Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production

Supplementation of Alternanthera sissoo pellets on feed digestion, rumen fermentation, and protozoal population in Thai native beef cattle

The objective of this experiment was to study the effects of Brazilian spinach (Alternanthera sissoo) pellet (BSP) supplementation on rumen fermentation, protozoal population, and methane (CH4) estimation in beef cattle. Four male Thai native beef cattle, 3 years old, with an average bodyweight of 180 ± 5 kg, were randomly arranged in a 4 × 4 Latin square design. The cattle were supplemented (on-top) with four levels of BSP (2, 4, 6, and 8% dry matter intake (DMI), respectively). The roughage component, derived from rice straw, was fed at 40 % of DMI, while the concentrate diet was fed at 60 % of DMI. The result of the experiment demonstrated that BSP supplementation had no effect on the DMI, nutrient intake, or nutrient digestibility (p > 0.05). Rumen pH and ammonia-nitrogen concentration were not significant, while the average protozoal population linearly decreased (p = 0.002) with BSP supplementation. Mean blood urea-nitrogen concentration was linearly increased (p = 0.004) when increasing the level of BSP. Brazilian spinach pellet had no significant effect on total volatile fatty acids (TVFA), VFA profiles, and CH4 estimation (p > 0.05). Nitrogen balance was no different from the supplementation of BSP. The study indicates that Brazilian spinach pellet supplementation showed no noticeable effects on feed intake, rumen parameters, and nitrogen utilization; however, at 6-8% of DMI, there was a decrease in protozoal population, with no corresponding reduction in CH4 estimation.

Rumen fermentation parameters and papillae development in Simmental growing bulls with divergent residual feed intake

Residual feed intake (RFI), a widespread index used to measure animal feed efficiency, is influenced by various individual biological factors related to inter-animal variation that need to be assessed. Herein, 30 Simmental bulls, raised under the same farm conditions, were divided on the basis of RFI values into a high efficient group (HE, RFI =  - 1.18 ± 0.33 kg DM/d, n = 15) and a low efficient group (LE, RFI = 0.92 ± 0.35 kg DM/d, n = 15). Subsequently, bulls were slaughtered at an average BW of 734 ± 39.4 kg. Their ruminal fermentation traits were analysed immediately after slaughtering and after 24 h of in vitro incubation. Furthermore, ruminal micro-biota composition and ruminal papillae morphology were examined. The LE group exhibited a higher propionate concentration as a percentage of total volatile fatty acids (17.3 vs 16.1%, P = 0.04) in the rumen fluid collected during slaughtering, which was also confirmed after in vitro fermentation (16.6 vs 15.4% respectively for LE and HE, P = 0.01). This phenomenon resulted in a significant alteration in the acetate-to-propionate ratio (A:P) with higher values for the HE group, both after slaughter (4.01 vs 3.66, P = 0.02) and after in vitro incubation (3.78 vs 3.66, P = 0.02). Methane production was similar in both groups either as absolute production (227 vs 218 mL for HE and LE, respectively) or expressed as a percentage of total gas (approximately 22%). Even if significant differences (P < 0.20) in the relative abundance of some bacterial genera were observed for the two RFI groups, no significant variations were observed in the alpha (Shannon index) and beta (Bray-Curtis index) diversity. Considering the papillae morphology, the LE subjects have shown higher length values (6.26 vs 4.90 mm, P < 0.01) while HE subjects have demonstrated higher papillae density (46.4 vs 40.5 n/cm2, P = 0.02). Histo-morphometric analysis did not reveal appreciable modifications in the total papilla thickness, boundaries or surface between the experimental groups. In conclusion, our results contribute to efforts to analyse the factors affecting feed efficiency at the ruminal level. Propionate production, papillae morphology and a few bacterial genera certainly play a role in this regard, although not a decisive one.

Determination of gas flux and animal performance test duration of growing cattle in confined conditions

Data from three experiments was analyzed to determine the number of visits and days to assess gas flux (CH4, CO2, and O2), dry matter intake (DMI), and average daily gain (ADG) from growing animals under confined conditions. In experiment 1, 213 animals (461 ± 91 kg initial body weight [BW]) were fed a backgrounding diet and evaluated for 60 d. In experiment 2, 169 steers (488 ± 37 kg initial BW) were fed a finishing diet and assessed for 70 d. In experiment 3, 64 steers (514 ± 42 kg initial BW) were fed a finishing diet and evaluated for 80 d. In each experiment, animals were placed in one pen with one Greenfeed and five SmartFeeds to collect gas flux and feed intake simultaneously. Gas flux was analyzed using data from 161 animals from the three experiments with 100 visits for 2 or more min or 3 or more min. Also, metabolic heat production (MHP) was estimated using the individual gas flux. Daily DMI was calculated as the daily feed intake corrected by the dry matter concentration. ADG was computed as the slope of the regression of the shrunk BW (96% BW) throughout each of the experimental periods. The mean gas flux and MHP were estimated for increasing or decreasing 5-visit intervals starting with the first or the last 5 visits and increasing or decreasing until the full 100-visit dataset was utilized, respectively. Intervals of DMI were estimated for increasing or decreasing 5-d intervals starting with the first or the last 5 d and increasing or decreasing until the end of the experimental period, respectively. Intervals of ADG were estimated for increasing or decreasing measurement period intervals until the end of the experimental period, respectively. Pearson and Spearman correlations were computed between the maximum visits or days and each shortened visit or day interval. The minimum number of visits and days was determined when correlations with the total visits were greater than 0.95. The results indicated that the minimum number of visits needed to quantify CO2, O2, and MHP accurately was 40, while CH4 was 60. A visitation length of 2 min or more or 3 min or more did not modify the gas flux determination. Thus, based on the average daily visitation in these experiments, gas flux data could be collected for 25 d. Additionally, the required days to determine DMI was 30, while ADG could not be assessed in a shorter than 60-d period.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Climate change and dairy farming sustainability; a causal loop paradox and its mitigation scenario

It is arguable at this time whether climate change is a cause or effect of the disruption in dairy farming. Climate change drastically affects the productive performance of livestock, including milk and meat production, and this could be attributed to the deviation of energy resources towards adaptive mechanisms. However, livestock farming also contributes substantially to the existing greenhouse gas pool, which is the causal of the climate change. We gathered relevant information from the recent publication and reviewed it to elaborate on sustainable dairy farming management in a changing climatic scenario, and efforts are needed to gather this material to develop methods that could help to overcome the adversities associated with livestock industries. We summarize the intervention points to reverse these adversities, such as application of genetic technology, nutrition intervention, utilization of chemical inhibitors, immunization, and application of metagenomics, which may help to sustain farm animal production in the changing climate scenario.

