Dry matter intake in US Holstein cows: exploring the genomic and phenotypic impact of milk components and body weight composite.

Large data sets allow estimating feed required for individual milk components or body maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake (DMI) records from 8,513 lactations of 6,621 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk, age-parity subclass, trial date, management group, and body weight change during 28- and 42-d feeding trials in mid-lactation. Phenotypic regressions of DMI on milk (0.014 ± 0.006), fat (3.06 ± 0.01), and protein (4.79 ± 0.25) were much less than corresponding genomic regressions (0.08 ± 0.03, 11.30 ± 0.47, and 9.35 ± 0.87) or sire genomic regressions multiplied by 2 (0.048 ± 0.04, 6.73 ± 0.94, and 4.98 ± 1.75). Thus, marginal feed costs as fractions of marginal milk revenue were higher from genetic than phenotypic regressions. According to the energy-corrected milk formula, fat production requires 69% more DMI than protein production. In the phenotypic regression, it was estimated that protein production requires 56% more DMI than fat. However, the genomic regression for the animal showed a difference of only 21% more DMI for protein compared with fat, while the sire genomic regressions indicated approximately 35% more DMI for fat than protein. Estimates of annual maintenance in kg DMI / kg body weight/lactation were similar from phenotypic regression (5.9 ± 0.14), genomic regression (5.8 ± 0.31), and sire genomic regression multiplied by 2 (5.3 ± 0.55) and are larger than those estimated by NASEM (2021) based on NEL equations. Multiple regressions on genomic evaluations for the 5 type traits in body weight composite (BWC) showed that strength was the type trait most associated with body weight and DMI, agreeing with the current BWC formula, whereas other traits were less useful predictors, especially for DMI. The Net Merit formula used to weight different genetic traits to achieve an economically optimal overall selection response was revised in 2021 to better account for these estimated regressions. To improve profitability, breeding programs should select smaller cows with negative residual feed intake that produce more milk, fat, and protein.

Periparturient Mineral Metabolism: Implications to Health and Productivity.

Mineral metabolism, in particular Ca, and to a lesser extent phosphorus (P) and magnesium (Mg), is altered with the onset of lactation because of extensive irreversible loss to synthesize colostrum and milk. The transient reduction in the concentration of Ca in blood, particularly when it lasts days, increases the risk of mineral-related disorders such as hypocalcemia and, to a lesser extent, hypophosphatemia. Although the incidence of clinical hypocalcemia can be reduced by prepartum dietary interventions, subclinical hypocalcemia remains prevalent, affecting up to 60% of the dairy cows in the first 3 d postpartum. More importantly, strong associations exist between hypocalcemia and increased susceptibility to other peripartum diseases and impaired reproductive performance. Mechanistic experiments have demonstrated the role of Ca on innate immune response in dairy cows, which presumably predisposes them to other diseases. Hypocalcemia is not related to inadequate Ca intake as prepartum diets marginal to deficient in Ca reduce the risk of the disease. Therefore, the understanding of how Ca homeostasis is regulated, in particular how calciotropic hormones such as parathyroid hormone and 1,25-dihydroxyvitamin D3, affect blood Ca concentrations, gastrointestinal Ca absorption, bone remodeling, and renal excretion of Ca become critical to develop novel strategies to prevent mineral imbalances either by nutritional or pharmacological interventions. A common method to reduce the risk of hypocalcemia is the manipulation of the prepartum dietary cation-anion difference. Feeding acidogenic diets not only improves Ca homeostasis and reduces hypocalcemia, but also reduces the risk of uterine diseases and improves productive performance. Feeding diets that induce a negative Ca balance in the last weeks of gestation also reduce the risk of clinical hypocalcemia, and recent work shows that the incorporation of mineral sequestering agents, presumably by reducing the absorption of P and Ca prepartum, increases blood Ca at calving, although benefits to production and health remain to be shown. Alternative strategies to minimize subclinical hypocalcemia with the use of vitamin D metabolites either fed prepartum or as a pharmacological agent administered immediately after calving have shown promising results in reducing hypocalcemia and altering immune cell function, which might prove efficacious to prevent diseases in early lactation. This review summarizes the current understanding of Ca homeostasis around parturition, the limited knowledge of the exact mechanisms for gastrointestinal Ca absorption in bovine, the implications of hypocalcemia on the health of dairy cows, and discusses the methods to minimize the risk of hypocalcemia and their impacts on productive performance and health in dairy cows.

