision-making in beef production.</p>","abstract_has_math":false,"creators":["Forson, Emmanuel"],"institution":null,"degree_name":"Master of Science - Mathematical Sciences","degree_level":"Thesis","degree_discipline":"Mathematics and Statistics","degree_department":null,"school":null,"contributors":["Dr. Jacob Pratsher","Dr. Jacob Turner","Dr. Kent Riggs"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-08T07:00:00Z","date_published":"2025-05-08T07:00:00Z","updated_at":"2026-07-24T04:30:45Z","subjects":["Predictive Models","Beef Carcasses","Steer","Carcass Traits","Nebraska Retail Value Steer Challenge","Beef Industry","Animal Sciences","Applied Statistics","Beef Science","Data Science","Meat Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.sfasu.edu/etds/601","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Jacob Pratsher","Dr. Jacob Turner","Dr. Kent Riggs"]},{"key":"dc:creator","label":"Author","values":["Forson, Emmanuel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-05-07T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics and Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science - Mathematical Sciences"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Predictive Models","Beef Carcasses","Steer","Carcass Traits","Nebraska Retail Value Steer Challenge","Beef Industry","Animal Sciences","Applied Statistics","Beef Science","Data Science","Meat Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.sfasu.edu/etds/601"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The beef industry plays a vital role in global agriculture, with carcass quality and consumer preference being key determinants of market success. This thesis examines predictive modeling techniques for estimating the Total Score of beef carcasses, a composite measure representing yield and quality, primarily used by the Nebraska Cattlemen Association. Using data from the Nebraska Cattlemen’s Foundation Retail Value Steer Challenge (2000–2023), the study compares the performance of First Order Multiple Linear Regression (MLR) with three machine learning techniques: K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting Machine (GBM).
The analysis focuses on six key predictors: Hot Carcass Weight, Back Fat Thickness, Ribeye Area, Final Yield Grade, Final Carcass Retail Value, and Year. Two modeling scenarios are evaluated: (1) training on a large dataset (2000–2021) and validating on recent years (2022–2023), and (2) smaller rolling window subsets (five-year training to predict the next two years, iterated across the full dataset). Performance is assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Accuracy Percentage.
Results show that while MLR provides interpretable models, modern machine learning models offer higher predictive accuracy in scenarios with limited and complex data, effectively capturing non-linear relationships and interactions. This research extends previous works that relied solely on MLR by integrating modern machine learning techniques into beef carcass prediction, offering practical recommendations for stakeholders and setting a benchmark for data-driven evaluation methods that can support improved decision-making in beef production.
"]},{"key":"dc:title","label":"Title","values":["Evaluating Predictive Models for Predicting Total Score of Beef Carcasses"]}]}],"canonical_facts":{"dc:contributor":["Dr. Jacob Pratsher","Dr. Jacob Turner","Dr. Kent Riggs"],"dc:creator":["Forson, Emmanuel"],"dc:date.available":["2025-05-07T07:00:00Z"],"dc:description.abstract":["The beef industry plays a vital role in global agriculture, with carcass quality and consumer preference being key determinants of market success. This thesis examines predictive modeling techniques for estimating the Total Score of beef carcasses, a composite measure representing yield and quality, primarily used by the Nebraska Cattlemen Association. Using data from the Nebraska Cattlemen’s Foundation Retail Value Steer Challenge (2000–2023), the study compares the performance of First Order Multiple Linear Regression (MLR) with three machine learning techniques: K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting Machine (GBM).
The analysis focuses on six key predictors: Hot Carcass Weight, Back Fat Thickness, Ribeye Area, Final Yield Grade, Final Carcass Retail Value, and Year. Two modeling scenarios are evaluated: (1) training on a large dataset (2000–2021) and validating on recent years (2022–2023), and (2) smaller rolling window subsets (five-year training to predict the next two years, iterated across the full dataset). Performance is assessed using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Accuracy Percentage.
Results show that while MLR provides interpretable models, modern machine learning models offer higher predictive accuracy in scenarios with limited and complex data, effectively capturing non-linear relationships and interactions. This research extends previous works that relied solely on MLR by integrating modern machine learning techniques into beef carcass prediction, offering practical recommendations for stakeholders and setting a benchmark for data-driven evaluation methods that can support improved decision-making in beef production.
"],"dc:identifier":["https://scholarworks.sfasu.edu/etds/601"],"dc:subject":["Predictive Models","Beef Carcasses","Steer","Carcass Traits","Nebraska Retail Value Steer Challenge","Beef Industry","Animal Sciences","Applied Statistics","Beef Science","Data Science","Meat Science"],"dc:title":["Evaluating Predictive Models for Predicting Total Score of Beef Carcasses"],"thesis:degree_discipline":["Mathematics and Statistics"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science - Mathematical Sciences"]},"updated_at":"2026-07-24T04:30:45Z"}