Exploring definitions of daily enteric methane emission phenotypes for genetic evaluations using a population of indoor-fed multi-breed growing cattle with feed intake data

Genetic selection has been identified as a promising approach for reducing enteric methane (CH4) emissions; a prerequisite for genetic evaluations; however, these are estimates of the necessary genetic parameters based on a population representative of where the genetic evaluations will be used. The objective of this study was, therefore, to derive genetic parameters for a series of definitions of CH4, carbon dioxide (CO2), and dry matter intake (DMI) as well as genetic correlations between CH4, CO2, and DMI in a bid to address the paucity of studies involving methane emissions measured in beef cattle using GreenFeed systems. Lastly, estimated breeding values (EBV) were generated for nine alternative definitions of CH4 using the derived genetic parameters; the EBV were validated against both phenotypic performance (adjusted for non-genetic effects) and the Legarra and Reverter method comparing EBV generated for a subset of the dataset compared to EBV generated from the entire dataset. Individual animal CH4 and CO2 records were available from a population of 1,508 multi-breed growing beef cattle using 10 GreenFeed Emission Monitoring systems. Nine trait definitions for CH4 and CO2 were derived: individual spot measures, the average of all spot measures within a 3-h, 6-h, 12-h, 1-d, 5-d, 10-d, and 15-d period and the average of all spot measures across the full test period (20 to 114 d on test). Heritability estimates from 1,155 animals, for CH4, increased as the length of the averaging period increased and ranged from 0.09 ± 0.03 for the individual spot measures trait to 0.43 ± 0.11 for the full test average trait; a similar trend existed for CO2 with the estimated heritability ranging from 0.17 ± 0.04 to 0.50 ± 0.11. Enteric CH4 was moderately to strongly genetically correlated with DMI with a genetic correlation of 0.72 ± 0.02 between the spot measures of CH4 and a 1-d average DMI. Correlations, adjusted for heritability, between the adjusted phenotype and (parental average) EBV ranged from 0.56 to 1.14 across CH4 definitions and the slope between the adjusted phenotype and EBV ranged from 0.92 to 1.16 (expectation = 1). Validation results from the Legarra and Reverter regression method revealed a level bias of between -0.81 and -0.45, a dispersion bias of between 0.93 and 1.17, and ratio accuracy (ratio of the partial evaluation accuracies on whole evaluation accuracies) from 0.28 to 0.38. While EBV validation results yielded no consensus, CH4 is a moderately heritable trait, and selection for reduced CH4 is achievable.

Association of genomically enhanced residual feed intake with performance, feed efficiency, feeding behavior, gas flux, and nutrient digestibility in growing Holstein heifers

Residual feed intake (RFI), a metric of feed efficiency, is moderately heritable and independent of body size and productivity, making it an ideal trait for investigation as a selection criterion to improve the feed efficiency of growing cattle. The objective of this study was to examine the differences in performance, feed efficiency, feeding behavior, gas flux, and nutrient digestibility in Holstein heifers with divergent genomically enhanced breeding values for RFI (RFIg). Holstein heifers (n = 55; BW = 352 ± 64 kg) with low (n = 29) or high (n = 26) RFIg were selected from a contemporary group of 453 commercial Holstein heifers. Heifers were rotated between 1 of 2 pens, each equipped with 4 electronic feed bunks and 1 pen with a GreenFeed emissions monitoring (GEM) system. Individual dry matter intake (DMI) and feeding behavior data were collected for 84-d. Body weight (BW) was measured weekly and spot fecal samples were collected at weighing. Phenotypic RFI (RFIp) was calculated as the residual from the regression of DMI on average daily gain (ADG) and mid-test metabolic BW (BW0.75). A mixed model including the fixed effect of RFIg classification and the random effect of group was used to evaluate the effect of RFIg classification on response variables. There were no differences (P > 0.05) in BW and ADG for heifers with divergent RFIg; however, low RFIg heifers consumed 7.5% less (P < 0.05) feed per day. Consequently, low RFIg heifers exhibited a more favorable (P < 0.05) RFIp compared to high RFIg heifers (-0.196 vs 0.222 kg/d, respectively). Low RFIg heifers had 8.7% fewer (P < 0.05) bunk visit events per day and tended to have an 11.2% slower (P < 0.10) eating rate. Low RFIg heifers had 7.7% lower (P < 0.05) methane (CH4) emissions (g/d), 6.1% lower (P ≤ 0.05) carbon dioxide (CO2) production (g/d), and 5.6% lower (P ≤ 0.05) heat production (Mcal/d) than high RFIg heifers. However, CH4 yield and CO2 yield (g/kg DMI), and heat production per unit DMI (Mcal/kg DMI) did not differ (P > 0.05) between heifers with divergent RFIg. Dry matter (DM) and nutrient digestibility did not differ (P > 0.05) between heifers with divergent RFIg. Results suggest that selection based on RFIg provides opportunities to select cattle with favorable feed efficiency phenotypes to increase the economic and environmental sustainability of the cattle industry.

Gastrointestinal Parasite Infections and Environmental Sustainability of the Ovine Sector: Eimeria spp. Infections and Nitrogen and Phosphorus Excretions in Dairy Sheep in Italy

In sheep farming, gastrointestinal parasites can be responsible for significant reductions in animal health and production. Nitrogen (N) and phosphorus (P) fecal excretions are the main determining factors for N2O emissions from manure management and may pose other environmental problems, such as the acidification and eutrophication of natural habitats. By using the Mini-FLOTAC technique on fecal samples from sheep of different ages and physiological status from 19 dairy sheep farms in Tuscany (central Italy), gastrointestinal parasite infections were evaluated. The animal N and P fecal contents were also assessed, with the aim of evaluating possible relationships between the identified parasites and the environmental sustainability of the examined farms. The obtained results showed that Eimeria spp. (86.36%) and gastrointestinal strongyle (54.55%) infections are prevalent in the examined farms. Moreover, significantly higher (p ≤ 0.05) P and Eimeria oocyst/gram-of-feces (OPG) values were found in fecal samples from animals < 1 year of age, and a significant (p ≤ 0.05) positive correlation resulted between N content and Eimeria OPG in fecal samples from animals in the first month of lactation. The findings from this study suggest for the first time that Eimeria spp. infections may have an impact on the environmental sustainability of sheep farming.

