Use of methane production data for genetic prediction in beef cattle: A review.

Methane (CH4) is a greenhouse gas that is produced and emitted from ruminant animals through enteric fermentation. Methane production from cattle has an environmental impact and is an energetic inefficiency. In the beef industry, CH4 production from enteric fermentation impacts all three pillars of sustainability: environmental, social, and economic. A variety of factors influence the quantity of CH4 produced during enteric fermentation, including characteristics of the rumen and feed composition. There are several methodologies available to either quantify or estimate CH4 production from cattle, all with distinct advantages and disadvantages. Methodologies include respiration calorimetry, the sulfur-hexafluoride tracer technique, infrared spectroscopy, prediction models, and the GreenFeed system. Published studies assess the accuracy of the various methodologies and compare estimates from different methods. There are advantages and disadvantages of each technology as they relate to the use of these phenotypes in genetic evaluation systems. Heritability and variance components of CH4 production have been estimated using the different CH4 quantification methods. Agreement in both the amounts of CH4 emitted and heritability estimates of CH4 emissions between various measurement methodologies varies in the literature. Using greenhouse gas traits in selection indices along with relevant output traits could provide producers with a tool to make selection decisions on environmental sustainability while also considering productivity. The objective of this review was to discuss factors that influence CH4 production, methods to quantify CH4 production for genetic evaluation, and genetic parameters of CH4 production in beef cattle.

Heritability and variance component estimation for feed and water intake behaviors of feedlot cattle.

Feed and water intake are two important aspects of cattle production that greatly impact the profitability, efficiency, and sustainability of producers. Feed and, to a lesser degree, water intake have been studied previously; however, there is little research on their associated animal behaviors and there is a lack of standardized phenotypes for these behaviors. Feed and water intakes obtained with an Insentec system (Hokofarm Group, The Netherlands) from 830 crossbred steers were used to compute five intake behaviors for both feed and water: daily sessions (DS), intake rate (IR), session size (SS), time per session (TS), and session interval (SI). Variance components and heritabilities were estimated for each trait. Heritabilities for feed intake behaviors were 0.50 ±â€…0.12, 0.63 ±â€…0.12, 0.40 ±â€…0.13, 0.35 ±â€…0.12, and 0.60 ±â€…0.12 for DS, IR, SS, TS, and SI, respectively. Heritabilities for water intake behaviors were 0.56 ±â€…0.11, 0.88 ±â€…0.07, 0.70 ±â€…0.11, 0.54 ±â€…0.12, and 0.80 ±â€…0.10 for NS, IR, SS, TS, and SI, respectively. Daily dry matter intake (DDMI) and daily water intake (DWI) had heritabilities of 0.57 ±â€…0.11 and 0.44 ±â€…0.11. Phenotypic correlations varied between pairs of traits (-0.83 to 0.82). Genetic correlations between DDMI and feed intake behaviors were moderate to high, while genetic correlations between DWI and water intake behaviors were low to moderate. Several significant single nucleotide polymorphisms (SNP) were identified for the feed and water intake behaviors. Genes and previously reported quantitative trait loci near significant SNPs were evaluated. The results indicated that feed and water intake behaviors are influenced by genetic factors and are heritable, providing one additional route to evaluate or manipulate feed and water intake.

Feed and water intake are important aspects of cattle production to understand because they impact producer profitability and sustainability. While feed intake and, to a lesser degree, water intake have previously been studied, the associated feeding and drinking behaviors are relatively unknown and lack standardized phenotypes. Using individual animal feed and water intake records, five behaviors were evaluated for feed and water intake from crossbred feedlot steers. The behaviors evaluated were daily sessions (no./d), session size (kg), time per session (s), intake rate (g/s), and session interval (min). The impact of season (winter vs. summer) and bunk management (ad libitum vs. slick) on feeding and drinking behaviors was evaluated. Heritability and variance components were estimated for all feeding and drinking behaviors. Pairwise phenotypic correlations between behaviors were discussed. The relationship between intake and feeding or drinking behaviors was evaluated with genetic correlations. A genome-wide association study identified several significant single nucleotide polymorphisms for feeding and drinking behaviors. The results indicate that feeding and drinking behaviors are heritable and may be one additional route to evaluate feed and water intake.

