Genomic prediction ability and genetic parameters for residual feed intake calculated using different approaches and their associations with growth, reproductive, and carcass traits in Nellore cattle

This study aimed to estimate prediction ability and genetic parameters for residual feed intake (RFI) calculated using a regression equation for each test (RFI_test) and for the whole population (RFI_pop) in Nellore beef cattle. It also aimed to evaluate the correlations between RFI_pop and RFI_test with growth, reproductive, and carcass traits. Genotypic and phenotypic records from 8354 animals were used. An analysis of variance (ANOVA) was performed to verify the adequacy of the regression equations applied to estimate the RFI_test and RFI_pop. The (co)variance components were obtained using the single-step genomic best linear unbiased prediction under single and two-trait animal model analyses. The genetic and phenotypic correlations between RFI_test and RFI_pop with dry matter intake, frame, growth, reproduction, and carcass-related traits were evaluated. The prediction ability and bias were estimated to compare the RFI_test and RFI_pop genomic breeding values (GEBV). The RFI_pop ANOVA showed a higher significance level (p < 0.0001) than did the RFI_test for the fixed effects. The RFI_pop displayed higher additive genetic variance estimated than the RFI_test, although the RFI_pop and RFI_test displayed similar heritabilities. Overall, the RFI_test showed higher residual correlations with growth, reproductive, and carcass traits, while the RFI_pop displayed higher genetic correlations with such traits. The GEBV for the RFI_test was slightly biased than GEBV RFI_pop. The approach to calculate the RFI influenced the decomposition and estimation of variance components and genomic prediction for RFI. The application of RFI_pop would be more appropriate for genetic evaluation purpose to adjust or correct for non-genetic effects and to decrease the prediction bias for RFI.

Effects of diet on feed intake, weight change, and gas emissions in beef cows

The objective of this study was to examine the effects of diet energy density on ranking for dry matter intake (DMI), residual feed intake (RFI), and greenhouse gas emissions. Forty-two mature, gestating Angus cows (600 ± 69 kg body weight [BW]; body condition score [BCS] 5.3 ± 1.1) with a wide range in DMI expected progeny difference (-1.38 to 2.91) were randomly assigned to two diet sequences; forage then concentrate (FC) or concentrate then forage (CF). The forage diet consisted of long-stem native grass hay plus protein supplement (HAY; 1.96 Mcal ME/kg DM). The concentrate diet consisted of 35% chopped grass hay and 65% concentrate feeds on a dry matter basis (MIX; 2.5 Mcal ME/kg DM). The GreenFeed Emission Monitoring system was used to determine carbon dioxide (CO2), oxygen (O2), and methane (CH4) flux. Cow performance traits, ultrasound back fat and rump fat, feed DMI, and gas flux data were analyzed in a crossover design using a mixed model including diet, period, and sequence as fixed effects and pen and cow within sequence as random effects. For all measured traits excluding DMI, there was a diet × sequence interaction (P < 0.05). The correlation between MIX and HAY DMI was 0.41 (P = 0.067) and 0.47 (P = 0.03) for FC and CF sequences, respectively. There was no relationship (P > 0.66) between HAY and MIX average daily gain (ADG), regardless of sequence. Fifty-seven percent of the variation in DMI was explained by metabolic BW, ADG, and BCS for both diets during the first period. During the second period, the same three explanatory variables accounted for 38% and 37% of the variation in DMI for MIX and HAY diets, respectively. The negative relationship between BCS and DMI was more pronounced when cows consumed the MIX diet. There was no relationship between MIX and HAY RFI, regardless of sequence (P > 0.18). During the first period, correlations for CO2, CH4, and O2 with MIX DMI were 0.69, 0.81, and 0.56 (P ≤ 0.015), respectively, and 0.76, 0.74, and 0.64 (P < 0.01) with HAY DMI. During the second period, correlations for CO2, CH4, and O2 with MIX DMI were 0.62, 0.47, and 0.56 (P ≤ 0.11), respectively. However, HAY DMI during the second period was not related to gas flux (P > 0.47). Results from this experiment indicate that feed intake of two energy-diverse diets is moderately correlated while ADG while consuming the two diets is not related. Further experimentation is necessary to determine if gas flux data can be used to predict feed intake in beef cows.