Genomic evaluation of feed efficiency in US Holstein heifers

There is growing interest in improving feed efficiency traits in dairy cattle. The objectives of this study were to estimate the genetic parameters of residual feed intake (RFI) and its component traits [dry matter intake (DMI), metabolic body weight (MBW), and average daily gain (ADG)] in Holstein heifers, and to develop a system for genomic evaluation for RFI in Holstein dairy calves. The RFI data were collected from 6,563 growing Holstein heifers (initial body weight = 261 ± 52 kg; initial age = 266 ± 42 d) for 70 d, across 182 trials conducted between 2014 and 2022 at the STgenetics Ohio Heifer Center (South Charleston, OH) as part of the EcoFeed program, which aims to improve feed efficiency by genetic selection. The RFI was estimated as the difference between a heifer's actual feed intake and expected feed intake, which was determined by regression of DMI against midpoint MBW, age, and ADG across each trial. A total of 61,283 SNPs were used in genomic analyses. Animals with phenotypes and genotypes were used as training population, and 4 groups of prediction population, each with 2,000 animals, were selected from a pool of Holstein animals with genotypes, based on their relationship with the training population. All traits were analyzed using univariate animal model in DMU version 6 software. Pedigree information and genomic information were used to specify genetic relationships to estimate the variance components and genomic estimated breeding values (GEBV), respectively. Breeding values of the prediction population were estimated by using the 2-step approach: deriving the prediction equation of GEBV from the training population for estimation of GEBV of prediction population with only genotypes. Reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of training population GEBV and magnitudes of genomic relationships between individuals in the training and prediction population. Heifers had DMI (mean ± SD) of 8.11 ± 1.59 kg over the trial period, with growth rate of 1.08 ± 0.25 kg/d. The heritability estimates (mean ± SE) of RFI, MBW, DMI, and growth rate were 0.24 ± 0.02, 0.23 ± 0.02, 0.27 ± 0.02, and 0.19 ± 0.02, respectively. The range of genomic predicted transmitted abilities (gPTA) of the training population (-0.94 to 0.75) was higher compared with the range of gPTA (-0.82 to 0.73) of different groups of prediction population. Average reliability of breeding values from the training population was 58%, and that of prediction population was 39%. The genomic prediction of RFI provides new tools to select for feed efficiency of heifers. Future research should be directed to find the relationship between RFI of heifers and cows, to select individuals based on their lifetime production efficiencies.

Applying assisted reproductive technology and reproductive management to reduce CO2-equivalent emission in dairy and beef cattle: a review

Methane emission from beef and dairy cattle combined contributes around 4.5-5.0% of total anthropogenic global methane. In addition to enteric methane (CH4) produced by the rumen, cattle production also contributes carbon dioxide (CO2) (feed), nitrous oxide (N2O) (feed production, manure) and other CH4 (manure) to the total greenhouse gas (GHG) budget of beef and dairy production systems. The relative contribution in standard dairy systems is typically enteric CH4 58%, feed 29% and manure 10%. Herds with low production efficiency can have an enteric CH4 contribution up to 90%. Digestibility of feed can impact CH4 emission intensity. Low fertility herds also have a greater enteric CH4 contribution. Animals with good feed conversion efficiency have a lower emission intensity of CH4/kg of meat or milk. Feed efficient heifers tend to be lean and have delayed puberty. Fertility is a major driver of profit in both beef and dairy cattle, and it is highly important to apply multi-trait selection when shifting herds towards improved efficiency and reduced CH4. Single nucleotide polymorphisms (SNPs) have been identified for feed efficiency in cattle and are used in genomic selection. SNPs can be utilized in artificial insemination and embryo transfer to increase the proportion of cattle that have the attributes of efficiency, fertility and reduced enteric CH4. Prepubertal heifers genomically selected for favourable traits can have oocytes recovered to produce IVF embryos. Reproductive technology is predicted to be increasingly adopted to reduce generation interval and accelerate the rate of genetic gain for efficiency, fertility and low CH4 in cattle. The relatively high contribution of cattle to anthropogenic global methane has focussed attention on strategies to reduce enteric CH4 without compromising efficiency and fertility. Assisted reproductive technology has an important role in achieving the goal of multiplying and distributing cattle that have good efficiency, fertility and low CH4.

Microbial composition, rumen fermentation parameters, enteric methane emissions, and lactational performance of phenotypically high and low methane-emitting dairy cows

This experiment was designed to investigate the relation of high and low methane-yield phenotypes with body weight (BW), dry matter intake (DMI), lactation performance, enteric CH4 emissions, and rumen fermentation parameters in lactating dairy cows. A total of 130 multi- and primiparous Holstein cows were screened for enteric CH4 emissions using the GreenFeed system (C-Lock Inc.). Out of these 130 cows, 5 were identified as phenotypically high (HM) and 5 as phenotypically low (LM) CH4 emitters. Cows in the LM group had lower daily enteric CH4 emissions than cows in the HM group (on average 346 vs. 439 g/d, respectively), lower CH4 yield (15.5 vs. 20.4 g of CH4/kg of DMI), and CH4 intensity (13.2 vs. 17.0 g of CH4/ kg of energy-corrected milk yield). Enteric emissions of CO2 and H2 did not differ between HM and LM cows. These 10 cows were blocked by parity, days in milk, and milk production, and were used in a 5-wk randomized complete block design experiment. Milk composition, production, and BW were also not different between LM and HM cows. The concentration of total volatile fatty acids in ruminal contents did not differ between CH4 phenotypes, but LM cows had a lower molar proportion of acetate (57 vs. 62.1%), a higher proportion of propionate (27.5 vs. 21.6%, respectively), and therefore a lower acetate-to-propionate ratio than HM cows. Consistently, the 16S cDNA analysis revealed the abundance of Succinivibrionaceae and unclassified Veillonellaceae to be higher in LM cows compared with HM cows, bacteria that were positively correlated with ruminal propionate concentration. Notably, Succinivibrionaceae trigger the formation of propionate via oxaloacetate pathway from phosphoenolpyruvate via Enzyme Commission: 4.1.1.49, which showed a trend to be higher in LM cows compared with HM cows. Additionally, LM cows possessed fewer transcripts of a gene encoding for methyl-CoM reductase enzyme compared with HM. In this study, low and high CH4-yield cows have similar production performance and milk composition, but total-tract apparent digestibility of organic matter and fiber fractions was lower in the former group of animals.

Technology for Carbon Neutral Animal Breeding

Animal breeding techniques are to genetically select highly productive animals with less GHG emission intensity, thereby reducing the number of animals required to produce the same amount of food. Shotgun metagenomics provides a platform to identify rumen microbial communities and genetic markers associated with CH4 emissions, allowing the selection of cattle with less CH4 emissions. Moreover, breeding is a viable option to make real progress towards carbon neutrality with a very high rate of return on investment and a very modest cost per tonne of CO2 equivalents saved regardless of the accounting method. Other high technologies include the use of cloned livestock animals and the manipulation of traits by controlling target genes with improved productivity.

