Cow- and herd-level risk factors for on-farm mortality in Midwest US dairy herds.

The objectives of this study were to describe on-farm mortality and to investigate cow- and herd-level risk factors associated with on-farm mortality in Midwest US dairy herds using lactation survival analysis. We analyzed a total of approximately 5.9 million DHIA lactation records from 10 Midwest US states from January 2006 to December 2010. The cow-level independent variables used in the models were first test-day milk yield, milk fat percent, milk protein percent, fat-to-protein ratio, milk urea nitrogen, somatic cell score, previous dry period, previous calving interval, stillbirth, calf sex, twinning, calving difficulty, season of calving, parity, and breed. The herd-level variables included herd size, calving interval, somatic cell score, 305-d mature-equivalent milk yield, and herd stillbirth percentage. Descriptive analysis showed that overall cow-level mortality rate was 6.4 per 100 cow-years and it increased from 5.9 in 2006 to 6.8 in 2010. Mortality was the primary reason of leaving the herd (19.4% of total culls) followed by reproduction (14.6%), injuries and other (14.0%), low production (12.3%), and mastitis (10.5%). Risk factor analysis showed that increased hazard for mortality was associated with higher fat-to-protein ratio (>1.6 vs. 1 to 1.6), higher milk fat percent, lower milk protein percent, cows with male calves, cows carrying multiple calves, higher milk urea nitrogen, increasing parity, longer previous calving interval, higher first test-day somatic cell score, increased calving difficulty score, and breed (Holstein vs. others). Decreased hazard for mortality was associated with higher first test-day milk yield, higher milk protein, and shorter dry period. For herd-level factors, increased hazard for mortality was associated with increased herd size, increased percentage of stillbirths, higher somatic cell score, and increased herd calving interval. Cows in herds with higher milk yield had lower mortality hazard. Results of the study indicated that first test-day records, especially those indicative of negative energy balance in cows, could be helpful to identify animals at high risk for mortality. Higher milk yield per cow did not have a negative association with mortality. In addition, the association between herd-level factors and mortality indicated that management quality could be an important factor in lowering on-farm mortality, thereby improving cow welfare.

A study of methods for evaluating the success of the transition period in early-lactation dairy cows.

Three transition monitors were developed in this study that serve on 2 levels: the individual cow level and the herd level. On the first level they screen all cows for potential onset of postparturient health disorders and could be used to trigger implementation of more specific diagnostic initiatives. On the second level they can be used within herd to monitor the implementation of transition protocols and evaluate the transition management on the farm, signaling potential problems before clinical disease onset. The performance of 3 transition monitors based on daily milk yield (MY) within the first 7d in milk was evaluated in 3 herds with differing transition management intensity. The 3 monitors considered were increase in MY (LINE), average MY (MY7), and the difference between MY7 and expected MY (transition success measure, TSM). Transition monitors were evaluated not only as within-herd predictors of individual cow transition problems but also as indicators of herd transition management failures by relating their value with probability of early-lactation health disorders, culling, and treatment cost. Analysis of logistic models, correlations, and sensitivity and specificity estimates identified TSM as the most reliable measure of transition failure on both the individual cow level as well as the farm level across all study herds, with best performance achieved in herds with the most intensive postpartum cow management. As evaluated by logistic regression models, TSM was able to successfully predict the probability of a cow remaining healthy for the first 21d of lactation (c-statistic between 0.68 and 0.78), and probability of culling by 100d in milk (c-statistic between 0.73 and 0.86). Total cost of treatment by 21d in milk also showed the strongest correlation with TSM, with correlation coefficients ranging between 0.2 and 0.4. Statistical-process control cumulative sum charts for TSM designed to monitor postpartum management process in the herd identified transition failure events with at least 90% sensitivity at specificity above 92% within a 14-d window of 7d before and 7d after the event.

Short communication: influence of sampling interval on the accuracy of predicting bulk milk somatic cell count.