Variance component estimation and genome-wide association of predicted methane production in crossbred beef steers.

Enteric methane is a potent greenhouse gas and represents an escape of energy from the ruminant digestive system. Additive genetic variation in methane production suggests that genetic selection offers an opportunity to diminish enteric methane emissions. Logistic and monetary difficulties in directly measuring methane emissions can make genetic evaluation on an indicator trait such as predicted methane production a more appealing option, and inclusion of genotyping data can result in greater genetic progress. Three predicted methane production traits were calculated for 830 crossbred steers fed in seven groups. The methane prediction equations used included mathematical models from Ellis et al. (2007), Mills et al. (2003), and IPCC (2019). Pearson correlations between the traits were all greater than 0.99, indicating that each prediction equation behaved similarly. Further, the Spearman correlations between the estimated breeding values for each trait were also 0.99, which suggests any of the predicted methane models could be used without substantially changing the ranking of the selection candidates. The heritabilities of Ellis, Mills, and IPCC predicted methane production were 0.60, 0.62, and 0.59, respectively. A genome-wide association study identified one single nucleotide polymorphism (SNP) that reached the threshold for significance for all of the traits on chromosome 7 related to oxidoreductase activity. Additionally, the SNP slightly below the significance threshold indicate genes related to collagen, intracellular microtubules, and DNA transcription may play a role in predicted methane production or its component traits.

Cattle produce methane, a greenhouse gas, as a byproduct of their digestion. It is possible to breed for animals which naturally produce less methane; however, measuring animals for methane production can be difficult or expensive and is required for effective selection. Therefore, an alternative solution is to use a mathematical model to predict methane production and select for animals with low predicted methane. The heritability of predicted methane production from each model ranged from 0.59 to 0.62. Animals were ranked nearly identical, regardless of model used. A genome-wide association study was also conducted to determine what loci may be related to predicted methane production. One significant locus was identified on chromosome 7 related to oxidoreductase activity. Other loci approaching significance showed that genes related to collagen production, intracellular microtubule binding, and DNA transcription may be related to predicted methane production. In particular, collagen turnover may have a relationship to predicted methane because it affects growth rate, which is driven by dry matter intake, which, in turn, is the primary driver of predicted methane production.

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed.

Enteric fermentation from cattle results in greenhouse gas production that is an environmental concern and also an energetic loss. Several methods exist to quantify gas fluxes; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from grazing cattle. While previous literature has proven the accuracy of an OCGQS, little work has been done to establish the minimum number of spot samples required to best evaluate an individual grazing animal's gas fluxes and metabolic heat production. A GreenFeed system (C-Lock Inc.) was used to collect at least 100 spot samples each from 17 grazing cows. The mean gas fluxes and metabolic heat production were computed starting from the first 10 visits (forward) and increasing by increments of 10 until an animal had 100 visits. Mean gas fluxes and metabolic heat production were also computed starting from visit 100 (reverse) in increments of 10 using the same approach. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. A large increase in correlations were seen between 30 and 40 visits. Thus, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The results indicated that the minimum numbers of spot samples needed for accurate quantification of CH4, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Metabolic heat production can be calculated using gas fluxes collected by the OCGQS with 36 spot samples. Practically, calculation of metabolic heat production will require 40 spot samples because the component gases for metabolic heat calculation require up to 40 spot samples. Published literature from nongrazing (confined) environments recommended a similar number of total spot samples. Large variation existed around the average number of spot samples for an animal per day, therefore a wide range of test durations may be needed to meet the same number of spot samples in different populations. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.

