A comparison of the economic value for enteric methane emissions with other biological traits associated with dairy cows

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms

Strategies and tools for genetic selection in dairy cattle and their application to improving animal welfare

Genetic improvement of farm animals, especially selection within breeds focussed on high production and efficiency, is often cited as a potential threat to animal welfare. However, many animal welfare issues can be addressed, at least partially, by animal breeding and genetics. In this chapter, we explore the relationship between genetic selection and animal welfare, the strategies and tools for genetic improvement and how they can contribute to improved animal welfare. A growing public awareness of animal welfare and environmental issues has led to breeding goals being broadened beyond farmer profitability. As animal welfare and behaviour are complex and multi-factorial, so the emergence of selection indices that include a large number of traits to optimise animal welfare in a way that is consistent with enterprise sustainability for the farmer is necessary. This trend is likely to continue and will be aided by the advent of new technologies for measuring animal welfare in conjunction with DNA-based predictions of genetic merit (genomic selection). The dairy cattle industry has been exemplary for the application of genomic selection, in addition to enabling selection decisions to be made earlier in life, it can be used to select for traits where it was not possible to select for previously. These include important welfare-related traits, such as improved disease resistance and heat tolerance. Dairy cattle breeding is a very international activity with just a few breeding companies dominating the market in semen for the most numerous breeds, especially the Holstein. Consequently, genetic diversity within breeds is diminishing and although genetic gain has been significant, the rate of inbreeding now presents itself as a threat to the future success of breeding programmes. A greater emphasis on diversity in breeding programmes and the traits under selection is needed as major themes in research and application. Innovation in methods to measure these new traits, (e.g. molecular phenotyping, sensor development, digitalisation data science, etc.) could dramatically transform selection for animal welfare, as these technologies can enable large-scale objective measurements of animal behaviours. In addition to animal-based outcome measures, factors like housing, feeding, specific management practices pose other risks to welfare. Risk factors and their interactions have an impact on the development of diseases or other challenges to welfare. Collaborative efforts between animal behaviour scientists, geneticists, engineers, data scientists, and others will potentially provide solutions to these challenges

A comparison of the economic value for enteric methane emissions with other biological traits associated with dairy cows

This is the first study to derive the economic value of enteric methane produced by a ruminant animal. There is considerable interest globally in selecting for low methane-emitting ruminant livestock, as methane is a potent greenhouse gas. However, before enteric methane can be included in a genetic selection index for breeding, the economic value for enteric methane needs to be derived. An animal model including a partial budget was used to derive economic values for a range of production and fitness (health and fertility) traits typically used in genetic selection of dairy cows with the addition of enteric methane. This study found that enteric methane (kilograms/lactation) has an economic value of -£1.68 per kg increase in methane per lactation. The economic value for enteric methane was of similar magnitude to the traits of milk fat yield (£1.14 per unit change in milk fat) and mastitis (-£1.55 per % incidence). Based on the variation seen in the dairy cow population in the UK, genetic selection on enteric methane has potential to increase herd profit per cow and reduce emissions. Even if the economic and abatement gains associated with selecting low methane producing livestock are relatively small, reductions in enteric methane emissions appear possible if a reliable and repeatable measure becomes available for use on commercial farms

The repeatability and heritability of traits derived from accelerometer sensors associated with grazing and rumination time in an extensive sheep farming system

IntroductionThe automated collection of phenotypic measurements in livestock is of interest to both researchers and farmers. Real-time, low-cost, and accurate phenotyping can enhance precision livestock management and could lead to the optimized utilization of pasture and breeding of efficient animals. Wearable sensors provide the tools for researchers to develop novel phenotypes across all production systems, which is especially valuable for grazing conditions. The objectives of this study were to estimate the repeatability and heritability of traits related to grazing and rumination activities and their correlations with other traits.MethodsThis study was conducted on a commercial Merino farm in the west of Victoria, Australia, from 4 May 2020 to 29 May 2020. A total of 160 ActiGraph sensors embedded in halters were attached to the left side of the muzzles of Merino sheep (M = 74, F = 86) aged 10–11 months while the sheep were grazing on pasture. Support vector machine (SVM) algorithms classified the sensor output into the categories of grazing, rumination, walking, idle, and other activities. These activities were further classified into daily grazing time (GT), number of grazing events (NGE), grazing length (GL), rumination time (RT), number of rumination events (NRE), rumination length (RL), walking time (WT), and idle time (IT). The data were analyzed using univariate and bivariate models in ASReml-SA to estimate the repeatability, heritability, and phenotypic correlations among traits.ResultsThe heritability of GT was estimated to be 0.44 ± 0.23, whereas the other traits had heritability estimates close to zero. The estimated repeatability for all traits was moderate to high, with the highest estimate being for GT (0.70 ± 0.03) and the lowest for RT (0.44 ± 0.03). The intraclass correlation or repeatability at a 1-day interval (i.e., 2 consecutive days) was high for all traits, and steadily reduced when the interval between measurements was longer than 1 week.DiscussionThe estimated repeatability for the grazing traits showed that wearable sensors and SVM methods are reliable methods for recording sheep activities on pasture, and have a potential application in the ranking of animals for selective breeding