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.

Evaluation of feed efficiency traits in different Hereford populations and their effect on variance component estimation

Context Residual feed intake is a relevant trait for beef cattle, given the positive impact on reducing feeding costs and greenhouse gas emissions. The lack of large databases is a restriction when estimating accurate genetic parameters for dry matter intake (DMI) and residual feed intake (RFI), and combining different data sets could be an alternative to increase the amount of data and achieve better estimations. Aim The main objective was to compare Uruguayan data (URY; 780 bulls) and Canadian data (CAN; 1597 bulls), and to assess the adequacy of pooling both data sets (ALL) for the estimation of genetic parameters for DMI and RFI. Methods Feed intake and growth traits phenotypes in both data sets were measured following the same protocols established by the Beef Improvement Federation. Pedigree connections among data sets existed, but were weak. Performance data were analysed for each data set, and individual partial regression coefficients for each energy sink on DMI were obtained and compared. Univariate and multivariate variance components were estimated by the restricted maximum likelihood (REML) for DMI, RFI and their energy sinks traits (average daily gain, metabolic mid weight and back fat thickness). Key results There were some differences in phenotypic performance among data (P < 0.01); however, no differences (P > 0.1) were observed for phenotypic values of RFI between sets. Heritability estimates for DMI were 0.42 (URY), 0.41 (CAN) and 0.45 for ALL data, whereas heritability estimates for RFI were 0.34 (URY), 0.20 (CAN) and 0.25 for ALL data. The results obtained indicate selection on reducing RFI could lead to a decrease in DMI, without compromising other performance traits, as genetic correlations between RFI, growth and liveweight were low or close to 0 (−0.12–0.07). Conclusions As genetic parameters were similar between national data sets (URY, CAN), pooling data (ALL) provided more accurate parameter estimations, as they presented smaller standard deviations, especially in multivariate analysis. Implications Parameters estimated here may be used in international or national genetic evaluation programs.

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.

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.

Ruminal microbiota is associated with feed-efficiency phenotype of fattening bulls fed high-concentrate diets

Abstract Context Improving feed efficiency in livestock production is of great importance to reduce feeding costs. Aims To examine the relationship between ruminal microbiota and variation in feed efficiency in beef cattle fed concentrate-based diets. Methods Residual feed intake of 389 fattening bulls, supplied with corn-based concentrate and forage ad libitum, was used to estimate animals’ feed efficiency. Faeces and ruminal fluid samples, from 48 bulls chosen at random, were collected to estimate their forage intake and to determine their apparent digestibility, ruminal fermentation and microbiota. Those animals with extreme values of feed efficiency (high-efficiency (HE, n = 12) and low-efficiency (LE, n = 13)) were subjected to further comparisons. Alpha biodiversity was calculated on the basis of the normalised sequence data. Beta diversity was approached through performing a canonical correspondence analysis based on log-transformed sequence data. Genera differential abundance was tested with an ANOVA-like differential expression analysis and genera interactions were determined applying the sparse correlations for compositional data technique. Key results No differences in dry matter intake were found between the two categories of feed efficiency (P = 0.699); however, HE animals had higher apparent digestibility of dry matter (P = 0.002), organic matter (P = 0.003) and crude protein (P = 0.043). The concentration of volatile fatty acids was unaffected by feed efficiency (P = 0.676) but butyrate proportion increased with time in LE animals (P = 0.047). Ruminal microbiota was different between HE and LE animals (P = 0.022); both α biodiversity and genera network connectance increased with time in LE bulls (P = 0.005 for Shannon index and P = 0.020 for Simpson index), which suggests that LE animals hosted a more robust ruminal microbiota. Certain genera usually related to high energy loss through methane production were found to establish more connections with other genera in LE animals’ rumen than in HE ones. Microbiota function capability suggested that methane metabolism was decreased in HE finishing bulls. Conclusions Rumen microbiota was associated with feed efficiency phenotypes in fattening bulls fed concentrate-based diets. Implications The possible trade-off between feed efficiency and robustness of ruminal microbiota should be taken into account for the optimisation of cattle production, especially in systems with intrinsic characteristics that may constitute a disturbance to rumen microbial community.

Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.

Feed and production efficiency of young crossbred beef cattle stratified on a terminal total merit index

Few studies have attempted to quantify the association between a terminal total merit index with phenotypic feed and production efficiency in beef cattle, particularly when feed efficiency is itself explicitly absent as a goal trait in the index. The objective of the present study was to quantify the differences in phenotypic performance for feed intake, feed efficiency, and carcass traits of crossbred bulls, steers, and heifers differing in a terminal total merit index. A validation population of 614 bulls, steers, and heifers that were evaluated for feed intake and efficiency in the same feedlot and subsequently slaughtered at the end of their test period was constructed. The Irish national genetic evaluations for a terminal index of calving performance, docility, feed intake, and carcass traits were undertaken with the phenotypic records of animals present in the validation population masked. The validation population animals were subsequently stratified into four groups, within sex, according to their terminal index value. Mixed models were used to quantify the association between terminal genetic merit and phenotypic performance; whether the associations differed by sex were also investigated. The regression coefficient of phenotypic feed intake, carcass weight, carcass conformation, or carcass fat on its respective estimated breeding values was 0.86 kg dry matter 0.91 kg, 1.01 units, and 1.29 units, respectively, which are close to the expectation of one. On average, cattle in the very high terminal index stratum had a 0.63 kg DM/d lower feed intake, a 25.05 kg heavier carcass, a 1.82 unit better carcass conformation (scale 1 to 15), and a 1.24 unit less carcass fat score (scale 1 to 15), relative to cattle in the very low terminal index stratum. Cattle of superior total genetic merit were also more feed efficient (i.e., had a lower energy conversion ratio, lower residual feed intake, and greater residual gain), had a greater proportion of their live-weight as carcass weight (i.e., better dressing percentage) and were slaughtered at a younger age relative to their inferior total genetic merit counterparts. This study provides validation of an all-encompassing total merit index and demonstrates the benefits of selection on a total merit index for feed and production efficiency, which should impart confidence among stakeholders in the contribution of genetic selection to simultaneous improvements in individual animal performance and efficiency.

Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency

In this study, Bos Taurus cattle offered one high concentrate diet (92% concentrate-8% straw) during two independent trials allowed us to classify 72 animals comprising of two cattle breeds as "Low" or "High" feed efficiency groups. Digesta samples were taken from individual beef cattle at the abattoir. After metagenomic sequencing, the rumen microbiome composition and genes were determined. Applying a targeted approach based on current biological evidence, 27 genes associated with host-microbiome interaction activities were selected. Partial least square analysis enabled the identification of the most significant genes and genera of feed efficiency (VIP > 0.8) across years of the trial and breeds when comparing all potential genes or genera together. As a result, limited number of genes explained about 40% of the variability in both feed efficiency indicators. Combining information from rumen metagenome-assembled genomes and partial least square analysis results, microbial genera carrying these genes were determined and indicated that a limited number of important genera impacting on feed efficiency. In addition, potential mechanisms explaining significant difference between Low and High feed efficiency animals were analyzed considering, based on the literature, their gastrointestinal location of action. High feed efficiency animals were associated with microbial species including several Eubacterium having the genetic capacity to form biofilm or releasing metabolites like butyrate or propionate known to provide a greater contribution to cattle energy requirements compared to acetate. Populations associated with fucose sensing or hemolysin production, both mechanisms specifically described in the lower gut by activating the immune system to compete with pathogenic colonizers, were also identified to affect feed efficiency using rumen microbiome information. Microbial mechanisms associated with low feed efficiency animals involved potential pathogens within Proteobacteria and Spirochaetales, releasing less energetic substrates (e.g., acetate) or producing sialic acid to avoid the host immune system. Therefore, this study focusing on genes known to be involved in host-microbiome interaction improved the identification of rumen microbial genetic capacities and potential mechanisms significantly impacting on feed efficiency in beef cattle fed high concentrate diet.

