Symposium review: Genomic selection for reducing environmental impact and adapting to climate change

Unraveling the genetic basis of methane emission in dairy cattle: a comprehensive exploration and breeding approach to lower methane emissions

Ruminant animals, such as dairy cattle, produce CH4, which contributes to global warming emissions and reduces dietary energy for the cows. While the carbon foot print of milk production varies based on production systems, milk yield and farm management practices, enteric fermentation, and manure management are major contributors togreenhouse gas emissions from dairy cattle. Recent emerging evidence has revealed the existence of genetic variation for CH4 emission traits among dairy cattle, suggests their potential inclusion in breeding goals and genetic selection programs. Advancements in high-throughput sequencing technologies and analytical techniques have enabled the identification of potential metabolic biomarkers, candidate genes, and SNPs linked to methane emissions. Indeed, this review critically examines our current understanding of carbon foot print in milk production, major emission sources, rumen microbial community and enteric fermentation, and the genetic architecture of methane emission traits in dairy cattle. It also emphasizes important implications for breeding strategies aimed at halting methane emissions through selective breeding, microbiome driven breeding, breeding for feed efficiency, and breeding by gene editing.

Impact on prime animal beef merit from breeding solely for lighter dairy cows

As the proportion of prime carcasses originating from dairy herds increases, the focus is shifting to the beef merit of the progeny from dairy herds. Several dairy cow total merit indexes include a negative weight on measures of cow size. However, there is a lack of knowledge on the impact of genetic selection, solely for lighter or smaller-sized dairy cows, on the beef performance of their progeny. Therefore, the objective of this study was to quantify the genetic correlations among cow size traits (i.e., cow body weight (BW), cow carcass weight (CW)), cow body condition score (BCS), cow carcass conformation (CC), and cow carcass fat cover (CF), as well as the correlations between these cow traits and a series of beef performance slaughter-related traits (i.e., CW, CC, CF, and age at slaughter (AS)) in their progeny. After data editing, there were 52,950 cow BW and BCS records, along with 57,509 cow carcass traits (i.e., CW, CC, and CF); carcass records from 346,350 prime animals along with AS records from 316,073 prime animals were also used. Heritability estimates ranged from moderate to high (0.18 to 0.62) for all cow and prime animal traits. The same carcass trait in cows and prime animals were strongly genetically correlated with each other (0.76 to 0.85), implying that they are influenced by very similar genomic variants. Selecting exclusively for cows with higher BCS (i.e., fatter) will, on average, produce more conformed prime animals carcasses, owing to a moderate genetic correlation (0.30) between both traits. Genetic correlations revealed that selecting exclusively for lighter BW or CW cows will, on average, result in lighter prime animal carcasses of poor CC, while also delaying slaughter age. Nonetheless, selective breeding through total merit indexes should be successful in breeding for smaller dairy cows, and desirable prime animal carcass traits concurrently, because of the non-unity genetic correlations between the cow and prime animal traits; this will help to achieve a more ethical, environmentally sustainable, and economically viable dairy-beef industry.

Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra

Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22-0.36, RMSE = 57-70 g/d). Equations using CH4 averages that had been pre-corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre-correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.

Are farmers motivated to select for heat tolerance? Linking attitudinal factors, perceived climate change impacts, and social trust to farmers' breeding desires

This study provides an understanding of dairy farmers' willingness to include heat tolerance in breeding goals and the modulating effect of sociopsychological factors and farm profile. A survey instrument including a choice experiment was designed to specifically address the trade-off between heat tolerance and milk production level. A total of 122 farmers across cattle, goat, and sheep farms were surveyed face-to-face. The results of the experiment show that most farmers perceive that heat stress and climate change are increasingly important problems, and that farming communities should invest more in generating knowledge and resources on mitigation strategies. However, we found limited initial support for selection for heat tolerance. This attitude changed when farmers were presented with objective information on the benefits and limitations of the different breeding choices, after which most farmers supported selection for heat tolerance, but only if doing so would compromise milk production gains to a small extent. Our results show that farmers' selection choices are driven by the interactions between heat stress risk perception, attitudes toward breeding tools, social trust, the species reared, and farm production level. In general, farmers willing to support selection of heat-tolerant animals are those with positive attitudes toward genetic values and genomic information and a strong perception of climate change and heat stress impacts on farms. On the contrary, negative support for selection for heat tolerance is found among farmers with high milk production levels; high trust in farming magazines, livestock farmers' associations, and veterinarians; and low trust in environmental and animalist groups.

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.

