Reducing the period of data collection for intake and gain to improve response to selection for feed efficiency in beef cattle

Abstract

Shortening the period of recording individual feed intake may improve selection response for feed efficiency by increasing the number of cattle that can be recorded given facilities of fixed capacity. Individual DMI and ADG records of 3,462 steers and 2,869 heifers over the entire intake recording period (range 62 to 154 d; mean 83 d; DMI83 and ADG83, respectively), DMI and ADG for the first 42 d of the recording period (DMI42 and ADG42, respectively), and postweaning ADG based on the difference between weaning and yearling weights (PADG) were analyzed. Genetic correlations among DMI42 and DMI83, ADG42 and ADG83, ADG42 and PADG, and ADG83 and PADG were 0.995, 0.962, 0.852, and 0.822, respectively. Four objective functions [feed:gain ratio in steers (FGS) and heifers (FGH); residual gain (RG); and residual feed intake (RFI)] based on DMI83 and ADG83 were considered. Indices using DMI42 and ADG42 (I42); DMI42 and PADG (IPW); and DMI42, ADG42, and PADG (IALL) were developed. Accuracy of the 5 EBV, 4 objectives, and 12 objective × index combinations were computed for all 12,033 animals in the pedigree. Accuracies of indices (IA) were summarized for animals with accuracies for objectives (OA) of 0.25, 0.5, 0.75, and 1. For the RG objective and animals with OA of 0.75, indices I42, IPW, and IALL had IA of 0.63, 0.55, and 0.67, respectively. Differences in IA increased with increased emphasis on ADG83 in the objective. Differences in IA between I42 and IPW usually increased with OA. Relative efficiency (RE) of selection on 42-d tests compared with 83 d was computed based on differences in IA and selection intensities of 5%, 25%, 50%, and 75% under the 83-d scenario, assuming 65% more animals could be tested for 42 d. For 25% selected for the RG objective, and animals with OA of 0.75, indices I42, IPW, and IALL had RE of 1.02, 0.90, and 1.10, respectively. As % selected, OA, and emphasis on DMI increased, RE increased. Relative efficiency varied considerably according to assumptions. One-half of the scenarios considered had RE > 1.15 with a maximum of 2.02 and 77% RE > 1.0. A shorter period of recording DMI can improve selection response for feed efficiency. Selection for the efficiency objectives would not affect PADG. It will be most effective if ADG over the period coinciding with intake recording and ADG over a much longer period of time are simultaneously included in a multiple-trait genetic evaluation with DMI and used in a selection index for efficiency.

Longitudinal analysis of body weight and average daily feed intake during the feedlot test period in Angus cattle

“…For long feeding regimes, the tracking of weight change at the later stages of feeding may also be a good indication of feed efficient animals. Previous studies have documented that shortening the test duration of FI, from 70 days to 35 days (Archer et al, ) and 42 days (Thallman et al, ), would not impact selection for feed efficiency (primarily RFI) based on estimates of additive genetic variances and heritability. However, these studies did not consider the increasing and unfavourable genetic correlations among feed efficiency component traits towards the end of the feedlot test period.…”

“…The measurement of traits that determine feed efficiency in beef cattle, such as body weight (BW) and feed intake (FI), is still expensive and needs to be recorded with the greatest possible accuracy and precision (Hill, 2012). Several authors have recommended different test periods to achieve this precision (Archer et al, 1999;Thallman et al, 2018;Wang et al, 2006). For example, Archer, Arthur, Herd, Parnell, and Pitchford (1997) recommended a minimum feedlot test period of 70 days for measuring BW (at 2-weekly intervals) and a minimum of 35 days for measuring FI measurements.…”

Alternative models for genetic evaluation of feed intake by Afrikaner cattle

“…The rank correlation between EBV from R3 and F3 for bulls evaluated in Phase D was 0.952 compared with a rank correlation of 0.936 between the pedigree EBV from R2 and the EBV from R3. 1 F1: single trait model for analysis of average daily feed intake (DFI) with contemporary group effects fixed; R1: single trait model for analysis of DFI with contemporary group effects random; F2: bivariate analysis model of average daily gain (ADGC) and DFI by bulls tested in phase C with contemporary group effects fixed; R2: model similar to F2, but with contemporary group effects random; F3: three-trait model of ADGC and DFI and average daily gain (ADGD) by bulls tested in phase D with contemporary groups fixed; and R3: three-trait model of average daily gain and average daily feed intake by bulls tested in phase C and average daily gain by bulls tested in phase D with contemporary groups random Genetic evaluations for efficiency traits can be based on indexes of EBV from multiple trait analyses (Kennedy et al, 1993;Thallman et al, 2018). Decoupling recording of average daily gain and feed intake would facilitate reduced cost of data collection by shortening the feed intake test period (Nielsen et al, 2013;Manafiazar et al, 2017;Thallman et al, 2018) with the potential downstream effect of more animals being phenotyped for feed intake at a reduced cost per animal (Culbertson et al, 2015).…”

Average daily gain divergence in beef steers is associated with altered plasma metabolome and whole blood immune-related gene expression

“…Another possible concern about this study was the use of ADG as the feed efficiency-related trait because residual feed intake has been widely used for classifying feed efficiency in beef cattle ( Koch et al, 1963 ). However, ADG with similar DM intake is reported to provide the most accurate mathematical description of cause and effect and is recommended as the preferred measure of biological efficiency ( Koch et al, 1963 ; Thallman et al, 2018 ).…”

“…Feed efficiency continues to be of significant interest due to rising feed costs ( Connor, 2015 ). Thus, several studies have evaluated several ways to optimize feed efficiency ( Artegoitia et al, 2017 ; Thallman et al, 2018 ) via increased feed nutrient use for better growth performance ( Parsons et al, 2012 ). Metabolism of macronutrients and energy yield from feeds have been shown to be associated with growth performance of animals ( Herd et al, 2004 ).…”