An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows.

Genetic selection has remarkably helped U.S. dairy farms to decrease their carbon footprint by more than doubling milk production per cow over time. Despite the environmental and economic benefits of improved feed and milk production efficiency, there is a critical need to explore phenotypical variance for feed utilization to advance the long-term sustainability of dairy farms. Feed is a major expense in dairy operations, and their enteric fermentation is a major source of greenhouse gases in agriculture. The challenges to expanding the phenotypic database, especially for feed efficiency predictions, and the lack of understanding of its drivers limit its utilization. Herein, we leveraged an artificial intelligence approach with feature engineering and ensemble methods to explore the predictive power of the rumen microbiome for feed and milk production efficiency traits, as rumen microbes play a central role in physiological responses in dairy cows. The novel ensemble method allowed to further identify key microbes linked to the efficiency measures. We used a population of 454 genotyped Holstein cows in the U.S. and Canada with individually measured feed and milk production efficiency phenotypes. The study underscored that the rumen microbiome is a major driver of residual feed intake (RFI), the most robust feed efficiency measure evaluated in the study, accounting for 36% of its variation. Further analyses showed that several alpha-diversity metrics were lower in more feed-efficient cows. For RFI, [Ruminococcus] gauvreauii group was the only genus positively associated with an improved feed efficiency status while seven other taxa were associated with inefficiency. The study also highlights that the rumen microbiome is pivotal for the unexplained variance in milk fat and protein production efficiency. Estimation of the carbon footprint of these cows shows that selection for better RFI could reduce up to 5 kg of diet consumed per cow daily, potentially reducing up to 37.5% of CH4. These findings shed light that the integration of artificial intelligence approaches, microbiology, and ruminant nutrition can be a path to further advance our understanding of the rumen microbiome on nutrient requirements and lactation performance of dairy cows to support the long-term sustainability of the dairy community.

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.

What a 31-yr multibreed herd taught us about the influence of B. indicus genetics on reproductive performance of cows.

Bos taurus × Bos indicus crosses are widespread in tropical and subtropical regions, nonetheless, quantitative information about the influence of B. indicus genetics on the reproductive performance of beef cattle is lacking. Herein, we determined the association between level of B. indicus genetics and reproduction from a 31-yr dataset comprising sequential breeding seasons of the University of Florida multibreed herd (n = 6,503 Angus × Brahman cows). The proportion of B. indicus genetics in this herd is evenly distributed by each 1/32nd or approximately 3-percentage points. From 1989 to 2020, the estrous cycle of cows was synchronized for artificial insemination (AI) based on detected estrus or timed-AI (TAI) using programs based on gonadotropin-releasing hormone and prostaglandin, and progestin/progesterone. All cows were exposed to natural service after AI and approximately 90-d breeding seasons, considering the day of AI as day 0. The proportion of B. indicus genetics of cows was associated negatively with pregnancy per AI, ranging from 51.6% for cows with 0%-19% of B. indicus genetics to 37.4% for cows with 81%-100% of B. indicus genetics. Similar association was found for estrous response at the end of the synchronization protocol, ranging from 66.3% to 38.4%, respectively. This reduced estrous response helped to explain the pregnancy results, once the pregnancy to AI of cows showing estrus was 2.3-fold greater than for those not showing estrus and submitted to TAI. Despite reduced pregnancy per AI, the increase in the proportion of B. indicus genetics of cows was not associated with a reduction in the proportion of pregnant cows at the end of the breeding season. Nevertheless, the interval from entering the breeding season to pregnancy was lengthened as the proportion of B. indicus genetics of cows increased. The median days to pregnancy was extended by 25 when the proportion of B. indicus genetics surpassed 78% compared with less than 20%. Thus, the increase in the proportion of B. indicus genetics of cows was related to a reduction in pregnancy per AI and lengthening the interval to attain pregnancy during the breeding season, but not with the final proportion of pregnant cows. As a result, reproductive management strategies directed specifically to cows with a greater proportion of B. indicus genetics are needed to improve the rate of pregnancy in beef herds.