Mitochondrial abundance and function in skeletal muscle and liver from Simmental beef cattle divergent for residual feed intake

Cellular mitochondrial function has been suggested to contribute to variation in feed efficiency (FE) among animals. The objective of this study was to determine mitochondrial abundance and activities of various mitochondrial respiratory chain complexes (complex I (CI) to complex IV (CIV)) in liver and muscle tissue from beef cattle phenotypically divergent for residual feed intake (RFI), a measure of FE. Individual DM intake (DMI) and growth were measured in purebred Simmental heifers (n = 24) and bulls (n = 28) with an initial mean BW (SD) of 372 kg (39.6) and 387 kg (50.6), respectively. All animals were offered concentrates ad libitum and 3 kg of grass silage daily, and feed intake was recorded for 70 days. Residuals of the regression of DMI on average daily gain (ADG), mid-test BW0.75 and backfat (BF), using all animals, were used to compute individual RFI coefficients. Animals were ranked within sex, by RFI into high (inefficient; top third of the population), medium (middle third of population) and low (efficient; bottom third of the population) terciles. Statistical analysis was carried out using the MIXED procedure of SAS v 9.3. Overall mean ADG (SD) and daily DMI (SD) for heifers were 1.2 (0.4) and 9.1 (0.5) kg, respectively, and for bulls were 1.8 (0.3) and 9.5 (1.02) kg, respectively. Heifers and bulls ranked as high RFI consumed 10% and 15% more (P < 0.05), respectively, than their low RFI counterparts. There was no effect of RFI on mitochondrial abundance in either liver or muscle (P > 0.05). An RFI × sex interaction was apparent for CI activity in muscle. High RFI animals had an increased activity (P < 0.05) of CIV in liver tissue compared to their low RFI counterparts; however, the relevance of that observation is not clear. Our data provide no clear evidence that cellular mitochondrial function within either skeletal muscle or hepatic tissue has an appreciable contributory role to overall variation in FE among beef cattle.

Feed efficiency and carcass metrics in growing cattle1

Some definitions of feed efficiency such as residual energy intake (REI) and residual gain (RG) may not truly reflect production efficiency. The energy sinks used in the derivation of the traits include metabolic live-weight; producers finishing cattle for slaughter are, however, paid on the basis of carcass weight, as opposed to live-weight. The objective of the present study was to explore alternative definitions of REI and RG which are more reflective of production efficiency, and quantify their relationship with performance, ultrasound, and carcass traits across multiple breeds and sexes of cattle. Feed intake and live-weight records were available on 5,172 growing animals, 2,187 of which also had information relating to carcass traits; all animals were fed a concentrate-based diet representative of a feedlot diet. Animal linear mixed models were used to estimate (co)variance components. Heritability estimates for all derived REI traits varied from 0.36 (REICWF; REI using carcass weight and carcass fat as energy sinks) to 0.50 (traditional REI derived with the energy sinks of both live-weight and ADG). The heritability for the RG traits varied from 0.24 to 0.34. Phenotypic correlations among all definitions of the REI traits ranged from 0.90 (REI with REICWF) to 0.99 (traditional REI with REI using metabolic preslaughter live-weight and ADG). All were different (P < 0.001) from one suggesting reranking of animals when using different definitions of REI to identify efficient cattle. The derived RG traits were either weakly or not correlated (P > 0.05) with the ultrasound and carcass traits. Genetic correlations between the REI traits with carcass weight, dressing difference (i.e., live-weight immediately preslaughter minus carcass weight) and dressing percentage (i.e., carcass weight divided by live-weight immediately preslaughter) implies that selection on any of the REI traits will increase carcass weight, lower the dressing difference and increase dressing percentage. Selection on REICW (REI using carcass weight as an energy sink), as opposed to traditional REI, should increase the carcass weight 2.2 times slower but reduce the dressing difference 4.3 times faster. While traditionally defined REI is informative from a research perspective, the ability to convert energy into live-weight gain does not necessarily equate to carcass gain, and as such, traits such as REICW and REICWF provide a better description of production efficiency for feedlot cattle.

