The Effects of Breed and Residual Feed Intake Divergence on the Abundance and Active Population of Rumen Microbiota in Beef Cattle

To assess the effects of residual feed intake (RFI) and breed on rumen microbiota, the abundance (DNA) and active population (RNA) of the total bacteria, archaea, protozoa, and fungi in the rumen of 96 beef steers from three different breeds (Angus (AN), Charolais (CH), and Kinsella Composite (KC)), and divergent RFIs (High vs Low), were estimated by measuring their respective maker gene copies using qRT-PCR. All experimental animals were kept under the same feedlot condition and fed with the same high-energy finishing diet. Rumen content samples were collected at slaughter and used for the extraction of genetic material (DNA and RNA) and further analysis. There was a significant difference (p < 0.01) between the marker gene copies detected for abundance and active populations for all four microbial groups. AN steers had a higher abundance of bacteria (p < 0.05) and a lower abundance of eukaryotes (protozoa and fungi, p < 0.05) compared to KC steers, while the abundance of protozoa (p < 0.05) in the AN cattle and fungi (p < 0.05) in the KC cattle were lower and higher, respectively, than those in the CH steers. Meanwhile, the active populations of bacteria, archaea, and protozoa in the KC steers were significantly lower than those in the AN and CH animals (p < 0.01). This work demonstrates that cattle breed can affect rumen microbiota at both the abundance and activity level. The revealed highly active protozoal populations indicate their important role in rumen microbial fermentation under a feedlot diet, which warrants further study.

The effect of feed efficiency classification on visceral organ mass in finishing steers

Individual feed intake of crossbred beef steers (one contemporary group/year, 2 yr) was recorded during finishing to investigate visceral organ mass in steers divergent for feed efficiency. Based on residual feed intake (RFI), the 20% most efficient (HE, n = 8/year) and 20% least efficient (LE; n = 8/year) steers with 12th rib fat ≥1.02 cm were slaughtered. High efficiency steers had less DM intake (P < 0.001), greater G:F (P < 0.001), and similar ADG and hot carcass weight (HCW). High efficiency steers tended to have less (P ≤ 0.10) small intestinal mass (actual and relative to BW and HCW) in year 1. In year 2, HE steers tended to have greater (P ≤ 0.10) large intestinal actual and relative masses. Low efficiency steers tended to have greater (P = 0.06) actual omasum mass and had greater (P ≤ 0.03) relative omasum masses compared with HE. Stomach complex, total gastrointestinal tract, liver, and kidney masses tended to be greater (P ≤ 0.10) relative to BW, and were greater (P ≤ 0.05) relative to HCW, in LE. Data suggest that visceral organ mass, especially of the gastrointestinal tract, plays a role in overall metabolic efficiency of finishing steers.

Genetic and phenotypic parameters for feed efficiency and component traits in American mink

Feed cost is the largest expense of mink production systems, and therefore, improvement of feed efficiency (FE) through selection for high feed efficient mink is a practical way to increase the mink industry's sustainability. In this study, we estimated the heritability, phenotypic and genetic correlations for different FE measures and component traits, including harvest weight (HW), harvest length (HL), final body length (FBL), final body weight (FBW), average daily gain (ADG), daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG), and Kleiber ratio (KR), using data from 2,288 American mink (for HW and HL), and 1,038-1,906 American mink (for other traits). Significance (P < 0.05) of fixed effects (farm, sex, and color-type), a covariate (age of animal), and random effects (additive genetic, maternal, and common litter) were evaluated through univariate models implemented in ASReml-R version 4. Genetic parameters were estimated via fitting a set of bivariate models using ASReml-R version 4. Estimates of heritabilities (±SE) were 0.28±0.06, 0.23±0.06, 0.28±0.10, 0.27±0.11, 0.25±0.09, 0.26±0.09, 0.20±0.09, 0.23±0.09, 0.21±0.10, 0.25±0.10, and 0.26±0.10 for HW, HL, FBL, FBW, ADG, DFI, FCR, RFI, RG, RIG, and KR, respectively. RIG had favorable genetic correlations with DFI (-0.62±0.24) and ADG (0.58±0.21), and non-significant (P > 0.05) genetic correlations with FBW (0.14±0.31) and FBL (-0.15±0.31). These results revealed that RIG might be superior trait as it guarantees reduced feed intake with faster-growing mink yet with no negative impacts on body weight and length. In addition, the strong positive genetic correlations (±SE) between KR with component traits (0.88±0.11 with FBW; 0.68±0.17 with FBL; and 0.97±0.02 with ADG) suggested KR as an applicable indirect measure of FE for improvement of component traits as it did not require the individual feed intake to be measured. Overall, our results confirmed the possibility of including FE traits in mink breeding programs to effectively select feed-efficient animals.

Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency

Despite the significant improvement of feed efficiency (FE) in pigs over the past decades, feed costs remain a major challenge for producers profitability. Improving FE is a top priority for the global swine industry. A deeper understanding of the biology underlying FE is crucial for making progress in genetic improvement of FE traits. This review comprehensively discusses the topics related to the FE in pigs including: measurements, genetics, genomics, biological pathways and the advanced technologies and methods involved in FE improvement. We first provide an update of heritability for different FE indicators and then characterize the correlations of FE traits with other economically important traits. Moreover, we present the quantitative trait loci (QTL) and possible candidate genes associated with FE in pigs and outline the most important biological pathways related to the FE traits in pigs. Finally, we present possible ways to improve FE in swine including the implementation of genomic selection, new technologies for measuring the FE traits, and the potential use of genome editing and omics technologies.

Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions

In the last decade, several countries have included feed efficiency (as residual feed intake; RFI) in their breeding goal. Recent studies showed that RFI is favorably correlated with methane emissions. Thus, selecting for lower emitting animals indirectly through RFI could be a short-term strategy in order to achieve the intended reduction set by the EU Commission (-55% for 2030). The objectives were to 1) estimate genetic parameters for six methane traits, including genetic correlations between methane traits, production, and feed efficiency traits, 2) evaluate the expected correlated response of methane traits when selecting for feed efficiency with or without including methane, 3) quantify the impact of reducing methane emissions in dairy cattle using the Danish Holstein population as an example. A total of 26,664 CH4 breath records from 647 Danish Holstein cows measured over 7 years in a research farm were analyzed. Records on dry matter intake (DMI), body weight (BW), and energy corrected milk (ECM) were also available. Methane traits were methane concentration (MeC, ppm), methane production (MeP; g/d), methane yield (MeY; g CH4/kg DMI), methane intensity (MeI; g CH4/kg ECM), residual methane concentration (RMeC), residual methane production (RMeP, g/d), and two definitions of residual feed intake with or without including body weight change (RFI1, RFI2). The estimated heritability of MeC was 0.20 ± 0.05 and for MeP, it was 0.21 ± 0.05, whereas heritability estimates for MeY and MeI were 0.22 ± 0.05 and 0.18 ± 0.04, and for the RMeC and RMeP, they were 0.23 ± 0.06 and 0.16 ± 0.02, respectively. Genetic correlations between methane traits ranged from moderate to highly correlated (0.48 ± 0.16–0.98 ± 0.01). Genetic correlations between methane traits and feed efficiency were all positive, ranging from 0.05 ± 0.20 (MeI-RFI2) to 0.76 ± 0.09 (MeP-RFI2). Selection index calculations showed that selecting for feed efficiency has a positive impact on reducing methane emissions’ expected response, independently of the trait used (MeP, RMeP, or MeI). Nevertheless, adding a negative economic value for methane would accelerate the response and help to reach the reduction goal in fewer generations. Therefore, including methane in the breeding goal seems to be a faster way to achieve the desired methane emission reductions in dairy cattle.

Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle

•

The Feed Saved (FS) estimated breeding value (EBV) was updated by doubling the number of Australian and overseas cows.

•

The reliability of the residual feed intake component of FS has increased from 11% (2015 model) to 20% (current model).

•

The mean reliability of FS EBV in Holstein bulls that were born in the last 10 years has improved by 10%.

•

The genetic trend of FS EBV has been stabilizing since 2015.

Although selection for increased milk production traits has led to a genetic increase in body weight (BW), the genetic gain in milk production has exceeded the gain in BW, so gross feed efficiency has improved. Nonetheless, greater gains may be possible by directly selecting for a measure of feed efficiency. Australia first introduced Feed Saved (FS) estimated breeding value (EBV) in 2015. Feed Saved combines residual feed intake (RFI) genomic EBV and maintenance requirements calculated from mature BW EBV. The FS EBV was designed to enable the selection of cows for reduced energy requirements with similar milk production. In this study, we used a reference population of 3,711 animals in a multivariate analysis including Australian heifers (AUSh), Australian cows (AUSc), and overseas cows (OVEc) to update the Australian EBV for lifetime RFI (i.e., a breeding value that incorporated RFI in growing and lactating cows) and to recalculate the FS EBV in Australian Holstein bulls (AUSb). The estimates of genomic heritabilities using univariate (only AUSc or AUSh) to trivariate (including the OVEc) analyses were similar. Genomic heritabilities for RFI were estimated as 0.18 for AUSc, 0.27 for OVEc, and 0.36 for AUSh. The genomic correlation for RFI between AUSc and AUSh was 0.47 and that between AUSc and OVEc was 0.94, but these estimates were associated with large standard errors (range: 0.18–0.28). The reliability of lifetime RFI (a component of FS) in the trivariate analysis (i.e., including OVEc) increased from 11% to 20% compared with the 2015 model and was greater, by 12%, than in a bivariate analysis in which the reference population included only AUSc and AUSh. By applying the prediction equation of the 2020 model, the average reliability of the FS EBV in 20,816 AUSb that were born between 2010 and 2020 improved from 33% to 43%. Previous selection strategies—that is, using the predecessor of the Balanced Performance Index (Australian Profit Ranking index) that did not include FS—have resulted in an unfavorable genetic trend in FS. However, this unfavorable trend has stabilized since 2015, when FS was included in the Balanced Performance Index, and is expected to move in a favorable direction with selection on Balanced Performance Index or the Health Weighted Index. Doubling the reference population, particularly by incorporating international data for feed efficiency, has improved the reliability of the FS EBV. This could lead to increased genetic gain for feed efficiency in the Australian industry.

Phenotypic differences for growth, feed efficiency, and age of first calving of Brazilian zebu females

This study investigated phenotypic differences of zebu females from four breeds using variables of growth, feed efficiency, and age at first calving. Weights throughout the life were recorded, and a growth curve was fitted using the Gompertz model. The growth was also evaluated at standardized ages (205, 365, and 550 days) using the body weight and the total and daily weight gains. The Kleiber index and age at first calving were used as measures of feed efficiency and sexual precocity, respectively, totaling 25 variables. New variables were created using the factor analysis and used in new multivariate analyzes. Only six factors explained 95.41% of the total variance and were used for the subsequent analyses. The factors were defined as maturity, precocity, feed efficiency postweaning, feed efficiency post 1 year of age, puberty, and birth weight. There were differences between breeds according to the multivariate analysis of variance. Each breed appeared in a quadrant on the Biplot graph, showing relationship with different factors, demonstrating the diversity of zebu females. There is a difference in growth, feed efficiency, and sexual precocity in Brazilian zebu females, allowing the identification of potentials of the animals and help breeders and decision-makers.

Emerging Roles of Non-Coding RNAs in the Feed Efficiency of Livestock Species

A global population of already more than seven billion people has led to an increased demand for food and water, and especially the demand for meat. Moreover, the cost of feed used in animal production has also increased dramatically, which requires animal breeders to find alternatives to reduce feed consumption. Understanding the biology underlying feed efficiency (FE) allows for a better selection of feed-efficient animals. Non-coding RNAs (ncRNAs), especially micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs), play important roles in the regulation of bio-logical processes and disease development. The functions of ncRNAs in the biology of FE have emerged as they participate in the regulation of many genes and pathways related to the major FE indicators, such as residual feed intake and feed conversion ratio. This review provides the state of the art studies related to the ncRNAs associated with FE in livestock species. The contribution of ncRNAs to FE in the liver, muscle, and adipose tissues were summarized. The research gap of the function of ncRNAs in key processes for improved FE, such as the nutrition, heat stress, and gut–brain axis, was examined. Finally, the potential uses of ncRNAs for the improvement of FE were discussed.

Evaluation of Ingestive Behavior, Ruminal and Blood Parameters, Performance, and Thermography as a Phenotypic Divergence Markers of Residual Feed Intake in Rearing Dairy Heifers

Simple Summary

The selection of highly efficient animals will support meeting the world’s future demand for products and food of animal origin. Thus, the identification of efficient animals and an understanding of the mechanisms inherent to this efficiency is fundamental for the progress of breeding systems. In the present study, we identify highly efficient animals for residual feed intake in dairy heifers. This animal category is unexplored in relation to this index. We utilized the classical parameters evaluated in cattle of different ages to carry out the study on these animals.

Abstract

The objectives of this study were: (1) to identify and rank phenotypically divergent animals for residual feed intake (RFI) regarding their efficiency (high: HE or low: LE); (2) to evaluate their relationships with ingestive behavior, ruminal and blood parameters, performance, and infrared thermography; and (3) to determine if such measurements can be used as feed efficiency markers in rearing dairy heifers. Thirty-eight heifers, 143 d ± 4 (Mean ± SD) of age and 108.7 kg ± 17.9 of body weight were used. The animals were fed with a total mixed ration during the 91 d of the trial. A phenotypic divergence of DMI for RFI was observed between −0.358 and 0.337 kg/d for HE and LE, respectively. Dry matter intake (DMI) was lower in the HE (2.5 kg DMI/d vs. 3.1 kg DMI/d), as was the number of visits to the feed bin with consumption (59 vs. 71). Feed intake was the best predictor of said divergence. Water intake and number of visits to the feed bin were presented moderate correlations with RFI. The ruminal fermentation variables, blood metabolites, blood hormones (such as the other ingestive behavior variables), and infrared thermography were not able to accurately predict HE or LE animals.

