Partitioning among-animal variance of energy utilization in lactating Jersey cows

Animals vary in the way in which they utilize energy due to diet, genetics, and management. Energy consumed by the animal supports milk production, but considerable variation among-animals in energy utilization is thought to exist. The study objective was to estimate the among-animal variance in energy utilization in data collected from Jersey cows using indirect calorimetry. Individual animal-period data from 15 studies (n = 560) were used. The data set included 115 animals from 44 to 410 DIM producing 11.5 to 39.1 kg/d of milk. On average, the 63 treatments in the data set ranged 14.8 to 19.5% CP, 21.4 to 43.0% NDF, 16.2 to 33.3% starch, and 2.21 to 6.44% crude fat. Data were analyzed with the Glimmix procedure of SAS (9.4) with random effects of cow, treatment nested within period, square, and experiment. The percentage of among-animal, dietary treatment, and experimental variance was calculated as the variance associated with each fraction divided by the sum of variance from animal, dietary treatment, experiment, and residual which was considered the total variance. The percentage of among-animal variance was characterized as high or low when the value was greater than or less than the mean value of 29.2%. Among-animal variance explained approximately 29.3 - 42.5% of the total variance in DM intake (DMI), gross energy (GE), digestible energy (DE), metabolizable energy (ME), and net energy of lactation (NEL) in Mcal/d. When energetic components of feces, urine, and heat in Mcal/d were expressed per unit of DMI the among-animal variance decreased by 20.4, 4.82, and 9.55% units, respectively. However, among-animal variance explained 4.80, 8.78, and 5.02% units more of the total variation for methane energy, lactation energy, and tissue energy in Mcal/d when expressed per unit of DMI. Variance in energetic efficiencies of DE/GE, ME/GE, and ME/DE were explained to a lesser extent by among-animal variance (averaging 17.8 ± 1.95%). The among-animal contribution to total variance in milk energy was 28.8%. Milk energy was a large proportion of the energy efficiency calculation which included milk energy plus corrected tissue energy over net energy intake which likely contributed to the 22.2% of total among-animal variance in energy efficiency. Results indicate that among-animal variance explains a large proportion of the total variation in DMI. This contributes to the variance observed for energy fractions as well as energy components when expressed in Mcal/d. Variation in energetic loss associated with methane was primarily explained by differences among-animals and was increased when expressed per unit of DMI highlighting the role of inherent animal differences in these losses.

Recent Advances in Enteric Methane Mitigation and the Long Road to Sustainable Ruminant Production

Mitigation of methane emission, a potent greenhouse gas, is a worldwide priority to limit global warming. A substantial part of anthropogenic methane is emitted by the livestock sector, as methane is a normal product of ruminant digestion. We present the latest developments and challenges ahead of the main efficient mitigation strategies of enteric methane production in ruminants. Numerous mitigation strategies have been developed in the last decades, from dietary manipulation and breeding to targeting of methanogens, the microbes that produce methane. The most recent advances focus on specific inhibition of key enzymes involved in methanogenesis. But these inhibitors, although efficient, are not affordable and not adapted to the extensive farming systems prevalent in low- and middle-income countries. Effective global mitigation of methane emissions from livestock should be based not only on scientific progress but also on the feasibility and accessibility of mitigation strategies.

Genetic parameters and evaluation of mortality and slaughter rate in Holstein and Jersey cows

The longevity of dairy cattle has economic, animal welfare, and health implications and is influenced by the frequency of mortality on the farm and sale for slaughter. In this study cows removed from the herd due to death or slaughter during the lactation were coded 1 and cows that were not terminated were coded 0. Genetic parameters for mortality rates (MR) and slaughter rates (SR) were estimated for Holstein (H) and Jersey (J) breeds by applying both linear (LM) and threshold (TM) sire models using about 1.2 million H and 286,000 J cows. Estimated breeding values (EBV) for MR and SR were predicted using animal models to assess the opportunity for selection and genetic trends. Cow termination data, recorded between 1990 and 2020 on a voluntary basis by Australian dairy farmers, were analyzed. Cow MR has increased from below 1% in the 1990s to 4.1% and 3.6% in recent years in H and J cows, respectively. Most dead cows (∼36%) left the herd before 120 d of lactation, while cows that were slaughtered left the herd toward the end of the lactation. Using the LM, heritability (h2) estimates for MR were lower (1%) than those for SR (2%-3.5%). When h2 were estimated using a TM, the estimates for both traits varied between 4% and 20%, suggesting that the difference in incidence level is one of the reasons for the difference in the h2 values between MR and SR. Early test-day milk yield (MY) and 305-d MY (305-d MY) have unfavorable genetic correlations (0.32-0.41) with MR in both breeds. The genetic correlations of calving interval with MR were stronger (0.54-0.68) than with SR (0.28-0.45) suggesting that poor fertility can serve as an early indicator of poor cow health that may lead to increased risk of death. High early test-day somatic cell count is genetically associated with increased likelihood of slaughter (0.24-0.46), but not with increased likelihood of death. In H, 305-d protein yield (PY) had the strongest genetic correlation (-0.34 to -0.40) with SR whereas in J, both 305-d PY and fat yield showed high genetic (-0.64 to -0.70) and moderate environmental (-0.35 to -0.37) correlations with SR. The genetic correlation of removal from the herd due to death and slaughter was negative (-0.3) in J and zero in H. Strong selection for improved fertility and survival and less selection emphasis for MY, has led to an improvement in the genetic trend for cow MR in H and the trend in J has stabilized. Although genetic evaluations for cow MR are feasible, the reliabilities of the EBV are low and the level of cow MR in Australia are relatively low compared with similar countries. Therefore, genetic evaluation for survival based on mortality and slaughter data could be sufficient in the current selection circumstances where breeding objectives are broadly defined. Nevertheless, all Australian farmers should be encouraged to continue recording mortality and slaughter data for monitoring of the trends and for future development of genetic evaluations.