Enteric Methane Emissions in Dairy Cows with Different Genetic Groups in the Humid Tropics of Costa Rica

Enteric methane (CH₄) is one of the main greenhouse gases emitted in livestock production systems with ruminants. Among the options to reduce such emissions, animal genetics is one of the factors that is taking relevance in recent years. The aim of the present study was to assess the emission of enteric CH₄ in dairy cows with different genetic backgrounds. Sixteen cows belonging to the following three genetic groups were selected for this study: seven F1 (50% Jersey × 50% Gyr), five Triple cross (50% Jersey × 31% Holstein × 19% Sahiwal) and four Jersey. Enteric CH₄ emissions were measured in all cows for 15 months, at the middle of each month, using the SF₆ technique. Enteric CH₄ emissions did not differ (p > 0.05) among genetic groups, although it varied with the stage of lactation, due to differences in milk yield and dry matter intake (DMI). Pasture DMI and the intensity of CH₄ emissions (g kg^-1 DMI) differed (p < 0.05) between dry and lactating cows, with higher DMI in the lactation period, while CH₄ emission intensity was higher for dry cows. Cows with the highest proportion of Bos taurus genes presented a higher annual mean methane conversion factor (Y_m), with 7.22, 7.05 and 5.90% for the Triple cross, purebred Jersey and F1, respectively. In conclusion, non-significant differences in enteric CH₄ emissions and Y_m were detected among dairy cows with different genetic backgrounds. However, F1 cows tended to show lower enteric CH₄ emission and Y_m, compared to those with more Bos taurus genes.

Predicting dry matter intake in beef cattle

Technology that facilitates estimations of individual animal dry matter intake (DMI) rates in group-housed settings will improve production and management efficiencies. Estimating DMI in pasture settings or facilities where feed intake cannot be monitored may benefit from predictive algorithms that use other variables as proxies. This study examined the relationships between DMI, animal performance, and environmental variables. Here we determined whether a machine learning approach can predict DMI from measured water intake variables, age, sex, full body weight, and average daily gain (ADG). Two hundred and five animals were studied in a drylot setting (152 bulls for 88 d and 53 steers for 50 d). Collected data included daily DMI, water intake, daily predicted full body weights, and ADG using In-Pen-Weighing Positions and Feed Intake Nodes. After exclusion of 26 bulls of low-frequency breeds and one severe (>3 standard deviations) outlier, the final number of animals used for modeling was 178 (125 bulls, 53 steers). Climate data were recorded at 30-min intervals throughout the study period. Random Forest Regression (RFR) and Repeated Measures Random Forest (RMRF) were used as machine learning approaches to develop a predictive algorithm. Repeated Measures ANOVA (RMANOVA) was used as the traditional approach. Using the RMRF method, an algorithm was constructed that predicts an animal's DMI within 0.75 kg. Evaluation and refining of algorithms used to predict DMI in drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive group field settings.

Milk metabolomics analyses of lactating dairy cows with divergent residual feed intake reveals physiological underpinnings and novel biomarkers

The opportunity to select for feed efficient cows has been limited by inability to cost-effectively record individual feed efficiency on an appropriate scale. This study investigated the differences in milk metabolite profiles between high- and low residual feed intake (RFI) categories and identified biomarkers of residual feed intake and models that can be used to predict residual feed intake in lactating Holsteins. Milk metabolomics analyses were undertaken at early, mid and late lactation stages and residual feed intake was calculated in 72 lactating dairy cows. Cows were ranked and grouped into high residual feed intake (RFI >0.5 SD above the mean, n = 20) and low residual feed intake (RFI <0.5 SD below the mean, n = 20). Milk metabolite profiles were compared between high residual feed intake (least efficient) and low residual feed intake (most efficient) groups. Results indicated that early lactation was predominantly characterized by significantly elevated levels of medium chain acyl carnitines and glycerophospholipids in high residual feed intake cows. Citrate cycle and glycerophospholipid metabolism were the associated pathways enriched with the significantly different metabolites in early lactation. At mid lactation short and medium chain acyl carnitines, glycerophospholipids and amino acids were the main metabolite groups differing according to residual feed intake category. Late lactation was mainly characterized by increased levels of amino acids in high residual feed intake cows. Amino acid metabolism and biosynthesis pathways were enriched for metabolites that differed between residual feed intake groups at the mid and late lactation stages. Receiver operating characteristic curve analysis identified candidate biomarkers: decanoylcarnitine (area under the curve: AUC = 0.81), dodecenoylcarnitine (AUC = 0.81) and phenylalanine (AUC = 0.85) at early, mid and late stages of lactation, respectively. Furthermore, panels of metabolites predicted residual feed intake with validation coefficient of determination (R ²) of 0.65, 0.37 and 0.60 at early, mid and late lactation stages, respectively. The study sheds light on lactation stage specific metabolic differences between high-residual feed intake and low-residual feed intake lactating dairy cows. Candidate biomarkers that distinguished divergent residual feed intake groups and panels of metabolites that predict individual residual feed intake phenotypes were identified. This result supports the potential of milk metabolites to select for more efficient cows given that traditional residual feed intake phenotyping is costly and difficult to conduct in commercial farms.

Predicting fecal composition, intake, and nutrient digestibility in beef cattle consuming high forage diets using near infrared spectroscopy

The objective of this study was to develop near infrared spectroscopy (NIRS) calibrations to predict fecal nutrient composition, intake, and diet digestibility from beef cattle fed high forage diets. Heifers were fed 12 different forage-based diets (>95% forage dry matter basis) in 3 total collection digestibility studies, resulting in individual fecal samples and related spectra (n = 135), corresponding nutrient intake, and apparent total tract digestibility (aTTD) data. Fecal samples were also collected from steers grazing two annual and two perennial forage mixtures over two growing seasons. Samples (n = 13/paddock) were composited by paddock resulting in 30 samples from year 1, and 24 from year 2. The grazing fecal spectra (n = 54) were added to the existing fecal composition spectral library. Dried and ground fecal samples were scanned using a FOSS DS2500 scanning monochromator (FOSS, Eden Prairie, MN). Spectra were mathematically treated for detrend and scatter correction and modified partial least squares (MPLS) regression was performed. The coefficient of determination for cross validation (R²_cv) and standard error of cross validation (SECV) were used to evaluate the quality of calibrations. Prediction equations were developed for fecal composition [organic matter (OM), nitrogen (N), amylase-treated ash-corrected neutral detergent fiber (aNDFom), acid detergent fiber (ADF), acid detergent lignin (ADL), undigestible NDF after 240 h of in vitro incubation (uNDF), calcium (Ca), and phosphorus (P)], digestibility [DM, OM, aNDFom, N], and intake [DM, OM, aNDFom, N, uNDF]. The calibrations for fecal OM, N, aNDFom, ADF, ADL, uNDF, Ca, P resulted in R²_cv between 0.86 and 0.97 and SECV of 1.88, 0.07, 1.70, 1.10, 0.61, 2.00, 0.18, and 0.06, respectively. Equations predicting intake of DM, OM, N, aNDFom, ADL, and uNDF resulted in R²_cv values between 0.59 and 0.91, SECV values of 1.12, 1.10, 0.02, 0.69, 0.06, 0.24 kg·d^-1, respectively, and SECV values between 0.00 and 0.16 when expressed as % body weight (BW). Digestibility calibrations for DM, OM, aNDFom, and N resulted in R²_cv ranging from 0.65 to 0.74 and SECV values from 2.20 to 2.82. We confirm the potential of NIRS to predict fecal chemical composition, digestibility, and intake of cattle fed high forage diets. Future steps include validation of the intake calibration equations for grazing cattle using forage internal marker and modelling energetics of grazing growth performance.

