California’s approximately 660,000 head of beef cattle are highly dependent on range bulls used to produce offspring that can perform across the state’s diverse ecological regions. While knowledge of bull breeding behavior and the technology used to monitor cattle movement and behavior is critical to maximize conception rates and the overall efficiency in natural service sires on rangelands. Bulls need to be functional on rugged coastal landscapes, rolling foothills, deserts, and in high-elevation terrain. Few data exist that indicate factors related to selection, maintenance, and longevity of bulls used in rangeland landscapes. Objectives herein, were to assess factors influencing bull purchasing, management, and culling decisions of California beef producers. Another objective was to examine the relationship between activity level and the number of offspring born from a breeding season. Surveys were mailed to the California Cattlemen’s Association membership (n = 1,410) with ~ 16% response rate (n = 227). Mean age and years of bull selection experience of respondents was 61 ± 1 years and 27 ± 1 years, respectively. Respondents managed cattle on a total of 694,949 ha of owned, private leased, and leased public rangelands in California and surrounding states. Cow-calf herd size was 333 ± 92 head and bull battery averaged 18 ± 2 head with average bull longevity of 5 ± 1.3 years. The average price paid for bulls in the last 2 years was $5007 ± 163.33, while the highest price paid in the last 5 years was $7291 ± 335.40. Survey responses were used to define current factors driving management after purchase and for subsequent breeding seasons. After bull purchase, 48% of producers turned bulls out directly with females, while 52% held bulls until the following breeding season. Additionally, most producers (70%) did not manage bulls to reduce condition after purchase. Semen quality analysis, a major component of a breeding soundness exam, was evaluated annually by 45% of respondents, while 20% of respondents never evaluated semen quality. Respondents indicated bull age (35%) and structural soundness (29%) as the most common factors for culling bulls. Global Positioning Systems (GPS) units were affixed to bulls prior to the start of the breeding season and distance traveled was determined. Additionally, calf tissue samples for DNA parentage were collected and DNA was processed by Neogen’s SireSeek program. Changes in carcass traits, such as intramuscular fat (IMF), marbling (MARB), ribeye area (REA), and rib fat (RIBFAT), were significant in the ranch*year interaction (P < 0.05). This highlights the fact that carcass traits were the most influenced by the ranch location they had per year. While the survey research shows that despite the variability in operation demographics, there were similarities in beef bull selection and management across the state. Additionally, these data suggest the need for additional research focused on bull selection and management to maximize producer investment in reproduction.

Thesis (MSc. (Animal breeding and genetics )) --University of Limpopo, 2006 / The objective of this study was to estimate genetic parameters for weekly body weight of feed intake of individually fed beef bulls at centralized testing stations in South Africa using random regression models (RRM). The model for cumulative feed intake included the fixed linear regression on third order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77 and 84 day) for starting age group and contemporary group effects. Random regressions on third order orthogonal Legendre polynomials were included for the additive genetic effect of the animal and the additional random effect of weaning-herd-year (WHY) and on fourth order for the additional random permanent environmental effect of the animal. The model for body weights included the fixed linear regression on fourth order orthogonal Legendre polynomials of the actual days on test for starting age group and contemporary group effects. Random regressions on fourth order orthogonal Legendre polynomials were included for additive genetic effects and additional uncorrelated random effects of the WHY and the permanent environment. The residual effects for both traits were assumed to be independently distributed with heterogeneous variance for each measurement period. Variance ratios for additive genetic, permanent environment and WHY for cumulative feed intake at different days on test ranged from 0.07 to 0.10, 0.53 to 0.77 and 0.14 to 0.37, respectively. Variance ratios for additive genetic, permanent environment and WHY for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.43 and 0.26 to 0.34, respectively. Estimates of genetic correlation among the same trait (body weight or feed intake) measured at different test days were generally high (>0.80) for any give test pair. The WHY had a significant contribution in variation of performance of bulls on test, despite the 28-day adjustment period. RRM provided the opportunity to study changes in genetic variability within the studied traits over time. Random Regression Models could be used in the National Genetic Evaluation of beef bulls at central performance testing stations in South Africa.

Thirty-six half-sibling Angus bulls were assigned one of three diets: 1) 60% corn-based (CON; S = 0.18%; n = 12); 2) 60% DDGS replacing corn (60DDGS; S = 0.55% DM; n = 12); 3) CON diet + equivalent sulfur of 60DDGS added as calcium sulfate (SULF; S = 0.54%; n = 12) to evaluate the effects of feeding diets containing DDGS or calcium sulfate on performance and semen characteristics. Bulls began the study at 9 months of age and gained 1.6 kg/day for 112 days. Treatment by day interactions (P < 0.05) were observed for glutathione peroxidase and trace mineral concentrations in seminal plasma. Effects of treatment (P < 0.05) were observed for semen kinematics and triiodothyronine in serum. Alterations observed when feeding 60% DDGS to developing bulls occurred in a manner that is not dependent on dietary sulfur; therefore, observed changes could be related to other components within DDGS.

Thesis (M.Sc.) (Agriculture) --University of Limpopo, 2007 / The objectives of this study were to evaluate the effect of pre-weaning selection on estimates of genetic parameters for post-weaning average daily gain in Phases C (ADG-C) and D (ADG-D) and to estimate genetic relationships between average daily gain in the two phases. Performance records of the South African Angus cattle breed was used in the analysis. (Co)variance components and genetic parameters for weaning (WWT), AGD-C and ADG-D were estimated by REML procedures fitting three different models (Models 1, 2 and 3) that differed in how they integrated sequential selection in the analysis of post-weaning traits. Model 1 was a univariate model of WWT, ADG-C and ADG-D. Model 2 was a two trait model of WWT and either ADG-C or ADG-D. Model 3 was a three trait model of WWT, ADG-C and ADG-D. Estimates of heritability for ADG-C were 0.39±0.08, 0.42±0.06 and 0.44±0.01 from Model 1, 2 and 3 respectively. Corresponding estimates of heritability for ADG-D were 0.18±0.021, 0.19±0.021 and 0.21±0.02 respectively. An estimate of genetic correlation between ADG-C and ADG-D was 0.58± and it suggested that the two traits may not necessarily be under the same genetic control. Rank correlations for all bulls based on ADG-C estimated breeding values (EBV’s) were 0.92, 0.83 and 0.94 for Model 1 vs. Model 2, Model 1 vs. Model 3 and Model 2 vs. Model 3 respectively. Corresponding ADG-D EBV’s rank correlations were 0.88, 0.84 and 0.93. Rank correlations for top and bottom 1%, 5% and 10% were lower than those for all bulls in both ADG-C and ADG-D. Low rank correlations showed that the difference in magnitude of genetic parameters from different models was enough to alter bull rankings based on EBV’s of ADG-C and ADG-D. Hence it was concluded that inclusion of pre-weaning information in genetic analysis for post-weaning average daily gain is necessary to account for selection at weaning. / the THRIP (Technology and Human Resource for Industry Project) and NRF (National Research Foundation).