Although bulk milk somatic cell count (BMSCC) is, in most instances, not a good proxy for actual average herd somatic cell count (SCC), BMSCC is the only SCC parameter available to monitor trends in udder health for a large number of farms worldwide. The frequency of sampling BMSCC varies considerably between countries, and it is unknown to what extent the sampling interval of BMSCC or variation in BMSCC data itself influences the accuracy. The aim of this study was to assess the effect of sampling interval and variation of the BMSCC data on the accuracy to predict BMSCC. Because BMSCC is measured at regular time intervals, an artificial neural network (ANN) was used to determine both the effect of sampling interval and variation of the BMSCC data. The intervals examined in this study ranged from 4 to 14 d and were compared with the baseline of a standard 2-d sampling interval. The BMSCC data were collected every other day for a 24-mo period on 949 farms, and all series were created by exclusion of BMSCC data in between the original 2-d sampling interval series. The effect of variation of BMSCC was determined by comparing the error of the ANN model in 2 subsets of farms, those with the lowest SD (n=239) and those with a high SD of BMSCC data (n=236). No significant differences were found in any of the sampling intervals between the 2 cohorts of low and high SD in BMSCC. Overall, compared with the 2-d sampling interval, on average the error of the ANN model was 32,600 cells/mL for all farms included, ranging from 15,000 cells/mL (4-d interval) to 41,000 cells/mL (14-d sampling interval). Therefore, the length of the sampling interval greatly influences the usefulness of BMSCC data to monitor trends in udder health at the herd level.

Application of statistical process control charts to monitor changes in animal production systems.

Statistical process control (SPC) is a method of monitoring, controlling, and improving a process through statistical analysis. An important SPC tool is the control chart, which can be used to detect changes in production processes, including animal production systems, with a statistical level of confidence. This paper introduces the philosophy and types of control charts, design and performance issues, and provides a review of control chart applications in animal production systems found in the literature from 1977 to 2009. Primarily Shewhart and cumulative sum control charts have been described in animal production systems, with examples found in poultry, swine, dairy, and beef production systems. Examples include monitoring of growth, disease incidence, water intake, milk production, and reproductive performance. Most applications describe charting outcome variables, but more examples of control charts applied to input variables are needed, such as compliance to protocols, feeding practice, diet composition, and environmental factors. Common challenges for applications in animal production systems are the identification of the best statistical model for the common cause variability, grouping of data, selection of type of control chart, the cost of false alarms and lack of signals, and difficulty identifying the special causes when a change is signaled. Nevertheless, carefully constructed control charts are powerful methods to monitor animal production systems. Control charts might also supplement randomized controlled trials.

A novel method of analyzing daily milk production and electrical conductivity to predict disease onset.

This study evaluates the changes in milk production (yield; MY) and milk electrical conductivity (MEC) before and after disease diagnosis and proposes a cow health monitoring scheme based on observing individual daily MY and MEC. All reproductive and health events were recorded on occurrence, and MY and MEC were collected at each milking from January 2004 through November 2006 for 587 cows. The first 24 mo (January 2004 until December 2005) were used to investigate the effects of disease on MY and MEC, model MY and MEC of healthy animals, and develop a health monitoring scheme to detect disease based on changes in a cow's MY or MEC. The remaining 11 mo of data (January to November 2006) were used to compare the performance of the health monitoring schemes developed in this study to the disease detection system currently used on the farm. Mixed model was used to examine the effect of diseases on MY and MEC. Days in milk (DIM), DIM x DIM, and ambient temperature were entered as quantitative variables and number of calves, parity, calving difficulty, day relative to breeding, day of somatotropin treatment, and 25 health event categories were entered as categorical variables. Significant changes in MY and MEC were observed as early as 10 and 9 d before diagnosis. Greatest cumulative effect on MY over the 59-d evaluation period was estimated for miscellaneous digestive disorders (mainly diarrhea) and udder scald, at -304.42 and -304.17 kg, respectively. The greatest average daily effect was estimated for milk fever with a 10.36-kg decrease in MY and 8.3% increase in MEC. Milk yield and MEC was modeled by an autoregressive model using a subset of healthy cow records. Six different self-starting cumulative sum and Shewhart charting schemes were designed using 3 different specificities (98, 99, and 99.5%) and based on MY alone or MY and MEC. Monitoring schemes developed in this study issue alerts earlier relative to the day of diagnosis of udder, reproductive, or metabolic problems, are more sensitive, and give fewer false-positive alerts than the disease detection system currently used on the farm.

Water intake and dry matter intake changes as a feeding management tool and indicator of health and estrus status in dairy cows.

