Quantifying the effects of mastitis on the reproductive performance of dairy cows: A meta-analysis.

The objective of this study was to conduct a meta-analysis to quantitatively characterize the effects of mastitis on the reproductive performance of dairy cows as well as to identify factors that interact with this relationship. A total of 29 publications were identified that contributed 24, 41, 27, 38, and 13 trial results to the meta-analysis of how mastitis is related to time to first service (TFS), days open (DO), services per conception (SPC), pregnancies per insemination at first service (FSP/AI) and pregnancy loss (PL), respectively. The meta-analyses were conducted using multilevel linear mixed-effects models. Overall, high levels of heterogeneity were present and meta-regression models only explained a small amount of heterogeneity. Results suggest that cows with mastitis pre-first insemination experience, on average, an additional 13.29 d to first service [95% confidence interval (CI): 6.64, 19.95] when compared with cows with no mastitis in the same time period. Moreover, in relation to cows with no mastitis in the same time period, 22.34 additional DO (95% CI: 12.89, 31.79) were estimated, on average, for cows with clinical mastitis at pre-insemination leading to conception. Additionally, 32.41 added DO (95% CI: 20.58, 44.25) were estimated, on average, for cows with clinical mastitis at pre- or post-insemination leading to conception compared with cows with no mastitis in the same time period. Finally, 20.03 additional DO (95% CI: 3.11, 36.95) were estimated, on average, for cows with subclinical mastitis pre- or post-insemination leading to conception compared with cows with no mastitis in the same time period. Effect size estimates from the meta-regression models for SPC, in relation to cows with no mastitis in the same time period, suggest that, on average, SPC increases by 0.46 inseminations (95% CI: 0.30, 0.62) for a cow experiencing mastitis pre-insemination leading to conception. When mastitis occurs either pre- or post-insemination leading to conception, SPC is expected to increase, on average, by 0.72 inseminations (95% CI: 0.48, 0.95) compared with cows with no mastitis in the same time period. The estimated effect sizes for FSP/AI suggest a risk ratio of conceiving at first insemination of 0.90 (95% CI: 0.82, 0.99) for cows with mastitis diagnosed pre-first insemination with respect to cows with no mastitis in the same time period, and a risk ratio of 0.79 (95% CI: 0.72, 0.86) for cows diagnosed with mastitis either pre- or post-first insemination with respect to cows with no mastitis in the same time period. Publication bias was identified in 4 of the meta-analysis models (TFS, DO, SPC, and FSCR), but no influential trials were identified in any models; the reliability of the meta-analysis results should be interpreted carefully keeping these limitations in mind. Further meta-regression analysis would be valuable as additional studies are published that report other potential sources of heterogeneity.

Use of a stochastic simulation model to estimate the cost per case of digital dermatitis, sole ulcer, and white line disease by parity group and incidence timing.

A farm-level stochastic simulation model was modified to estimate the cost per case of 3 foot disorders (digital dermatitis, sole ulcer, and white line disease) by parity group and incidence timing. Disorder expenditures considered within the model included therapeutics, outside labor, and on-farm labor. Disorder losses considered within the model included discarded milk, reduced milk production, extended days open, an increased risk of culling, an increased risk of death (natural or euthanized), and disease recurrence. All estimates of expenditures and losses were defined using data from previously published research in stochastic distributions. Stochastic simulation was used to account for variation within the farm model; 1,000 iterations were run. Sensitivity of foot disorder costs to selected market prices (milk price, feed price, replacement heifer price, and slaughter price) and herd-specific performance variables (pregnancy rate) were analyzed. Using our model assumptions, the cost per disorder case over all combinations of parity group and incidence timing, regardless of incidence likelihood, was lowest for digital dermatitis ($64 ± 24; mean ± standard deviation), followed by white line disease ($152 ± 26) and sole ulcer ($178 ± 29). Disorder costs were greater in multiparous versus primiparous cows and were always highest at the beginning of lactation. The greatest contributing cost categories were decreased milk production, an increased risk of culling, and disease recurrence. The contribution of cost categories to the total cost of disorder varied by disorder type, parity group, and incidence timing. For all disorders, the cost per case increased as milk price or replacement heifer price increased and decreased as feed price, pregnancy rate, or slaughter price increased. Understanding how foot disorder costs change according to cow-specific conditions (i.e., disorder type, parity group, and days in milk at incidence) and herd-specific conditions (i.e., market prices and performance variables) can help improve on-farm decisions about treatment and prevention of foot disorders.