Enteric fermentation in ruminant livestock species produces methane (CH4), which has negative effects on the environment and producer profitability. Gas fluxes from livestock species can be quantified with an open-circuit gas quantification system (OCGQS) or GreenFeed (C-Lock Inc., Rapid City, SD). However, little work has been done to establish a standardized protocol for OCGQS use in grazing beef cows. The minimum number of spot samples for quantification of CH4, carbon dioxide (CO2), oxygen (O2), and metabolic heat production was determined for grazing beef cows. The minimum number of spot samples needed for accurate quantification of CH4 was 38. Forty spot samples were needed to quantify CO2 and O2. Metabolic heat production can be calculated using CH4, CO2, and O2 gas fluxes from the OCGQS with 36 spot samples. Approximately 30 d were needed for animals in the current study to obtain the recommended number of visits for gas quantification, but this could vary across studies depending on the frequency of animals visiting the unit. There was large variability in the duration needed to obtain the recommended number of spot samples. Therefore, OCGQS protocols should include a minimum number of spot samples rather than a test duration.

Genetic parameter estimates for feet and leg traits in Red Angus cattle.

The objective of this study was to estimate genetic parameters for feet and leg traits, relationships within feet and leg traits, and relationships between feet and leg traits and production traits in Red Angus cattle. Subjective scores for 14 traits including body condition score (BCS), front hoof angle (FHA), front heel depth (FHD), front claw shape (FCS), rear hoof angle (RHA), rear heel depth (RHD), rear claw shape (RCS), size of hoof (SIZE), front side view (FSV), knee orientation (KNEE), front hoof orientation (FHO), rear side view (RSV), rear view (RV), and a composite score (COMP) were collected by trained evaluators on 1,720 Red Angus cattle. All traits except COMP were scored as intermediate optimum traits. Performance data, and EPD were obtained on all animals measured and a three-generation pedigree was obtained from the Red Angus Association of America (RAAA) that contained 13,306 animals. Data were modeled using a linear bivariate animal model with random additive genetic and residual effects, and fixed effects of age and contemporary group (herd-year) implemented in ASREML 4.0. Heritability estimates of BCS, FHA, FHD, FCS, RHA, RHD, RCS, SIZE, FSV, KNEE, FHO, RSV, RV, and COMP were 0.11, 0.20, 0.17, 0.09, 0.19, 0.25, 0.17, 0.36, 0.16, 0.17, 0.17, 0.30, 0.14, and 0.12, respectively. These results demonstrate feet and leg traits are lowly to moderately heritable. Strong, positive genetic correlations were observed between FHA and FHD (0.89), FHA and RHA (0.88), FHD and RHA (0.85), FHA and RHD (0.85), FHD and RHD (0.94), and FHO and KNEE (0.95), indicating these traits were highly related to each other. Strong and negative genetic correlations were observed between KNEE and FSV (-0.59) and FHO and FSV (-0.75). The strongest Pearson correlation between front limb traits (FHA, FHD, FSV, FHO, KNEE, and COMP) and Stayability EPD (STAY) was FSV (r = 0.16) and for rear limb traits (RHA, RHD, RCS, RSV, RV, and COMP) and STAY was RCS (r = -0.12). This suggests cattle with more slope to the angle of the shoulder and cattle with less evidence of hoof curl may stay in the herd longer. Further investigation into the economic viability of feet and leg trait genetic prediction with a larger population of animals is required to help validate these findings.

Genetic parameter estimation for beef bull semen attributes.

