Cohort-level disease prediction by extrapolation of individual-level predictions in transition dairy cattle

Monitoring of Body Condition in Dairy Cows to Assess Disease Risk at the Individual and Herd Level

A retrospective longitudinal study assessing the explanatory and predictive capacity of body condition score (BCS) in dairy cows on disease risk at the individual and herd level was carried out. Data from two commercial grazing herds from the Argentinean Pampa were gathered (Herd A = 2100 and herd B = 2600 milking cows per year) for 4 years. Logistic models were used to assess the association of BCS indicators with the odds for anestrus at the cow and herd level. Population attributable fraction (AFP) was estimated to assess the anestrus rate due to BCS indicators. We found that anestrus risk decreased in cows calving with BCS ≥ 3 and losing ≤ 0.5 (OR: 0.07-0.41), and that anestrus rate decreased in cohorts with a high frequency of cows with proper BCS (OR: 0.22-0.45). Despite aggregated data having a good explanatory power, their predictive capacity for anestrus rate at the herd level is poor (AUC: 0.574-0.679). The AFP varied along the study in both herds and tended to decrease every time the anestrous rate peaked. We conclude that threshold-based models with BCS indicators as predictors are useful to understand disease risk (e.g., anestrus), but conversely, they are useless to predict such multicausal disease events at the herd level.

Weather conditions associated with death attributed to bovine respiratory disease complex in high-risk auction market-sourced male beef calves

OBJECTIVE

To evaluate associations between weather conditions and management factors with the incidence of death attributable to bovine respiratory disease complex (BRDC) in high-risk auction-sourced beef calves.

ANIMALS

Cohorts (n = 3,339) of male beef calves (545,866) purchased by 1 large cattle feeding operation from 216 locations and transported to 1 of 89 feeding locations (backgrounding location or feedlot) with similar management protocols.

PROCEDURES

Associations between weather conditions and management factors on the day of purchase (day P) and during the first week at the feeding location and cumulative BRDC mortality incidence within the first 60 days on feed were estimated in a mixed-effects negative binomial regression model.

RESULTS

Significant factors in the final model were weaning status; degree of commingling; body weight; transport distance; season; precipitation, mean wind speed, and maximum environmental temperature on day P; environmental temperature range in the first week after arrival at the feeding location; and interactions between distance and wind speed and between body weight and maximum environmental temperature. Precipitation and wind speed on day P were associated with lower cumulative BRDC mortality incidence, but wind speed was associated only among calves transported long distances (≥ 1,082.4 km). Higher mean maximum temperature on day P increased the incidence of cumulative mortality among calves with low body weights (< 275.5 kg).

CONCLUSIONS AND CLINICAL RELEVANCE

Several weather conditions on day P and during the first week after arrival were associated with incidence of BRDC mortality. The results may have implications for health- and economic-risk management, especially for high-risk calves and calves that are transported long distances.

Rumination time around dry-off relative to the development of diseases in early-lactation cows

Monitoring rumination time (RT) around the time of calving is an effective way of identifying cows at risk of disease in early lactation. However, this only allows for the identification of cows a few days before the onset of clinical signs; thus, effective preventive measures cannot be implemented. Recent research has suggested that biomarkers of immune and metabolic function measured at dry-off (DO) can predict higher disease risk in early lactation. Nevertheless, the extent to which RT around DO is associated with early-lactation disease risk remains unexplored. Thus, the objective of this study was to compare RT in the weeks before and after DO between cows that did and did not experience health disorders in early lactation. For this, we conducted an observational retrospective cohort study utilizing the records available from a large commercial dairy herd in which RT is recorded daily using an automated system. Daily RT from -7 to +14 d relative to DO from 2,258 DO cycles and their respective health records in the first 60 d in milk were used. Differences in RT between animals with and without a disease history were tested with the Student t-test with Bonferroni adjustment. Mixed linear regression analyses were performed to assess differences in RT around DO and the association of RT with the occurrence of mastitis, metritis, retained placenta, hyperketonemia, lameness, hypocalcemia, pneumonia, and displaced abomasum. Rumination time decreased abruptly at DO and remained lower for 3 to 4 d compared with the days before DO. On average, cows affected by hyperketonemia and lameness ruminated 9.83 ± 6.40 and 15.00 ± 6.08 min/d less than unaffected cows, respectively. Cows that developed lameness in the first 60 d in milk showed reduced RT from 1 to 3 d following DO compared with cows that were not diagnosed with lameness in early lactation. However, RT around DO was not associated with the occurrence of the other health disorders studied here. Our results demonstrate that DO is a stressful event for dairy cows resulting in decreased RT for several days. Furthermore, the association between RT around DO and some early-lactation diseases suggests that RT could be a useful tool to identify at-risk cows early enough to allow for preventive interventions. Further studies should investigate the diagnostic utility of incorporating RT data early in the dry period in the disease prediction algorithms of rumination sensors.

Microclimate modeling in naturally ventilated dairy barns during the hot season: Checking the accuracy of forecasts

Monitoring and predicting the microclimate in naturally ventilated barns (NVB) is important given the adverse effects of high summer temperatures on dairy cows in the context of global climate change. The aim of the work was to verify the accuracy of the microclimate forecast in a NVB using linear regression (LR). Our working hypothesis suggested that multiple periodic measurements of air temperature and relative humidity outside and inside the barns at the same time will allow us to build LR models for predicting the temperature-humidity index (THI). This was done not only for a specific dairy barn based on this indicator outside, but also in other dairy barns with a similar design, located in similar weather conditions. The results of the research indicate that the use of LR had a high accuracy of forecasting (93-96%) the THI in NVB of various designs during the summer heat. At the same time, differences were found between traits (air temperature, relative humidity as well as resulting THI) provided by meteorological weather stations and these data measured simultaneously next to the dairy barns. The proposed LR models can be used to predict THI in NVBs of different designs.

Prospects for predictive modeling of transition cow diseases

Transition cow diseases can negatively impact animal welfare and reduce dairy herd profitability. Transition cow disease incidence has remained relatively stable over time despite monitoring and management efforts aimed to reduce the risk of developing diseases. Dairy cattle disease risk is monitored by assessing multiple factors, including certain biomarker test results, health records, feed intake, body condition score, and milk production. However, these factors, which are used to make herd management decisions, are often reviewed separately without considering the correlation between them. In addition, the biomarkers that are currently used for monitoring may not be representative of the complex physiological changes that occur during the transition period. Predictive modeling, which uses data to predict future or current outcomes, is a method that can be used to combine the most predictive variables and their interactions efficiently. The use of an effective predictive model with relevant predictors for transition cow diseases will result in better targeted interventions, and therefore lower disease incidence. This review will discuss predictive modeling methods and candidate variables in the context of transition cow diseases. The next step is to investigate novel biomarkers and statistical methods that are best suited for the prediction of transition cow diseases.