Lameness is one of the most costly dairy cow diseases, yet adoption of lameness prevention strategies remains low. Low lameness prevention adoption might be attributable to a lack of understanding regarding total lameness costs. In this review, we evaluated the contribution of different expenditures and losses to total lameness costs. Evaluated expenditures included labor for treatment, therapeutic supplies, lameness detection and lameness control and prevention. Evaluated losses included non-saleable milk, reduced milk production, reduced reproductive performance, increased animal death, increased animal culling, disease interrelationships, lameness recurrence and reduced animal welfare. The previous literature on total lameness cost estimates was also summarized. The reviewed studies indicated that previous estimates of total lameness costs are variable and inconsistent in the expenditures and losses they include. Many of the identified expenditure and loss categories require further research to accurately include in total lameness cost estimates. Future research should focus on identifying costs associated with specific lameness conditions, differing lameness severity levels, and differing stages of lactation at onset of lameness to provide better total lameness cost estimates that can be useful for decision making at both the herd and individual cow level.

to explore the application of machine learning techniques to automatically collected data. Activity 4 level, lying bouts, lying time, rumination time, feeding time, and reticulorumen temperature 5showed differences between periods of estrus and non-estrus, but ear surface temperature did not. 6Additionally, applying machine learning techniques to automatically collected technology data 7shows potential for estrus detection.

Two experiments were conducted to evaluate a pregnancy-detection assay based on the measurement of pregnancy-associated glycoproteins (PAG) in milk samples. In experiment 1, milk samples were collected on the day of first pregnancy check (33-52 d postinsemination; n=119) or second check (60-74 d postinsemination; n=60). The accuracy in identification of pregnant and nonpregnant cows was 99% at first check. Only 6% of samples were found to be within an intermediate range of PAG concentrations and classified as requiring recheck by the assay. At second check, the accuracy of the assay was 98%. Fifteen percent of these samples were classified as requiring recheck. In experiments 2a (n=17 cows) and 2b (n=16 cows), milk and plasma samples were collected from cows at weekly intervals beginning 2 (experiment 2a) or 4 d (experiment 2b) after insemination. The earliest time point at which pregnant cows were accurately classified as pregnant by the assay was on d 30 postinsemination. A transient decline in PAG levels into the intermediate range was observed on d 46 to 72 postinsemination. This coincides with the time of recheck in experiment 1. Results obtained with the plasma samples were essentially the same. The accuracy of pregnancy identification based on milk samples from nonpregnant and pregnant cows was 99%. Levels of PAG in milk were useful in identifying 6 incidences of embryonic mortality. No consistent relationship was noted between the timing of the decline in PAG levels and the timing of luteal regression in this small number of cows.

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.

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.