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Introduction: Clinical metritis (CM) has significant costs to dairy producers. Current treatment strategy involves systemic antibiotics; however, there is increasing concern about judicious antibiotic use. The study objective was to evaluate the effects of a non-antibiotic treatment vs. systemic antibiotic therapy on the vaginal discharge microbiome of dairy cows diagnosed with CM at 7 ± 3 DIM (days in milk). We hypothesize that both treatment methods will have a similar impact on the reproductive microbiome due to broad-spectrum bactericidal activity; therefore, there will not be significant differences amongst the microbiota after the completion of therapy. Methods: Cows from a central Pennsylvania dairy were screened for CM at 7 ± 3 days DIM using a Metricheck™ device (n = 351). Cows with red-brown watery discharge were diagnosed with CM and eligible for enrollment. Eligible cows (n = 77) were blocked by parity and randomly allocated to one of two treatments starting on the day of diagnosis: (1) Intrauterine dextrose (DEX, n = 38): 1 l of an intrauterine 50% dextrose solution for 3 days, and (2) Systemic ceftiofur (CONV, n = 39): two injections of ceftiofur (6.6 mg/Kg of BW; Excede, Zoetis Inc.) 72 h apart. Cows were evaluated for clinical cure rate at 7 ± 3 and 14 ± 3 days post-diagnosis. Vaginal discharge samples were collected using the Metricheck™ at enrollment day [study day (sd) 0, pre-treatment], sd 7, and sd 14 for a subset of enrolled cows (DEX = 13, CONV = 14). Vaginal discharge samples were analyzed with 16S rRNA sequencing to evaluate changes in the microbiome between treatments. Results: After treatment, there were only minor differences within the microbiome between the two treatments indicating the potential suitability of dextrose as an antibiotic-alternative treatment. Alpha diversity did not differ (Welch's t -test) between the treatments at any of the time points. Beta diversity based on PERMANOVA analysis did differ between treatments at sd 0 (P = 0.014) and again at sd 14 (P = 0.028), but not at sd 7 (P = 0.261). Discussion: While 16S rRNA analysis does not provide information on bacterial viability, the relative similarity of the microbiome between the two groups immediately following treatment might suggest that intrauterine dextrose could be utilized as an alternative treatment for CM. [ABSTRACT FROM AUTHOR]

Artificially rearing lambs is an important component of United States sheep production as some flocks select for increasing litter size to boost productivity. However, there is a conception among shepherds that artificially reared ewe lambs will ultimately result in poor mothers since they were not naturally raised. The objective of this study was to determine if differences in maternal behavior could be detected between ewes raised artificially and those raised naturally and if dam rearing type had an impact on lamb growth performance. Forty-eight ewes were enrolled comprised of 4 equal sized groups: artificially reared primiparous, naturally reared primiparous, artificially reared multi-parous, and naturally reared multi-parous. Ewes were observed using remote cameras to record maternal behaviors toward the lambs during and shortly after parturition. Behavior of 29 ewes during the first hour after parturition was analyzed. Independent multivariable regression models were evaluated for: proportion of time spent grooming, proportion of time spent helping the lambs nurse, number of helpful bouts, and the number of negative bouts. No statistically significant difference (P  < 0.05) was detected between artificially reared and naturally reared ewes for the proportion of time spent grooming and the proportion of time spent helping the lambs nurse. Historical flock data were utilized to evaluate lamb growth between artificially and naturally reared ewes. No detrimental associations between artificially reared ewes and lamb weaning weights were observed. This study shows that artificially reared ewes are no different in terms of mothering behaviors and may be used as potential replacement candidates. [ABSTRACT FROM AUTHOR]

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of “2.0” for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day. [ABSTRACT FROM AUTHOR]

Genotype-by-environmental interactions (G x E) have been reported to cause sire reranking in dairy populations. However, the effects of such interactions are not included in the current US national dairy genetic evaluation. Phenotypes collected from animals across the country are considered the same trait, disregarding interactions between genetic effects and region or other environmental factors. Variations in production systems may interact with the genotypes of the animals, leading to distinct phenotypes. Separating data into regions could change decision-making on bull selection, which is the most important step of a breeding program. The goal of this study is to evaluate the extent of genotype-by-environment interaction between the state of California and the region of New England in the US Holstein Population. Data from Holstein cows in the aforementioned regions collected from 2010 to 2020 were provided by the Council on Dairy Cattle Breeding. The genetic evaluation models used for the national evaluation were modified to account for New England and California phenotypes as separated yet correlated traits. The mixed model accounted for additive genetic, sire-by-herd interaction, and permanent environmental effects as random, and for the management group, age of parity, birth year, and parity as fixed effects. In some scenarios, sire-by-herd interaction and permanent environment were removed due to convergence problems. Variance components were estimated using AIREML under the Method R procedure. Such a method allows computing variance components for large databases by splitting the data into several random groups. Ten random samples of data containing 100,000 records were used for each analysis. The scenarios included single trait for each state, single trait with both states, and two-trait with separated states. Breeding values from the top 100 bulls in each region were compared with evaluate potential sire reranking. Heritabilities varied across regions for all three traits (Table 1). California presented lower heritability than New England across methods. The genetic correlations on the two-trait analysis averaged at 0.96, 0.92, and 0.95 for yield, fat, and protein, respectively. The Spearman correlation coefficient between breeding values of the bulls with more than 200 daughters was 0.87 for milk yield, suggesting that the reranking is unimportant. The genetic correlations suggested that GxE between the two regions is small and that the traits should not be split in the current genetic evaluation. In conclusion, no critical effect of genotype-by-environment interaction was observed, even though top bulls may differ across regions. The next step of this project is to evaluate GxE for somatic cell score and to include genomic information in the evaluations. Finally, a genomewide association study across the two regions will be performed to investigate changes in genomic regions associated with the phenotypes. [ABSTRACT FROM AUTHOR]