Non-invasive metabolomics biomarkers of production efficiency and beef carcass quality traits

The inter-cattle growth variations stem from the interaction of many metabolic processes making animal selection difficult. We hypothesized that growth could be predicted using metabolomics. Urinary biomarkers of cattle feed efficiency were explored using mass spectrometry-based untargeted and targeted metabolomics. Feed intake and weight-gain was measured in steers (n = 75) on forage-based growing rations (stage-1, 84 days) followed by high-concentrate finishing rations (stage-2, 84 days). Urine from days 0, 21, 42, 63, and 83 in each stage were analyzed from steers with the greater (n = 14) and least (n = 14) average-daily-gain (ADG) and comparable dry-matter-intake (DMI; within 0.32 SD of the mean). Steers were slaughtered after stage-2. Adjusted fat-thickness and carcass-yield-grade increased in greater-ADG-cattle selected in stage-1, but carcass traits did not differ between ADG-selected in stage-2. Overall 85 untargeted metabolites segregated greater- and least-ADG animals, with overlap across diets (both stages) and breed type, despite sampling time effects. Total 18-bile acids (BAs) and 5-steroids were quantified and associated with performance and carcass quality across ADG-classification depending on the stage. Stepwise logistic regression of urinary BA and steroids had > 90% accuracy identifying efficient-ADG-steers. Urine metabolomics provides new insight into the physiological mechanisms and potential biomarkers for feed efficiency.

Are subjectively scored linear type traits suitable predictors of the genetic merit for feed intake in grazing Holstein-Friesian dairy cows?

Measuring dry matter intake (DMI) in grazing dairy cows using currently available techniques is invasive, time consuming, and expensive. An alternative to directly measuring DMI for use in genetic evaluations is to identify a set of readily available animal features that can be used in a multitrait genetic evaluation for DMI. The objectives of the present study were thus to estimate the genetic correlations between readily available body-related linear type traits and DMI in grazing lactating Holstein-Friesian cows, but importantly also estimate the partial genetic correlations between these linear traits and DMI, after adjusting for differences in genetic merit for body weight. Also of interest was whether the predictive ability derived from the estimated genetic correlations materialized upon validation. After edits, a total of 8,055 test-day records of DMI, body weight, and milk yield from 1,331 Holstein-Friesian cows were available, as were chest width, body depth, and stature from 47,141 first lactation Holstein-Friesian cows. In addition to considering the routinely recorded linear type traits individually, novel composite traits were defined as the product of the linear type traits as an approximation of rumen volume. All linear type traits were moderately heritable, with heritability estimates ranging from 0.27 (standard error = 0.14) to 0.49 (standard error = 0.15); furthermore, all linear type traits were genetically correlated (0.29 to 0.63, standard error 0.14 to 0.12) with DMI. The genetic correlations between the individual linear type traits and DMI, when adjusted for genetic differences in body weight, varied from -0.51 (stature) to 0.48 (chest width). These genetic correlations between DMI and linear type traits suggest linear type traits may be useful predictors of DMI, even when body weight information is available. Nonetheless, estimated genetic merit of DMI derived from a multitrait genetic evaluation of linear type traits did not correlate strongly with actual DMI in a set of validation animals; the benefit was even less if body weight data were also available.

The impact of genetic merit on ewe performance and efficiency parameters

The aim of this study was to investigate the impact of ewe genetic merit on ewe performance and efficiency parameters. The study consisted of three genetic merit groups (New Zealand [NZ], High Irish, and Low Irish) and ran from 2016 to 2019, inclusive. Each genetic merit group contained 30 purebred Suffolk and 30 purebred Texel ewes, which were selected based on their maternal genetic indexes in their country of origin, namely Ireland (€uro-star Replacement index) or New Zealand (New Zealand Maternal worth). Ewe body condition score (BCS), ewe body weight (BW), milk yield, milk composition, dry matter intake (DMI), and efficiency parameters were all analyzed using linear mixed models. Ewe BW was similar across all genetic merit groups at each time point (P > 0.05). In comparison to both High and Low Irish ewes, NZ ewes had a higher BCS at mating, mid-pregnancy, lambing, week 10 post-lambing (PL, P < 0.05). Ewe BW change was similar across genetic merit groups, except between mating and mid-pregnancy where ewe BW loss was greater for NZ ewes than Irish ewes (P < 0.05) and between weeks 6 PL and 10 PL, where NZ ewes gained BW and High and Low Irish ewes lost BW (P < 0.01). Ewe milk yield, milk fat, total solids, and gross energy content were superior for milk produced by NZ ewes at week 6 PL in comparison to milk produced by High Irish and Low Irish ewes (P < 0.01). NZ ewes produced a greater quantity of milk solids/kg of BW at week 6 PL compared with High Irish ewes (P < 0.01), whereas Low Irish ewes did not differ from either NZ or High Irish (P > 0.05). Low Irish ewes had a greater daily DMI than High Irish ewes in late lactation (week 10 PL, P < 0.05) and had a greater DMI/kg of ewe BW compared with the High Irish ewes at the same time point (P < 0.05). NZ ewes weaned a litter BW equivalent to 60.4% of their mating BW, which was more than the Low Irish ewes who weaned 57.1% of the ewe's BW at mating (P < 0.01), whereas the High Irish ewes did not differ from either the NZ or Low Irish ewes at 59.3% of the ewe's BW at mating (P > 0.05). This study presents a range of parameters across ewes of high and low genetic merit, demonstrating the ability to achieve gains through selection of animals of high genetic merit. Sheep producers should consider genetic indexes as a tool to assist in the decision-making process of selecting replacement ewes and/or breeding rams, once satisfied the animal is correct, and meeting the breeding objectives of the system.

Combining datasets in a dynamic residual feed intake model and comparison with linear model results in lactating Holstein cattle

A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model.

An alternative interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle

There has been increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. Residual feed intake phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. By rearranging the single-trait linear regression, we showed a causal RFI interpretation underlying the linear regression for RFI. It postulates recursive effects in energy allocation from energy sinks on dry matter intake, but the feedback or simultaneous effects are nonexistent. A Bayesian recursive structural equation model was proposed for directly predicting RFI and energy sinks and estimating relevant genetic parameters simultaneously. A simplified Markov chain Monte Carlo algorithm was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis.

Assessment of feed and economic efficiency of dairy farms based on multivariate aggregation of partial indicators measured on field

Many of the metrics used to evaluate farm performance are only partial indicators of farm operations, which are assumed to be best predictors of the whole farm efficiency. The main objective of this work was to identify aggregated multiple indexes of profitability using common partial indicators that are routinely available from individual farms to better support the short-term decision-making processes of the cattle-feeding process. Data were collected from face-to-face interviews with farmers from 90 dairy farms in Italy and used to calculate 16 partial indicators that covered almost all indicators currently used to target feeding and economic efficiency in dairy farms. These partial indicators described feed efficiency, energy utilization, feed costs, milk-to-feed price ratio, income over feed costs, income equal feed cost, money-corrected milk, and bargaining power for feed costs. Calculations of feeding costs were based on lactating cows or the whole herd, and income from milk deliveries was determined with or without considering the milk quality payment. Multivariate factor analysis was then applied to the 16 partial indicators to determine simplified and latent structures. The results indicated that 5 factors explained 70% of the variability. Each of the original partial indicator was associated with all factors in different proportions, as indicated by loading scores from the multivariate factor analysis. Based on the loading scores, we labeled these 5 factors as "economic efficiency," "energy utilization," "break-even point," "milk-to-feed price," and "bargaining power of the farm," in decreasing order of explained communality. The first 3 factors shared 83% of the total communality. Feed efficiency was similarly associated with factor 1 (53% loading) and factor 2 (66% loading). Only factor 4 was significantly affected by farm location. Milk production and herd size had significant effects on factor 1 and factor 2. Our multivariate approach eliminated the problem of multicollinearity of partial indicators, providing simple and effective descriptions of farm feeding economics. The proposed method allowed the evaluation, benchmarking, and ranking of dairy herd performance at the level of single farms and at territorial level with high opportunity to be used or replicated in other areas.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

