Reproductive performance is a key determinant of cow longevity in a pasture-based, seasonal dairy system. Unfortunately, direct fertility phenotypes such as intercalving interval or pregnancy rate tend to have low heritabilities and occur relatively late in an animal's life. In contrast, age at puberty (AGEP) is a moderately heritable, early-in-life trait that may be estimated using an animal's age at first measured elevation in blood plasma progesterone (AGEP4) concentrations. Understanding the genetic architecture of AGEP4 in addition to genetic relationships between AGEP4 and fertility traits in lactating cows is important, as is its relationship with body size in the growing animal. Thus, the objectives of this research were 3-fold. First, to estimate the genetic and phenotypic (co)variances between AGEP4 and subsequent fertility during first and second lactations. Second, to quantify the associations between AGEP4 and height, length, and BW measured when animals were approximately 11 mo old (standard deviation = 0.5). Third, to identify genomic regions that are likely to be associated with variation in AGEP4. We measured AGEP4, height, length, and BW in approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey crossbred yearling heifers across 54 pasture-based herds managed in seasonal calving farm systems. We also obtained calving rate (CR42, success or failure to calve within the first 42 d of the seasonal calving period), breeding rate (PB21, success or failure to be presented for breeding within the first 21 d of the seasonal breeding period) and pregnancy rate (PR42, success or failure to become pregnant within the first 42 d of the seasonal breeding period) phenotypes from their first and second lactations. The animals were genotyped using the Weatherby's Versa 50K SNP array (Illumina, San Diego, CA). The estimated heritabilities of AGEP4, height, length, and BW were 0.34 (90% credibility interval [CRI]: 0.30, 0.37), 0.28 (90% CRI: 0.25, 0.31), 0.21 (90% CRI: 0.18, 0.23), and 0.33 (90% CRI: 0.30, 0.36), respectively. In contrast, the heritabilities of CR42, PB21 and PR42 were all <0.05 in both first and second lactations. The genetic correlations between AGEP4 and these fertility traits were generally moderate, ranging from 0.11 to 0.60, whereas genetic correlations between AGEP4 and yearling body-conformation traits ranged from 0.02 to 0.28. Our GWAS highlighted a genomic window on chromosome 5 that was strongly associated with variation in AGEP4. We also identified 4 regions, located on chromosomes 14, 6, 1, and 11 (in order of decreasing importance), that exhibited suggestive associations with AGEP4. Our results show that AGEP4 is a reasonable predictor of estimated breeding values for fertility traits in lactating cows. Although the GWAS provided insights into genetic mechanisms underpinning AGEP4, further work is required to test genomic predictions of fertility that use this information.

Anogenital distance (AGD) is a moderately heritable trait that can be measured at a young age that may provide an opportunity to indirectly select for improved fertility in dairy cattle. In this study, we characterized AGD and its genetic and phenotypic relationships with a range of body stature and fertility traits. We measured AGD, shoulder height, body length, and body weight in a population of 5,010 Holstein-Friesian and Holstein-Friesian × Jersey crossbred heifers at approximately 11 mo of age (AGD1). These animals were born in 2018 across 54 seasonal calving, pasture-based dairy herds. A second measure of AGD was collected in a subset of herds (n = 17; 1,956 animals) when the animals averaged 29 mo of age (AGD2). Fertility measures included age at puberty (AGEP), then time of calving, breeding, and pregnancy during the first and second lactations. We constructed binary traits reflecting the animal's ability to calve during the first 42 d of their herd's seasonal calving period (CR42), be presented for breeding during the first 21 d of the seasonal breeding period (PB21) and become pregnant during the first 42 d of the seasonal breeding period (PR42). The posterior mean of sampled heritabilities for AGD1 was 0.23, with 90% of samples falling within a credibility interval (90% CRI) of 0.20 to 0.26, whereas the heritability of AGD2 was 0.29 (90% CRI 0.24 to 0.34). The relationship between AGD1 and AGD2 was highly positive, with a genetic correlation of 0.89 (90% CRI 0.82 to 0.94). Using a GWAS analysis of 2,460 genomic windows based on 50k genotype data, we detected a region on chromosome 20 that was highly associated with variation in AGD1, and a second region on chromosome 13 that was moderately associated with variation in AGD1. We did not detect any genomic regions associated with AGD2 which was measured in fewer animals. The genetic correlation between AGD1 and AGEP was 0.10 (90% CRI 0.00 to 0.19), whereas the genetic correlation between AGD2 and AGEP was 0.30 (90% CRI 0.15 to 0.44). The timing of calving, breeding, and pregnancy (CR42, PB21, and PR42) during first or second lactations exhibited moderate genetic relationships with AGD1 (0.19 to 0.52) and AGD2 (0.46 to 0.63). Genetic correlations between AGD and body stature traits were weak (≤0.16). We conclude that AGD is a moderately heritable trait, which may have value as an early-in-life genetic predictor for reproductive success during lactation.