Cow­calf operations in the tropics and sub-tropics have benefited from the environmental adaptation provided by Bos indicus genetics. However, reproductive performance has been a cause of concern, although poorly quantified. This study characterized how much the B. indicus genetics in crossbred cows influence herd reproduction. We analyzed data from cows with known proportions of Angus and Brahman genetics, from the same crossbred herd, for 31 sequential breeding seasons. The increase in the proportion of B. indicus genetics reduced estrous response and pregnancy per artificial insemination after estrous synchronization, but not the proportion of pregnant cows at the end of the breeding season. Interval from the beginning of the breeding season to pregnancy was extended by 25 d when the proportion of B. indicus genetics surpassed 78%. In conclusion, reproductive management strategies directed specifically to cows with a greater proportion of B. indicus genetics are needed to improve the rate of pregnancy in beef herds.

Impact of epistasis effects on the accuracy of predicting phenotypic values of residual feed intake in U. S Holstein cows.

The impact of genomic epistasis effects on the accuracy of predicting the phenotypic values of residual feed intake (RFI) in U.S. Holstein cows was evaluated using 6215 Holstein cows and 78,964 SNPs. Two SNP models and seven epistasis models were initially evaluated. Heritability estimates and the accuracy of predicting the RFI phenotypic values from 10-fold cross-validation studies identified the model with SNP additive effects and additive × additive (A×A) epistasis effects (A + A×A model) to be the best prediction model. Under the A + A×A model, additive heritability was 0.141, and A×A heritability was 0.263 that consisted of 0.260 inter-chromosome A×A heritability and 0.003 intra-chromosome A×A heritability, showing that inter-chromosome A×A effects were responsible for the accuracy increases due to A×A. Under the SNP additive model (A-only model), the additive heritability was 0.171. In the 10 validation populations, the average accuracy for predicting the RFI phenotypic values was 0.246 (with range 0.197-0.333) under A + A×A model and was 0.231 (with range of 0.188-0.319) under the A-only model. The average increase in the accuracy of predicting the RFI phenotypic values by the A + A×A model over the A-only model was 6.49% (with range of 3.02-14.29%). Results in this study showed A×A epistasis effects had a positive impact on the accuracy of predicting the RFI phenotypic values when combined with additive effects in the prediction model.

Investigating the Use of Dry Matter Intake and Energy Balance Prepartum as Predictors of Digestive Disorders Postpartum.

One objective was to evaluate the association of dry matter intake as a percentage of body weight (DMI%BW) and energy balance (EB) prepartum and postpartum, and energy-corrected milk (ECM) postpatum with digestive disorders postpartum. For this, ANOVA was used, and DMI%BW, EB, and ECM were the outcome variables, and left displaced abomasum (LDA), indigestion, and other digestive disorders (ODDZ) were the explanatory variables. The main objective was to evaluate prepartum DMI%BW and EB as predictors of digestive disorders. For this, logistic regression was used, and LDA, indigestion, and ODDZ were the outcome variables and DMI%BW and EB were the explanatory variables. Data from 689 cows from 11 experiments were compiled. Left displaced abomasum was not associated with prepartum DMI%BW or EB. Postpartum data were normalized to the day of the event (day 0). Cows that developed LDA had lesser postpartum DMI%BW on days -24, -23, -12, -7 to 0 and from days 1 to 8, 10 to 12, and 14 and 16, lesser postpartum EB from days -7 to -5, -3 to 0, and 12, and lesser postpartum energy-corrected milk on days -19, -2, -1, 0, 7, 9, 10, 15, and 17 relative to diagnosis than cows without LDA. Cows that developed indigestion had lesser prepartum DMI%BW and EB than cows without indigestion, and lesser postpartum DMI%BW on days -24, -1, 0, 1, and 2, and greater DMI%BW on day 26, lesser ECM on days -24, -2, -1, 0, 1, and 2 relative to diagnosis. Postpartum EB was not associated with indigestion postpartum. Cows that developed ODDZ had lesser prepartum DMI%BW on day -8 and from days -5 to -2, lesser prepartum EB on day -8 and from days -5 to -2, and lesser postpartum DMI%BW than cows without ODDZ. Each 0.1 percentage point decrease in the average DMI%BW and each Mcal decrease in the average EB in the last 3 days prepartum increased the odds of having indigestion by 9% each. Cutoffs for DMI%BW and EB during the last 3 days prepartum to predict indigestion were established and were ≤1.3%/day and ≤0.68 Mcal/day, respectively. In summary, measures of prepartum DMI%BW and EB were associated with indigestion and ODDZ postpartum and were predictors of indigestion postpartum, although the effect sizes were small.

