Journal of Dairy Science

BackgroundImproving feed efficiency has become a common target for dairy farmers to meet the requirement of producing more milk with fewer resources. To improve feed efficiency, a prerequisite is to ensure that the cows identified as most or least efficient will remain as such, independently of diet composition. Therefore, the current research analysed the ability of lactating dairy cows to maintain their feed efficiency while changing the energy density of the diet by changing its concentration in starch and fibre. A total of 60 lactating Holstein cows, including 33 primiparous cows, were first fed a high starch diet-low fibre (diet S+F-), then switched over to a low starch diet-high fibre (diet S-F+). To know if diet affect feed efficiency, we compared the ability of feed efficiency to be maintained within a diet over subsequent lactation stages, known as repeatability of feed efficiency, with its ability to be maintained across diets, known as reproducibility of feed efficiency. To do so we used two indicators: the estimation of the error of repeatability/reproducibility, which is commonly used in metrology, and the coefficient of correlation of concordance (CCC), which is used in biology. The effect of diet change could also lead to a change in cows sorting behaviour which could potentially affect feed efficiency if for example the most efficient cows select more concentrate than the least efficient. We therefore analysed the relationship between the differences in individual feed refusals composition and the differences in feed efficiency. To do so, the composition of each feed refusal was described with its near infra-red (NIR) spectroscopy and was performed on each individual feed ingredient, diet and refusals and used as composition variable. The variability of the NIR spectra of the refusals was described with its principal components thanks to a principal component analysis (PCA). The Pearson correlation was estimated to check the relationship between feed efficiency and refusals composition, i.e. sorting behaviour. ResultsThe error of reproducibility of feed efficiency across diets was 2.95 MJ/d. This error was significantly larger than the errors of repeatability estimated within diet, which were 2.01 MJ/d within diet S-F+ and 2.40 MJ/d within diet S+F-. The CCC was 0.64 between feed efficiency estimated within diet S+F- and feed efficiency estimated within diet S-F+. This CCC was smaller than the one observed for feed efficiency estimated within diet between two subsequent lactation stages (CCC = 0.72 within diet S+F- and 0.85 within diet S-F+). Feed efficiency was poorly correlated to the first two principal components, which explained 90% of the total variability of the NIR spectra of the individual refusals. This suggests that feed sorting behaviour did not explain differences in feed efficiency. ConclusionsFeed efficiency was significantly less reproducible across diets than repeatable within the same diet over subsequent lactation stages, but cows ranking for feed efficiency was not significantly affected by diet change. This loss in repeatability across diets could be due to a more pronounced feed sorting subsequent to the change in diet composition. However, the differences in sorting behaviour between cows were not associated to feed efficiency differences in this trial neither with the S+F- diet nor with the S-F+ diet. Those results have to be confirmed on diets having different forage to concentrate ratios to ensure that the least and most efficient cows will not change.

Heat stress is a major environmental challenge affecting dairy cattle, leading to behavioral changes, production losses, and welfare concerns. As heat stress events intensify and become more frequent due to climate change, identifying heat tolerant animals is crucial for sustainable dairy production. This study develops a pipeline to quantify the population-wise impact of heat stress on a dairy cattle population and subsequently defines individual-based heat tolerance traits. Data from 677,318 Dutch Holstein cows, including 15.6 million mid-infrared spectra and 762 million records from automated milking systems, were analyzed. An iterative approach using kernel regression was employed to estimate the population-wise effects of heat stress. Results indicate that fat and protein percentages decrease approximately linearly with increasing temperature humidity index (THI) with an absolute reduction of 0.3% from THI = 30 to THI = 70. In contrast, milk yield remains stable until a THI of 60, after which production losses increase quadratically reaching 5.0% at a THI of 75. The phenotype of an animal is subsequently defined as the slope in a linear regression model of the residuals of the population-wise models against THI for milk yield and concentration of fat, protein, lactose, and specific fatty acids. Compared to reaction-norm models, individual records per cow are combined before model fitting, thereby reducing computation times and allowing more flexibility in the design of the model. Heritabilities for heat tolerance traits ranged from 0.05 to 0.12, and genetic variances indicate substantial potential for breeding as an improvement of the population by one genetic standard deviation would already offset 69% of the losses in fat percentage, 65% in protein percentage, and 11% in milk yield. Heat tolerance based on milk yield showed favorable correlations with most commercial traits, whereas heat tolerance based on fat and protein percentage showed negative correlations to health and resilience. A genome-wide association study using both SNPs and haplotype blocks from the software HaploBlocker identified potential QTLs across the genome, with particularly strong signals on BTA5, 14, and 20. These findings support the potential of breeding for heat tolerance but highlight the need for complementary management strategies to mitigate heat stress impacts. Interpretive summaryThis study introduced a novel, computationally efficient method to quantify the impact of heat stress in dairy cattle and define novel heat tolerance traits based on milk production data from automated milking systems. Our results indicate quadratically increasing losses in milk yield with increasing heat load. The identified heat tolerance traits show substantial genetic variance, moderate heritabilities, and favorable correlation to key production traits. These findings highlight the potential for incorporating heat tolerance into dairy breeding goals to mitigate climate change impacts, improve animal welfare, and enhance sustainable milk production.