Phenotypic relationship and repeatability of methane emissions and performance traits in beef cattle using a GreenFeed system

Rumen methanogenesis results in the loss of 6% to 10% of gross energy intake in cattle and globally is the single most significant source of anthropogenic methane (CH4) emissions. The purpose of this study was to analyze greenhouse gas traits recorded in a commercial feedlot unit to gain an understanding into the relationships between greenhouse gas traits and production traits. Methane and carbon dioxide (CO2) data recorded via multiple GreenFeed Emission Monitoring (GEM), systems as well as feed intake, live weight, ultrasound scanning data, and slaughter data were available on 1,099 animals destined for beef production, of which 648 were steers, 361 were heifers, and 90 were bulls. Phenotypic relationships between GEM emission measurements with feed intake, weight traits, muscle ultrasound data, and carcass traits were estimated. Utilization of GEM systems, daily patterns of methane output, and repeatability of GEM system measurements across averaging periods were also assessed. Methane concentrations varied with visit number, duration, and time of day of visit to the GEM system. Mean CH4 and CO2 varied between sex, with mean CH4 of 256.1 g/day ± 64.23 for steers, 234.7 g/day ± 59.46 for heifers, and 156.9 g/day ± 55.98 for young bulls. A 10-d average period of GEM system measurements were required for steers and heifers to achieve a minimum repeatability of 0.60; however, higher levels of repeatability were observed in animals that attended the GEM system more frequently. In contrast, CO2 emissions reached repeatability estimates >0.6 for steers and heifers in all averaging periods greater than 2-d, suggesting that cattle have a moderately consistent CO2 emission pattern across time periods. Animals with heavier bodyweights were observed to have higher levels of CH4 (correlation = 0.30) and CO2 production (correlation = 0.61), and when assessing direct methane, higher levels of dry matter intake were associated with higher methane output (correlation = 0.31). Results suggest that reducing CH4 can have a negative impact on growth and body composition of cattle. Methane ratio traits, such as methane yield and intensity were also evaluated, and while easy to understand and compare across populations, ratio traits are undesirable in animal breeding, due to the unpredictable level of response. Methane adjusted for dry matter intake and liveweight (Residual CH4) should be considered as an alternative emission trait when selecting for reduced emissions within breeding goals.

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.

Board Invited Review: Enteric methane mitigation interventions

Mitigation of enteric methane (CH₄) presents a feasible approach to curbing agriculture’s contribution to climate change. One intervention for reduction is dietary reformulation, which manipulates the composition of feedstuffs in ruminant diets to redirect fermentation processes toward low CH₄ emissions. Examples include reducing the relative proportion of forages to concentrates, determining the rate of digestibility and passage rate from the rumen, and dietary lipid inclusion. Feed additives present another intervention for CH₄ abatement and are classified based on their mode of action. Through inhibition of key enzymes, 3-nitrooxypropanol (3-NOP) and halogenated compounds directly target the methanogenesis pathway. Rumen environment modifiers, including nitrates, essential oils, and tannins, act on the conditions that affect methanogens and remove the accessibility of fermentation products needed for CH₄ formation. Low CH₄-emitting animals can also be directly or indirectly selected through breeding interventions, and genome-wide association studies are expected to provide efficient selection decisions. Overall, dietary reformulation and feed additive inclusion provide immediate and reversible effects, while selective breeding produces lasting, cumulative CH₄ emission reductions.

Translational multi-omics microbiome research for strategies to improve cattle production and health

Cattle microbiome plays a vital role in cattle growth and performance and affects many economically important traits such as feed efficiency, milk/meat yield and quality, methane emission, immunity and health. To date, most cattle microbiome research has focused on metataxonomic and metagenomic characterization to reveal who are there and what they may do, preventing the determination of the active functional dynamics in vivo and their causal relationships with the traits. Therefore, there is an urgent need to combine other advanced omics approaches to improve microbiome analysis to determine their mode of actions and host-microbiome interactions in vivo. This review will critically discuss the current multi-omics microbiome research in beef and dairy cattle, aiming to provide insights on how the information generated can be applied to future strategies to improve production efficiency, health and welfare, and environment-friendliness in cattle production through microbiome manipulations.

Exploring Biological Impacts of Prenatal Nutrition and Selection for Residual Feed Intake on Beef Cattle Using Omics Technologies: A Review

Approximately 70% of the cost of beef production is impacted by dietary intake. Maximizing production efficiency of beef cattle requires not only genetic selection to maximize feed efficiency (i.e., residual feed intake (RFI)), but also adequate nutrition throughout all stages of growth and development to maximize efficiency of growth and reproductive capacity, even during gestation. RFI as a measure of feed efficiency in cattle has been recently accepted and used in the beef industry, but the effect of selection for RFI upon the dynamics of gestation has not been extensively studied, especially in the context of fluctuating energy supply to the dam and fetus. Nutrient restriction during gestation has been shown to negatively affect postnatal growth and development as well as fertility of beef cattle offspring. This, when combined with the genetic potential for RFI, may significantly affect energy partitioning in the offspring and subsequently important performance traits. In this review, we discuss: 1) the importance of RFI as a measure of feed efficiency and how it can affect other economic traits in beef cattle; 2) the influence of prenatal nutrition on physiological phenotypes in calves; 3) the benefits of investigating the interaction of genetic selection for RFI and prenatal nutrition; 4) how metabolomics, transcriptomics, and epigenomics have been employed to investigate the underlying biology associated with prenatal nutrition, RFI, or their interactions in beef cattle; and 5) how the integration of omics information is adding a level of deeper understanding of the genetic architecture of phenotypic traits in cattle.

Economic values of reproductive, growth, feed efficiency and carcass traits in Nellore cattle

A bioeconomic model was developed to calculate the economic value (ev) of reproductive and growth performance, feed efficiency and carcass traits of a seedstock Nellore herd. Data from a full-cycle cattle operation (1,436 dams) located in the Brazilian Cerrado were assessed. The ev was calculated by the difference in profit before and after one-unit improvement in the trait, with others remaining unchanged. The ev was standardized by the phenotypic standard deviation of each trait. Preweaning average daily gain (ADG) was the most economically important trait evaluated (R$ 58.04/animal/year), followed by age at first calving (R$ 44.35), postweaning ADG (R$ 31.43), weight at 450 days (R$ 25.36), accumulated productivity (R$ 21.43), ribeye area (R$ 21.35), calving interval (R$ 19.97), feed efficiency (R$ 15.24), carcass dressing per cent (R$ 8.27), weight at 120 days (R$ 6.22), weight at 365 days (R$ 6.06), weight at weaning (210 days, R$ 5.82), stayability (R$ 5.70) and the probability of early calving (R$ 0.32). The effects of all traits on profits are evidence that their selection may result in the economic and genetic progress of the herd if there is genetic variability.

