Effect of a dairy farmworker stewardship training program on antimicrobial drug usage in dairy cows

Antimicrobial use (AMU) is critical to preserving animal health and welfare. However, the development of antimicrobial resistance (AMR) represents a public health threat. Although most antimicrobials used on the farm require a veterinarian prescription, farmworkers make daily on-farm treatment decisions. Therefore, farmworker training is vital to promote responsible AMU. This research project aimed to evaluate the effect of farmworker antimicrobial stewardship (AMS) training on the quantity of AMU on dairy farms in Ohio and California. We hypothesized that farms receiving AMS training would have reduced AMU in dairy cattle compared with farms where training wasn't administered. We designed a quasiexperimental study with 18 conventional dairy farms enrolled in Ohio and California. Twelve farms received AMS training, and 6 farms did not. The AMS program included a 12-wk training focused on accurate identification of cows requiring antimicrobial treatment. We quantified AMU by collecting used drug containers, manually counted by researchers during farm visits. Treatment incidence (TI) was calculated using animal daily doses (ADD), and Poisson regression models were used to analyze the TI data. Disease incidence rate (DIR) in lactating cows was 2.2, 1.5, 1.0, 0.4, 0.3, 0.2, and 0.03/1,000 cow-days for mastitis, lameness, metritis, pneumonia, retained placenta, diarrheas, and other diseases (e.g., conjunctivitis, injuries), respectively. The highest TI by antimicrobial class (provided in ADD/1,000 cow-days) was cephalosporin (5.9), penicillin (5.2), tetracyclines (0.4), lincosamides (0.2), and sulfonamides (0.1). Among the trained farms using a within-treatment group analysis, no significant differences were observed in TI pre-intervention (10.9 ADD/1,000 cow-days) compared with TI postintervention (10.3 ADD/1,000 cow-days). Treatment incidence from the training group was 10.8 ADD/1,000 cow-days and although numerically lower, it was not significantly different compared with TI in the control groups at 13.9 ADD/1,000 cow-days (rate ratio = 0.77, CI = 0.25-2.38). Future research on AMS should incorporate farmworker training with social science approaches to overcome barriers and promote on-farm responsible use.

Implications of supplementing mid-lactation multiparous Holstein cows fed high by-product low-forage diets with rumen-protected methionine and lysine in a commercial dairy

Supplementation of rumen-protected amino acids may improve dairy cow performance but few studies have evaluated the implications of supplementing low-forage diets. Our objective was to evaluate the effects of supplementing rumen-protected methionine (Met) and lysine (Lys) on milk production and composition as well as on mammary gland health of mid-lactating Holstein cows from a commercial dairy farm feeding a high by-product low-forage diet. A total of 314 multiparous cows were randomly assigned to control (CON; 107 g of dry distillers' grains) or rumen-protected Met and Lys (RPML; 107 g dry distillers' grains + 107 g of RPML). All study cows were grouped in a single dry-lot pen and fed the same total mixed ration diet twice a day for a total of 7 weeks. Treatments were top-dressed on the total mix ration immediately after morning delivery with 107 g of dry distillers' grains for 1 week (adaptation period) and then with CON and RPML treatments for 6 weeks. Blood samples were taken from a subset of 22 cows per treatment to determine plasma AA (d 0 and 14) and plasma urea nitrogen and minerals (d 0, 14, and 42). Milk yield and clinical mastitis cases were recorded daily, and milk components were determined bi-weekly. Body condition score change was evaluated from d 0 to 42 of the study. Milk yield and components were analyzed by multiple linear regression. Treatment effects were evaluated at the cow level considering parity and milk yield and composition taken at baseline as a covariate in the models. Clinical mastitis risk was assessed by Poisson regression. Plasma Met increased (26.9 vs 36.0 µmol/L), Lys tended to increase (102.5 vs 121.1 µmol/L), and Ca increased (2.39 vs 2.46 mmol/L) with RPML supplementation. Cows supplemented with RPML had higher milk yield (45.4 vs 46.0 kg/d) and a lower risk of clinical mastitis (risk ratio = 0.39; 95% CI = 0.17-0.90) compared to CON cows. Milk components yield and concentrations, somatic cell count, body condition score change, plasma urea nitrogen, and plasma minerals other than Ca were not affected by RPML supplementation. Results suggest that RPML supplementation increases milk yield and decreases the risk of clinical mastitis in mid-lactation cows fed a high by-product low-forage diet. Further studies are needed to clarify the biological mechanisms for mammary gland responses to RPML supplementation.

Integration of statistical inferences and machine learning algorithms for prediction of metritis cure in dairy cows

The study's objectives were to identify cow-level and environmental factors associated with metritis cure to predict metritis cure using traditional statistics and machine learning algorithms. The data set used was from a previous study comparing the efficacy of different therapies and self-cure for metritis. Metritis was defined as fetid, watery, reddish-brownish discharge, with or without fever. Cure was defined as an absence of metritis signs 12 d after diagnosis. Cows were randomly allocated to receive a subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid (Excede, Zoetis) at the day of diagnosis and 3 d later (n = 275); and no treatment at the time of metritis diagnosis (n = 275). The variables days in milk (DIM) at metritis diagnosis, treatment, season of the metritis diagnosis, month of metritis diagnostic, number of lactation, parity, calving score, dystocia, retained fetal membranes, body condition score at d 5 postpartum, vulvovaginal laceration score, the rectal temperature at the metritis diagnosis, fever at diagnosis, milk production from the day before to metritis diagnosis, and milk production slope up to 5, 7, and 9 DIM were offered to univariate logistic regression. Variables included in the multivariable logistic regression model were selected from the univariate analysis according to P-value. Variables were offered to the model to assess the association between these factors and metritis cure. Additionally, the univariate logistic regression variables were offered to a recursive feature elimination to find the optimal subset of features for a machine learning algorithms analysis. Cows without vulvovaginal laceration had 1.91 higher odds of curing of metritis than cows with vulvovaginal laceration. Cows that developed metritis at >7 DIM had 2.09 higher odds of being cured than cows that developed metritis at ≤7 DIM. For rectal temperature, each degree Celsius above 39.4°C led to lower odds to be cured than cows with rectal temperature ≤39.4°C. Furthermore, milk production slope and milk production difference from the day before to the metritis diagnosis were essential variables to predict metritis cure. Cows that had reduced milk production from the day before to the metritis diagnosis had lower odds to be cured than cows with moderate milk production increase. The results from the multivariable logistic regression and receiver operating characteristic analysis indicated that cows developing metritis at >7 DIM, with increase in milk production, and with a rectal temperature ≤39.40°C had increased likelihood of cure of metritis with an accuracy of 75%. The machine learning analysis showed that in addition to these variables, calving-related disorders, season, and month of metritis event were needed to predict whether the cow will cure or not from metritis with an accuracy ≥70% and F1 score (harmonic mean between precision and recall) ≥0.78. Although machine learning algorithms are acknowledged as powerful tools for predictive classification, the current study was unable to replicate its potential benefits. More research is needed to optimize predictive models of metritis cure.