The aim of the study was to describe the metabolic responses of protein metabolism to a period of negative nutrient balance induced by feed restriction (FR). Seven multiparous Holstein cows (93 ± 15 days in milk) were randomly assigned to 7 treatments in a 7 × 4 Youden square design. Daily intake was restricted to provide 60% of energy requirements during 5 d except for one treatment with ad libitum (AL) feeding. While 5 out of 7 experimental treatments involved abomasal supplementation of AA or glucose, in this article we evaluated only the effects of FR by comparing both control treatments (AL and FR). Data of 2 cows within the AL group were removed due to sickness and therefore it had n = 2. A rapid decrease of most amino acids in plasma was paired with an increase in blood urea N with its peak on d 2 and decreasing afterwards. On the other hand, Lys, Arg, Gly, Gln, and Cys were greater during FR. Comparing the fluctuation of all the essayed N components in circulation across the 5-d period, protein tissue mobilization may have supplied amino acids for catabolism to provide needs for N and energy precursors.Implications The short-term feed restriction model described in this article can serve as an alternative to study metabolic adaptations during the transition period. The response observed of the protein metabolism sets the baseline to measure the effect of nutrients supplementation and identify those candidates that will improve milk production and overall health after calving.Competing Interest StatementThe authors have declared no competing interest.View Full Text

The aim of the study was to describe the metabolic responses on energy metabolism to a period of negative nutrient balance induced by feed restriction (FR). Seven multiparous Holstein cows (93 ± 15 days in milk) were randomly assigned to 7 treatments in a 7 × 4 Youden square design. Daily intake was restricted to provide 60% of energy requirements during 5 d except for one treatment with ad libitum (AL) feeding. While 5 out of 7 experimental treatments involved abomasal supplementation of amino acids or glucose, in this article we evaluated only the effects of a negative nutrient balance by comparing both control treatments (AL and FR). Data of 2 cows within the AL group were removed due to sickness and therefore it had n = 2. Milk and energy corrected milk yield were reduced by FR. Yields of milk protein and lactose were lower during FR than during AL but the yield of milk fat only had a tendency (P > 0.06) to be lower with FR. Milk protein concentration was lower with FR than with AL but concentration of milk lactose and fat were not different between diets. The FR induced a decrease in plasma insulin and glucose concentrations, with quadratic decreasing trends both reaching nadirs on d 3. Simultaneously, non-esterified fatty acids (NEFA) concentration was greater and increased quadratically, peaking at d 3 during FR. There were no differences in daily β-hydroxybutyrate concentration, but it increased linearly until d 4 with FR. Comparison of the variation in concentration after feeding of insulin, NEFA and glucose could indicate a likely increased insulin sensitivity for peripheral NEFA uptake and a resistance for glucose uptake. This mechanism would contribute to decrease NEFA in circulation and sparing of glucose for lactose synthesis, respectively. Metabolic adaptations to a short-term reduction in dry matter intake include lipid mobilization, as well as modulation of peripheral tissue endocrine sensitivity in order to maintain yield of milk components production but prioritizing milk fat and lactose over milk protein.Implications The short-term feed restriction model described in this article can serve as an alternative to study metabolic adaptations during the transition period. The response of energy metabolism observed sets the baseline to measure the effect of nutrients supplementation and identify those candidates that will improve milk production and overall health after calving.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Mastitis in cattle poses a significant health challenge and results in substantial economic losses for the dairy industry. This study aimed to establish precision-cut bovine udder slices (PCBUS) as an in vitro model to explore the potential of stimulating trained immunity in the udder. The goal was to investigate whether this approach could influence the early pathophysiological processes of mastitis and pave the way for developing new therapeutic strategies for udder inflammation in future research. PCBUS remained viable in culture for up to two weeks. When stimulated with E. coli-derived lipopolysaccharide (LPS), zymosan (an inducer of trained immunity), or pre-incubated with zymosan followed by LPS stimulation, the slices exhibited distinct responses in terms of pro-inflammatory cytokine production and lipid mediator profiles. Additionally, cytokine production was influenced by the presence or absence of fetal calf serum (FCS), highlighting the potential limitations of FCS in in vitro studies. While the current experimental setup did not definitively confirm the induction of trained immunity in the bovine udder, it validated the utility of PCBUS as a robust in vitro model for studying bovine udder inflammation. This model offers a promising platform for developing innovative mastitis treatments, particularly given the growing concern over antimicrobial resistance. It also provides a valuable tool for advancing our understanding of immune responses in the bovine udder. By adapting the precision-cut tissue slice technique to bovine udders, this model enables extensive research into new therapeutic approaches and supports basic research efforts to characterize complex pathophysiological processes associated with mastitis.