Hens That Exhibit Poorer Feed Efficiency Produce Eggs with Lower Albumen Quality and Are Prone to Being Overweight

Simple Summary

The contemporary hybrid layer is an efficient producer of eggs, which is a source of high-quality nutrients. However, there is a lack of scientific knowledge on how feed efficiency (FE), an important measure of hen productivity, differs between individual hens during laying life and its association with other hen performance and egg quality traits. This study sought to investigate the production traits, egg composition and quality of laying hens in mid-lay when ranked based on FE in early-lay. The results showed that feed to egg conversion ratios (FCR) exhibited in early-lay were maintained until at least 40 weeks, with feed intake being the major driver of the difference in FE, not the mass of the egg. Further, hen and egg quality traits are associated during mid-lay, with high feed efficient hens having a lower body weight but producing eggs whose albumen has a higher height, Haugh unit and amino acid concentration. These results may provide important information to poultry breeders and egg producers towards improving the economics of egg production and generally improve management decision making, which is usually made based on accepting the expected average performance of a cohort of animals.

Abstract

Feed efficiency (FE) is an important measure of productivity in the layer industry; however, little is known about how FE differs between individual hens during the egg-laying cycle and the implications for egg quality parameters. Individual 25-week-old ISA Brown hens were observed for 42 days, ranked into three FE groups (n = 48 per High (HFE), Medium (MFE) and Low (LFE) FE groups and then monitored later in the laying cycle from 35–40 weeks. The groups exhibited different feed to egg conversion ratios (p < 0.001) from 35–40 weeks. Average daily feed intake and body weight were highest (p < 0.001) in the LFE group compared to the MFE and HFE groups, while albumen height, Haugh unit and amino acid concentrations of the albumen were significantly higher in the HFE groups compared to the LFE cohort (p < 0.001). This study concludes that FE status established in early lay is a stable variable until at least 40 weeks of age, and overweight, mid-laying hens that had poor FE produced inferior egg albumen quality measurements and composition. The distinct traits of the highly efficient hens and the poor feed efficient hens may provide important information to improving productivity in egg production.

Estimating methane coefficients to predict the environmental impact of traits in the Australian dairy breeding program

The dairy industry has been scrutinized for the environmental impact associated with rearing and maintaining cattle for dairy production. There are 3 possible opportunities to reduce emissions through genetic selection: (1) a direct methane trait, (2) a reduction in replacements, and (3) an increase in productivity. Our aim was to estimate the independent effects of traits in the Australian National Breeding Objective on the gross methane production and methane intensity (EI) of the Australian dairy herd of average genetic potential. Based on similar published research, the traits determined to have an effect on emissions include production, fertility, survival, health, and feed efficiency. The independent effect of each trait on the gross emissions produced per animal due to genetic improvement and change in EI due to genetic improvement (intensity value, IV) were estimated and compared. Based on an average Australian dairy herd, the gross emissions emitted per cow per year were 4,297.86 kg of carbon dioxide equivalents (CO₂-eq). The annual product output, expressed in protein equivalents (protein-eq), and EI per cow were 339.39 kg of protein-eq and 12.67 kg of CO₂-eq/kg of protein-eq, respectively. Of the traits included in the National Breeding Objective, genetic progress in survival and feed saved were consistently shown to result in a favorable environmental impact. Conversely, production traits had an unfavorable environmental impact when considering gross emissions, and favorable when considering EI. Fertility had minimal impact as its effects were primarily accounted for through survival. Mastitis resistance only affected IV coefficients and to a very limited extent. These coefficients may be used in selection indexes to apply emphasis on traits based on their environmental impact, as well as applied by governments and stakeholders to track trends in industry emissions. Although initiatives are underway to develop breeding values to reduce methane by combining small methane data sets internationally, alternative options to reduce emissions by utilizing selection indexes should be further explored.

Relationships among feed efficiency traits across production segments and production cycles in cattle

Understanding the relationships between feed efficiency traits measured in different stages of production is necessary to improve feed efficiency across the beef value chain. The objective of this study was to evaluate relationships among feed efficiency traits measured as growing heifers and breeding females and in their progeny in three full production cycles, and relationships of dam residual feed intake (RFI) with lifetime and lifecycle cow efficiency traits. Data were collected on 160 mixed-breed heifers from 240 d of age to weaning of their third progeny, and postweaning performance of progeny until harvest in experiments initiated in 1953, 1954, 1959, 1964, 1969, and 1974. Individual feed offered was recorded daily, and feed refusals measured every 28 d. Milk yield was measured at 14-d intervals throughout lactation by machine or hand milking. Females and progeny were weighed at 28-d intervals and progeny were harvested at a constant endpoint of live grade or age depending upon the experiment. Feed efficiency traits of RFI and residual BW gain (RG) were computed as the residual from linear regression for developing heifers, dams (RFI and residual energy-corrected milk [RECM]), and postweaning progeny. Feed:gain ratio (FCR) was computed for developing heifers and postweaning progeny, and feed:milk energy ratio (FME) was computed for dams. Various measures of cow efficiency were calculated on either a life cycle or lifetime basis using ratios of progeny and dam weight outputs to progeny and dam feed inputs. Pearson correlations were computed among traits adjusted for a random year-breed-diet group effect. Heifer RFI (0.74) and RG (-0.32) were correlated (P ≤ 0.05) with dam RFI in parity 1 only, but were not correlated (P > 0.05) with dam RECM in any parity. Heifer RFI was correlated (P ≤ 0.05) with progeny RFI (0.17) in parity 3 only. Heifer FCR was not correlated with dam FME or progeny FCR in any parity. Dam RFI was weakly correlated (r = 0.25 to 0.36; P ≤ 0.05) among parities, whereas dam FME and RECM were strongly correlated (r = 0.49 to 0.72; P ≤ 0.05) among parities. Dam RFI in parity 1 and 2 was weakly correlated (r = -0.20 to -0.33; P ≤ 0.05) with cow efficiency ratios that included dam weight as an output, whereas dam RFI in parity 3 was not correlated with any cow efficiency ratio. In conclusion, feed efficiency traits were poorly correlated across production segments, but moderately repeatable across production cycles.

Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows' Dry Matter Intake

Simple Summary

Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values.

Abstract

We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSE_CV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSE_CIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation.

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.