Residual energy intake, energy balance, and liability to diseases: Genetic parameters and relationships in German Holstein dairy cows

Residual energy intake (REI) is an often-suggested trait for direct selection of dairy cows for feed efficiency. Cows with lower REI seem to be more efficient but are also in a more severe negative energy balance (EB), especially in early lactation. A negative EB leads to a higher liability to diseases. Due to this fact, this study aims to investigate the genetic relationship between REI and liability to diseases. Health and production data were recorded from 1,370 German Holstein dairy cows from 8 research farms over a period of 2 yr. We calculated 2 phenotypes for REI that considered the following energy sinks: milk energy content, metabolic body weight, body weight change, body condition score, and body condition score change. Genetic parameters were estimated with threshold or linear random regression models from days in milk (DIM) 1 to 305. Heritabilities for REI, EB, and all diseases ranged from 0.12 to 0.39, 0.15 to 0.31, and 0.09 to 0.20, respectively. Genetic correlations between selected DIM for REI and EB were higher for adjacent DIM than for more distant DIM. Pearson correlation coefficients between estimated breeding values (EBV) for REI and EB varied between 0.47 and 0.81; they were highest in mid lactation. Correlations between EBV for all diseases and REI as well as EB were negative, with lowest values in early lactation. Within the first 50 DIM, proportions of diseased days for cows with lowest EBV for REI were almost twice as high as for cows with highest EBV for REI. In conclusion, selecting dairy cows for lower REI should be treated with caution because of an unfavorable relationship with liability to diseases, especially in early lactation.

Contribution of genetic variability to phenotypic differences in on-farm efficiency metrics of dairy cows based on body weight and milk solids yield

Milk solids per kilogram of body weight (BW) is growing in popularity as a measure of dairy cow lactation efficiency. Little is known on the extent of genetic variability that exist in this trait but also the direction and strength of genetic correlations with other performance traits. Such genetic correlations are important to know if producers are to consider actively selecting cows excelling in milk solids per kilogram of BW. The objective of the present study was to use a large data set of commercial Irish dairy cows to quantify the extent of genetic variability in milk solids per kilogram of BW and related traits but also their genetic and phenotypic inter-relationships. Mid-lactation BW and body condition score (BCS), along with 305-d milk solids yield (i.e., fat plus protein yield) were available on 12,413 lactations from 11,062 cows in 85 different commercial dairy herds. (Co)variance components were estimated using repeatability animal linear mixed models. The genetic correlation between milk solids and body weight was only 0.05, which when coupled with the observed large genetic variability in both traits, indicate massive potential to select for both traits in opposite directions. The genetic correlations between both milk solids and BW with BCS; however, need to be considered in any breeding strategy. The genetic standard deviation, heritability, and repeatability of milk solids per kilogram of BW was 0.08, 0.37, and 0.57, respectively. The genetic correlation between milk solids per kilogram of BW with milk solids, BW, and BCS was 0.62, -0.75, and -0.41, respectively. Therefore, based on genetic regression, each increase of 0.10 units in genetic merit for milk solids per kilogram of BW is expected to result in, on average, an increase in 16.1 kg 305-d milk solids yield, a reduction of 25.6 kg of BW and a reduction of 0.05 BCS units (scale of 1-5 where 1 is emaciated). The genetic standard deviation (heritability) for 305-d milk solids yield adjusted phenotypically to a common BW was 27.3 kg (0.22). The genetic correlation between this adjusted milk solids trait with milk solids, BW, and BCS was 0.91, -0.12, and -0.26, respectively. Once also adjusted phenotypically to a common BCS, the genetic standard deviation (heritability) for milk solids adjusted phenotypically to a common BW was 26.8 kg (0.22) where the genetic correlation with milk solids, BW and BCS was 0.91, -0.21, and -0.07, respectively. The genetic standard deviation (heritability) of BW adjusted phenotypically for differences in milk solids was 35.3 kg (0.61), which reduced to 33.2 kg when also phenotypically adjusted for differences in BCS. Results suggest considerable opportunity exists to change milk solids yield independent of BW, and vice versa. The opportunity is reduced slightly once also corrected for differences in BCS. Inter-animal BCS differences should be considered if selection on such metrics is contemplated.

Simultaneous Bayesian estimation of genetic parameters for curves of weight, feed intake, and residual feed intake in beef cattle

Rates of gain and feed efficiency are important traits in most breeding programs for growing farm animals. The rate of gain (GAIN) is usually expressed over a certain age period and feed efficiency is often expressed as residual feed intake (RFI), defined as observed feed intake (FI) minus expected feed intake based on live weight (WGT) and GAIN. However, the basic traits recorded are always WGT and FI and other traits are derived from these basic records. The aim of this study was to develop a procedure for simultaneous analysis of the basic records and then derive linear traits related to feed efficiency without retorting to any approximation. A bivariate longitudinal random regression model was employed on 13,791 individual longitudinal records of WGT and FI from 2,827 bulls of six different beef breeds tested for their own performance in the period from 7 to 13 mo of age. Genetic and permanent environmental covariance functions for curves of WGT and FI were estimated using Gibbs sampling. Genetic and permanent covariance functions for curves of GAIN were estimated from the first derivative of the function for WGT and finally the covariance functions were extended to curves for RFI, based on the conditional distribution of FI given WGT and GAIN. Furthermore, the covariance functions were extended to include GAIN and RFI defined over different periods of the performance test. These periods included the whole test period as normally used when predicting breeding values for GAIN and RFI for beef bulls. Based on the presented method, breeding values and genetic parameters for derived traits such as GAIN and RFI defined longitudinally or integrated over (parts of) of the test period can be obtained from a joint analysis of the basic records. The resulting covariance functions for WGT, FI, GAIN, and RFI are usually singular but the method presented here does not suffer from the estimation problems associated with defining these traits individually before the genetic analysis. All the results are thus estimated simultaneously, and the set of parameters is consistent.

Herd-level versus animal-level variation in methane emission prediction in grazing dairy cattle

In response to the increased concern over agriculture's contribution to greenhouse gas (GHG) emissions, more detailed assessments of current methane emissions and their variation, within and across individual dairy farms and cattle, are of interest for research and policy development. This assessment will provide insights into possible changes needed to reduce GHG emissions, the nature and direction of these changes, ways to influence farmer behavior and areas to maximize the adoption of emerging mitigation technologies. The objectives of this study were to (1) quantify the variation in enteric fermentation methane emissions within and among seasonal calving dairy farms with the majority of nutritional requirements met through grazed pasture; (2) use this variation to assess the potential of new individual animal emission monitoring technologies and their impact on mitigation policy. We used a large database of cow performance records for milk production and survival from 2 398 herds in New Zealand, and simulation to account for unobserved variation in feed efficiency and methane emissions per unit of feed. Results showed an average of 120 ± 31.4 kg predicted methane (CH₄) per cow per year after accounting for replacement costs, ranging 8.9-323 kg CH₄/cow per year. Whereas milk production, survival and predicted live weight were reasonably effective at predicting both individual and herd average levels of per cow feed intake, substantial within animal variation in emissions per unit of feed reduced the ability of these variables to predict variation in per animal methane output. Animal-level measurement technologies predicting only feed intake but not emissions per unit of feed are unlikely to be effective for advancing national policy goals of reducing dairy farming enteric methane output. This is because farmers seek to profitably utilize all farm feed resources available, so improvements in feed efficiency will not result in the reduction in feed utilization required to reduce methane emissions. At a herd level, average per cow milk production and live weight could form the basis of assigning a farm-level point of obligation for methane emissions. In conclusion, a comprehensive national database infrastructure that was tightly linked to animal identification and movement systems, and captured live weight data from existing farm-level recording systems, would be required to make this effective. Additional policy and incentivization mechanisms would still be required to encourage farmer uptake of mitigation interventions, such as novel feed supplements or vaccines that reduce methane emissions per unit of feed.