Fertility is one of the major factors affecting the efficiency of dairy herd, and genomic selection (GS) on milk yield, while ignoring fertility, has resulted in a decline in heifer fertility. Consider the cost of breeding, it is important to enhance the accuracy of GS with cost-effective way for fertility traits. This study investigates the genomic prediction (GP) of fertility traits in Holstein heifers using both machine learning (ML) and conventional methods, based on data from SNP arrays and low-coverage sequencing data. In this study, we collected 45,320 Holstein heifers with phenotype and pedigree records, from which we generated genomic data for 3,683 Holstein heifers using lcGWS. We first estimated the heritability for age at first service (AFS), gestation length (GLh) and age at first calving (AFC). We then compared the prediction performance of ML methods, kernel ridge regression (KRR), support vector regression, and random forest regression, with GBLUP, ssGBLUP and BayesR3 regarding GP accuracy and unbiasedness. Inputs for ML includes genomic relationship matrices (GRM), principal components, and SNPs. The results revealed that the heritability for the three fertility traits ranged from 0.09 to 0.48. Prediction accuracy from imputed low-coverage sequencing data was comparable to that from standard SNP chips. When both pedigree and genotypic data were used for GS, ssGBLUP yielded the highest prediction accuracy for AFS and GLh. Crucially, using only genomic data, KRR_GRM improved GP accuracy by up to 28.57% compared to GBLUP and by up to 9.46% compared to BayesR3. Our results highlight the effectiveness of low-coverage sequencing data in breeding applications and the MLs potential to enhance GP accuracy for fertility traits, offering practical insights for dairy breeding programs. ImplicationsTo improve the accuracy of GS with cost-effective way for fertility traits, this study confirms that low-coverage sequencing offers both accuracy and cost-effectiveness for fertility in Holstein heifers. The research provides a decision-making framework for breeding workers: the ssGBLUP model is optimal when combining pedigree data, while machine learning methods are superior with only genomic data. This study offers a practical tool for achieving efficient and economical genetic improvement in dairy cattle.

BackgroundInfertility or subfertility is a critical barrier to sustainable cattle production, including in heifers. The development of heifers that do not produce a calf within an optimum window of time is a critical factor for the profitability and sustainability of the cattle industry. The early identification of heifers with optimum fertility using molecular phenotyping is a promising approach to improving sustainability in beef and dairy cattle production. ResultsUsing a high-density single nucleotide polymorphism (SNP) chip, we collected genotypic data from 575,053 SNPs. We also produced quantitative transcriptome data for 12,445 genes (12,105 protein-coding genes, 228 long non-coding RNAs, and 112 pseudogenes) and proteome data for 213 proteins. We identified two SNPs significantly associated with heifer fertility (rs110918927, chr12: 85648422, P = 6.7x10-7; and rs109366560, chr11:37666527, P = 2.6x10-5). We identified two genes with differential transcript abundance (eFDR [&le;] 0.002) between the two groups (Fertile and Sub-Fertile): Adipocyte Plasma Membrane Associated Protein (APMAP, 1.16 greater abundance in the Fertile group) and Dynein Axonemal Intermediate Chain 7 (DNAI7, 1.23 greater abundance in the Sub-Fertile group). Our analysis revealed that the protein Alpha-ketoglutarate-dependent dioxygenase FTO was more abundant in the plasma collected from Fertile heifers relative to their Sub-Fertile counterparts (FDR < 0.05). Interestingly, two proteins did not reach the significance threshold in the model accounting for all samples (Apolipoprotein C-II, APOC2 (FDRglmm = 0.06) and Lymphocyte cytosolic protein 1, LCP1 (FDRglmm = 0.06)), but both proteins were less abundant in the plasma of Fertile Holstein heifers (P < 0.05). Lastly, an integrative analysis of the three datasets identified a series of features (SNPs, gene transcripts, and proteins) that can be useful for the discrimination of heifers based on their fertility. When all features were utilized together, 21 out of 22 heifers were classified correctly based on their fertility category. ConclusionsOur multi-omics analyses confirm the complex nature of female fertility. Very importantly, our results also highlight differences in the molecular profile of heifers associated with fertility that transcend the constraints of breed-specific genetic background.