This study investigates whether dry matter (DM) or water intake is affected by the presence of disease or estrus in dairy cows and whether water intake can serve as an accurate substitute for monitoring changes in DM intake (DMI). A combined cumulative sum (CUSUM) and Shewhart monitoring scheme is proposed to detect DMI changes and emerging disease or estrus. Daily readings from 35 inline water meters for 35 water cups in a tie-stall barn at the University of Minnesota were collected from September 2005 until June 2006. Two cows were assigned to each water cup. Individual DMI were recorded for each of the 70 cows on the study. All drug or hoof treatments administered to the cows along with breeding and calving events were also recorded and classified as 1 of the following 6 event categories: estrus, calving, mastitis, fever, hoof treatment, and other. Analysis of covariance was used to identify factors significantly changing intake. Only the first 150 d in milk (DIM) were considered in the analysis. Six event categories plus DIM, ambient temperature, relative humidity, and parity were entered as independents into the model. Calving, primiparity, and health events categorized as "other" were associated with decreased DM and water intake. Mastitis decreased DMI and fever negatively affected water intake. Both intakes increased with DIM, and water intake decreased with increase in humidity. Covariance analysis was used to investigate the relationship between DMI and water intake. In model 1, analysis was done for a pair of cows, whereas model 2 modeled DMI of the whole group of 70 cows. Water intake, ambient temperature, humidity, and DIM were entered as independents in both models and parity was entered in model 1. Polynomial models and 2-way interactions were also considered. Water intake, ambient temperature, DIM, and DIM(2) were kept in final models 1 and 2, and parity was kept in model 1. Final models for cow pairs and a group of 70 cows resulted in R(2) of 0.50 and 0.82, respectively. The proposed CUSUM-Shewhart DMI monitoring scheme successfully detected emerging disease even in the first week of lactation. Monitoring water intake can serve as an alternative to measurements of DMI for groups of cows and has the potential of predicting change in individual cow health and estrus status.

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part I: Analyzing variation.

The present study examines the relationship between the bulk tank somatic cell count (SCC) mean and sigma (an estimate of variation) and the probability of exceeding a SCC standard. Daily or every other day, bulk tank SCC data were collected for 24 mo from 1,501 herds. Mean and sigma were estimated for each herd monthly and were compared between months and herd production categories using Kruskal-Wallis nonparametric ANOVA. The effect of month on bulk tank SCC mean and sigma was significant, with estimates for all summer months and most of the spring and fall months being significantly greater than estimates of mean and sigma in December 2004. Logistic regression models were developed to examine the relationship between month and herd production and the odds of a herd exceeding a SCC standard. The odds of exceeding a bulk tank SCC standard were significantly greater in the summer months and for smaller herds. A grid was constructed determining the probability of exceeding any of 5 SCC standards (200,000 to 600,000 cells/mL, step 100,000 cells/mL) in the following month, based on the mean and sigma of the past month. The violation probability grid can be used to assess the prospect of meeting quality premium goals and to proactively encourage more consistent performance in all the processes affecting bulk tank SCC.

Predicting somatic cell count standard violations based on herd's bulk tank somatic cell count. Part II: Consistency index.

The present study examines the capability of 1,501 herds in the Upper Midwest and the performance of statistical process control charts and indices as a way of monitoring and controlling milk quality on the farm. For 24 mo, daily or every other day bulk tank somatic cell count (SCC) data were collected. Consistency indices for 5 different SCC standards were developed. The indices calculate the maximum variation allowed to meet a desired SCC level at a given mean bulk tank SCC and were used to identify herds not capable of meeting a specific SCC standard. Consistency index method was compared with a test identifying future bulk tank SCC standard violators based on herds' past violations. The performance of the consistency index test and the past violation method was evaluated by logistic regression. The comparison focused on detection probability and certainty associated with a result. For the 5 SCC levels, detection probability and certainty associated with a result ranged from 51 to 98%. Detection probability of all violators and certainty associated with a negative result was greater for the consistency index across all 5 SCC levels (by 0.7 to 7.4% and 2.1 to 5.1%, respectively). Control charts were plotted and monthly consistency indices calculated for individual farms. Charts in combination with the consistency indices would warn from 66 to 80% of the herds about an upcoming violation within 30 d before it occurred. They offer a proactive approach to maintaining consistently high milk quality. By assessing process capability and distinguishing between significant changes and random variation in bulk tank SCC, tools presented in this article encourage fact-based decisions in dairy farm milk quality management.

Performance and welfare of dairy cows in an alternative housing system in Minnesota.