Plasma proteins δ15N vs plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation

Identifying animals that are superior in terms of feed efficiency may improve the profitability and sustainability of the beef cattle sector. However, measuring feed efficiency is costly and time-consuming. Biomarkers should thus be explored and validated to predict between-animal variation of feed efficiency for both genetic selection and precision feeding. In this work, we aimed to assess and validate two previously identified biomarkers of nitrogen (N) use efficiency in ruminants, plasma urea concentrations and the ¹⁵N natural abundance in plasma proteins (plasma δ¹⁵N), to predict the between-animal variation in feed efficiency when animals were fed two contrasted diets (high-starch vs high-fibre diets). We used an experimental network design with a total of 588 young bulls tested for feed efficiency through two different traits (feed conversion efficiency [FCE] and residual feed intake [RFI]) during at least 6 months in 12 cohorts (farm × period combination). Animals reared in the same cohort, receiving the same diet and housed in the same pen, were considered as a contemporary group (CG). To analyse between-animal variations and explore relationships between biomarkers and feed efficiency, two statistical approaches, based either on mixed-effect models or regressions from residuals, were conducted to remove the between-CG variability. Between-animal variation of plasma δ¹⁵N was significantly correlated with feed efficiency measured through the two criteria traits and regardless of the statistical approach. Conversely, plasma urea was not correlated to FCE and showed only a weak, although significant, correlation with RFI. The response of plasma δ¹⁵N to FCE variations was higher when animals were fed a high-starch compared to a high-fibre diet. In addition, we identified two dietary factors, the metabolisable protein to net energy ratio and the rumen protein balance that influenced the relation between plasma δ¹⁵N and FCE variations. Concerning the genetic evaluation, and despite the moderate heritability of the two biomarkers (0.28), the size of our experimental setup was insufficient to detect significant genetic correlations between feed efficiency and the biomarkers. However, we validated the potential of plasma δ¹⁵N to phenotypically discriminate two animals reared in identical conditions in terms of feed efficiency as long as they differ by at least 0.049 g/g for FCE and 1.67 kg/d for RFI. Altogether, the study showed phenotypic, but non-genetic, relationships between plasma proteins δ¹⁵N and feed efficiency that varied according to the efficiency index and the diet utilised.

Influence of Residual Feed Intake and Cow Age on Dry Matter Intake Post-Weaning and Peak Lactation of Black Angus Cows

We evaluated heifer post-weaning residual feed intake (RFI) classification and cow age on dry matter intake (DMI) at two stages of production. Fifty-nine non-lactating, pregnant, (Study 1) and fifty-four lactating, non-pregnant (Study 2) commercial black Angus beef cows were grouped by age and RFI. Free-choice, hay pellets were fed in a GrowSafe feeding system. In Study 1, cow DMI (kg/d) and intake rate (g/min) displayed a cow age effect (p < 0.01) with an increase in DMI and intake rate with increasing cow age. In Study 2, cow DMI (kg/d) and intake rate (g/min) displayed a cow age effect (p < 0.02) with an increase in DMI and intake rate with increasing cow age. Milk production displayed a cow age × RFI interaction (p < 0.01) where both 5-6-year-old and 8-9-year-old low RFI cows produced more milk than high RFI cows. For both studies, intake and intake behavior were not influenced by RFI (p ≥ 0.16) or cow age × RFI interaction (p ≥ 0.21). In summary, heifer's post-weaning RFI had minimal effects on beef cattle DMI or intake behavior, however, some differences were observed in milk production.

Breeding goal traits accounting for feed intake capacity and roughage or concentrate intake separately

Current breeding tools aiming to improve feed efficiency use definitions based on total dry matter intake (DMI); for example, residual feed intake or feed saved. This research aimed to define alternative traits using existing data that differentiate between feed intake capacity and roughage or concentrate intake, and to investigate the phenotypic and genetic relationships among these traits. The data set contained 39,017 weekly milk yield, live weight, and DMI records of 3,164 cows. The 4 defined traits were as follows: (1) Feed intake capacity (FIC), defined as the difference between how much a cow ate and how much she was expected to eat based on diet satiety value and status of the cow (parity and lactation stage); (2) feed saved (FS), defined as the difference between the measured and the predicted DMI, based on the regression of DMI on milk components within experiment; (3) residual roughage intake (RRI), defined as the difference between the measured and the predicted roughage intake, based on the regression of roughage intake on milk components and concentrate intake within experiment; and (4) residual concentrate intake (RCI), defined as the difference between the measured and the predicted concentrate intake, based on the regression of concentrate intake on milk components and roughage intake within experiment. The phenotypic correlations were -0.72 between FIC and FS, -0.84 between FS and RRI, and -0.53 between FS and RCI. Heritability of FIC, FS, RRI, and RCI were estimated to be 0.21, 0.12, 0.15, and 0.03, respectively. The genetic correlations were -0.81 between FS and FIC, -0.96 between FS and RRI, and -0.25 between FS and RCI. Concentrate intake and RCI had low heritability. Genetic correlation between DMI and FIC was 0.98. Although the defined traits had moderate phenotypic correlations, the genetic correlations between DMI, FS, FIC, and RRI were above 0.79 (in absolute terms), suggesting that these traits are genetically similar. Therefore, selecting for FIC is expected to simply increase DMI and RRI, and there seems to be little advantage in separating concentrate and roughage intake in the genetic evaluation, because measured concentrate intake was determined by the feeding system in our data and not by the genetics of the cow.

