<p dir="ltr"> Improving feed efficiency in beef cattle is essential to meet rising global beef demand while reducing costs and environmental impacts. Genetic selection plays a significant role in identifying and breeding more feed-efficient animals, with multi-population data integration enhancing prediction accuracy. To optimize animals for selection or breeding programs related to efficiency, it is crucial to understand the traits associated with mature cow size and the genetic relationships between them. Mature cow size, defined by mature cow weight (MWT), height (MHT), and body condition score (BCS), is pivotal to cow-calf profitability, maintenance efficiency, and reproductive performance.<br><br> The objectives of the first study were to: 1) estimate variance components and genetic parameters for MWT, MHT, and BCS in the United States (US) and Australia (AUS); 2) estimate genetic correlations between mature cow size traits and early growth and carcass traits; and 3) estimate the genetic correlations among these traits across the two countries. The datasets provided by the American Angus Association and Angus Australia included 434,746 and 206,003 records for MWT, 213,875 and 15,379 records for MHT, and 382,156 and 36,184 records for BCS, respectively. Single-trait repeatability models were used to estimate heritabilities and variance components. Multiple-trait models were used to estimate phenotypic and genetic correlations between traits and across countries. Heritability estimates for MWT (US: 0.45; AUS: 0.40), MHT (US: 0.57; AUS: 0.63), and BCS (US: 0.18; AUS: 0.18) highlighted moderate-to-high genetic control. Genetic correlations within the US and Australian datasets between MWT and MHT, and MWT and BCS were > 0.50, and < 0.20 between MHT and BCS. Genetic correlations between MWT, MHT and early growth traits were generally positive and moderate-to-high, ranging from 0.51(0.01) to 0.92(0.003) in the US and 0.41(0.03) to 0.79(0.05) in Australia. Genetic correlations between the traits in the two countries were high for MWT = 0.91 (0.02) and MHT = 0.98 (0.02); and moderate for BCS = 0.65 (0.08). The results suggested that optimizing selection for mature cow traits is feasible, and that a joint evaluation between the US and Australia could be beneficial. </p><p dir="ltr"><br></p><p dir="ltr"> The objective of the second study was to investigate the impact of different modeling approaches on the estimation of breeding values for MWT, with a focus on how BCS was treated across models. The dataset provided by American Angus Association comprised 382,156 MWT and BCS records from 209,491 cows. Four modeling approaches were evaluated: Model 1 did not consider BCS; Model 2 treated BCS as a categorical fixed effect; Model 3 used pre-adjusted records standardized for BCS and age; and Model 4 used a recursive model to assess MWT as a genetically independent trait from BCS. Spearman correlations between models ranged from 0.78 to 0.95, with model choice influencing sire rankings by 5–22% and top 10% concordance differing by up to 40%. Model selection can significantly affect rankings, highlighting the importance of carefully selecting the model that aligns best with the breeding goals. The recursive approach appears to effectively derive MWT that is genetically independent of BCS. </p><p dir="ltr"> This thesis analyzes genetic relationships among mature cow size traits; mature cow weight, height, and body condition score, providing insights for selection programs aimed at optimizing cow’s efficiency. Through variance analysis and genetic correlation studies across North American and Australian Angus populations, it highlights the potential of joint evaluations across countries. It also assesses how different modeling approaches for estimating breeding values for mature cow weight can affects sire rankings and selection decisions, underscoring the importance of model alignment with breeding objectives. Ultimately, this work contributes to the goal of a more sustainable beef industry, where mature cow size is optimized.</p>