Effect of presynchronization with prostaglandin F2α before the 5-d timed AI protocol on ovarian responses and pregnancy in dairy heifers.

The objectives were the determine the effects of presynchronization with PGF2α 2 days before the 5-d timed artificial insemination (AI) protocol on ovarian responses and pregnancy per AI (P/AI) in dairy heifers. The hypothesis was that PGF2α would induce responsive heifers to be in proestrus at the initiation of the timed AI protocol, which was expected to improve ovulatory responses and P/AI. Weekly cohorts of Holstein heifers were blocked by age and, within block, randomly assigned to remain as control (CON; n = 255) or receive PGF2α on experiment Day -10 (PG; n = 255). All heifers were subjected to the 5-d timed AI protocol (Day -8, GnRH + intravaginal progesterone controlled internal drug release insert; Day -3, PGF2α and insert removal; Day -2, PGF2α; and Day 0, GnRH and AI). A subset of 22 blocks of heifers (n = 43) had their ovaries scanned by ultrasonography on experiment Days -8, -3, 0, and 2 and blood was sampled and analyzed for concentrations of progesterone on experiment Days -8, -7, -5, and -3. Pregnancy was diagnosed on experiment Days 32 and 60. On the day of the first GnRH of the timed AI protocol, PG heifers had smaller concentration of progesterone in plasma (CON = 4.5 ±â€¯0.5 vs. PG = 0.5 ±â€¯0.5 ng/mL), but larger follicular diameter (CON = 9.1 ±â€¯0.5 vs. PG = 11.0 ±â€¯0.5 mm), and a greater proportion of them had a follicle with at least 8.0 mm in diameter (CON = 61.9 vs. PG = 90.9%) than CON heifers, which resulted in increased ovulation to GnRH (CON = 19.0 vs. PG = 86.3%). Ovulation to the initial GnRH of the protocol increased as the concentration of progesterone in plasma decreased, from less than 20% when progesterone was greater than 5.0 ng/mL to more than 65% when progesterone was less than 1.0 ng/mL. More CON than PG heifers spontaenously ovulated before the day of timed AI. Detection of estrus on the day of timed AI did not differ between treatments (CON = 50.9 vs. PG = 46.6%), but P/AI on Days 32 (CON = 52.9 vs. PG = 61.1%) and 60 (CON = 49.0 vs. PG = 57.1%) after insemination tended to be greater for PG than CON; however, the benefit to presynchronization was observed in heifers inseminated with conventional (CON = 54.7 vs. PG = 67.4%), but not in heifers inseminated with sex-sorted semen (CON = 50.9 vs. PG = 52.8%). Administration of PGF2α 2 days before initiating the timed AI protocol induced heifers to be in proestrus, which enhanced ovulation to the initial GnRH and favored pregnancy per AI, particularly in heifers inseminated with conventional semen.

Shift of uterine microbiota associated with antibiotic treatment and cure of metritis in dairy cows.

Broad-spectrum antibiotics such as ceftiofur and ampicillin are recommended for the treatment of metritis in dairy cows. Nonetheless, little is known about the impacts of antibiotics on the uterine microbiota. Here, we evaluated the shift in uterine microbiota after treating metritic cows with ceftiofur or ampicillin, and also gained insight into the uterine microbiota associated with cure of metritis. Uterine swabs from ceftiofur-treated, ampicillin-treated, and untreated metritic Holstein cows were collected on the day of metritis diagnosis (D1) and on D6 and then used for genomic DNA extraction and sequencing of the V4 hypervariable region of the 16S rRNA gene on the Illumina MiSeq platform. The uterine microbiota consolidated over time by decreasing species richness and increasing evenness; therefore, becoming more homogeneous. The uterine microbial community showed distinct clustering patterns on D6 according to antibiotic treatment, which could be attributed to more dynamic changes in the microbial structure from D1 to D6 in ceftiofur-treated cows. Ceftiofur led to significant changes at the community level, phylum level, and genus level, whereas the changes in ampicillin and untreated cows, although following the same pattern, were mostly non-significant. Bacteroidetes was significantly increased in ceftiofur-treated cows but was not changed after ampicillin and no treatment. Different responses to antibiotics were observed in Porphyromonas, which increased in relative abundance with ceftiofur and decreased with ampicillin. Regardless of treatment group, failure to cure metritis was associated with a decrease in diversity of uterine microbiota and an increase in the relative abundance of Bacteroides, Porphyromonas, and Fusobacterium.