Estimating the value of infectious or noninfectious foot disorder prevention strategies within dairy farms, as influenced by foot disorder incidence rates and prevention effectiveness.

A farm-level stochastic simulation model was adapted to estimate the value of implementing foot disorder prevention on a dairy farm by calculating the return on investment associated with implementation of foot disorder prevention. Two potential strategies for foot disorder prevention were tested: strategy 1 was prevention focused on reducing infectious foot disorders (i.e., digital dermatitis) in the model, and strategy 2 was prevention focused on reducing noninfectious foot disorders (i.e., sole ulcer and white line disease) in the model. For each strategy, we evaluated the effect of foot disorder incidence on the value of prevention by setting the incidence of foot disorders at 3 levels. For strategy 1, the level of digital dermatitis incidence without prevention in all parities was 20, 40, or 60%, and the incidence level of the noninfectious foot disorders in the model were held constant. For strategy 2, levels of sole ulcer and white line disease incidence without prevention in parity ≥3 cows were 5, 15, or 25%, and the incidence level of the infectious foot disorders included in the model were held constant; the incidence levels of noninfectious foot disorders in younger cows were adjusted to be lower. Overall, 6 scenarios were run, 1 for each prevention strategy × foot disorder incidence rate combination. To evaluate how the effectiveness of each prevention strategy would influence the investment value, the effectiveness of prevention could vary from a prevention risk ratio (RR) of 0.0 (100% reduction in disorder incidence) to 1.0 (0% reduction in disorder incidence). When implementing strategy 1, the return on prevention investment per cow-year (mean ± standard deviation) when prevention effectiveness was low (prevention RR = 0.91 to 1.0) and the digital dermatitis incidence rate was originally 20, 40, or 60% was $0.6 ± 0.4, $1.2 ± 0.9, and $1.8 ± 1.3, respectively. In comparison, the return on prevention investment per cow-year when prevention effectiveness was high (prevention RR = 0.00 to 0.09) and the digital dermatitis incidence rate was originally 20, 40, or 60% was $12.2 ± 3.0, $24.4 ± 6.0, and $36.5 ± 9.0, respectively. When implementing strategy 2, the return on prevention investment per cow-year when prevention effectiveness was low and noninfectious foot disorder incidence rates were originally 5, 15, or 25% in parity ≥3 cows was $0.6 ± 0.4, $1.9 ± 1.1, and $3.2 ± 1.9, respectively. In comparison, the return on prevention investment per cow-year when prevention effectiveness was high and noninfectious foot disorder incidence rates were originally 5, 15, or 25% in parity ≥3 cows was $12.4 ± 1.5, $37.3 ± 4.6, and $62.2 ± 7.6, respectively. The return on investment for foot disorder prevention would depend on the cost of the prevention strategy and the other benefits associated with the selected prevention strategy. This model could be used as a decision support tool to help identify the amount that could be paid to implement a selected prevention strategy.

Short communication: A comparison of cow cleanliness, fly population, and fly avoidance behaviors among docked, switch-trimmed, and switch-intact dairy cows in 3 commercial dairy herds.

Tail docking is a controversial practice in the dairy industry. Proponents claim that tail docking keeps the udder cleaner, and therefore improves milk quality and decreases somatic cell count. Opponents of tail docking cite that it causes unnecessary pain, backed by multiple studies that demonstrate no positive benefits of tail docking and that tail docking increases aggravation from flies. The objective of this study was to evaluate and compare cow cleanliness, fly population, and fly-avoidance behaviors among cows with docked, switch-trimmed, and switch-intact tails. A total of 206 cows from 3 Kentucky dairy herds were included in the longitudinal observational study. Each farm included previously docked cows, switch-intact cows, and cows whose switches were trimmed at the initial farm visit. Researchers visited each farm every 2 wk for 8 wk to record cow cleanliness, teat cleanliness, fly population, and fly-avoidance behavior scores. No significant differences were found in cow cleanliness scores, teat cleanliness scores, fly population scores, skin twitching, or foot stomping counts among docked, switch-trimmed, or switch-intact cows. Although the fly population scores did not differ, the amount of tail swings among docked, switch-intact, and switch-trimmed cows were significantly different. The odds of exhibiting tail swinging were 2.63 times greater for docked cows than for switch-trimmed cows and 1.92 times greater than for switch-intact cows. Overall, switch trimming resulted in similar outcomes to tail docking, although neither showed improvements over intact tails.