The effect of 2-day heat stress on the lipid composition of bovine milk and serum

Milk and serum were collected from dairy cows before and during a 2-day heat challenge. The concentrations of free short-chain fatty acids (SCFAs), the fatty acid (FA) profile, and the abundance of the major species of phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM) were measured, and samples collected during heat exposure were compared with those collected prior to heat exposure. It was found that a 2-day heat challenge did not alter the global FA composition of milk fat nor the content of the major phospholipids. Although the concentration of SCFAs C3 and C4 and some lysophosphatidylcholine (LPC) species in milk was found to be associated with the forage type, neither of these lipid molecules can be used as an indicator of acute heat stress. While it is a positive finding that short-term heat stress has no detrimental effect on the FA composition or the nutritive quality of milk fat, this study highlights the complexity of validating a milk lipid biomarker for heat stress in dairy cows

Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle

The current research was carried out to determine the associations between the rumen microbiota and traits related with feed efficiency in a Holstein cattle population (n = 30) using whole metagenome sequencing. Improving feed efficiency (FE) is important for a more sustainable livestock production. The variability for the efficiency of feed utilization in ruminants is partially controlled by the gastrointestinal microbiota. Modulating the microbiota composition can promote a more sustainable and efficient livestock. This study revealed that most efficient cows had larger relative abundance of Bacteroidetes (P = 0.041) and Prevotella (P = 0.003), while lower, but non-significant (P = 0.119), relative abundance of Firmicutes. Methanobacteria (P = 0.004) and Methanobrevibacter (P = 0.003) were also less abundant in the high-efficiency cows. A de novo metagenome assembly was carried out using de Bruijn graphs in MEGAHIT resulting in 496,375 contigs. An agnostic pre-selection of microbial contigs allowed high classification accuracy for FE and intake levels using hierarchical classification. These microbial contigs were also able to predict FE and intake levels with accuracy of 0.19 and 0.39, respectively, in an independent population (n = 31). Nonetheless, a larger potential accuracy up to 0.69 was foreseen in this study for datasets that allowed a larger statistical power. Enrichment analyses showed that genes within these contigs were mainly involved in fatty acids and cellulose degradation pathways. The findings indicated that there are differences between the microbiota compositions of high and low-efficiency animals both at the taxonomical and gene levels. These differences are even more evident in terms of intake levels. Some of these differences remain even between populations under different diets and environments, and can provide information on the feed utilization performance without information on the individual intake level.info:eu-repo/semantics/publishedVersio

The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO₂-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO₂-eq emissions per cow over the past decade were made by multiplying predicted CO₂-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO₂-eq/cow.year after 10 years of selection. The same approach was applied to future CO₂-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO₂-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO₂-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO₂-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over the past decade and production has increased; altogether, we estimate that the net impact has been a reduction of CO₂-eq emissions of ~1.0% in total emissions from the dairy industry per year. Using two future scenarios of either keeping the number of cows or amount of product static, we predict that net GHG emissions will reduce by ~0.6%/year of total dairy emissions if milk production remains static, compared with 0.3%/year, if cow numbers remain the same and there is genetic improvement in milk-production traits

The use of mid-infrared spectra to map genes affecting milk composition.

The aim of this study was to investigate the feasibility of using mid-infrared (MIR) spectroscopy analysis of milk samples to increase the power and precision of genome-wide association studies (GWAS) for milk composition and to better distinguish linked quantitative trait loci (QTL). To achieve this goal, we analyzed phenotypic data of milk composition traits, related MIR spectra, and genotypic data comprising 626,777 SNP on 5,202 Holstein, Jersey, and crossbred cows. We performed a conventional GWAS on protein, lactose, fat, and fatty acid concentrations in milk, a GWAS on individual MIR wavenumbers, and a partial least squares regression (PLS), which is equivalent to a multi-trait GWAS, exploiting MIR data simultaneously to predict SNP genotypes. The PLS detected most of the QTL identified using single-trait GWAS, usually with a higher significance value, as well as previously undetected QTL for milk composition. Each QTL tends to have a different pattern of effects across the MIR spectrum and this explains the increased power. Because SNP tracking different QTL tend to have different patterns of effect, it was possible to distinguish closely linked QTL. Overall, the results of this study suggest that using MIR data through either GWAS or PLS analysis applied to genomic data can provide a powerful tool to distinguish milk composition QTL