Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows

OBJECTIVE

This study aimed to evaluate whether the methane (CH4) to carbon dioxide (CO2) ratio (CH4/CO2) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH4 emissions.

METHODS

The sniffer method was used to simultaneously measure the concentrations of CH4 and CO2 during milking in each milking box of the automatic milking system to obtain CH4/CO2. Methane-related traits, which included CH4 emissions, CH4 per energy-corrected milk, methane conversion factor (MCF), and residual CH4, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH4/CO2 and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets.

RESULTS

The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH4/CO2 (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH4/CO2 was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH4/CO2 and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms.

CONCLUSION

This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH4 emissions.

Estimates of the genetic contribution to methane emission in dairy cows: a meta-analysis

The present study aimed to perform a meta-analysis using the three-level model to integrate published estimates of genetic parameters for methane emission traits [methane yield (METY), methane intensity (METINT), and methane production (METP)] in dairy cows. Overall, 40 heritability estimates and 32 genetic correlations from 17 papers published between 2015 and 2021 were used in this study. The heritability estimates for METY, METINT, and METP were 0.244, 0.180, and 0.211, respectively. The genetic correlation estimates between METY and METINT with corrected milk yield for fat, protein, and or energy (CMY) were negative (- 0.433 and - 0.262, respectively). Also, genetic correlation estimates between METINT with milk fat and protein percentages were 0.254 and 0.334, respectively. Although the genetic correlation estimate of METP with daily milk yield was 0.172, its genetic correlation with CMY was 0.446. All genetic correlation estimates between METP with milk fat and protein yield or percentage ranged from 0.005 (between METP-milk protein yield) to 0.185 (between METP-milk protein percentage). The current meta-analysis confirmed the presence of additive genetic variation for methane emission traits in dairy cows that could be exploited in genetic selection plans.

Genetic and genomic analyses for predicted methane-related traits in Japanese Black steers

The objectives of this study were to estimate genetic parameters and to perform a genome‐wide association study (GWAS) for predicted methane‐related traits in Japanese Black steers. The methane production and yield traits were predicted using on‐farm measurable traits, such as dry matter intake and average daily gain. A total of 4,578 Japanese Black steers, which were progenies of 362 sires genotyped with imputed 551,995 single nucleotide polymorphisms (SNPs), had phenotypes of predicted methane‐related traits during the total fattening period (52 weeks). For the estimation of genetic parameters, the estimated heritabilities were moderate (ranged from 0.57 to 0.60). In addition, the estimated genetic correlations of methane production traits with most of carcass traits and feed‐efficiency traits were unfavorable, but those of methane yield traits were favorable or low. For the GWAS, no genome‐wide significant SNP was detected, but a total of four quantitative trait locus (QTL) regions that explained more than 5.0% of genetic variance were localized on the genome, and some candidate genes associated with growth and feed‐efficiency traits were located on the regions. Our results suggest that the predicted methane‐related traits are heritable and some QTL regions for the traits are localized on the genome in Japanese Black steers.

Assessment of methane emission traits in ewes using a laser methane detector: genetic parameters and impact on lamb weaning performance

The aim of the present study was to derive individual methane (CH4) emissions in ewes separated in CH4 respiration and eructation traits. The generated longitudinal CH4 data structure was used to estimate phenotypic and genetic relationships between ewe CH4 records and energy efficiency indicator traits from same ewes as well as from their lambs (intergenerational perspective). In this regard, we recorded CH4 emissions via mobile laser methane detector (LMD) technique, body weight (EBW), backfat thickness (BFT) and body condition score (BCS) from 330 ewes (253 Merinoland (ML), 77 Rhön sheep (RH)) and their 629 lambs (478 ML, 151 RH). The interval between repeated measurements (for ewe traits and lamb body weight (LBW)) was 3 weeks during lactation. For methane concentration (µL L-1) determinations in the exhaled air, we considered short time measurements (3 min). Afterwards, CH4 emissions were portioned into a respiration and eructation fraction, based on a double normal distribution. Data preparation enabled the following CH4 trait definitions: mean CH4 concentration during respiration and eructation (CH4r+e), mean CH4 concentration during respiration (CH4r), mean CH4 concentration during eructation (CH4e), sum of CH4 concentrations per minute during respiration (CH4rsum), sum of CH4 concentrations per minute during eructation (CH4esum), maximal CH4 concentration during respiration (CH4rmax), maximal CH4 concentration during eructation (CH4emax), and eructation events per minute (CH4event). Large levels of ewe CH4 emissions representing energy losses were significantly associated with lower LBW (P<0.05), lower EBW (P<0.01) and lower BFT (P<0.05). For genetic parameter estimations, we applied single- and multiple-trait animal models. Heritabilities and additive genetic variances for CH4 traits were small, i.e., heritabilities in the range from <0.01 (CH4r+e, CH4r, CH4rmax, CH4esum) to 0.03 (CH4rsum). We estimated negative genetic correlations between CH4 traits and EBW in the range from -0.44 (CH4r+e) to -0.05 (CH4rsum). Most of the CH4 traits were genetically negatively correlated with BCS (-0.81 for CH4esum) and with BFT (-0.72 for CH4emax), indicating same genetic mechanisms for CH4 output and energy efficiency indicators. Addressing the intergenerational aspect, genetic correlations between CH4 emissions from ewes and LBW ranged between -0.35 (CH4r+e) and 0.01 (CH4rsum, CH4rmax), indicating that breeding on reduced CH4 emissions (especially eructation traits) contribute to genetic improvements in lamb weaning performance.