To investigate how the disturbances associated with a relocation to a bedded-pack barn, such as a housing system change, a milking system change and a social regrouping, impacts the behavior of lactating dairy cows, 38 cows from a total of 9 tie-stall or free-stall commercial farms were moved to a newly built bedded-pack barn on an enrollment basis, with a social regrouping occurring after 2 weeks. Scan sampling of video data was done to assess behavior expression in the pen, and live data was collected to assess milking reactivity and animal handling procedures. Results indicate that the cows adapted quickly to the relocation to the new housing system as there were no changes in the locations in the pen, the body positions or the behaviors of cows in time between arrival and regrouping. The social regrouping had a bigger impact with a decrease in 16% of the observed time spent lying and an increase of 9.7% of the observed time spent feeding. Cows also adapted quickly to the milking procedures with a rapid decrease in the occurrence of negative social interactions between cows at the parlor, and in needing less human-animal manipulations and less time to be brought to the parlor. The housing system of origin had a slight effect on behaviors with cows from tie-stalls spending 1.7 times more of the observed time lying than free-stall cows, and free-stall cows spending 1.6 times more of the observed time feeding than tie-stall cows. This study provides a better understanding of how dairy cows respond to disturbances and is encouraging for producers that need to make changes to their current housing system as cows were shown to be quickly adaptable to the challenges presented to them. SummaryDairy cows from cubicle systems were shown to adapt quickly after a relocation to a bedded pack barn, the first use of a milking parlor, and a social regrouping. This was supported by limited changes observed in their behaviors after the disturbances, and observed deviations were temporary and short-lived. Animal handling procedures also observed a quick improvement in time with the trips to the milking parlor needing 2x less time and 3.5x fewer physical contacts from handlers after 5 days. These results are encouraging to producers needing to make changes to their barns.

To identify factors affecting rumination time (RT), factorial analyses in a dairy farm were performed. In univariate analyses, differences in distribution were observed between low (< 45 kg/d) and high ([&ge;] 45 kg/d) milk yield (MY) before the heat season, early and peak lactation periods and mid and late at the start of the heat season, and 1-2 and [&ge;] 3 parity at the start of the heat season. Multiple regression analysis confirmed that RT was affected by low MY, early to peak and 1-2 parity cows, low MY, mid to late and 1-2 parity cows, and low MY, early to peak and [&ge;] 3 parity cows (coefficients = -37.039, -25.353, -44.805 respectively, P < 0.05). When the cows were classified by MY before the heat season, the RT and MY of the high-MY group remained higher than the low-MY group until before the severe heat (THI [&ge;] 84 continued). However, when cows were classified by RT before the heat season, there was no difference in MY between the low-RT (< 485 min/d) and the high-RT group (> 485 min/d). In conclusions, MY is a factor affecting RT up to moderate heat but RT is not a sufficient condition affecting MY.

BackgroundGenetic evaluation is a central component of a breeding program. In advanced economies, most genetic evaluations depend on large quantities of data that are recorded on commercial farms. Large herd sizes and widespread use of artificial insemination create strong genetic connectedness that enables the genetic and environmental effects of an individual animals phenotype to be accurately separated. In contrast to this, herds are neither large nor have strong genetic connectedness in smallholder dairy production systems of many low to middle-income countries (LMIC). This limits genetic evaluation, and furthermore, the pedigree information needed for traditional genetic evaluation is typically unavailable. Genomic information keeps track of shared haplotypes rather than shared relatives. This information could capture and strengthen genetic connectedness between herds and through this may enable genetic evaluations for LMIC smallholder dairy farms. The objective of this study was to use simulation to quantify the power of genomic information to enable genetic evaluation under such conditions.\n\nResultsThe results from this study show: (i) the genetic evaluation of phenotyped cows using genomic information had higher accuracy compared to pedigree information across all breeding designs; (ii) the genetic evaluation of phenotyped cows with genomic information and modelling herd as a random effect had higher or equal accuracy compared to modelling herd as a fixed effect; (iii) the genetic evaluation of phenotyped cows from breeding designs with strong genetic connectedness had higher accuracy compared to breeding designs with weaker genetic connectedness; (iv) genomic prediction of young bulls was possible using marker estimates from the genetic evaluations of their phenotyped dams. For example, the accuracy of genomic prediction of young bulls from an average herd size of 1 (=1.58) was 0.40 under a breeding design with 1,000 sires mated per generation and a training set of 8,000 phenotyped and genotyped cows.\n\nConclusionsThis study demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in-situ records collected from farms with herd sizes of four cows or less. Across a range of breeding designs, genomic data enabled accurate genetic evaluation of phenotyped cows and genomic prediction of young bulls using data sets that contained small herds with weak genetic connections. The use of smallholder dairy data in genetic evaluations would enable the establishment of breeding programs to improve in-situ germplasm and, if required, would enable the importation of the most suitable external germplasm. This could be individually tailored for each target environment. Together this would increase the productivity, profitability and sustainability of LMIC smallholder dairy production systems. However, data collection, including genomic data, is expensive and business models will need to be carefully constructed so that the costs are sustainably offset.