A survey of United States dairy hoof care professionals on costs associated with treatment of foot disorders.

The objective of this study was to collect information regarding hoof care professionals' billing practices and to gather their opinions about foot disorders and the value of their prevention. Responses were gathered from veterinarians (n = 18) and hoof trimmers (n = 116) through both online and paper survey platforms. Because of the limited number of respondents, veterinarian responses were not further analyzed. Of the 6 foot disorders included in the survey, the treatment cost per case was greatest for toe ulcers (mean ± standard deviation; $20.2 ± 8.5), sole ulcers ($19.7 ± 8.6), white line disease ($19.5 ± 8.1), and thin soles ($18.1 ± 8.1), and least for infectious disorders (foot rot and digital dermatitis; $8.0 ± 7.6 and $7.5 ± 9.6, respectively). Of the disorders, digital dermatitis represented most of the foot disorder cases treated by respondents over the past year (43.9 ± 20.4%), whereas toe ulcers and thin soles represented the least (5.3 ± 4.1 and 5.3 ± 5.7%, respectively). Respondents that served mostly large herds (>500 lactating cows) reported a lower prevalence of digital dermatitis (31.6 ± 4.2 vs. 44.4 ± 3.4 and 46.7 ± 3.2% in small and medium herds, respectively) and a higher prevalence of sole ulcers (23.1 ± 3.0 vs. 13.4 ± 2.4 and 13.3 ± 2.3% in small and medium herds, respectively). Region of the United States (Northeast, Midwest, or other) also influenced foot disorder prevalence; respondents from the Northeast reported more sole ulcers than respondents from other regions (22.1 ± 2.3 vs. 12.4 ± 3.3%). When respondents were asked which disorder was associated with the greatest total cost per case to the producer (treatment and labor costs plus the reduction in milk yield, reduced reproductive performance, and so on), hoof trimmers ranked digital dermatitis as having the greatest total cost per case and thin soles as having the least total cost per case. Finally, respondents indicated that the most important benefits of reducing foot disorders were enhanced animal welfare and increased milk production, whereas the least important benefit was reduced veterinary and hoof trimmer fees. Results from this survey can be used to improve the accuracy of foot disorder cost estimates and contribute to better decision-making regarding both foot disorder treatment and prevention.

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring.

Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

Retention payoff-based cost per day open regression equations: Application in a user-friendly decision support tool for investment analysis of automated estrus detection technologies.

Assessing the economic implications of investing in automated estrus detection (AED) technologies can be overwhelming for dairy producers. The objectives of this study were to develop new regression equations for estimating the cost per day open (DO) and to apply the results to create a user-friendly, partial budget, decision support tool for investment analysis of AED technologies. In the resulting decision support tool, the end user can adjust herd-specific inputs regarding general management, current reproductive management strategies, and the proposed AED system. Outputs include expected DO, reproductive cull rate, net present value, and payback period for the proposed AED system. Utility of the decision support tool was demonstrated with an example dairy herd created using data from DairyMetrics (Dairy Records Management Systems, Raleigh, NC), Food and Agricultural Policy Research Institute (Columbia, MO), and published literature. Resulting herd size, rolling herd average milk production, milk price, and feed cost were 323 cows, 10,758kg, $0.41/kg, and $0.20/kg of dry matter, respectively. Automated estrus detection technologies with 2 levels of initial system cost (low: $5,000 vs. high: $10,000), tag price (low: $50 vs. high: $100), and estrus detection rate (low: 60% vs. high: 80%) were compared over a 7-yr investment period. Four scenarios were considered in a demonstration of the investment analysis tool: (1) a herd using 100% visual observation for estrus detection before adopting 100% AED, (2) a herd using 100% visual observation before adopting 75% AED and 25% visual observation, (3) a herd using 100% timed artificial insemination (TAI) before adopting 100% AED, and (4) a herd using 100% TAI before adopting 75% AED and 25% TAI. Net present value in scenarios 1 and 2 was always positive, indicating a positive investment situation. Net present value in scenarios 3 and 4 was always positive in combinations using a $50 tag price, and in scenario 4, the $5,000, $100, and 80% combination. Overall, the payback period ranged from 1.6 yr to greater than 10 yr. Investment analysis demonstration results were highly dependent on assumptions, especially AED system initial investment and labor costs. Dairy producers can use herd-specific inputs with the cost per day open regression equations and the decision support tool to estimate individual herd results.

