High genetic merit dairy heifers grazing low quality forage had similar weight gain and urinary nitrogen excretion to those of low genetic merit heifers

Climate variability and increasing drought events have become significant concerns in recent years. However, there is limited published research on body weight (BW) change of dairy heifers with different genetic merit when grazing on drought impacted pastures in southern Australia. Achieving target body weight (BW) is vital for dairy heifers, especially during critical stages like mating and calving. This study aimed to assess dry matter (DM) intake, BW change, urinary nitrogen excretion, and grazing behaviours of high vs. low genetic dairy heifers grazing pasture during a 43-day experimental period in a drought season. Forty-eight Holstein Friesian heifers grazed on ryegrass-dominant pasture and were divided into two groups based on their high and low Balanced Performance Index (HBPI and LBPI, respectively). Each group was further stratified into six plots, with similar BW, resulting in four heifers per replication group. Data from the five measurement days were averaged for individual cows to analyse the dry matter intake, nitrogen intake and nitrogen excretion. The statistical model included the treatment effect of BPI (H and L) and means were analysed using ANOVA. The pasture quality was poor, with metabolizable energy 9.3 MJ/Kg DM and crude protein 5.9% on a DM basis. Nitrogen intake and urinary nitrogen excretion were significantly higher (p  <  0.05) in HBPI compared to the LBPI. However, despite these differences, the study did not find any advantages of having HBPI heifer grazing on low quality forage in terms of BW performance.

Benchmarking in a beef cattle breeding program: Lessons from the best breeders

Beef cattle breeding programs offer genetic evaluations and consulting services on animal breeding practices to help breeders improve the genetic merit of their herds. Some breeders are more willing to apply best practices and technologies than others. Consequently, the average genetic merit and genetic trends differ across herds. We benchmarked some parameters of an average herd (AVE) and the corresponding parameters of herds with higher genetic merit (TOP), both participating in a commercial Nellore breeding program. Expected progeny differences (EPD) for growth, reproductive and carcass traits and a selection index (SI) of animals born from 2005 to 2019 on 128 farms located in Brazil, Bolivia and Paraguay were used to compute the AVE parameters. The 20 herds with higher mean SI of animals born in the last five birth seasons were classified as TOP herds. The mean SI and EPD of animals born in the last five seasons in the TOP herds were, respectively, 89% and 79% to 206% higher (p ≤ 0.001) than those of animals from the AVE herd. Genetic trends over the entire period were also higher (50% for SI and 31% to 88% separately for each trait, p ≤ 0.006) in the TOP herds compared to the AVE herd. Although the difference in the numbers of cows, bulls and calves between the TOP and AVE herds did not reach statistical significance (p = 0.175, p = 0.273 and p = 0.061, respectively), the numbers of progeny per cow and per bull were 21% (p = 0.012) and 26% (p = 0.047) higher in the TOP herds, respectively. Multiple ovulation and embryo transfer and in vitro fertilization and embryo transfer (MOET/IVF) accounted for a higher percentage of births in the TOP herds compared to AVE (24.6% vs. 12.5%, p = 0.002). The generation interval was 17% shorter (p < 0.001) in the TOP herds compared to AVE. The average inbreeding coefficient of animals from the TOP herds (1.08 ± 0.52%) did not differ (p = 0.78) from that of AVE animals (1.26 ± 0.96%). In general, AVE herds are evolving in the desirable direction but differences in genetic merit between AVE and TOP herds are increasing over time. The more frequent use of MOET/IVF, a lower cow-to-bull ratio, and a larger family size (progeny per cow or per bull) can help achieve larger selection differentials and increase genetic trends and average genetic merits of TOP herds compared to AVE herds.

Associations among body energy status, feeding duration and activity with respect to diet energy and protein content in housed dairy cows

The study in this research paper was undertaken with a hypothesis that accelerometer data can be used to improve monitoring of energy balance in dairy cows. Animals of high (select, S) and average (control, C) genetic-merit lines were allocated to two feeding systems, by-product (BP) and homegrown (HG). This culminated in four production systems referred to as BPS, BPC, HGS and HGC. Cows between their first and fourth lactations were included and a total of 8602 records were used. The target crude protein (CP) and metabolisable energy (ME) content in the BP diet was 185 g/kg DM and 12.3 MJ/kg DM while it was 180 g/kg DM, and 11.5 MJ/kg DM for the HG diet, respectively. Milk yield, body energy content (BEC) and animal activity were monitored while the animals were all housed for winter. Results showed that cows on homegrown feeds were significantly (P < 0.05) more active than cows on by-product feeds as indicated by higher motion index and number of steps per day. Feeding duration was not significantly different (P > 0.05) between cows under by-product feeding system irrespective of the energy balance of the cows. However, there were significant differences for cows under homegrown feeding system. Cows in negative energy balance had a longer feeding duration per day than cows in positive energy balance. Milk yield was negatively correlated (P < 0.05) to motion index and number of steps per day but not to lying time and feeding duration. The results showed differences in cow activity were related to diet content and body energy status. This is useful in precision farming where feeds are provided according to specific animal behaviour and feed requirements.