How does reproduction account for dairy farm sustainability?

Sustainability - the new hype of the 21st century has brought discomfort for the government and society. Sustainable agriculture is essential to face our most concerning challenges: climate change, food security, and the environmental footprint, all of which add to consumers' opinions and choices. Improvements in reproductive indexes can enhance animal production and efficiency, guaranteeing profit and sustainability. Estrus detection, artificial insemination (AI), embryo transfer (ET), estrus synchronization (ES), and multiple ovulations are some strategies used to improve animal reproduction. This review highlights how reproductive strategies and genetic selection can contribute to sustainable ruminant production. Improved reproductive indices can reduce the number of nonproductive cows in the herd, reducing methane emissions and land use for production while preserving natural resources.

Review: Diving into the cow hologenome to reduce methane emissions and increase sustainability

Interest on methane emissions from livestock has increased in later years as it is an anthropogenic greenhouse gas with an important warming potential. The rumen microbiota has a large influence on the production of enteric methane. Animals harbour a second genome consisting of microbes, collectively referred to as the "microbiome". The rumen microbial community plays an important role in feed digestion, feed efficiency, methane emission and health status. This review recaps the current knowledge on the genetic control that the cow exerts on the rumen microbiota composition. Heritability estimates for the rumen microbiota composition range between 0.05 and 0.40 in the literature, depending on the taxonomical group or microbial gene function. Variables depicting microbial diversity or aggregating microbial information are also heritable within the same range. This study includes a genome-wide association analysis on the microbiota composition, considering the relative abundance of some microbial taxa previously associated to enteric methane in dairy cattle (Archaea, Dialister, Entodinium, Eukaryota, Lentisphaerae, Methanobrevibacter, Neocallimastix, Prevotella and Stentor). Host genomic regions associated with the relative abundance of these microbial taxa were identified after Benjamini-Hoschberg correction (Padj < 0.05). An in-silico functional analysis using FUMA and DAVID online tools revealed that these gene sets were enriched in tissues like brain cortex, brain amigdala, pituitary, salivary glands and other parts of the digestive system, and are related to appetite, satiety and digestion. These results allow us to have greater knowledge about the composition and function of the rumen microbiome in cattle. The state-of-the art strategies to include methane traits in the selection indices in dairy cattle populations is reviewed. Several strategies to include methane traits in the selection indices have been studied worldwide, using bioeconomical models or economic functions under theoretical frameworks. However, their incorporation in the breeding programmes is still scarce. Some potential strategies to include methane traits in the selection indices of dairy cattle population are presented. Future selection indices will need to increase the weight of traits related to methane emissions and sustainability. This review will serve as a compendium of the current state of the art in genetic strategies to reduce methane emissions in dairy cattle.

Udder Health Monitoring for Prevention of Bovine Mastitis and Improvement of Milk Quality

To maximize milk production, efficiency, and profits, modern dairy cows are genetically selected and bred to produce more and more milk and are fed copious quantities of high-energy feed to support ever-increasing milk volumes. As demands for increased milk yield and milking efficiency continue to rise to provide for the growing world population, more significant stress is placed on the dairy cow's productive capacity. In this climate, which is becoming increasingly hotter, millions of people depend on the capacity of cattle to respond to new environments and to cope with temperature shocks as well as additional stress factors such as solar radiation, animal crowding, insect pests, and poor ventilation, which are often associated with an increased risk of mastitis, resulting in lower milk quality and reduced production. This article reviews the impact of heat stress on milk production and quality and emphasizes the importance of udder health monitoring, with a focus on the use of emergent methods for monitoring udder health, such as infrared thermography, biosensors, and lab-on-chip devices, which may promote animal health and welfare, as well as the quality and safety of dairy products, without hindering the technological flow, while providing significant benefits to farmers, manufacturers, and consumers.