A comparison of timed artificial insemination and automated activity monitoring with hormone intervention in 3 commercial dairy herds.

The objective of this study was to compare the reproductive performance of cows inseminated based on automated activity monitoring with hormone intervention (AAM) to cows from the same herds inseminated using only an intensive timed artificial insemination (TAI) program. Cows (n=523) from 3 commercial dairy herds participated in this study. To be considered eligible for participation, cows must have been classified with a body condition score of at least 2.50, but no more than 3.50, passed a reproductive tract examination, and experienced no incidences of clinical, recorded metabolic diseases in the current lactation. Within each herd, cows were balanced for parity and predicted milk yield, then randomly assigned to 1 of 2 treatments: TAI or AAM. Cows assigned to the TAI group were subjected to an ovulation synchronization protocol consisting of presynchronization, Ovsynch, and Resynch for up to 3 inseminations. Cows assigned to the AAM treatment were fitted with a leg-mounted accelerometer (AfiAct Pedometer Plus, Afimilk, Kibbutz Afikim, Israel) at least 10 d before the end of the herd voluntary waiting period (VWP). Cows in the AAM treatment were inseminated at times indicated by the automated alert system for up to 90 d after the VWP. If an open cow experienced no AAM alert for a 39±7-d period (beginning at the end of the VWP), hormone intervention in the form of a single injection of either PGF2α or GnRH (no TAI) was permitted as directed by the herd veterinarian. Subsequent to hormone intervention, cows were inseminated when alerted in estrus by the AAM system. Pregnancy was diagnosed by ultrasound 33 to 46 d after insemination. Pregnancy loss was determined via a second ultrasound after 60 d pregnant. Timed artificial insemination cows experienced a median 11.0 d shorter time to first service. Automated activity-monitored cows experienced a median 17.5-d shorter service interval. No treatment difference in probability of pregnancy to first AI, probability of pregnancy to repeat AI, pregnancy loss, time to pregnancy, or proportion of pregnant cows at 90 d past the VWP existed. Based on these results, inseminating cows using AAM with hormone intervention can achieve a level of reproductive performance comparable to TAI. Considering the strict cow selection criteria used in this study, interpretation of results for on-farm implementation should be performed cautiously; the results cannot be directly extrapolated to whole herds of cows.

Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies.

This study included 2 objectives. The first objective was to describe estrus-related changes in parameters automatically recorded by the CowManager SensOor (Agis Automatisering, Harmelen, the Netherlands), DVM bolus (DVM Systems LLC, Greeley, CO), HR Tag (SCR Engineers Ltd., Netanya, Israel), IceQube (IceRobotics Ltd., Edinburgh, UK), and Track a Cow (Animart Inc., Beaver Dam, WI). This objective was accomplished using 35 cows in 3 groups between January and June 2013 at the University of Kentucky Coldstream Dairy. We used a modified Ovsynch with G7G protocol to partially synchronize ovulation, ending after the last PGF2α injection (d 0) to allow estrus expression. Visual observation for standing estrus was conducted for four 30-min periods at 0330, 1000, 1430, and 2200h on d 2, 3, 4, and 5. Eighteen of the 35 cows stood to be mounted at least once during the observation period. These cows were used to compare differences between the 6h before and after the first standing event (estrus) and the 2wk preceding that period (nonestrus) for all technology parameters. Differences between estrus and nonestrus were observed for CowManager SensOor minutes feeding per hour, minutes of high ear activity per hour, and minutes ruminating per hour; twice daily DVM bolus reticulorumen temperature; HR Tag neck activity per 2h and minutes ruminating per 2h; IceQube lying bouts per hour, minutes lying per hour, and number of steps per hour; and Track a Cow leg activity per hour and minutes lying per hour. No difference between estrus and nonestrus was observed for CowManager SensOor ear surface temperature per hour. The second objective of this study was to explore the estrus detection potential of machine-learning techniques using automatically collected data. Three machine-learning techniques (random forest, linear discriminant analysis, and neural network) were applied to automatically collected parameter data from the 18 cows observed in standing estrus. Machine learning accuracy for all technologies ranged from 91.0 to 100.0%. When we compared visual observation with progesterone profiles of all 32 cows, we found 65.6% accuracy. Based on these results, machine-learning techniques have potential to be applied to automatically collected technology data for estrus detection.