Breeding for reduced methane emission and feed-efficient Holstein cows: An international response

Selecting for lower methane (CH₄) emitting animals is one of the best approaches to reduce CH₄ given that genetic progress is permanent and cumulative over generations. As genetic selection requires a large number of animals with records and few countries actively record CH₄, combining data from different countries could help to expedite accurate genetic parameters for CH₄ traits and build a future genomic reference population. Additionally, if we want to include CH₄ in the breeding goal, it is important to know the genetic correlations of CH₄ traits with other economically important traits. Therefore, the aim of this study was first to estimate genetic parameters of 7 suggested methane traits, as well as genetic correlations between methane traits and production, maintenance, and efficiency traits using a multicountry database. The second aim was to estimate genetic correlations within parities and stages of lactation for CH₄. The third aim was to evaluate the expected response of economically important traits by including CH₄ traits in the breeding goal. A total of 15,320 methane production (MeP, g/d) records from 2,990 cows belonging to 4 countries (Canada, Australia, Switzerland, and Denmark) were analyzed. Records on dry matter intake (DMI), body weight (BW), body condition score, and milk yield (MY) were also available. Additional traits such as methane yield (MeY; g/kg DMI), methane intensity (MeI; g/kg energy-corrected milk), a genetic standardized methane production, and 3 definitions of residual methane production (g/d), residual feed intake, metabolic BW (MBW), BW change, and energy-corrected milk were calculated. The estimated heritability of MeP was 0.21, whereas heritability estimates for MeY and MeI were 0.30 and 0.38, and for the residual methane traits heritability ranged from 0.13 to 0.16. Genetic correlations between different methane traits were moderate to high (0.41 to 0.97). Genetic correlations between MeP and economically important traits ranged from 0.29 (MY) to 0.65 (BW and MBW), being 0.41 for DMI. Selection index calculations showed that residual methane had the most potential for inclusion in the breeding goal when compared with MeP, MeY, and MeI, as residual methane allows for selection of low methane emitting animals without compromising other economically important traits. Inclusion of residual feed intake in the breeding goal could further reduce methane, as the correlation with residual methane is moderate and elicits a favorable correlated response. Adding a negative economic value for methane could facilitate a substantial reduction in methane emissions while maintaining an increase in milk production.

Assessment of trade-offs between feed efficiency, growth-related traits, and immune activity in experimental lines of layer chickens

Background

In all organisms, life-history traits are constrained by trade-offs, which may represent physiological limitations or be related to energy resource management. To detect trade-offs within a population, one promising approach is the use of artificial selection, because intensive selection on one trait can induce unplanned changes in others. In chickens, the breeding industry has achieved remarkable genetic progress in production and feed efficiency over the last 60 years. However, this may have been accomplished at the expense of other important biological functions, such as immunity. In the present study, we used three experimental lines of layer chicken—two that have been divergently selected for feed efficiency and one that has been selected for increased antibody response to inactivated Newcastle disease virus (ND3)—to explore the impact of improved feed efficiency on animals’ immunocompetence and, vice versa, the impact of improved antibody response on animals’ growth and feed efficiency.

Results

There were detectable differences between the low (R+) and high (R−) feed-efficiency lines with respect to vaccine-specific antibody responses and counts of monocytes, heterophils, and/or T cell population. The ND3 line presented reduced body weight and feed intake compared to the control line. ND3 chickens also demonstrated an improved antibody response against a set of commercial viral vaccines, but lower blood leucocyte counts.

Conclusions

This study demonstrates the value of using experimental chicken lines that are divergently selected for RFI or for a high antibody production, to investigate the modulation of immune parameters in relation to growth and feed efficiency. Our results provide further evidence that long-term selection for the improvement of one trait may have consequences on other important biological functions. Hence, strategies to ensure optimal trade-offs among competing functions will ultimately be required in multi-trait selection programs in livestock.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-021-00636-z.

Effects of Incorporating Dry Matter Intake and Residual Feed Intake into a Selection Index for Dairy Cattle Using Deterministic Modeling

The inclusion of feed efficiency in the breeding goal for dairy cattle has been discussed for many years. The effects of incorporating feed efficiency into a selection index were assessed by indirect selection (dry matter intake) and direct selection (residual feed intake) using deterministic modeling. Both traits were investigated in three ways: (1) restricting the trait genetic gain to zero, (2) applying negative selection pressure, and (3) applying positive selection pressure. Changes in response to selection from economic and genetic gain perspectives were used to evaluate the impact of including feed efficiency with direct or indirect selection in an index. Improving feed efficiency through direct selection on residual feed intake was the best scenario analyzed, with the highest overall economic response including favorable responses to selection for production and feed efficiency. Over time, the response to selection is cumulative, with the potential for animals to reduce consumption by 0.16 kg to 2.7 kg of dry matter per day while maintaining production. As the selection pressure increased on residual feed intake, the response to selection for production, health, and fertility traits and body condition score became increasingly less favorable. This work provides insight into the potential long-term effects of selecting for feed efficiency as residual feed intake.

Invited review: Disentangling residual feed intake-Insights and approaches to make it more fit for purpose in the modern context

Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

Invited review: Beef-on-dairy-The generation of crossbred beef × dairy cattle

Because a growing proportion of the beef output in many countries originates from dairy herds, the most critical decisions about the genetic merit of most carcasses harvested are being made by dairy producers. Interest in the generation of more valuable calves from dairy females is intensifying, and the most likely vehicle is the use of appropriately selected beef bulls for mating to the dairy females. This is especially true given the growing potential to undertake more beef × dairy matings as herd metrics improve (e.g., reproductive performance) and technological advances are more widely adopted (e.g., sexed semen). Clear breed differences (among beef breeds but also compared with dairy breeds) exist for a whole plethora of performance traits, but considerable within-breed variability has also been demonstrated. Although such variability has implications for the choice of bull to mate to dairy females, the fact that dairy females themselves exhibit such genetic variability implies that "one size fits all" may not be appropriate for bull selection. Although differences in a whole series of key performance indicators have been documented between beef and beef-on-dairy animals, of particular note is the reported lower environmental hoofprint associated with beef-on-dairy production systems if the environmental overhead of the mature cow is attributed to the milk she eventually produces. Despite the known contribution of beef (i.e., both surplus calves and cull cows) to the overall gross output of most dairy herds globally, and the fact that each dairy female contributes half her genetic merit to her progeny, proxies for meat yield (i.e., veal or beef) are not directly considered in the vast majority of dairy cow breeding objectives. Breeding objectives to identify beef bulls suitable for dairy production systems are now being developed and validated, demonstrating the financial benefit of using such breeding objectives over and above a focus on dairy bulls or easy-calving, short-gestation beef bulls. When this approach is complemented by management-based decision-support tools, considerable potential exists to improve the profitability and sustainability of modern dairy production systems by exploiting beef-on-dairy breeding strategies using the most appropriate beef bulls.

Strategies to improve the efficiency of beef cattle production

Globally, there are approximately one billion beef cattle, and compared with poultry and swine, beef cattle have the poorest conversion efficiency of feed to meat. However, these metrics fail to consider that beef cattle produce high-quality protein from feeds that are unsuitable for other livestock species. Strategies to improve the efficiency of beef cattle are focusing on operational and breeding management, host genetics, functional efficiency of rumen and respiratory microbiomes, and the structure and composition of feed. These strategies must also consider the health and immunity of the herd as well as the need for beef cattle to thrive in a changing environment. Genotyping can identify hybrid vigor with positive consequences for animal health, productivity, and environmental adaptability. The role of microbiome–host interactions is key in efficient nutrient digestion and host health. Microbial markers and gene expression patterns within the rumen microbiome are being used to identify hosts that are efficient at fibre digestion. Plant breeding and processing are optimizing the feed value of both forages and concentrates. Strategies to improve the efficiency of cattle production are a prerequisite for the sustainable intensification needed to satisfy the future demand for beef.