AIMS:To explore animal- and herd-level risk factors influencing age at puberty in predominantly Holstein-Friesian dairy heifers managed in seasonal, pasture-based systems. METHODS:Heifers born in spring 2018 (n = 5,010) from 54 commercial dairy herds in New Zealand were visited on three occasions when the mean heifer age, within herd, was 10 (visit 1; V1), 11 (V2) and 12 (V3) months old. Blood samples were collected on each visit and liveweight, stature and anogenital distance (AGD) were measured at V2. Heifers were defined as having reached puberty at the first visit where blood progesterone was elevated (≥ 1 ng/mL). Animal-level response variables included pubertal status by V1, V2 and V3, and age at puberty (or age at V3 plus 31 days for those that had not attained puberty by V3). To explore herd-level management factors, farmers answered a questionnaire relating to animal location, land type, health, feeding, and management between weaning and mating. A partial least squares regression was undertaken to identify herd-level factors associated with the greatest influence on puberty rate within herd. RESULTS:The mean age at puberty was 352 (SD 34.9) days. Heavier animals at a greater proportion of expected mature liveweight based on their breeding value for liveweight, or animals with a higher breed proportion of Jersey and lower breed proportion of Holstein, were associated with earlier puberty. Herd puberty rates varied widely among enrolled herds, and averaged 20%, 39% and 56% by V1, V2 and V3, respectively. Liveweight, followed by breed and land type, had the greatest influence on the herd puberty rate. Heifer herds with a greater mean liveweight (absolute and proportion of expected mature weight) or greater Jersey proportion had more animals that reached puberty at any visit, whereas herds located on steep land or with greater Holstein breed proportions had lower puberty rates. Management-related factors such as vaccinations, provision of feed supplements, and weighing frequency were also herd-level risk factors of puberty but had less influence. CONCLUSIONS AND CLINICAL RELEVANCE:This study highlights the importance of having well-grown heifers for increasing the chances of earlier puberty onset and the effect of breed and youngstock management to achieve growth targets. These outcomes have important implications for the optimal management of heifers to achieve puberty before their maiden breeding and for the timing of measurements to potentially incorporate a puberty trait in genetic evaluations.

Clinical mastitis (CM) incidence is considerable in terms of cows affected per year, but cases are much less common in terms of detections per cow per milking. From a modeling perspective, where predictions are made every time any cow is milked, low CM incidence per cow day makes training, evaluating, and applying CM prediction models a challenge. The objective of this study was to build models for predicting CM incidence using time-series sensor data and choose models that maximize net return based on a cost matrix. Data collected from 2 university dairy farms, the University of Florida and Virginia Polytechnic Institute and State University, were used to gather representative data, including 110,156 milkings and 333 CM cases. Variables used in the models were milk yield, protein, lactose, fat, electrical conductivity, days in milk, lactation number, and activity as the number of steps, lying time, lying bouts, and lying bout duration. Models that predicted either likelihood of CM caused by gram-negative (GN) or gram-positive (GP) bacteria on each day were derived using extreme gradient boosting with weighting favoring true-positive cases, logistic responses, and log-loss errors. Model accuracies were determined using data randomly held out from the training set on each run. All variables considered were in terms of change (slope) over previous days, including the day CM was visually detected. The GN models had a median sensitivity (Se) of 52.6% and specificity (Sp) of 99.8%, whereas the GP models had a median Se of 37.5% and Sp of 99.9% when tested on the held-out data. In our models optimized to reduce cost from predictions, the Se was much less than Sp, suggesting that CM models might benefit from greater model weighting placed on Sp. Results also highlight the importance of positive predictive value (true positive cases per predicted positive case) along with Sp and Se, as models built on sparse data tend to predict too many false-positive cases. The calculated partial net return of our GN and GP models were -$0.15 and -$0.10 per cow per lactation, respectively, whereas International Organization for Standardization (ISO) standard models with Se of 80% and Sp of 99% would return -$1.32 per cow per lactation. Models chosen that minimized the cost to the farmer differed markedly from models that met ISO guidelines, showing asymmetry in targets between Sp and Se when the disease incidence rate is low. Because of the unique challenges that low-incidence diseases like CM present, we recommend that future CM predictive models consider the economic and practical implications in addition to the traditional model evaluation metrics.