Comparative analyses of enteric methane emissions, dry matter intake, and milk somatic cell count in different residual feed intake categories of dairy cows

This study compared the different residual feed intake (RFI) categories of lactating Holsteins with respect to methane (CH4) emissions, dry matter intake (DMI, kg), milk somatic cell count (SCC, 103∙mL−1), and β-hydroxybutyrate (BHB, mmol∙L−1). The RFI was calculated in 131 lactating Holstein cows that were then categorized into −RFI (RFI < 0) vs. +RFI (RFI > 0) and low- [RFI < −0.5 standard deviation (SD)] vs. high-RFI (RFI > 0.5 SD) groups. Milk traits were recorded in 131 cows, whereas CH4 and carbon dioxide were measured in 83. Comparisons of −RFI vs. +RFI and low- vs. high-RFI showed 7.9% (22.3 ± 0.40 vs. 24.2 ± 0.39) and 12.8% (21.1 ± 0.40 vs. 24.2 ± 0.45) decrease (P < 0.05) in DMI of −RFI and low-RFI groups, respectively. Similarly, −RFI and low-RFI cows had lower (P < 0.05) CH4 (g∙d−1) by 9.7% (343.5 ± 11.1 vs. 380.4 ± 10.9) and 15.5% (332.5 ± 12.9 vs. 393.5 ± 12.6), respectively. Milk yield was not different (P > 0.05) in −RFI vs. +RFI and low vs. high comparisons. The −RFI and low-RFI cows had lower (P < 0.05) SCC in −RFI vs. +RFI and low-RFI vs. high-RFI comparisons. The BHB was lower (P < 0.05) in low-RFI compared with the high-RFI group. Low-RFI dairy cows consumed less feed, emitted less CH4 (g∙d−1), and had lower milk SCC and BHB without differing in milk yield.

Animal food products: policy, market and social issues and their influence on demand and supply of meat

The present paper aims to contribute to the contentious debate regarding the role of meat as part of a sustainable diet. It uses secondary data to examine the patterns of meat consumption across the globe, and drawing on academic and grey literature, it outlines some of the policy, market and social trends and issues influencing demand and supply of meat. It also presents an overview of the scientific evidence base regarding the pros and cons of meat consumption. The results show that consumption patterns are not homogeneous globally, nor across meat types, with overall meat consumption increasing strongly in developing countries but stagnating in developed countries, and demand for poultry increasing in most regions in contrast to beef. They also illustrate the evolving impact of factors such as income on consumption and the increasing impact of non-economic factors, such as social and policy influences relating to health and the environment, on food choice behaviours, to the extent that such behaviours are increasingly entering a moral space. Given the solid scientific evidence that simultaneously substantiates arguments to increase and decrease meat consumption, it is clear that dietary recommendations need to be context-specific. An important part of the context is the strategies being pursued by researchers and supply chain actors, from farmers through to processors, retailers and food service operators, to improve the sustainability credentials of livestock production. As new evidence emerges from such initiatives, the context will change which means that dietary guidelines will require continuous review.

Key traits for ruminant livestock across diverse production systems in the context of climate change: perspectives from a global platform of research farms

Ruminant livestock are raised under diverse cultural and environmental production systems around the globe. Ruminant livestock can play a critical role in food security by supplying high-quality, nutrient-dense food with little or no competition for arable land while simultaneously improving soil health through vital returns of organic matter. However, in the context of climate change and limited land resources, the role of ruminant-based systems is uncertain because of their reputed low efficiency of feed conversion (kilogram of feed required per kilogram of product) and the production of methane as a by-product of enteric fermentation. A growing human population will demand more animal protein, which will put greater pressure on the Earth's planetary boundaries and contribute further to climate change. Therefore, livestock production globally faces the dual challenges of mitigating emissions and adapting to a changing climate. This requires research-led animal and plant breeding and feeding strategies to optimise ruminant systems. This study collated information from a global network of research farms reflecting a variety of ruminant production systems in diverse regions of the globe. Using this information, key changes in the genetic and nutritional approaches relevant to each system were drawn that, if implemented, would help shape more sustainable future ruminant livestock systems.

Impact of protein on the composition and metabolism of the human gut microbiota and health

The composition and metabolic activity of the bacteria that inhabit the large intestine can have a major impact on health. Despite considerable inter-individual variation across bacterial species, the dominant phyla are generally highly conserved. There are several exogenous and gut environmental factors that play a role in modulating the composition and activities of colonic bacteria including diet with intakes of different macronutrients, including protein, accounting for approximately 20% of the microbial variation. Certain bacterial species tend to be considered as generalists and can metabolise a broad range of substrates, including both carbohydrate- and protein-derived substrates, whilst other species are specialists with a rather limited metabolic capacity. Metabolism of peptides and amino acids by gut bacteria can result in the formation of a wide range of metabolites several of which are considered deleterious to health including nitrosamines, heterocyclic amines and hydrogen sulphide as some of these products are genotoxic and have been linked to colonic disease. Beneficial metabolites however include SCFA and certain species can use amino acids to form butyrate which is the major energy source for colonocytes. The impact on health may however depend on the source of these products. In this review, we consider the impact of diet, particularly protein diets, on modulating the composition of the gut microbiota and likely health consequences and the potential impact of climate change and food security.

Radiated temperature from thermal imaging is related to feed consumption, growth rate and feed efficiency in grower pigs

Individual feed consumption and animal weight were continuously recorded in grower pigs using an automated feeding system. Infrared images were recorded each time a pig entered the feeding system and infrared thermography provided radiated thermal measurements of the dorsal surface of each animal. Feed was withdrawn and the animals fasted for a period of 24 h three times during the growth of the animals at body weights of approximately 35, 65 and 105 kg. There was a significant reduction of 0.28 °C in the maximum surface temperature (Tmax), and 0.48 °C in the average surface temperature (Tmean) during the periods of fasting. Maximum and average pig temperatures exhibited negative correlations to feed consumption and growth variables. There were negative correlations of residual feed intake (RFI) to Tmax and Tmean radiated temperatures. There were positive correlations of residual gain (RG) and residual intake and gain (RIG) with Tmax and Tmean. The Tmax and Tmean temperature responses to fasting were negatively associated with feed consumption and growth variables. Absolute temperature and temperature response variables were positively associated with RFI and negatively associated with residual intake and gain (RIG). These findings provide support for the concept of radiated heat losses as a measure of metabolic activity and a predictor of growth performance.

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.