BackgroundFood intake is a complex trait in living organisms, where the genetics of food intake have been widely studied in humans, mice, Drosophila, cattle, pigs, chicken, and fish. In dairy cattle, intake of feed is highly linked to individuals energy balance, health, production, efficiency, and the environmental footprint of the individual to the society. Recent studies have provided solid evidence of the genetic variation of feed intake (FI) in dairy cattle population, but the genetic basis and molecular mechanism of dairy feed intake is still far from clear especially considering the lactation cycles of dairy cattle. This study aims to integrate stage-dependent genome-wide association (GWA) analyses, regional heritability mapping (RHM), and RNA-seq gene expression analyses to identify temporal functional variants associated with cattle dry matter intake (DMI) across multiple stages in lactation cycles. A total of 750,000 daily DMI records from 7,500 lactations of 2,300 cows were available with animals genotype and pedigree information. Total RNA-seq from blood were generated for 121 individuals in this population from 2 lactation stages. Data were split into multiple lactations stages for GWA, RHM, and transcriptomic analyses. ResultsStage-dependent GWAS and RHM identified 21 significant loci associated with DMI across multiple lactation stages. A total of 45 candidate genes were identified from GWA and RHM. Among all the 45 genes, six genes were later found significantly differently expressed between high and low feed intake animal groups using gene expression information from RNA-seq data. These genes show links to sugar and adipose metabolism, milk production, body weight, dopamine-reward pathways and immune functions. ConclusionsOur multi-omics analyses provide molecular evidence that the genetic basis of cattle DMI across lactation is not static. Temporal genomic variants associated with FI were identified with their transcriptomic patterns investigated, decoding the molecular mechanisms underlying DMI. Overall, the associated variants and candidate genes uncovered herein decoded genetic architecture of dairy feed intake on a temporal and multi-omics basis, enhancing the understanding of basic biology of dairy feed intake and informing breeding strategies aimed at improving dairy feed efficiency.

BackgroundHeat stress (HS) poses a major challenge to the dairy industry by reducing milk production, yet its cell type-specific effects in the bovine mammary gland remain incompletely defined. In this study, we recorded production traits and collected mammary biopsies from cows under thermoneutral (TN), HS, and pair-fed (PF) conditions. ResultsClinical measurements confirmed HS-induced physiological alterations. Compared with TN cows, HS cows exhibited reduced dry matter intake (DMI), milk yield, and yields of fat, protein, and lactose, along with increased water intake and milk urea nitrogen. The use of PF controls indicated that decreased DMI accounted for 45% of the milk-yield reduction, whereas direct HS effects accounted for the remaining reduction. We applied single-nucleus RNA-seq (snRNA-seq) on mammary biopsies to generate cell-resolved HS responses. We identified 14 distinct cell clusters, including epithelial, immune, and stromal populations. Under the TN condition, casein genes (e.g., CSN1S1, CSN2) were broadly expressed across luminal cells but were attenuated under HS, whereas luminal alveolar cells showed relative upregulation. Heat shock protein genes were strongly induced by HS, primarily in epithelial clusters. Gene-set enrichment analyses revealed increased ribosomal activities across HS-responsive clusters and enrichment of protein folding and metabolic pathways in luminal alveolar cells, suggesting elevated proteostasis demands under stress. Pseudotime analysis positioned luminal cells along a progenitor-to-secretory trajectory under TN, accompanied by increased casein gene expression, whereas under HS, mature luminal cells shifted toward a homeostasis regulatory state. Cell-cell communication analysis demonstrated HS-induced remodeling of interepithelial signaling, including altered ERBB4-mediated signaling from luminal hormone-sensing to alveolar lineages. Finally, transcription factor activity profiling highlighted cell type-specific HS-activated regulators and their downstream target genes. ConclusionsTogether, this cell type-resolved atlas delineates how HS alters bovine mammary epithelial function, developmental state, and intercellular crosstalk. These findings point to proteostasis pressure, disrupted signaling pathways, and rewired regulatory networks as mechanistic contributors to reduced lactational performance under HS, offering insights for improving heat resilience in dairy cattle.