Improvements in bull reproductive performance are necessary to optimize the efficiency of cattle production. Female fertility has been enhanced through assisted reproductive technologies as well as genetic selection; however, improving beef bull fertility has been largely ignored. Phenotypes routinely collected at bull semen collection facilities are believed to affect fertility and provide the phenotypes necessary for a genetic evaluation. The first objective of this study was to determine the significant fixed effects for modeling beef bull fertility using data from bull semen collection facilities. The second objective was to estimate variance components, heritabilities, repeatabilities, and correlations between beef bull semen attributes. Beef bull fertility phenotypes including volume (VOL), concentration (CONC), number of spermatozoa (NSP), initial motility (IMot), post-thaw motility (PTMot), 3-h post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SEC) were obtained from two bull semen collection facilities. A total of 1,819 Angus bulls with 50,624 collection records were analyzed. Of the fixed class and covariate effects tested, the significant class effects were collection location and collection day within year and the significant covariate effects included age at collection, days since previous collection, and cumulative comprehensive climate index (CCI). For this study, the CCI was calculated for a 75-d period including the 61-d spermatogenesis cycle and 14-d epididymal transit time. The 75 d prior to collection accounted for the environmental stress a bull may have experienced over the course of development of the spermatozoa, which was more significant than the CCI calculated for collection day or spermatogenesis start date. Pre-thaw beef bull semen traits had low heritability estimates of 0.11 ± 0.02 (VOL), 0.09 ± 0.02 (CONC), 0.08 ± 0.02 (NSP), and 0.12 ± 0.03 (IMot). Heritabilities of post-thaw beef bull semen attributes were more variable at 0.10 ± 0.02 (PTMot), 0.05 ± 0.04 (3HRPTMot), 0.10 ± 0.04 (%NORM), 0.03 ± 0.03 (PRIM), and 0.18 ± 0.04 (SEC). Correlations of breeding values for these traits with scrotal circumference (SC) expected progeny difference (EPD) are low. The low to moderate heritability estimates indicate that genetic improvement can be made in beef bull semen quality traits if new tools are developed to augment the scrotal circumference EPD that are currently available within the industry.

Selection for bull fertility: a review.

Fertility is a critically important factor in cattle production because it directly relates to the ability to produce the offspring necessary to offset costs in production systems. Female fertility has received much attention and has been enhanced through assisted reproductive technologies, as well as genetic selection; however, improving bull fertility has been largely ignored. Improvements in bull reproductive performance are necessary to optimize the efficiency of cattle production. Selection and management to improve bull fertility not only have the potential to increase conception rates but also have the capacity to improve other economically relevant production traits. Bull fertility has reportedly been genetically correlated with traits such as average daily gain, heifer pregnancy, and calving interval. Published studies show that bull fertility traits are low to moderately heritable, indicating that improvements in bull fertility can be realized through selection. Although female fertility has continued to progress according to increasing conception rates, the reported correlation between male and female fertility is low, indicating that male fertility cannot be improved by selection for female fertility. Correlations between several bull fertility traits, such as concentration, number of spermatozoa, motility, and number of spermatozoa abnormalities, vary among studies. Using male fertility traits in selection indices would provide producers with more advanced selection tools. The objective of this review was to discuss current beef bull fertility measurements and to discuss the future of genetic evaluation of beef bull fertility and potential genetic improvement strategies.

Environmental effects on water intake and water intake prediction in growing beef cattle.

Water is an essential nutrient, but there are few recent studies that evaluate how much water individual beef cattle consume and how environmental factors affect an individual's water intake (WI). Most studies have focused on WI of whole pens rather than WI of individual animals. Thus, the objective of this study was to evaluate the impact of environmental parameters on individual-animal WI across different seasons and develop prediction equations to estimate WI, including within different environments and management protocols. Individual daily feed intake and WI records were collected on 579 crossbred steers for a 70-d period following a 21-d acclimation period for feed and water bunk training. Steers were fed in 5 separate groups over a 3-yr period from May 2014 to March 2017. Individual weights were collected every 14 d and weather data were retrieved from the Oklahoma Mesonet's Stillwater station. Differences in WI as a percent of body weight (WI%) were analyzed accounting for average temperature (TAVG), relative humidity (HAVG), solar radiation (SRAD), and wind speed (WSPD). Seasonal (summer vs. winter) and management differences (ad libitum vs. slick bunk) were examined. Regression analysis was utilized to generate 5 WI prediction equations (overall, summer, winter, slick, and ad libitum). There were significant (P < 0.05) differences in WI between all groups when no environmental parameters were included in the model. Although performance was more similar after accounting for all differences in weather variables, significant (P < 0.05) seasonal and feed management differences were still observed for WI%, but were less than 0.75% of steer body weight. The best linear predictors of daily WI (DWI) were dry mater intake (DMI), metabolic body weights (MWTS), TAVG, SRAD, HAVG, and WSPD. Slight differences in the coefficient of determinations for the various models were observed for the summer (0.34), winter (0.39), ad libitum (0.385), slick bunk (0.41), and overall models (0.40). Based on the moderate R2 values for the WI prediction equations, individual DWI can be predicted with reasonable accuracy based on the environmental conditions that are present, MWTS, and DMI consumed, but substantial variation exists in individual animal WI that is not accounted for by these models.

