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The objective of this study was to analyze the associations of body condition score (BCS) and BCS change (∆BCS) during the dry period and the first 100 d of lactation with daily milk yield. Examining the involvement of health status in the associations between BCS and milk yield was a secondary objective of this research. Data included 12,042 lactations in 7,626 Holstein cows calving between April 2019 and January 2022 in a commercial dairy operation located in Colorado, USA. BCSs were generated daily by an automated BCS camera system located at the exit of the milking parlor. The assessment points selected for this study were dry-off (BCSdry), calving (BCS1), 7 DIM (BCS7), 14 DIM (BCS14), 21 DIM (BCS21), and nadir (nBCS; defined as the lowest daily BCS from calving to 100 DIM). Subsequently, these BCS were categorized considering quartiles (Q1 = 25% lowest BCS; Q4 = 25% greatest BCS), separately for primiparous and multiparous cows. Changes in BCS were calculated from dry-off to calving (multiparous); and from calving to 7 DIM, 14 DIM, 21 DIM, and nadir and assigned into quartile categories considering Q1 as the 25% of cows with the greatest decrease of BCS. Lactations were classified based on the number of health events before nadir as healthy, affected by one event, or having multiple events. Data were examined in primiparous and multiparous cows separately using ANOVA. The least square means for daily milk at 60 DIM and 305 DIM were calculated by category of BCS and ∆BCS at multiple time points and time periods. Subsequently, lactation curves were created by BCS and ∆BCS categories and by health status. Multivariable models included calving season and BCS1 as covariables. The largest differences in milk yield among categories of BCS and ∆BCS were identified for BCS originated at nadir and for the ∆BCS between calving and nadir. The differences in average daily milk yield between cows in the lowest and the greatest nBCS category (Q1 vs. Q4) were 3.3 kg/d (60 DIM) and 3.4 kg/d (305 DIM) for primiparous cows and 2.4 kg/d (60 DIM) and 2.1 kg/d (305 DIM) for multiparous cows. During the period from calving to nadir, primiparous cows in Q1 (greatest decrease of BCS) produced 4.3 kg/d (60 DIM) and 3.8 kg/d (305 DIM) more than cows in Q4. For multiparous cows, the differences were 3.0 kg/d (60 DIM) and 1.9 kg/d (305 DIM) in favor of Q1 cows. Overall, the associations between BCS and ∆BCS categories and milk yield were not consistent across time and they depended on the parity category. Nonetheless, as the assessment of BCS and ∆BCS approached the nadir, the association between greater milk yield and lower BCS or greater reduction in BCS became more evident., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The economically optimal dry period length (DPL) of dairy cows remains a topic of interest. Increasing daily milk production and improved management of the transition period require frequent evaluation of the optimal DPL. The economically optimal DPL also depends on the most limiting farm resource such as milking capacity, housing capacity, or fat quota. Therefore, the objective of this study was to determine economically optimal DPL under farm constraints on milking capacity, housing capacity, and fat quota given variations in 12 input factor levels. We developed a deterministic whole herd simulation model, including a nonlinear optimizer of the DPL in the first 3 parities. The model included estimates of milk, fat, and protein yield deviations in the subsequent parity and hazard ratios of culling risk and pregnancy rates as functions of the DPL in the current parity. The DPL could vary between 20 and 90 d with step size of 1 d. In addition to a one-factor-at-a-time analysis, we used a definitive screening design and a space-filling design with Latin hypercube sampling to determine important linear and curvature effects of input factors and their interactions. Results indicated that the economically optimal DPL were typically between 35 and 50 d under a large variation in input factor levels. The opportunity costs of equal DPL in all parities were small compared with optimal policies where the DPL were allowed to vary between parities. The DPL under the parlor constraint were generally less than 5 d longer than the optimal DPL under the housing constraint. The optimal DPL under the quota constraint were between those under the parlor and housing constraints. Opportunity costs compared with 50 d dry were often small, but in some cases large. A formal global sensitivity analysis revealed important interactions of input factors that were not discovered with one-factor-at-a-time analyses. In conclusion, economically optimal DPL were often shorter than typically are recommended. Adding uncertainty about the date of calving at the date of dry-off might extend these optimal DPL by some days depending on the risk attitude of the decision maker., (© 2021, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Calving patterns and milk production are seasonal throughout the United States; however, the distribution of seasonality, and the extent to which this seasonality is due to direct effects of climate on milk production and reproductive performance or farm management, is not well quantified. Summer-to-winter (SW) ratios have been used as measures of seasonality, but other measures such as low-to-peak (LP) ratios have been proposed. Our objectives were (1) to describe the distribution of seasonality in calving pattern and milk production among herds in the United States, (2) to compare SW and LP ratios of calving pattern and milk production, (3) to quantify the effect of a seasonal calving pattern, parity, and percentage of dry cows on seasonality of milk production, and (4) to describe the association between seasonality in calving pattern and milk production, herd size, and daily milk production per cow. The final data set contained Dairy Herd Improvement Association lactation records from 2015 from 5,292 (calving pattern) and 5,200 (milk production) herds for 41 states in the United States. We used generalized linear regression models with 1 sinusoidal curve to model calving pattern and milk production per cow for each herd. For milk production, a model adjusting for days in milk (DIM) and the interaction of DIM and parity (ADJ) and a model that was not adjusted (NO) were run. Both models included the effect of the percentage of dry cows. We used SW and LP ratios calculated from the parameters of the