The effect of methane-mitigating feed additives on dairy cows has been widely explored; however, confusing conclusions have been reached due to factors such as the inclusion of different doses and experimental conditions in which the additives are tested, or even a small sample size. We present the first extensive study assessing the effects of methane-mitigating feed additives on milk production across several commercial dairy farms. This study used a previously developed predictive AI-driven model based on microbiome samples; the model predicts farms where a significant reduction of methane emissions is expected due to the applied feed additives. Thus, in this study, each feed additive was supplied to a large number of farms, widely distributed across different climatic areas in Israel. The data analysis followed two simulated scenarios: (1) a naive approach, where feed additives are supplied indiscriminately, and (2) an optimized approach, where feed additives are supplied only to farms with a high likelihood of being positively impacted in terms of reduced enteric methane emissions (50% of the farms). The results show that each feed additive significantly increased milk production compared to the control groups. This increase in milk production was significantly higher in the optimized scenario. Other related parameters such as somatic cells were also improved. Our results suggest that the feed additives positively affect milk production, reaching a maximum expression when the AI-driven model is applied.

Heifer Infertility and disease are important challenges in dairy cattle production. We investigated genetic differences between Holstein heifers with varying fertility potential and health. We carried out a genome-wide association analysis comparing heifers that conceived at first insemination against those requiring multiple attempts or failing to become pregnant, as well as heifers culled due to health issues. There were 12 significant SNPs (P<5x10-5) associated with fertility and 35 SNPs associated with health traits. There were 166 significant SNPs when infertile, sub-fertile and animals culled due to health issues were grouped. Two SNPs identified in the analysis of infertility were found near NUFIP1 and within TENM4 genes, both genes are linked to embryonic lethality in mouse knockouts. Follow-up CRISPR-Cas9 mediated disruption of NUFIP1 significantly (P<0.05) reduced in vitro blastocyst development in cattle embryos, while TENM4 editing did not alter in vitro blastocyst development. Additionally, SNPs overlapped with previously identified reproduction-related QTL (CNTN4, DLG2, PARP10, PRICKLE, TMEM150B) or health-related QTL (FAM162A, PARP10). We also identified genes within or near genes previously associated with age at menarche (CADM2, DLG2, FHIT, LSAMP and TENM4) or lung function or pulmonary diseases (ASCC2, BCAS3, BTBD9, CADM2, CNTN4, CPEB4, CTNNA2, DEUP1, DGKH, DLG2, ENOX1, EPHB1, ERC2, ERGIC1, EYA2, FAM162A, FGF18, FHIT, GRID1, KCNIP4, LINGO2, LRMDA, MALRD1, NEBL, PLA2G6, PLXDC2, PRPF18, SLC8A1, TEAD4, TSPAN9) in humans. These results further support genetic components of fertility and health in cattle. The findings also show overlapping genetic architecture between fertility and health traits, with a degree of conservation across mammals. Summary sentenceSeveral genetic variants that influence female fertility and health in cattle were identified, and many genes harboring or near significant polymorphisms are common to equivalent phenotypes in mice and humans.