Lameness is a major animal welfare and economic issue for the dairy industry and is a challenge to overcome due to multifaceted causes. Digital cushion thickness (DCT) is a strong predictor of lameness and is phenotypically associated with incidence of claw horn disruption lesions (CHDL ; sole ulcers and white line disease). We hypothesized that DCT varies between digits and across lactation within the cow. This variation could be characterized to predict the occurrence of CHDL or compromised locomotion. BCS, visual locomotion score (VLS), DCT, and presence or absence of lesions were collected at 4 time points: <40 d prepartum (DPP), 1 to 30 d in milk (DIM), 90 to 120 DIM, and ≥255 DIM for 183 commercial Holstein cows enrolled in the study. Cows underwent digital sonographic examination for the measurement of DCT evaluated at the typical sole ulcer site beneath the flexor tuberosity for the right front medial and lateral digits and right hind medial and lateral digits. Factors such as parity number and stage in lactation were obtained from farm management software (DairyComp 305; Valley Agricultural Software, Tulare, CA). Cows were grouped by parity: primiparous (parity = 1) or multiparous (parity ≥ 2). The prevalence of CHDL among time points ranged from 0% to 4.2% for primiparous cows vs. 2.5% to 25% for multiparous cows, whereas the prevalence of lameness based on VLS of 3 to 5 ranged from 1.7% to 8.3% for primiparous cows vs. 12.7% to 33% for multiparous cows. DCT varied within primiparous and multiparous cows based on stage of lactation and digit (P < 0.05) and was thicker for both parity groups prior to dry off (≥255 DIM) and thinnest prior to calving (<40 DPP) and after peak lactation (90 to 120 DIM). The DCT of the front medial digit was thickest for primiparous heifers, whereas the hind lateral digit was thickest for multiparous cows. The DCT of the hind medial digit was thinnest for both parity groups. Parity group and DCT of the hind lateral digit <40 DPP were important predictors of CHDL (P < 0.05), whereas parity group and DCT of the hind lateral digit and front lateral digit at 1 to 30 DIM were key predictors of VLS lameness (P < 0.05). These results may help identify animals with higher odds of developing these diseases by highlighting key time points and specific digits of importance for monitoring. In addition, it improves our biological understanding of the relationship between DCT and lameness. [ABSTRACT FROM AUTHOR]

We define sustainable agriculture as a balance of practices protecting the natural environment, promoting economic vitality, and building healthy communities in the present without compromising the future. The dairy industry has been working for decades with these goals in mind, but under the brand of productive efficiency. This has resulted in tremendous gains in farm profitability and cow performance, though dairy systems are continually impacted by resource availability, advancing technology, and changing consumer values that all affect the environment in which cows must perform. As we redefine selection goals for sustainability we should prioritize overall fitness, encompassing a dairy cattle population that is genetically diverse, resilient, and adaptable. Greater genetic gains correspond to a faster rate of inbreeding, which has long been recognized as an antagonist to health and performance. Male genetic variation is very limited due to the marketability of high genetic merit sires and so conserving female genetic diversity must be a top priority. National genetic and genomic evaluations are delivered with the cooperation of key industry partners through a complex system of data sharing and management; incorporating new phenotypes (e.g., emissions) will require operating within the parameters of this system. Examples of changing industry trends include the rise in embryo transfer, increasing interest in evaluations for heat tolerance, higher frequency of robotic milkers in the United States, and the popularity of beef-on-dairy crossbreds as managers seek to sustain farm profits. Genetic improvement tools need to be just as dynamic as the dairy industry, but they all depend upon reliable and accurate data flow. Autonomous, sensor-based phenotyping represents a huge opportunity for genomic improvement, but translating this rapidly generated data into something that can be used for genomic selection will be a new challenge. [ABSTRACT FROM AUTHOR]