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.

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.