Liver and Muscle Transcriptomes Differ in Mid-Lactation Cows Divergent in Feed Efficiency in the Presence or Absence of Supplemental Rumen-Protected Choline

Improving dairy cow feed efficiency is critical to the sustainability and profitability of dairy production, yet the underlying mechanisms that contribute to individual cow variation in feed efficiency are not fully understood. The objectives of this study were to (1) identify genes and associated pathways that are altered in cows with high- or low-residual feed intake (RFI) using RNA sequencing, and (2) determine if rumen-protected choline supplementation during mid-lactation would influence performance or feed efficiency. Mid-lactation (134 ± 20 days in milk) multiparous Holstein cows were randomly assigned to either supplementation of 0 g/d supplementation (CTL; n = 32) or 30 g/d of a rumen-protected choline product (RPC; 13.2 g choline ion; n = 32; Balchem Corp., New Hampton, NY, USA). Residual feed intake was determined as dry matter intake regressed on milk energy output, days in milk, body weight change, metabolic body weight, and dietary treatment. The 12 cows with the highest RFI (low feed efficient; LE) and 12 cows with the lowest RFI (high feed efficient; HE), balanced by dietary treatment, were selected for blood, liver, and muscle analysis. No differences in production or feed efficiency were detected with RPC supplementation, although albumin was greater and arachidonic acid tended to be greater in RPC cows. Concentrations of β-hydroxybutyrate were greater in HE cows. Between HE and LE, 268 and 315 differentially expressed genes in liver and muscle tissue, respectively, were identified through RNA sequencing. Pathway analysis indicated differences in cell cycling, oxidative stress, and immunity in liver and differences in glucose and fatty acid pathways in muscle. The current work indicates that unique differences in liver and muscle post-absorptive nutrient metabolism contribute to sources of variation in feed efficiency and that differences in amino acid and fatty acid oxidation, cell cycling, and immune function should be further examined.

Association of Phenotypic Markers of Heat Tolerance with Australian Genomic Estimated Breeding Values and Dairy Cattle Selection Indices

Dairy cattle predicted by genomic breeding values to be heat tolerant are known to have less milk production decline and lower core body temperature increases in response to elevated temperatures. In a study conducted at the University of Melbourne's Dookie Robotic Dairy Farm during summer, we identified the 20 most heat-susceptible and heat-tolerant cows in a herd of 150 Holstein Friesian lactating cows based on their phenotypic responses (changes in respiration rate, surface body temperature, panting score, and milk production). Hair samples were collected from the tip of the cows' tails following standard genotyping protocols. The results indicated variation in feed saved and HT genomic estimated breeding values (GEBVs) (p ≤ 0.05) across age, indicating a potential for their selection. As expected, the thermotolerant group had higher GEBVs for HT and feed saved but lower values for milk production. In general, younger cows had superior GEBVs for the Balanced Performance Index (BPI) and Australian Selection Index (ASI), whilst older cows were superior in fertility, feed saved (FS), and HT. This study demonstrated highly significant (p ≤ 0.001) negative correlations (-0.28 to -0.74) between HT and GEBVs for current Australian dairy cattle selection indices (BPI, ASI, HWI) and significant (p ≤ 0.05) positive correlations between HT and GEBVs for traits like FS (0.45) and fertility (0.25). Genomic selection for HT will help improve cow efficiency and sustainability of dairy production under hot summer conditions. However, a more extensive study involving more lactating cows across multiple farms is recommended to confirm the associations between the phenotypic predictors of HT and GEBVs.

Genome-wide expression analysis reveals different heat shock responses in indigenous (Bos indicus) and crossbred (Bos indicus X Bos taurus) cattle

Environmental heat stress in dairy cattle leads to poor health, reduced milk production and decreased reproductive efficiency. Multiple genes interact and coordinate the response to overcome the impact of heat stress. The present study identified heat shock regulated genes in the peripheral blood mononuclear cells (PBMC). Genome-wide expression patterns for cellular stress response were compared between two genetically distinct groups of cattle viz., Hariana (B. indicus) and Vrindavani (B. indicus X B. taurus). In addition to major heat shock response genes, oxidative stress and immune response genes were also found to be affected by heat stress. Heat shock proteins such as HSPH1, HSPB8, FKB4, DNAJ4 and SERPINH1 were up-regulated at higher fold change in Vrindavani compared to Hariana cattle. The oxidative stress response genes (HMOX1, BNIP3, RHOB and VEGFA) and immune response genes (FSOB, GADD45B and JUN) were up-regulated in Vrindavani whereas the same were down-regulated in Hariana cattle. The enrichment analysis of dysregulated genes revealed the biological functions and signaling pathways that were affected by heat stress. Overall, these results show distinct cellular responses to heat stress in two different genetic groups of cattle. This also highlight the long-term adaptation of B. indicus (Hariana) to tropical climate as compared to the crossbred (Vrindavani) with mixed genetic makeup (B. indicus X B. taurus).

Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r_auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01-0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88-0.96) than between lactations 1 and 2 or 3 (0.34-0.88) for dPert, LnVar, and r_auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r_auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.

