The objectives of this study were to estimate heritability for scrotal circumference (SC) and semen traits and their genetic correlations (rg) with birth weight (BRW). Semen traits were recorded for Line 1 Hereford bulls (n = 841), born in 1963 or from 1967 to 2000, that were selected for use at Fort Keogh (Miles City, MT) or for sale. Semen was collected by electroejaculation when bulls were a mean age of 446 d. Phenotypes were BRW, SC, ejaculate volume, subjective scores for ejaculate color, swirl, sperm concentration and motility, and percentages of sperm classified as normal and live or having abnormal heads, abnormal midpieces, proximal cytoplasmic droplets (primary abnormalities), bent tails, coiled tails, or distal cytoplasmic droplets (secondary abnormalities). Percentages of primary and secondary also were calculated. Data were analyzed using multiple-trait derivative-free REML. Models included fixed effects for contemporary group, age of dam, age of bull, inbreeding of the bull and his dam, and random animal and residual effects. Random maternal and permanent maternal environmental effects were also included in the model for BRW. Estimates of heritability for BRW, SC, semen color, volume, concentration, swirl, motility, and percentages of normal, live, abnormal heads, abnormal midpieces, proximal cytoplasmic droplets, bent tails, coiled tails, distal cytoplasmic droplets, and primary and secondary abnormalities were 0.34, 0.57, 0.15, 0.09, 0.16, 0.21, 0.22, 0.35, 0.22, 0.00 0.16, 0.37, 0.00 0.34 0.00, 0.30, and 0.33, respectively. Estimates of rg for SC with color, volume, concentration, swirl, motility, and percentages of live, normal, and primary and secondary abnormalities were 0.73, 0.20, 0.77, 0.40, 0.34, 0.63, 0.33, -0.36, and -0.45, respectively. Estimates of rg for BRW with SC, color, volume, concentration, swirl, motility, and percentages live, normal, and primary and secondary abnormalities were 0.28, 0.60, 0.08, 0.58, 0.44, 0.21, 0.34, 0.20, -0.02, and -0.16, respectively. If selection pressure was applied to increase SC, all of the phenotypes evaluated would be expected to improve. Predicted correlated responses in semen characteristics per genetic SD of selection applied to SC were 0.87 genetic SD or less. If selection pressure was applied to reduce BRW, the correlated responses would generally be smaller but antagonistic to improving all of the phenotypes evaluated. Predicted correlated responses in SC and semen characteristics per genetic SD of selection applied to BRW were less than 0.35 genetic SD.
Our objectives were to estimate genetic parameters for carcass traits and evaluate the influence of slaughter end point on estimated breeding values (BV). Data provided by the American Simmental Association were divided into three sets: 1) 9,604 records of hot carcass weight (CW) and percentage retail cuts (PRC), 2) 6,429 records of CW, PRC, and marbling score (MS), and 3) 1,780 records of CW, PRC, MS, fat thickness (FT), and longissimus muscle area (LMA). Weaning weights (WW) from animals with carcass data and from their weaning contemporaries were used. Data were analyzed with a multiple-trait animal model and REML procedures to estimate genetic parameters and BV on an age-, CW-, MS-, or FT-constant basis. The model for carcass traits included fixed contemporary group and covariates for breed, heterozygosity, and slaughter end point and random additive direct genetic and residual effects. Weaning weight was preadjusted for founder effects, direct and maternal heterosis, age of dam, and age of calf. The model for WW included fixed contemporary group and random additive direct genetic, maternal genetic, maternal permanent environment, and residual effects. Heritabilities from data set 1 were 0.34 for CW and 0.25 for PRC on an age-constant basis and 0.25 for PRC on a CW end point. Heritabilities for data set 2 were 0.35, 0.24, and 0.36 for CW, PRC, and MS, respectively, on an age-constant basis. Data set 2 heritabilities were 0.25 for PRC and 0.34 for MS on a CW-constant basis and 0.33 for CW and 0.25 for PRC at a constant MS end point. Heritabilities on an age-constant basis for data set 3 were as follows: CW, 0.32; PRC, 0.09; MS, 0.12; FT, 0.10; and LMA, 0.26. Heritability estimates for data set 3 on a CW-, MS-, and FT-constant basis were similar to those on an age-constant basis. Heritabilities were 0.12 for PRC, 0.12 for MS, 0.14 for FT, and 0.22 for LMA on a CW-constant basis; 0.30 for CW, 0.09 for PRC, 0.10 for FT, and 0.28 for LMA at a constant MS end point; and 0.33, 0.17, 0.13, and 0.29 for CW, PRC, MS, LMA on a FT-constant basis. Genetic correlations among traits varied across groups and end points but suggested that it should be possible to select for improved lean yield without sacrificing quality grade. Correlations were calculated among BV computed at different end points. Adjustment to various end points resulted in some changes in BV and reranking of sires, especially for PRC; however, the number of records available had a larger influence than slaughter end point.
A mathematical computer model of beef cattle production systems was developed at Montana State University. The objective of this report was to describe the rationale and procedures used to simulate animal and system performance. The model was designed to simulate the dynamic relationships among cattle genotype, physiological state, forage quality, and management in range environments. Forage intake, energy and protein metabolism, growth, reproduction, lactation, and changes in chemical body composition are simulated for individual animals over complete life cycles. Expenses driven by animal performance, management decisions, and land resources are tabulated. Several biological and economic measures of system performance can be computed, including ratios of inputs (e.g., DM, CP, ME, dollars) to outputs (e.g., weight, lean), break even prices, and annual gross margin per cow or ranch. Primary uses of the model include the evaluation of system responses to changes in breeding strategies and management in range production/marketing systems.
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