Genome-wide association identifies methane production level relation to genetic control of digestive tract development in dairy cows

The global temperatures are increasing. This increase is partly due to methane (CH4) production from ruminants, including dairy cattle. Recent studies on dairy cattle have revealed the existence of a heritable variation in CH4 production that enables mitigation strategies based on selective breeding. We have exploited the available heritable variation to study the genetic architecture of CH4 production and detected genomic regions affecting CH4 production. Although the detected regions explained only a small proportion of the heritable variance, we showed that potential QTL regions affecting CH4 production were located within QTLs related to feed efficiency, milk-related traits, body size and health status. Five candidate genes were found: CYP51A1 on BTA 4, PPP1R16B on BTA 13, and NTHL1, TSC2, and PKD1 on BTA 25. These candidate genes were involved in a number of metabolic processes that are possibly related to CH4 production. One of the most promising candidate genes (PKD1) was related to the development of the digestive tract. The results indicate that CH4 production is a highly polygenic trait.

Heritability of methane emissions from dairy cows over a lactation measured on commercial farms

Methane emission is currently an important trait in studies on ruminants due to its environmental and economic impact. Recent studies were based on short-time measurements on individual cows. As methane emission is a longitudinal trait, it is important to investigate its changes over a full lactation. In this study, we aimed to estimate the heritability of the estimated methane emissions from dairy cows using Fourier-transform infrared spectroscopy during milking in an automated milking system by implementing the random regression method. The methane measurements were taken on 485 Polish Holstein-Friesian cows at 2 commercial farms located in western Poland. The overall daily estimated methane emission was 279 g/d. Genetic variance fluctuated over the course of lactation around the average level of 1,509 (g/d), with the highest level, 1,866 (g/d), at the end of the lactation. The permanent environment variance values started at 2,865 (g/d) and then dropped to around 846 (g/d) at 100 d in milk (DIM) to reach the level of 2,444 (g/d) at the end of lactation. The residual variance was estimated at 2,620 (g/d). The average repeatability was 0.25. The heritability level fluctuated over the course of lactation, starting at 0.23 (SE 0.12) and then increasing to its maximum value of 0.3 (SE 0.08) at 212 DIM and ending at the level of 0.27 (SE 0.12). Average heritability was 0.27 (average SE 0.09). We have shown that estimated methane emission is a heritable trait and that the heritability level changes over the course of lactation. The observed changes and low genetic correlations between distant DIM suggest that it may be important to consider the period in which methane phenotypes are collected.

Dairy system, parity, and lactation stage affect enteric methane production, yield, and intensity per kilogram of milk and cheese predicted from gas chromatography fatty acids

Ruminants (and milk production) contribute to global climate change through enteric methane emissions (EME), and any attempt to reduce them is complicated by the fact that they are difficult and expensive to measure directly. In the case of dairy cows, a promising indirect method of estimating EME is to use the milk fatty acid profile as a proxy, as a relationship exists between microbial activity in the rumen and the molecules available for milk synthesis in the mammary gland. In the present study, we analyzed the detailed fatty acid profiles (through gas chromatography) of a large number of milk samples from 1,158 Brown Swiss cows reared on 85 farms with the aim of testing in the field 2 equations for estimating EME taken from a published meta-analysis. The average estimated methane yield (CH₄ emission per kg of dry matter intake, 21.34 ± 1.60 g/kg) and methane intensity (per kg of corrected milk, 14.17 ± 1.78 g/kg), and the derived methane production (CH₄ emissions per day per cow, 357 ± 109 g/d) were similar to those previously published. Using data from model cheese makings from individual cows, we also calculated estimated methane intensity per kilogram of fresh cheese (99.7 ± 16.4 g/kg) and cheese solids (207.5 ± 30.9 g/kg). Dairy system affected all EME estimates. Traditional dairy farms, and modern farms including corn silage in the TMR exhibited greater estimated methane intensities. We found very wide variability in estimated EME traits among different farms within dairy system (0.33 to 0.61 of total variance), suggesting the need to modify the farms' feeding regimens and management practices to mitigate emissions. Among the individual factors, parity order affected all estimated EME traits excepted methane yield, with an increase from first lactation to the following ones. Lactation stage exhibited more favorable estimated EME traits during early lactation, concomitant with the availability of nutrients from body tissue mobilization for mammary synthesis of milk. Our results showed a coherence between the EME traits estimated from the analysis of milk fatty acids and the expectations according to current knowledge. Further research is needed to validate the results obtained in this study in other breeds and populations, to assess the magnitude of the genetic variation and the potential of these phenotypes to be exploited in breeding programs with the aim to mitigate emissions.

Invited review: A perspective on the future of genomic selection in dairy cattle

Genomic evaluation has been successfully implemented in the United States, Canada, Great Britain, Ireland, New Zealand, Australia, France, the Netherlands, Germany, and the Scandinavian countries. Adoption of this technology in the major dairy producing countries has led to significant changes in the worldwide dairy industry. Gradual elimination of the progeny test system has led to a reduction in the number of sires with daughter records and fewer genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Although genomic selection has been successful in increasing rates of genetic gain, we still know very little about the genetic architecture of quantitative variation. Apparently, a very large number of genes affect nearly all economic traits, in accordance with the infinitesimal model for quantitative traits. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and on environmentally friendly production with reduced waste and gas emission. Genetic variance for economic traits is maintained by the increase in frequency of rare alleles, new mutations, and changes in selection goals and management. Thus, it is unlikely that a selection plateau will be reached in the near future.