Estimation of genetic parameters and breeding values for feed intake and energy balance using pedigree relationships or single-step genomic evaluation in Holstein Friesian cows

At the beginning of lactation, high-performing dairy cows often experience a severe energy deficit, which in turn is associated with metabolic stress. Increasing feed intake (FI) or reducing the energy deficit during this period could improve the metabolic stability and thus the health of the animals. Genomic selection for the first time enables the inclusion of this hard-to-measure trait in breeding programs. The objective of the current study was the estimation of genetic parameters and genomic breeding values for FI and energy balance (EB). For this purpose, 1,374 Holstein Friesian (HF) dairy cows from 8 German research farms were phenotyped with standardized FI data protocols. After data editing, phenotypic data of HF comprised a total of 40,012 average weekly FI records with a mean of 21.8 ± 4.3 kg/d. For EB 33,376 average weekly records were available with a mean of 3.20 ± 29.4 MJ of NE_L/d. With the Illumina Bovine SNP50 BeadChip (Illumina Inc., San Diego, CA) 1,128 of phenotyped cows were genotyped. Thirty-five female candidates of the HF population were genotyped but not phenotyped. Pedigree information contained sires and dams 4 generations back. The random regression animal model included the fixed effects of herd test week (alternatively, herd group test week), parity, and stage of lactation, modeled by the function according to Ali and Schaeffer (1987). For both the random permanent environmental effect across lactations and the random additive genetic effect, third-order Legendre polynomials were chosen. Additionally, a random permanent environmental cow effect within lactation was included. Analyses for heritabilities, genetic correlations between different lactation stages, and breeding values were estimated using, respectively, pedigree relationships and single-step genomic evaluation, carried out with the DMU software package (Madsen et al., 2013). This allowed for comparison of conventional reliabilities with genomic-assisted reliabilities based on real data, to evaluate the gain of genotyping. Heritability estimates ranged between 0.12 and 0.50 for FI, and 0.15 and 0.48 for EB, and increased toward the end of lactation. Genetic correlations were weak between early and late lactation, with a value of 0.05 for FI and negative with a value of -0.05 for EB. Reliabilities for genomic values of cows for FI and EB ranged between 0.33 and 0.61, and 0.27 and 0.47, respectively. For the genotyped cows without phenotypes, the inclusion of genomic relationship leads to an increase of the average reliability of the breeding value for FI by nearly 9% and for EB by 4%. The results show the possibility of combining pedigree, genotypes, and phenotypes for increasing FI or EB to reduce health and reproductive problems, especially at the beginning of lactation. Nevertheless, the reference population needs to be extended to reach higher breeding value reliabilities.

Variation in residual feed intake depends on feed on offer

Two small pen trials with cattle and sheep both clearly demonstrated that while there is significant variation in residual feed intake when on high energy supply, there is negligible variation when energy supply is limited. A review of literature demonstrated that this is also the case when energy supply is limited by heat or physiological state, such as peak lactation, and in multiple species. There is little evidence of variation in efficiency of maintenance requirements, growth or lactation. Nor is there strong evidence for large variation in digestibility within breeds, despite some differences between divergent breeds. Thus, the primary source of variation in residual feed intake must be in appetite and, in variable environments, it is possible that those with greater appetite are more resilient during times of feed shortage.