Serum Metabolite Biomarkers for Predicting Residual Feed Intake (RFI) of Young Angus Bulls

Residual feed intake (RFI) is a feed efficiency measure commonly used in the livestock industry to identify animals that efficiently/inefficiently convert feed into meat or body mass. Selection for low-residual feed intake (LRFI), or feed efficient animals, is gaining popularity among beef producers due to the fact that LRFI cattle eat less and produce less methane per unit weight gain. RFI is a difficult and time-consuming measure to perform, and therefore a simple blood test that could distinguish high-RFI (HRFI) from LRFI animals (early on) would potentially benefit beef farmers in terms of optimizing production or selecting which animals to cull or breed. Using three different metabolomics platforms (nuclear magnetic resonance (NMR) spectrometry, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and inductively coupled plasma mass spectrometry (ICP-MS)) we successfully identified serum biomarkers for RFI that could potentially be translated to an RFI blood test. One set of predictive RFI biomarkers included formate and leucine (best for NMR), and another set included C4 (butyrylcarnitine) and LysoPC(28:0) (best for LC-MS/MS). These serum biomarkers have high sensitivity and specificity (AUROC > 0.85), for distinguishing HRFI from LRFI animals. These results suggest that serum metabolites could be used to inexpensively predict and categorize bovine RFI values. Further validation using a larger, more diverse cohort of cattle is required to confirm these findings.

Association between residual feed intake, digestion, ingestive behavior, enteric methane emission and nitrogen metabolism in Nellore beef cattle

This study aimed to evaluate nutrient intake and digestibility, enteric methane emission and nitrogen utilization efficiency in Nellore cattle ranked by residual feed intake (RFI). Twenty-four Nellore bulls at 466 ± 24 days of age and with 352 ± 14.6 kg of body weight, classified as low and high RFI, were evaluated. Animals were kept in individual pens for three periods of 28 days and variables were measured. Data were analyzed as repeated measures over time, considering as fixed effects RFI class, period and RFI class x period interaction, and linear (co)variate of age. No significant differences in dry matter or nutrient intake were detected between RFI classes, but total digestible nutrients intake tended to be lower in low RFI animals, and apparent nutrient digestibility was higher in high RFI animals. Partial efficiency of growth tended to be lower in high RFI animals. RFI class did not interfere with enteric methane production or microbial protein synthesis, but fecal nitrogen output was higher in low RFI animals. The greater efficiency of low RFI animals is consequence of lower maintenance requirements, since energy from higher nutrients digestibility in high RFI animals was spent on metabolic processes other than body tissue deposition.

Genetic Parameters of Different FTIR-Enabled Phenotyping Tools Derived from Milk Fatty Acid Profile for Reducing Enteric Methane Emissions in Dairy Cattle

This study aimed to infer the genetic parameters of five enteric methane emissions (EME) predicted from milk infrared spectra (13 models). The reference values were estimated from milk fatty acid profiles (chromatography), individual model-cheese, and daily milk yield of 1158 Brown Swiss cows (85 farms). Genetic parameters were estimated, under a Bayesian framework, for EME reference traits and their infrared predictions. Heritability of predicted EME traits were similar to EME reference values for methane yield (CH₄/DM: 0.232-0.317) and methane intensity per kg of corrected milk (CH₄/CM: 0.177-0.279), smaller per kg cheese solids (CH₄/SO: 0.093-0.165), but greater per kg fresh cheese (CH₄/CU: 0.203-0.267) and for methane production (dCH₄: 0.195-0.232). We found good additive genetic correlations between infrared-predicted methane intensities and the reference values (0.73 to 0.93), less favorable values for CH₄/DM (0.45-0.60), and very variable for dCH₄ according to the prediction method (0.22 to 0.98). Easy-to-measure milk infrared-predicted EME traits, particularly CH₄/CM, CH₄/CU and dCH₄, could be considered in breeding programs aimed at the improvement of milk ecological footprint.

Dietary mitigation of enteric methane emissions from ruminants: A review of plant tannin mitigation options

Methane gas from livestock production activities is a significant source of greenhouse gas (GHG) emissions which have been shown to influence climate change. New technologies offer a potential to manipulate the rumen biome through genetic selection reducing CH4 production. Methane production may also be mitigated to varying degrees by various dietary intervention strategies. Strategies to reduce GHG emissions need to be developed which increase ruminant production efficiency whereas reducing production of CH4 from cattle, sheep, and goats. Methane emissions may be efficiently mitigated by manipulation of natural ruminal microbiota with various dietary interventions and animal production efficiency improved. Although some CH4 abatement strategies have shown efficacy in vivo, more research is required to make any of these approaches pertinent to modern animal production systems. The objective of this review is to explain how anti-methanogenic compounds (e.g., plant tannins) affect ruminal microbiota, reduce CH4 emission, and the effects on host responses. Thus, this review provides information relevant to understanding the impact of tannins on methanogenesis, which may provide a cost-effective means to reduce enteric CH4 production and the influence of ruminant animals on global GHG emissions.

Inclusive inheritance for residual feed intake in pigs and rabbits

Non‐genetic information (epigenetic, microbiota, behaviour) that results in different phenotypes in animals can be transmitted from one generation to the next and thus is potentially involved in the inheritance of traits. However, in livestock species, animals are selected based on genetic inheritance only. The objective of the present study was to determine whether non‐genetic inherited effects play a role in the inheritance of residual feed intake (RFI) in two species: pigs and rabbits. If so, the path coefficients of the information transmitted from sire and dam to offspring would differ from the expected transmission factor of 0.5 that occurs if inherited information is of genetic origin only. Two pigs (pig1, pig2) and two rabbits (rabbit1, rabbit2) datasets were used in this study (1,603, 3,901, 5,213 and 4,584 records, respectively). The test of the path coefficients to 0.5 was performed for each dataset using likelihood ratio tests (null model: transmissibility model with both path coefficients equal to 0.5, full model: unconstrained transmissibility model). The path coefficients differed significantly from 0.5 for one of the pig datasets (pig2). Although not significant, we observed, as a general trend, that sire path coefficients of transmission were lower than dam path coefficients in three of the datasets (0.46 vs 0.53 for pig1, 0.39 vs 0.44 for pig2 and 0.38 vs 0.50 for rabbit1). These results suggest that phenomena other than genetic sources of inheritance explain the phenotypic resemblance between relatives for RFI, with a higher transmission from the dam's side than from the sire's side.

The Role of the Gut Microbiome in Cattle Production and Health: Driver or Passenger?

Ruminant production systems face significant challenges currently, driven by heightened awareness of their negative environmental impact and the rapidly rising global population. Recent findings have underscored how the composition and function of the rumen microbiome are associated with economically valuable traits, including feed efficiency and methane emission. Although omics-based technological advances in the last decade have revolutionized our understanding of host-associated microbial communities, there remains incongruence over the correct approach for analysis of large omic data sets. A global approach that examines host/microbiome interactions in both the rumen and the lower digestive tract is required to harness the full potential of the gastrointestinal microbiome for sustainable ruminant production. This review highlights how the ruminant animal production community may identify and exploit the causal relationships between the gut microbiome and host traits of interest for a practical application of omic data to animal health and production.