Progress and opportunities through use of genomics in animal production

The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.

Animal board invited review: Genomic-based improvement of cattle in response to climate change

Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding.

Predictive assessment of single-step BLUP with linear and non-linear similarity RKHS kernels: A case study in chickens

Single-step GBLUP (ssGBLUP) to obtain genomic prediction was proposed in 2009. Many studies have investigated ssGBLUP in genomic selection in animals and plants using a standard linear kernel (similarity matrix) called genomic relationship matrix (G). More general kernels should allow capturing non-additive effects as well, whereas GBLUP is based on additive gene action. In this study, we generalized ssBLUP to accommodate two non-linear kernels, the averaged Gaussian kernel (AK) and the recently developed arc-cosine deep kernel (DK). We evaluated the methodology using body weight (BW) and hen-housing production (HHP) traits, recorded on a sample of phenotyped and genotyped commercial broiler chickens. There were, thus, different ssGBLUP models corresponding to G, AK and DK. We used random replication of training (TRN) and testing (TST) layouts at different genotyping rates (20%, 40%, 60% and 80% of all birds) in three selective genotyping scenarios. The selections were genotyping the youngest individuals in the pedigree (YS), random genotyping (RS) and genotyping based on parent average (PA). Predictive abilities were measured using rank correlations between the observed and the predictive phenotypic values in TST for each random partition. Prediction accuracy was influenced by the type of kernel when a large proportion of birds was genotyped. An advantage of non-linear kernels (AK and DK) was more apparent when 60 and 80% of birds had been genotyped. For BW, the lowest rank correlations were obtained with G (0.093 ± 0.015 using RS by 20% genotyped individuals) and the highest values with DK (0.320 ± 0.016 in the PA setting with 80% genotyped individuals). For HHP, the lowest and highest rank correlations were obtained by AK with 20% and 80% genotyped individuals, 0.071 ± 0.016 (in RS) and 0.23 ± 0.016 (in PA) respectively. Our results indicated that AK and DK are more effective than G when a large proportion of the target population is genotyped. Our expectation is that ssGBLUP with AK or DK models can perform even better than G when non-additive genetic effects influence the underlying variability of complex traits.

Sustainability of the Dairy Industry: Emissions and Mitigation Opportunities

Dairy cattle provide a major benefit to the world through upcycling human inedible feedstuffs into milk and associated dairy products. However, as beneficial as this process has become, it is not without potential negatives. Dairy cattle are a source of greenhouse gases through enteric and waste fermentation as well as excreting nitrogen emissions through their feces and urine. However, these negative impacts vary widely due to how and what these animals are fed. In addition, there are many promising opportunities for further reducing emissions through feed and waste additives. The present review aims to further expand on where the industry is today and the potential avenues for improvement. This area of research is still not complete and additional information is required to further improve our dairy systems impact on sustainable animal products.

Greenhouse gas emissions and nitrogen efficiency of dairy cows of divergent economic breeding index under seasonal pasture-based management

Greenhouse gas (GHG) emissions and nitrogen (N) efficiencies were modeled for 2 genetic groups (GG) of Holstein-Friesian cows across 3 contrasting feeding treatments (FT). The 2 GG were (1) high economic breeding index (EBI) animals representative of the top 5% of cows nationally (elite) and (2) EBI representative of the national average (NA). The FT represented (1) generous feeding of pasture, (2) a slight restriction in pasture allowance, and (3) a high-concentrate feeding system with adequate pasture allowance. Greenhouse gas and N balance models were parameterized using outputs generated from the Moorepark Dairy Systems model, a stochastic budgetary simulation model, having integrated biological data pertaining to the 6 scenarios (2 GG × 3 FT) obtained from a 4-yr experiment conducted between 2013 and 2016. On a per hectare basis, total system GHG emissions were similar for both elite and NA across the 3 FT. Per unit of product, however, the elite group had 10% and 11% lower GHG emissions per kilogram of fat- and protein-corrected milk and per kilogram of milk solids (MSO; fat + protein kg), respectively, compared with the NA across the 3 FT. The FT incorporating high concentrate supplementation had greater absolute GHG emissions per hectare as well as GHG per kilogram of fat- and protein-corrected milk and MSO. The elite group had a slightly superior N use efficiency (N output/N input) and lower N surplus (N input - N output) compared with the NA group. The high concentrate FT had an inferior N use efficiency and a higher N surplus. The results of the current study demonstrate that breeding for increased EBI will lead to a general improvement in GHG emissions per unit of product as well as improved N efficiency. The results also illustrate that reducing concentrate supplementation will reduce GHG emissions, GHG emissions intensity, while improving N efficiency in the context of pasture-based dairy production.

Gene Editing for Improved Animal Welfare and Production Traits in Cattle: Will This Technology Be Embraced or Rejected by the Public?