Holstein x Gyr and Holstein are the primary dairy breed used in tropical systems, but when rearing under pasture, feed intake, behavior, and performance might differ between them. This study aimed to evaluate the voluntary intake, nutrient digestibility, performance, and ingestive behavior of Holstein and Holstein x Gyr ([1/2] Holstein x [1/2] Gyr) heifers managed in an intermittent grazing system of Guinea grass (Panicum maximum Jacq. cv. Mombaca). The experiment was conducted during the summer season throughout four periods of 21 d. Two 8-heifers (four Holstein and four Holstein x Gyr) groups, averaging 258.6 {+/-} 24.8 kg and 157.1 {+/-} 24.99 kg body weight, were used. Each group grazed a separate set of 16 paddocks, and all heifers received a concentrate supplement daily. Heifers were weighed at the beginning and end of the experiment. Fecal, forage and concentrate samples were evaluated for their dry matter (DM), crude protein (CP), crude fat, ash, neutral detergent fiber (NDF), and indigestible NDF. Feeding behavior was evaluated through 24 h of live observation for 48 h of each experimental period. Grazing, ruminating, resting, and intake of concentrate times were recorded, and rumination criteria, bout criteria, mealtime, meal frequency, and meal duration were estimated. There was no difference in dry matter intake (DMI). The Holstein x Gyr heifers had greater NDF intake and average daily gain (ADG), and feed efficiency tended to show greater CP and NDF digestibilities. The forage DMI of Holstein x Gyr was 11.70% greater than the Holstein heifers. Holstein grazed less than Holstein x Gyr heifers in the afternoon. Ruminating time was 18.43% lower for Holstein than Holstein x Gyr heifers, and rumination criteria were greater for Holstein heifers. Holstein heifers presented more prolonged rumination bouts and resting time than Holstein x Gyr heifers. Holstein x Gyr can ingest and ruminate greater amounts of fibrous material. Holstein heifers select lower fiber material, and they need to spend more time ruminating small portions of feed. Overall, we do not recommend using young Holstein heifers in tropical pasture conditions because their ADG is low because of its lower adaptability to fibrous feed and heat stress. However, this management condition is appropriate for Holstein x Gyr heifers and results in an adequate performance. ImplicationsThis study was the first to evaluate the performance and behavior of young Holstein x Gyr and Holsteins heifers in tropical grazing systems under the same nutritional and environmental conditions. Crossbreed and purebred heifers interacted differently with the pasture; however, without noticeable variation in grazing time. As expected, Holstein heifers performance in the tropical pasture was impaired by a reduction in intake and grazing time. The greater performance observed for Holstein x Gyr heifers was assigned to greater forage intake, rumination time, and efficient forage nutrient use, showing animals adaptability to management conditions.

Most common housing systems for dairy cows restrict their movement, which can influence welfare, gait, and hoof health of dairy cows. Outdoor access has been proposed as a management practice to offset these restrictions, but reported effects on cows locomotion vary and may not always be captured by traditional clinical assessments. In this study, we investigated gait and hoof through clinical (i.e., visual locomotion scoring and hoof lesion assessment) and subclinical (3D motion analysis, kinetic assessment, hoof infrared thermography and measuring claw conformation) methods to assess how limited provision of outdoor access affects non-lame cows housed in movement restricted environment. Thirty-six Holstein tie-stall cows were either given 1day/week (EX1) or 3days/week (EX3) of outdoor access (1h/day) during 5 consecutive weeks. Clinical and subclinical assessments of gait and hoof were performed before (Pre-trial), after 5 weeks of outing (Post-trial) and 8 weeks after outing (Follow-up). The results of this study revealed no clinical effect of outdoor access on cows locomotion score and hoof lesion prevalence. However, for subclinical assessment, both groups showed an increase in stride and stance time at Post-trial, with an increase in pressure applied by cows while standing in EX3 group and a reduction in coronary band temperature in both groups at Post-trial and Follow-up. Contact area and claw conformation changed after provision of outdoor access in both groups. This study illustrates that with the use of subclinical methods; we can reveal effects of outdoor access on gait and hoof health that might not be visible using the traditional methods.

Data from 80,713 first-calving cows (1984 to1989) of the Holstein, Mambi, and Siboney breeds, belonging to seven large dairy enterprises in Cuba and progenies of 1,297 sires, were analyzed. For each cow, the average across all lactations for at least 14 years after first calving was defined as individual productivity (PI), and the corresponding lifetime sum as accumulated productivity (PA); both traits were. Two genetic models were fitted: a classical Animal Model (M1) and a Sire maternal grandsire model (Sire MGS; M2), aimed at partitioning additive genetic variance into paternal and maternal-line components. Heritability estimates under model M1 were moderate (h2 {approx} 0.135 to 0.140), whereas M2 yielded higher values (h2 {approx} 0.158 to 0.170), reflecting increased additive variance due to a better connectedness across herds. Using estimated breeding values (EBV) for PI and PA, a global cow merit index (H1) was defined under M1. Under M2, a parental index (IM2) combining four standardized predictors (paternal and maternal-grandsire EBV for PI and PA) was constructed. Multiple regression of H1 on IM2 showed that the paternal and maternal-grandsire paths accounted for 73% and 27% of the variation, respectively, indicating a non-negligible maternal-line contribution. Model M2 provided the best overall fit according to information criteria and cross validation using two independent subsamples and the full population yielded correlations of 0.870 to 0.881, demonstrating strong predictive ability and stability of IM2 rankings. These results support the Sire MGS model as a structural extension of the Animal Model for breeding programs targeting lifetime productivity in tropical dairy cattle.