Vaccination against coliform mastitis has become part of mastitis control programs in the past 3 decades, as a means of reducing the severity of clinical mastitis. Our study objective was to evaluate the effect of 2 commercially available vaccines on clinical, behavioral, and antibody response following Escherichia coli intramammary challenge in cows near peak lactation. Cows (n = 12 per group) were vaccinated with vaccine 1 (V1) or vaccine 2 (V2) at dry-off, 21 d pre-calving, and 14 d post-calving. Twelve cows served as unvaccinated controls (CTL). Cows were challenged with E. coli in a rear quarter at approximately 100 d in milk. Milk samples were collected pre- and post-challenge to enumerate E. coli and determine somatic cell count. Serum was collected before each vaccination and at d 0, 1, 2, 3, 6, 30, and 60 relative to challenge, to study antibody response. Milk IgA and tumor necrosis factor-α concentrations were determined in whey. Vaginal temperature, cow activity, and milk yield and components were monitored post-challenge. Bacterial count, somatic cell score, milk yield and component decline, vaginal temperature, activity measures, and antibody and cytokine response were analyzed for treatment differences. The effects of parity, breed, and a repeated measure of time were also tested. Seven cows had to be removed from the study post-challenge for antibiotic treatment (CTL and V1, n = 3 each; V2, n = 1), 2 of which were euthanized (both CTL). Vaccinated cows exhibited fever (vaginal temperature ≥39.4°C) 3 h earlier than CTL cows, but we found no differences between treatments for bacterial count, somatic cell score, or milk yield reduction. Vaccinated cows spent more time lying per rest bout 2 d post-challenge, but total daily lying time was not different from CTL cows during the 7 d post-challenge. The vaccines differed in antibody response: V1 cows had greater serum IgG1 and IgG2 post-challenge. A parity effect was also evident: primiparous cows had lower bacterial counts, somatic cell score and a smaller milk yield decline than multiparous cows, but also had lower antibody production. Immunization with either J5 bacterin did not reduce clinical signs of mastitis in cows challenged at 100 d in milk, demonstrating that the effects of J5 vaccination had diminished at peak lactation.

Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d −7 and −5, d −7 and −4, d −7 and −3, d −7 and −2, and d −7 and −1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d −10, −7, and −3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d −3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d −10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems.

The performance of a commercial, real-time PCR assay was compared with traditional bacterial culture for the identification of Streptococcus uberis and Staphylococcus aureus in bovine milk collected at different stages of lactation. Initial validation tests using fresh and frozen quarter milk samples identified factors that affected the success of the PCR. Therefore, the standard protocol was adjusted for samples collected at the first milking postpartum (colostrum) and from clinical mastitis cases. The adjustment involved PCR testing both undiluted and diluted (1 in 10 with sterile water) DNA extracts. The performance comparison between culture and the PCR assay used milk samples collected aseptically from individual quarters of mixed-age spring-calving dairy cows, during early, mid, and late lactation. Bacterial culture results were used to select a subset of samples for PCR testing (n = 315) that represented quarters with a current or prior Strep. uberis or Staph. aureus infection. Compared with culture, PCR had a sensitivity of 86.8% and specificity of 87.7% for detecting Strep. uberis (kappa = 0.74) and 96.4% and 99.7%, respectively, for detecting Staph. aureus (kappa = 0.96). The dilution of DNA extracts for colostrum and clinical samples increased the relative sensitivity from 79.2% to 86.8% for Strep. uberis detection and from 92.9% to 96.4% for Staph. aureus, presumably through diluting unidentified PCR inhibitors. The sensitivity for detecting Strep. uberis using PCR, relative to culture, was similar throughout lactation (85-89%), whereas relative specificity was lowest immediately postcalving (64%) but improved in mid and late lactation (98%). Specificity estimates for samples collected in early lactation can be optimized by reducing the cutoff cycle threshold (Ct) value from the recommended value of 37 to 34. Although using this value improved specificity (77%), it reduced test sensitivity (77%). The PCR assay lacked agreement with culture in early lactation, specifically for diagnosing Strep. uberis. Thus, PCR should not be used as the only tool for diagnosing mastitis in early lactation.

Rapid identification of the pathogen responsible for an intramammary infection in a dairy cow can support herd management decisions. Polymerase chain reaction (PCR) has become available to identify mastitis pathogens in milk, offering a rapid and sensitive test. The performance of a commercial, real-time PCR assay was compared with traditional bacterial culture for the identification of the most-frequent pathogen in New Zealand, Streptococcus uberis, during three stages of lactation. Aseptically collected quarter-milk samples (n=6,848) were analysed by culture, and a subset (n=315) selected for PCR analysis based on S. uberis infection status. Using culture as the reference test, PCR had a relative sensitivity of 89.0%, and a specificity of 64.3% (kappa=0.49) for detecting S. uberis in milk collected at the first milking post-calving. The relative sensitivity and specificity for samples collected in mid-late lactation were 89.3% and 97.7%, respectively, indicating a greater agreement between the two tests later in lactation (kappa=0.88). Accurate diagnostic tests are necessary for mastitis management. In early lactation, when S. uberis prevalence is high, bacterial culture should be used. The PCR assay tested in this study is most useful after the first month of lactation.