Integrating technology into agricultural systems has gained considerable traction, particularly over the last half century. Agricultural systems that incorporate the public’s concerns regarding farm animal welfare are more likely to be socially accepted in the long term, a key but often forgotten component of sustainability. Gene editing is a tool that has received considerable attention in the last five years, given its potential capacity to improve farm animal health, welfare, and production efficiency. This study aimed to explore the attitudes of Brazilian citizens regarding the applications of gene editing in cattle that generate offspring without horns; are more resistant to heat; and have increased muscle tissue. Using a mixed-methods approach, we surveyed participants via face-to-face, using in-depth interviews (Study 1) and an online questionnaire containing closed-ended questions (Study 2). Overall, the acceptability of gene editing was low and in cases where support was given it was highly dependent on the type and purpose of the application proposed. Using gene editing to improve muscle tissue growth was viewed as less acceptable compared to using gene editing to reduce heat stress or to produce hornless cattle. Support declined when the application was perceived to harm animal welfare, to be profit motivated or to reinforce the status quo of intensive livestock systems. The acceptability of gene editing was reduced when perceptions of risks and benefits were viewed as unevenly or unfairly distributed among consumers, corporations, different types of farmers, and the animals. Interviewees did not consider gene editing a “natural” process, citing dissenting reasons such as the high degree of human interference and the acceleration of natural processes. Our findings raised several issues that may need to be addressed for gene editing to comply with the social pillar of sustainable agriculture.

Genetic mixing for population management: From genetic rescue to provenancing

Animal and plant species around the world are being challenged by the deleterious effects of inbreeding, loss of genetic diversity, and maladaptation due to widespread habitat destruction and rapid climate change. In many cases, interventions will likely be needed to safeguard populations and species and to maintain functioning ecosystems. Strategies aimed at initiating, reinstating, or enhancing patterns of gene flow via the deliberate movement of genotypes around the environment are generating growing interest with broad applications in conservation and environmental management. These diverse strategies go by various names ranging from genetic or evolutionary rescue to provenancing and genetic resurrection. Our aim here is to provide some clarification around terminology and to how these strategies are connected and linked to underlying genetic processes. We draw on case studies from the literature and outline mechanisms that underlie how the various strategies aim to increase species fitness and impact the wider community. We argue that understanding mechanisms leading to species decline and community impact is a key to successful implementation of these strategies. We emphasize the need to consider the nature of source and recipient populations, as well as associated risks and trade-offs for the various strategies. This overview highlights where strategies are likely to have potential at population, species, and ecosystem scales, but also where they should probably not be attempted depending on the overall aims of the intervention. We advocate an approach where short- and long-term strategies are integrated into a decision framework that also considers nongenetic aspects of management.

Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

A method for implementing methane breeding values in Australian dairy cattle

Context There has been a lot of interest in recent years in developing estimated breeding values (EBVs) to reduce methane emissions from the livestock sector. However, while a major limitation is the availability of high-quality methane phenotypes measured on individual animals required to develop these EBVs, it has been recognised that selecting for improved efficiency of milk production, longevity, feed efficiency and fertility may be an effective strategy to genetically reduce methane emissions in dairy cows. Aim Applying carbon dioxide equivalents (CO2-eq) weights to these EBVs, we hypothesise that it is possible to develop a genetic tool to reduce greenhouse-gas emissions (GHG). Methods We calculated the effect of an EBV unit change in each trait in the Balanced Performance Index on CO2-eq emissions per cow per year. The estimated environmental weights were used to calculate a prototype index of CO2-eq emissions. The final set of EBVs selected for inclusion in the GHG subindex were milk volume, fat yield and protein yield, survival and feed saved, as these traits had an independent effect on emissions. Feed saved is the Australian feed efficiency trait. A further modification was to include a direct methane trait in the GHG subindex, which is a more direct genomic evaluation of methane estimated from measured methane data, calculated as the difference between actual and predicted emissions, for example, a residual methane EBV. Key results The accuracy of the GHG subindex (excluding residual methane EBV) is ~0.50, calculated as the correlation between the index and gross methane (using 3-day mean gross methane phenotypes corrected for fixed effects, such as batch and parity and adjusting for the heritability). The addition of the residual methane EBV had a minimal effect with a correlation of 0.99 between the indexes. This was likely to be due to limited availability of methane phenotypes, resulting in residual methane EBVs with low reliabilities. Conclusions We expect that as more methane data becomes available and the accuracy of the residual methane trait increases, the two GHG subindexes will become differentiated. When the GHG subindex estimates are applied to bull EBVs, it can be seen that selecting for bulls that are low emitters of GHG can be achieved with a small compromise in the BPI of ~20 BPI units (standard deviation of BPI = 100). Implications Therefore, selection for more sustainable dairy cattle, both economic and environmental, may be promptly implemented until sufficient data are collected on methane.