Predicted carcass meat yield and primal cut yields in cattle divergent in genetic merit for a terminal index

Several studies have clearly demonstrated the favorable impact of genetic selection on increasing beef cattle performance within the farm gate. Few studies, however, have attempted to quantify the value of genetic selection to downstream sectors of the beef industry, such as the meat processing sector. The objective of the current study was to characterize detailed carcass attributes of animals divergent in genetic merit for a terminal index as well as individual measures of genetic merit for carcass weight, conformation, and fat. The data used consisted of 53,674 young bulls and steers slaughtered between the years 2010 and 2013 in multiple Irish processing plants. All animals had a genetic evaluation as well as phenotypic measures of carcass characteristics. A terminal index, based on pedigree index for calving performance, feed intake, and carcass traits, calculated from the Irish national genetic evaluations, was obtained for each animal. Animals were categorized into four terminal index groups based on genetic merit estimates derived prior to the expression of the carcass phenotype by the animal. The association between genetic merit for terminal index with predicted phenotypic carcass red meat yield, carcass fat, carcass bone, and carcass composition, as well as between genetic merit for carcass weight, conformation, and fat with predicted phenotypic carcass red meat yield and composition were all quantified using linear mixed models. A greater terminal index value was associated with, on average, heavier phenotypic weights of each wholesale cut category. A greater terminal index value was also associated with a greater weight of meat and bone, but reduced carcass fat. Relative to animals in the lowest 25% genetic merit group, animals in the highest 25% genetic merit group had, on average, a greater predicted yield of very high value cuts (4.52 kg), high value cuts (13.13 kg), medium value cuts (6.06 kg), low value cuts (13.25 kg) as well as more total meat yield (37 kg). The results from the present study clearly signify a benefit to meat processers from breeding programs for terminal characteristics; coupled with the previously documented benefits to the producer, the benefits of breeding programs across the entire food production chain are obvious.

The impact of using old germplasm on genetic merit and diversity-A cattle breed case study

Artificial selection and high genetic gains in livestock breeds led to a loss of genetic diversity. Current genetic diversity conservation actions focus on long-term maintenance of breeds under selection. Gene banks play a role in such actions by storing genetic materials for future use and the recent development of genomic information is facilitating characterization of gene bank material for better use. Using the Meuse-Rhine-Issel Dutch cattle breed as a case study, we inferred the potential role of germplasm of old individuals for genetic diversity conservation of the current population. First, we described the evolution of genetic merit and diversity over time and then we applied the optimal contribution (OC) strategy to select individuals for maximizing genetic diversity, or maximizing genetic merit while constraining loss of genetic diversity. In the past decades, genetic merit increased while genetic diversity decreased. Genetic merit and diversity were both higher in an OC scenario restricting the rate of inbreeding when old individuals were considered for selection, compared to considering only animals from the current population. Thus, our study shows that gene bank material, in the form of old individuals, has the potential to support long-term maintenance and selection of breeds.

Is there a relationship between genetic merit and enteric methane emission rate of lactating Holstein-Friesian dairy cows?

The present study was undertaken to examine the effect of cow genetic merit on enteric methane (CH4) emission rate. The study used a data set from 32 respiration calorimeter studies undertaken at this Institute between 1992 and 2010, with all studies involving lactating Holstein-Friesian dairy cows. Cow genetic merit was defined as either profit index (PIN) or profitable lifetime index (PLI), with these two United Kingdom genetic indexes expressing the expected improvement in profit associated with an individual cow, compared with the population average. While PIN is based solely on milk production, PLI includes milk production and a number of other functional traits including health, fertility and longevity. The data set had a large range in PIN (n=736 records, -£30 to +£63) and PLI (n=548 records, -£131 to +£184), days in milk (18 to 354), energy corrected milk yield (16.0 to 45.6 kg/day) and CH(4) emission (138 to 598 g/day). The effect of cow genetic merit (PIN or PLI) was evaluated using ANOVA and linear mixed modelling techniques after removing the effects of a number of animal and diet factors. The ANOVA was undertaken by dividing each data set of PIN and PLI into three sub-groups (PIN:£15, PLI:£67) with these being categorised as low, medium and high genetic merit. Within the PIN and PLI data sets there was no significant differences among the three sub-groups in terms of CH(4) emission per kg feed intake or per kg energy corrected milk yield, or CH(4) energy (CH(4)-E) output as a proportion of energy intake. Linear regression using the whole PIN and PLI data sets also demonstrated that there was no significant relationship between either PIN or PLI, and CH(4) emission per kg of feed intake or CH(4)-E output as a proportion of energy intake. These results indicate that cow genetic merit (PIN or PLI) has little effect on enteric CH(4) emissions as a proportion of feed intake. Instead enteric CH(4) production may mainly relate to total feed intake and dietary nutrient composition.

Bayesian inference on genetic merit under uncertain paternity

A hierarchical animal model was developed for inference on genetic merit of livestock with uncertain paternity. Fully conditional posterior distributions for fixed and genetic effects, variance components, sire assignments and their probabilities are derived to facilitate a Bayesian inference strategy using MCMC methods. We compared this model to a model based on the Henderson average numerator relationship (ANRM) in a simulation study with 10 replicated datasets generated for each of two traits. Trait 1 had a medium heritability (h2) for each of direct and maternal genetic effects whereas Trait 2 had a high h2 attributable only to direct effects. The average posterior probabilities inferred on the true sire were between 1 and 10% larger than the corresponding priors (the inverse of the number of candidate sires in a mating pasture) for Trait 1 and between 4 and 13% larger than the corresponding priors for Trait 2. The predicted additive and maternal genetic effects were very similar using both models; however, model choice criteria (Pseudo Bayes Factor and Deviance Information Criterion) decisively favored the proposed hierarchical model over the ANRM model.