sinusoidal component of the models as measures of seasonality. The variability within states for all seasonality measures was large. The median LP ratio of calving pattern was 0.61, and small herds were more seasonal (LP ratio 0.56) than large herds (LP ratio 0.75). For milk production, the median LP ratio-NO was 0.88, and the LP ratio-ADJ was 0.90. Small herds were more seasonal (0.89) than large herds (0.92) when their LP ratios-ADJ were compared. States in the south of the United States were the most seasonal for calving patterns and milk production. Adjusting for DIM and parity increased the LP ratio of milk production by 8.9% for 66% of the herds. Adjusting for the percentage of dry cows increased the LP ratio in 72.9% of the herds by a median value of 21.8%. The correlations between SW and LP ratios were weak. Herds that were more seasonal for milk production had a lower average daily milk per cow than less-seasonal herds. In conclusion, seasonality in calving patterns and milk production among herds varied greatly across the United States. Sinusoidal models with covariates allowed for quantification of the effects of calving pattern, DIM, and parity on the seasonality in milk production. The LP ratios captured the maximum seasonality better than SW ratios did., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Tunnel ventilation is an increasingly popular approach to mitigate the effects of heat stress on dairy cattle. Tunnel-ventilation barns use a bank of high-power fans to move air horizontally from one end of the barn to the other at cow level. The overall objective of the present experiments was to determine whether tunnel ventilation is superior to housing with fans and sprinklers with respect to rectal temperature during heat stress and seasonal variation in milk yield. In the first study, rectal temperatures were measured for 1097 lactating Holstein cows in six freestall barns with fans and sprinklers and 575 lactating Holsteins in four tunnel-ventilated freestall barns at a time point between 14:00 and 16:00 h during the months of June to August in Florida, USA. Rectal temperatures were lower for cows in tunnel-ventilation barns than sprinkler-and-fan barns when the tunnel-ventilation barns were built de novo but not when the tunnel-ventilation barns were produced by retrofitting a sprinkler-and-fan barn (interaction, P = 0.0129). In the second study, average daily milk yield in the first 90 days in milk was examined for 8470 lactating Holsteins housed in three sprinkler-and-fan barns and two tunnel-ventilation barns. Milk production for cows calving in cool weather (October to March) was greater (P < 0.0001) than for cows calving in hot weather (April to September). The seasonal reduction in milk yield was less for cows (P = 0.037) in tunnel-ventilation barns (3.5% decrease) than for cows in sprinkler-and-fan barns (5.8% decrease). With this difference in impact of heat stress, it was estimated at a dairy farm could invest up to a $332 more per cow space in a tunnel-ventilated barn than in a sprinkler-and-fan barn. It was concluded that housing cows in tunnel-ventilation barns can reduce the impact of heat stress on body temperature regulation and milk yield.

Rapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes' theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of clinical mastitis, and time windows make comparisons across studies difficult. We found the DLM/NBC method to be a flexible method for combining multiple sensor and nonsensor data sources to predict clinical mastitis and accommodate missing observations. Further research is needed before practical implementation is possible. In particular, the performance of our method needs to be improved in the first 2 wk of lactation. The DLM method produces forecasts that are based on continuously estimated multivariate normal distributions, which makes forecasts and forecast errors easy to interpret, and new sensors can easily be added., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Dairy cattle productive lifespan averages approximately 3 yr after first calving. Changes in the last decade in reproductive performance, genetic merit, and societal concerns regarding animal welfare and the environmental footprint of dairy products warrant a critical review of decision making regarding dairy cattle productive lifespan. The objective of this study is to provide such a review. Economic decision making drives the majority of culling decisions and, by extension, dairy cattle productive lifespan. Historically, models focused on optimizing replacement decisions for individual cows found economically optimal productive lifespans of 40 mo or more. However, cow performance and prices have changed and the average findings of these models may no longer hold. Management and housing may affect productive lifespan through improvements in health care and cow comfort. Improvements in reproductive efficiency and the availability of sexed semen are leading to an abundance of dairy heifers on many dairy farms, which often results in shorter productive lifespans in herds of fixed sizes. There is also a growing interest in the use of beef semen in dairy cattle, which does not add to the supply of dairy heifers. Acceleration of genetic gain due to genomic testing should likely result in shorter productive lifespans. Younger herds capitalize on genetic progress but have fewer efficient mature cows and have greater replacement costs. Extending dairy cattle productive lifespan might decrease the environmental footprint of milk production because fewer heifers need to be raised. Short productive lifespans, especially as a result of much forced culling early in lactation, are often signs of reduced welfare. Possible extensions of productive lifespan through improved welfare may alleviate public concerns about dairy production, although longer productive lifespans for healthy cows are not necessarily more profitable. A simple model of the economically optimal productive lifespan illustrates the tradeoffs between herd replacement cost, maturity and aging costs, genetic opportunity cost, and calf value opportunity cost. Combined, these factors suggest that an average productive lifespan of approximately 5 yr is warranted. In conclusion, increases in genetic gain, reproductive efficiency, cow comfort, and health care will increase the opportunity of herd managers to change productive lifespan to increase profitability, improve societal acceptance of dairy production, or both. [ABSTRACT FROM AUTHOR]