BackgroundThe use of genome-wide association studies (GWAS) has led to the identification of numerous quantitative trait loci and candidate genes in dairy cattle. To obtain sufficient power of GWAS and to identify quantitative trait nucleotides, whole-genome sequence data is required. Sequence data facilitates the identification of potential causal variants; however, sequencing of whole genomes is still expensive for a large number of animals. Imputation is a quick and efficient way of obtaining sequence data from large datasets. Milk production traits are complex and influenced by many genetic and environmental factors. Although extensive research has been performed for these traits, with many associations unveiled thus far, due to their crucial economic importance, complex genetic architecture, and the fact that causative variants in cattle are still scarce, there is a need for a better understanding of their genetic background. In this study, we aimed to identify new candidate loci associated with milk production traits in German Holstein cattle, the most important dairy breed in Germany and worldwide. For that purpose, 252,285 cattle were imputed to the sequence level and large-scale GWAS was carried out to identify new association signals. ResultsWe confirmed many known and identified 30 previously unreported candidate genes for milk, fat, and protein yield. While all of the genes were functionally associated with the traits, some showed pleiotropic effects as well. Specifically, association with mammary gland development, fatty acid synthesis, metabolism of lipids, or milk production QTLs in other farm animals has been reported. Variants associated with these genes explained a large percentage of genetic variance, compared to random ones. ConclusionsOur findings proved the power of large samples and sequence-based GWAS in detecting new association signals. In order to fully exploit the power of GWAS, one should aim at very large samples combined with whole-genome sequence data. Although milk production traits in cattle are comprehensively researched, the genetic background of these traits is still not fully understood, with the potential for many new associations to be revealed, as shown in our study. With constantly growing sample sizes, we expect more insights into the genetic architecture of production traits in the future.

The GeneMax (GMX) Advantage test, developed by Zoetis, uses approximately 50,000 single nucleotide polymorphisms (SNP) to predict the genomic potential of a commercial Angus heifer. Genetic predictions are provided for Calving Ease Maternal, Weaning Weight, Heifer Pregnancy, Milk, Mature Weight, Dry Matter Intake, Carcass Weight, Marbling, Yield, and three economic selection indices. Test results can inform selection and culling decisions made by commercial beef cattle producers. To measure the accuracy of the trait predictions, data from commercial Angus females and their progeny at the University of Missouri Thompson Research Center were utilized to analyze weaning weight, milk, marbling, fat, ribeye area, and carcass weight. Progeny phenotypic data were matched to the respective dam, then the cows genomic predictions were compared to the calfs age-adjusted phenotypes using correlation and linear model effect sizes. All tested GeneMax scores of the dam were significantly correlated with and predicted calf performance. Our predicted effect sizes, except for fat thickness, were similar to those reported by Zoetis. In conclusion, the GeneMax Advantage test accurately ranks animals based on their genetic merit and is an effective selection tool in commercial cowherds.

Field methods to diagnose bovine respiratory disease (BRD) do not accurately identify airway inflammation and lack clinical sensitivity. New diagnostic modalities, such as thoracic ultrasound (TU), computer-assisted lung auscultation (CALA), and transtracheal wash (TTW), have recently emerged which may deliver accurate diagnosis and prediction of BRD in clinical settings. Therefore, we sought to compare TU, CALA, and TTW fluid cytologic assessment in stocker cattle at risk for BRD in a pilot study. We enrolled 17 high-risk mixed-breed beef steers, sampled 10 and 21 days after arrival and conventional management, in a pilot cross-sectional observational study. Cattle were examined daily for 82 days for clinical BRD. On day 10, 16 cattle received CALA, and 10 and 8 of these received TU and TTW, respectively. On day 21, 12 cattle received CALA and TTW, and 10 received TU. CALA was scored as 1-5. Lung consolidation and/or comet tails were evaluated by TU. TTW was evaluated by 200-cell differential count, with inflammation defined as >20% neutrophils. Relationships between each diagnostic test, and between diagnostic tests and clinical BRD, were evaluated by logistic regression (P<0.10). Fourteen cattle were treated for BRD. CALA scores ranged 1-3; three cattle had lung consolidation. On day 10, 5 of 6 cattle previously treated for BRD and 0 of 3 not treated had >20% TTW neutrophils. On day 21, 5 of 9 treated cattle and 1 of 3 not treated had >20% TTW neutrophils. No significant relationship between CALA, TU, and TTW inflammation existed. TTW inflammation was associated with BRD diagnosis (P=0.0586). CALA and TU results were unrelated to TTW inflammation. Cytologic assessment of TTW may improve antemortem diagnosis of BRD.