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

Genomic evaluation of residual feed intake in US Holstein cows: insights into lifetime feed efficiency

Residual feed intake (RFI) is an important trait of feed efficiency that has been increasingly considered in the breeding objectives for dairy cattle. The objectives of this study were to estimate the genetic parameters of RFI and its component traits, namely, dry-matter intake (DMI), body weight (BW), and energy-corrected milk (ECM), in lactating Holstein cows; we thus developed a system for genomic evaluation of RFI in lactating Holstein cows and explored the associations of the RFI of heifers and cows. The RFI values were calculated from 2,538 first (n = 2,118) and second (n = 420) lactation Holsteins cows between 2020 and 2024 as part of the STgenetics EcoFeed® program. Of the animals, 1,516 were heifers from the same research station with previously established RFI values . After quality control, 61,283 single-nucleotide polymorphisms were used for the analyses. Univariate analyses were performed to estimate the heritabilities of RFI and its components in lactating cows; bivariate analyses were then performed to estimate the genetic correlations between the RFI of heifers and lactating cows using the genomic best unbiased linear prediction method. Animals with phenotypes and genotypes were used as the training population, and animals with only genotypes were considered the prediction population. The reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of the training population's genomic estimated breeding values (GEBVs) and magnitudes of genomic relationships between the individuals in the training and prediction populations. The heritability estimates (mean ± SE) of the RFI, DMI, ECM, and BW were 0.43 ± 0.07, 0.44 ± 0.04, 0.40 ± 0.05, and 0.46 ± 0.04, respectively. The average reliability of the GEBVs for RFI from the training and prediction populations were 44% and 30%, respectively. The genetic correlations for the RFI were 0.42 ± 0.08 between heifers and first lactation cows and 0.34 ± 0.06 between heifers and first and second lactation cows. Our results show that the genetic components of RFI are not fully carried over from heifers to cows and that there is re-ranking of the individuals at different life stages. Selection of animals for feed efficiency on a lifetime basis thus requires accounting for the efficiencies during animal growth and milk production as a lactating cow.

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.

Evaluating enteric methane emissions within a herd of genetically divergent grazing dairy cows

Enteric methane (CH4) emissions of 3 genetic groups (GG) of dairy cows were recorded across the grazing season (early March to late October). The 3 GG were (1) high economic breeding index (EBI) Holstein-Friesian (HF) representative of the top 1% of dairy cows in Ireland at the time of the study (elite), (2) national average (NA) EBI, which were representative of the average HF dairy cow in Ireland, and (3) purebred Jersey (JE) cows. Enteric CH4 was recorded using GreenFeed technology. Seasonal variation in CH4 was observed, with the lowest daily CH4 emissions and CH4 expressed per unit of dry matter intake occurring in spring (253 g/d and 15.56 g/kg, respectively), intermediate in summer (303 g/d and 18.26 g/kg, respectively), and greatest in autumn (324 g/d and 19.80 g/kg, respectively). Seasonal variation was also observed in the proportion of gross energy intake converted to CH4 (Ym); in the spring the Ym was lowest at 0.046, increasing to 0.053 and 0.058 in the summer and autumn, respectively. There was no difference in daily CH4 between the elite and NA, whereas JE had lower CH4 emissions compared with the elite. When expressed per unit of milk solids (fat + protein yield; MS), the elite and JE produced 6.8% and 9.7% less CH4 per kilogram of MS, respectively, compared with NA. There was no difference between the GG for CH4 per unit of DMI or the Ym. This research emphasizes the variation in CH4 emissions across the grazing season and among cows of differing genetic merit for CH4 emission intensities but not for CH4 per unit of DMI or the Ym.