Test duration for water intake, ADG, and DMI in beef cattle.

Water is an essential nutrient, but the effect it has on performance generally receives little attention. There are few systems and guidelines for collection of water intake (WI) phenotypes in beef cattle, which makes large-scale research on WI a challenge. The Beef Improvement Federation has established guidelines for feed intake (FI) and ADG tests, but no guidelines exist for WI. The goal of this study was to determine the test duration necessary for collection of accurate WI phenotypes. To facilitate this goal, individual daily WI and FI records were collected on 578 crossbred steers for a total of 70 d using an Insentec system at the Oklahoma State University Willard Sparks Beef Research Unit. Steers were fed in five groups and were individually weighed every 14 d. Within each group, steers were blocked by BW (low and high) and randomly assigned to one of four pens containing approximately 30 steers per pen. Each pen provided 103.0 m2 of shade and included an Insentec system containing six feed bunks and one water bunk. Steers were fed a constant diet across groups and DMI was calculated using the average of weekly percent DM within group. Average FI and WI for each animal were computed for increasingly large test durations (7, 14, 21, 28, 35, 42, 49, 56, 63, and 70 d), and ADG was calculated using a regression formed from BW taken every 14 d (0, 14, 28, 42, 56, and 70 d). Intervals for all traits were computed starting from both the beginning (day 0) and the end of the testing period (day 70). Pearson and Spearman correlations were computed for phenotypes from each shortened test period and for the full 70-d test. Minimum test duration was determined when the Pearson correlations were greater than 0.95 for each trait. Our results indicated that minimum test duration for WI, DMI, and ADG were 35, 42, and 70 d, respectively. No comparable studies exist for WI; however, our results for FI and ADG are consistent with those in the literature. Although further testing in other populations of cattle and areas of the country should take place, our results suggest that WI phenotypes can be collected concurrently with DMI, without extending test duration, even if following procedures for decoupled intake and gain tests.

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

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

Decision-making tools: stochastic simulation model accounting for the impacts of biological variation on success of bovine embryo transfer programs.

The objective of the project was to create an economic risk analysis tool for user-defined embryo transfer (ET) programs as an aid in decision-making. Distributions defining the biological uncertainty for many reproductive outcomes are estimated through extensive literature review and limited industry sources. Applying the Latin hypercube variation of Monte Carlo simulation, a sample value from the descriptive distribution associated with each stochastic variable is included in each iteration of the simulation. Through large numbers of iterations with dynamic combinations of variable values, the process culminates in a distribution of possible values for the net present value, annuity equivalent net present value, and return on investment associated with the modeled embryo production scenario. Two options for embryo production, multiple ovulation embryo transfer (MOET) and in vitro embryo production (IVP) from aspirated oocytes, are modeled. Within both MOET and IVP, the use of unsorted or sex-sorted semen is considered, as well as the exception or inclusion of follicular synchronization and/or stimulation before ovum pick-up in IVP procedures. Pretransfer embryo selection through embryo biopsy can also be accounted for when considering in vivo derived embryos. Ample opportunity exists for the commercial application of in-depth, alternative ET scenario assessment afforded through stochastic simulation methodology that the ET industry has not yet fully exploited.