Genetic association among feeding behavior, feed efficiency, and growth traits in growing indicine cattle

This study aimed to estimate genetic parameters, including genomic data, for feeding behavior, feed efficiency, and growth traits in Nellore cattle. The following feeding behavior traits were studied (861 animals with records): time spent at the feed bunk (TF), duration of one feeding event (FD), frequency of visits to the bunk (FF), feeding rate (FR), and dry matter intake (DMI) per visit (DMIv). The feed efficiency traits (1,543 animals with records) included residual feed intake (RFI), residual weight gain (RWG), and feed conversion (FC). The growth traits studied were average daily gain (ADG, n = 1,543 animals) and selection (postweaning) weight (WSel, n = 9,549 animals). The (co)variance components were estimated by the maximum restricted likelihood method, fitting animal models that did (single-step genomic best linear unbiased prediction) or did not include (best linear unbiased prediction) genomic information in two-trait analyses. The direct responses to selection were calculated for the feed efficiency traits, ADG, and WSel, as well as the correlated responses in feed efficiency and growth by direct selection for shorter TF. The estimated heritabilities were 0.51 ± 0.06, 0.35 ± 0.06, 0.27 ± 0.07, 0.34 ± 0.06, and 0.33 ± 0.06 for TF, FD, FF, FR, and DMIv, respectively. In general, TF and FD showed positive genetic correlations with all feed efficiency traits (RFI, RWG, and FC), ADG, DMI, and WSel. Additionally, TF showed high and positive genetic and phenotypic correlations with RFI (0.71 ± 0.10 and 0.46 ± 0.02, respectively) and DMI (0.56 ± 0.09 and 0.48 ± 0.03), and medium to weak genetic correlations with growth (0.32 ± 0.11 with ADG and 0.14 ± 0.09 with WSel). The results suggest that TF is a strong indicator trait of feed efficiency, which exhibits high heritability and a weak positive genetic correlation with growth. In a context of a selection index, the inclusion of TF to select animals for shorter TF may accelerate the genetic gain in feed efficiency by reducing RFI but with zero or slightly negative genetic gain in growth traits.

Genetic parameters for performance, feed efficiency, and carcass traits in Senepol heifers

Improving feed efficiency is a key breeding goal in the beef cattle industry. In this study, we estimated the genetic parameters for feed efficiency and carcass traits in Senepol cattle raised in tropical regions. Various indicators of feed efficiency [gain to feed ratio (G:F), feed conversion ratio (FCR), residual weight gain (RG), residual intake and body weight gain (RIG), and residual feed intake (RFI)] as well as growth [final BW, average daily gain (ADG), and DM intake (DMI)], and carcass [rib-eye area (REA), backfat thickness (BF), intramuscular fat score, and carcass conformation score] traits were included in the study. After data editing, records from 1 393 heifers obtained between 2009 and 2018 were used for the analyses. We fitted an animal model that included contemporary group (animals from the same farm that were evaluated in the same test season) as the fixed effect, and a linear effect of animal age at the beginning of the test as a covariate; in addition to random direct additive genetic and residual effects. The (co)variance components were estimated by Bayesian inference in uni- and bivariate analyses. Our results showed that feed efficiency indicators derived from residual variables such as RG, RIG, and RFI can be improved through genetic selection (h² = 0.14 ± 0.06, 0.13 ± 0.06, and 0.20 ± 0.08, respectively). Variables calculated as ratios such as G:F and FCR were more influenced by environmental factors (h² = 0.08 ± 0.05 and 0.09 ± 0.05), and were, therefore, less suitable for use in breeding programs. The traits with the greatest and impact on genetic progress in feed efficiency were ADG, REA, and BF. The traits with the greatest and least impact on growth and carcass traits were RG and RFI, respectively. Selection for feed efficiency will result in distinct overall effects on the growth and carcass traits of Senepol heifers. Direct selection for lower RFI may reduce DMI and increase carcass fatness at the finishing stage, but it might also result in reduced growth and muscle deposition. Residual BW gain is associated with the highest weight gain and zero impact on REA and BF, however, it is linked to higher feed consumption. Thus, the most suitable feed efficiency indicator was RIG, as it promoted the greatest decrease in feed intake concomitant with faster growth, with a similar impact on carcass traits when compared to the other feed efficiency indicators.

Selection criteria for feed efficiency-related traits and their association with growth, reproductive and carcass traits in Nelore cattle

Context Livestock feed costs have a higher impact on the profitability of beef production systems and are directly related to feed efficiency. However, these traits are hard and have high costs to measure, reducing the availability of phenotypic records and reliability of genetic evaluations. Thus, the use of genomic information can increase the robustness of genetic studies that address them. Aims The aim of the present study was to estimate genetic parameters for feed efficiency, growth, reproductive and carcass traits in Nelore cattle and the correlated response among them, using genomic information. Methods Residual feed intake (RFI), dry-matter intake, feed conversion ratio, feed efficiency (FE), residual average daily gain (RG), residual feed intake and average daily gain (RIG), birthweight, weight at 120, 240, 365 and 450 days of age, scrotal circumference at 365 and 450 days of age, rib-eye area, backfat thickness and rump fat thickness were evaluated. The genetic parameters were estimated using the single-step genomic best linear unbiased prediction approach. Key results The FE-related traits showed low to moderate heritability ranging from 0.07 to 0.23. Feed efficiency-related traits showed low genetic correlations with reproductive (–0.24 to 0.27), carcass (–0.17 to 0.27) and growth (–0.19 to 0.24) traits, except for growth with dry-matter intake (0.32–0.56) and weight at 365 days of age with FE (–0.40). Conclusions The selection to improve growth, reproductive and carcass traits would not change RFI, RG and RIG. The choice of the most adequate selection criterion depends on the production system, that is, RFI might be used for low-input beef cattle systems, and RIG would be used for more intensive and without-any-dietary-restrictions beef cattle systems. Implications The estimates of heritability and genetic correlations suggest that genetic selection for feed efficiency using RFI, RG and RIG in Nellore cattle leads to higher genetic gain than does that using FE and feed conversion ratio without affecting other profitability traits.

Water requirements of beef production can be reduced by genetic selection

Growing concerns regarding sustainability in agriculture include the availability of drinking water, which is putting pressure on livestock production, especially the beef sector, for more efficient practices. Thus, genetic parameters were estimated for traits related to water intake and water use efficiency in Senepol cattle. Senepol females (n = 925) and males (n = 191) were evaluated in performance tests carried out from 2014 to 2019. Daily dry matter intake (DMI) and water intake (WI) were recorded by electronic feed and water bunks (Intergado Ltd.). Other traits assessed included average daily gain (ADG); mid-test metabolic BW (BW^0.75); residual water intake based on ADG (RWI_ADG), estimated as the residual of the linear regression equation of WI on ADG and BW^0.75; residual water intake based on DMI (RWI_DMI), estimated as the residual of the linear regression equation of WI on DMI and BW^0.75 (RWI_DMI); water conversion ratio (= WI/ADG); gross water efficiency (GWE = ADG/WI); residual feed intake estimated as the residual of the linear regression equation of DMI on ADG and BW^0.75 (RFI); feed conversion ratio (= DMI/ADG) and gross feed efficiency. Genetic (co)variances were estimated with bivariate analyses. The heritabilities for WI, RWI_ADG and RWI_DMI were 0.38, 0.36 and 0.33, respectively. Water conversion ratio, RWI_ADG and RWI_DMI showed positive genetic and phenotypic correlations with WI, whereas GWE was negatively correlated with WI, suggesting that traits related to water use efficiency may be useful to identify cattle with reduced WI. Water intake showed positive genetic (r = 0.79) and phenotypic (r = 0.60) correlations with DMI, suggesting the use of WI to estimate DMI in future studies. Both RWI_ADG and RWI_DMI were genetically correlated with RFI (0.67 and 0.57, respectively) and ADG (0.49 and 0.44, respectively), showing that RWI is positively associated with feed efficiency, but has an antagonistic relationship with growth. This antagonism, however, may be managed using selection indexes. Genetic improvement of water use efficiency in Senepol cattle is possible through selection and may reduce the water requirements of beef production systems.

Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing

The identification of functional genetic variants and associated candidate genes linked to feed efficiency may help improve selection for feed efficiency in dairy cattle, providing economic and environmental benefits for the dairy industry. This study used RNA-sequencing data obtained from liver tissue from 9 Holstein cows [n = 5 low residual feed intake (RFI), n = 4 high RFI] and 10 Jersey cows (n = 5 low RFI, n = 5 high RFI), which were selected from a single population of 200 animals. Using RNA-sequencing, 3 analyses were performed to identify: (1) variants within low or high RFI Holstein cattle; (2) variants within low or high RFI Jersey cattle; and (3) variants within low or high RFI groups, which are common across both Holstein and Jersey cattle breeds. From each analysis, all variants were filtered for moderate, modifier, or high functional effect, and co-localized quantitative trait loci (QTL) classes, enriched biological processes, and co-localized genes related to these variants, were identified. The overlapping of the resulting genes co-localized with functional SNP from each analysis in both breeds for low or high RFI groups were compared. For the first two analyses, the total number of candidate genes associated with moderate, modifier, or high functional effect variants fixed within low or high RFI groups were 2,810 and 3,390 for Holstein and Jersey breeds, respectively. The major QTL classes co-localized with these variants included milk and reproduction QTL for the Holstein breed, and milk, production, and reproduction QTL for the Jersey breed. For the third analysis, the common variants across both Holstein and Jersey breeds, uniquely fixed within low or high RFI groups were identified, revealing a total of 86,209 and 111,126 functional variants in low and high RFI groups, respectively. Across all 3 analyses for low and high RFI cattle, 12 and 31 co-localized genes were overlapping, respectively. Among the overlapping genes across breeds, 9 were commonly detected in both the low and high RFI groups (INSRR, CSK, DYNC1H1, GAB1, KAT2B, RXRA, SHC1, TRRAP, PIK3CB), which are known to play a key role in the regulation of biological processes that have high metabolic demand and are related to cell growth and regeneration, metabolism, and immune function. The genes identified and their associated functional variants may serve as candidate genetic markers and can be implemented into breeding programs to help improve the selection for feed efficiency in dairy cattle.

A new method to estimate residual feed intake in dairy cattle using time series data

In dairy, the usual way to measure feed efficiency is through the residual feed intake (RFI) method. However, this method is, in its classical form, a linear regression, which, by construction, does not take into account the evolution of the RFI components across time, inducing approximations in the results. We present here a new approach that incorporates the dynamic dimension of the data. Using a multitrait random regression model, the correlations between milk, live weight, DM intake (DMI) and body condition score (BCS) were investigated across the lactation. In addition, at each time point, by a matrix regression on the variance-covariance matrix and on the animal effects from the three predictor traits, a predicted animal effect for intake was estimated, which, by difference with the actual animal effect for intake, gave a RFI estimation. This model was tested on historical data from the Aarhus University experimental farm (1 469 lactations out of 740 cows). Correlations between animal effects were positive and high for milk and DMI and for weight and DMI, with a maximum mid-lactation, stable across time at around 0.4 for weight and BCS, and slowly decreasing along the lactation for milk and weight, DMI and BCS, and milk and BCS. At the Legendre polynomial coefficient scale, the correlations were estimated with a high accuracy (averaged SE of 0.04, min = 0.02, max = 0.05). The predicted animal effect for intake was always extremely highly correlated with the milk production and highly correlated with BW for the most part of the lactation, but only slightly correlated with BCS, with the correlation becoming negative in the second half of the lactation. The estimated RFI possessed all the characteristics of a classical RFI, with a mean at zero at each time point and a phenotypic independence from its predictors. The correlation between the averaged RFI over the lactation and RFI at each time point was always positive and above 0.5, and maximum mid-lactation (>0.9). The model performed reasonably well in the presence of missing data. This approach allows a dynamic estimation of the traits, free from all time-related issues inherent to the traditional RFI methodology, and can easily be adapted and used in a genetic or genomic selection context.

Sire Effects on Post-Weaning Growth of Beef-Cross-Dairy Cattle: A Case Study in New Zealand

Simple Summary

Cattle born in the dairy industry are a very important source of beef. This study evaluated the growth of calves born to dairy cows and sired by a range of Angus and Hereford bulls. The sire had effects on live weights of their progeny at all ages. Growth trajectories differed among beef sires and the differences in live weights between the lightest and heaviest sires increased as live weight increased. Live weight at early age could explain a large proportion of live weights at later ages. Thus, using beef sires selected for growth has the potential to increase the live weight of cattle born on dairy farms for meat production.

Abstract

Little is known about the growth performance of beef sires used over dairy cows in New Zealand. This experiment aimed to evaluate the growth of Angus and Hereford sires via progeny testing of beef-cross-dairy offspring born to dairy cows and grown on hill country pasture. Live weights at 131, 200, 400, 600 and 800 days were analysed from a dataset of 5208 records from 1101 progeny of 73 sires. The means of the progeny group means for live weight were 118.6 kg at 131 days, 159.1 kg at 200 days, 284.2 kg at 400 days, 427.0 kg at 600 days and 503.6 kg at 800 days, and the overall daily growth rate was 0.58 kg/day from 131 to 800 days. The sire affected (p < 0.05) the live weight of their progeny at all ages. Differences in live weights between the lightest and heaviest progeny group means increased from 19 kg at 131 days to 90 kg at 800 days. Even though growth of calves was likely restricted to 200 days, live weight at 200 days explained 51–56% of the variation in live weights at 400 and 600 days (p < 0.05). Thus, the use of beef sires selected for growth has the potential to increase the live weight of cattle born on dairy farms for meat production.

Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.

Associations of feed efficiency with circulating IGF-1 and leptin, carcass traits and meat quality of lambs

The aim of this study was to investigate the effects of feed efficiency classifications on live animal measurements, circulating IGF-1 and leptin concentrations, and carcass, non-carcass and meat quality traits of lambs. One-hundred and two lambs approximately 70 days-old with initial live weight of 24.6 ± 3.71 kg (mean ± SD) were individually fed for 56 days to determine residual feed intake (RFI) and residual feed intake and gain (RIG). Lambs were then classified as phenotypically Low-, Medium- or High-RFI and Low-, Medium- or High-RIG phenotypes. Circulating leptin and IGF-1 concentrations were higher in more efficient lambs (Low-RFI or High-RIG). Variation in RFI and RIG did not affect meat redness or tenderness, but High-RIG lambs had darker meat. These findings show that the phenotypically more efficient Low-RFI and High-RIG lambs produced carcasses with similar characteristics and meat quality as the less efficient High-RFI and Low-RIG lambs but have a strategic advantage of lower feed intake to achieve similar production outcomes.