Exploration of Biological Markers of Feed Efficiency in Young Bulls

The efficiency with which ruminants convert feed to desirable products is difficult to measure under normal commercial settings. We explored the use of potential biological markers from easily obtainable samples, that is, blood, hair, and feces, to characterize potential causes of divergent efficiency when considered as residual feed intake (RFI) or feed conversion efficiency (FCE). A total of 54 Charolais bulls, 20 in period 1 and 34 in period 2, were examined for individual dry matter intake (DMI) and growth. Bulls were offered a diet of 70:30 wrapped grass silage to concentrate for 99 d. At the conclusion of the test period, blood samples were collected for the determination of vitamins B₂ and B₆, and plasma used for the determination of metabolites, natural isotopic ¹⁵N abundance (¹⁵N NIA, expressed as δ¹⁵N ‰) and fractionation (Δ¹⁵N_{plasma proteins-diet} and Δ¹³C_{plasma proteins-diet}) and near-infrared spectroscopy (NIRS). Feces were analyzed by NIRS. Bulls were slaughtered at 15-17 months of age and carcass characteristics determined. Bulls were ranked according to RFI with extremes (SD ± 0.5; n = 31) classified as either efficient (Neg-RFI) or inefficient (Pos-RFI). Extreme bulls were then classified for FCE (high vs low FCE), changing the groups. Pos-RFI bulls consumed 14% more feed than Neg-RFI bulls for the same level of weight gain. Low FCE bulls tended to eat more, but had lower weight gains than high FCE bulls. No differences were detected in carcass conformation, fat scores, hot carcass weight, or dressing percentage. Yet, heart and bladder weights were heavier in Pos-RFI, and rumen weight tended to be heavier in Pos-RFI bulls. RFI did not affect bulk ¹⁵N or ¹³C fractionation. A negative correlation was observed between FCE and Δ¹⁵N_{plasma proteins-diet}. Inefficient bulls (Pos-RFI) had higher δ¹⁵N in glycine compared to Neg-RFI bulls. Similarly, metabolomic analysis showed a tendency for concentrations of glycine and sarcosine to be elevated in Pos-RFI bulls, whereas aspartic acid and carnosine tended to be elevated, and serine tended to be lower in High FCE. Among vitamins, only flavin adenine dinucleotide concentration was higher in the blood of bulls with High FCE. These results suggest that the two feed efficiency metrics differ in the underlying mechanisms of metabolism, where RFI is driven by differences in the energetic requirements of visceral organs and the extent of AA catabolism.

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors

Feed efficiency (FE), characterized as the fraction of feed nutrients converted into salable milk or meat, is of increasing economic importance in the dairy industry. We conjecture that FE is a complex trait whose variation and relationships or partial efficiencies (PE) involving the conversion of dry matter intake to milk energy and metabolic body weight may be highly heterogeneous across environments or management scenarios. In this study, a hierarchical Bayesian multivariate mixed model was proposed to jointly infer upon such heterogeneity at both genetic and nongenetic levels on PE and variance components (VC). The heterogeneity was modeled by embedding mixed effects specifications on PE and VC in addition to those directly specified on the component traits. We validated the model by simulation and applied it to a joint analysis of a dairy FE consortium data set with 5,088 Holstein cows from 13 research stations in Canada, the Netherlands, the United Kingdom, and the United States. Although no differences were detected among research stations for PE at the genetic level, some evidence was found of heterogeneity in residual PE. Furthermore, substantial heterogeneity in VC across stations, parities, and ration was observed with heritability estimates of FE ranging from 0.16 to 0.46 across stations.