The compost bedded pack dairy barn is an alternative housing system for lactating cows that has received increased attention in the last 2 yr. No descriptive data were available about this housing system. Therefore, a study of 12 compost dairy barns in Minnesota was conducted between late June 2005 and September 2005. The objectives of this study were to describe the housing system, identify management practices used in these herds, observe cow welfare, analyze herd performance and udder health prior to and following the change in housing system, and measure producer satisfaction with the system. Producers were interviewed on various aspects related to the housing system and herd management, samples of milk were collected, and cows were scored for locomotion, body condition, hygiene, and hock lesions. In addition, historical bulk tank information and Dairy Herd Improvement Association data were collected when available. At the time of the visit, the Dairy Herd Improvement Association somatic cell count (SCC) was 325,000 +/- 172,000 cells/mL, rolling herd average was 10,457 +/- 1,138 kg per cow, and herd size was 73 +/- 35.5 lactating cows. The body condition score was 3.04 +/- 0.11, the cow hygiene score was 2.66 +/- 0.19, and 7.8% of all cows were clinically lame (locomotion score > or = 3 on a 1 to 5 scale). No hock lesions were present on 74.9% of the cows; 24.1% of cows had a mild lesion (hair loss), and 1.0% had a severe lesion (swollen hock). Historical analysis of the bulk tank SCC showed that 3 out of the 7 herds analyzed had a significant reduction in bulk tank SCC when compared with the previous housing system. Mastitis infection rates decreased significantly by 12% on 6 of the 9 farms analyzed. Reproductive performance significantly improved for 4 out of the 7 herds analyzed, with 25.9 and 34.5% improvement in heat detection rates and pregnancy rates, respectively. The main reasons producers reported for building this type of housing system were for improved cow comfort, cow health and longevity, and ease of completing daily chores. The largest concern was the cost and availability of bedding, especially as additional compost barns are built. Overall, all producers were satisfied with their decision to build a compost barn.

Bulk tank somatic cell counts analyzed by statistical process control tools to identify and monitor subclinical mastitis incidence.

The objective of this study was to examine the relationship between monthly Dairy Herd Improvement (DHI) subclinical mastitis and new infection rate estimates and daily bulk tank somatic cell count (SCC) summarized by statistical process control tools. Dairy Herd Improvement Association test-day subclinical mastitis and new infection rate estimates along with daily or every other day bulk tank SCC data were collected for 12 mo of 2003 from 275 Upper Midwest dairy herds. Herds were divided into 5 herd production categories. A linear score [LNS = ln(BTSCC/100,000)/0.693147 + 3] was calculated for each individual bulk tank SCC. For both the raw SCC and the transformed data, the mean and sigma were calculated using the statistical quality control individual measurement and moving range chart procedure of Statistical Analysis System. One hundred eighty-three herds of the 275 herds from the study data set were then randomly selected and the raw (method 1) and transformed (method 2) bulk tank SCC mean and sigma were used to develop models for predicting subclinical mastitis and new infection rate estimates. Herd production category was also included in all models as 5 dummy variables. Models were validated by calculating estimates of subclinical mastitis and new infection rates for the remaining 92 herds and plotting them against observed values of each of the dependents. Only herd production category and bulk tank SCC mean were significant and remained in the final models. High R2 values (0.83 and 0.81 for methods 1 and 2, respectively) indicated a strong correlation between the bulk tank SCC and herd's subclinical mastitis prevalence. The standard errors of the estimate were 4.02 and 4.28% for methods 1 and 2, respectively, and decreased with increasing herd production. As a case study, Shewhart Individual Measurement Charts were plotted from the bulk tank SCC to identify shifts in mastitis incidence. Four of 5 charts examined signaled a change in bulk tank SCC before the DHI test day identified the change in subclinical mastitis prevalence. It can be concluded that applying statistical process control tools to daily bulk tank SCC can be used to estimate subclinical mastitis prevalence in the herd and observe for change in the subclinical mastitis status. Single DHI test day estimates of new infection rate were insufficient to accurately describe its dynamics.

Comparison of number of Streptococcus uberis calculated on a volume or weight basis in sand and sawdust bedding.

OBJECTIVE: To determine a method for comparing counts of Streptococcus uberis in sand and sawdust and account for the influence of weight or volume of the bedding material. SAMPLE POPULATION: 2 sources of kiln-dried sawdust and 2 sources of washed sand. PROCEDURES: Sterilized bedding material (100 ml) was weighed and uniformly distributed in an aluminum pan. Each sterilized bedding material was inoculated with a mean of 3.6 X 10(6) (experiment 1) or 2.4 X 10(7) (experiment 2) colony-forming units (CFU) of S uberis/ml of bedding material. Without allowing time for replication of S uberis, inoculated bedding materials were washed with sterile saline (0.9% NaCl) solution. A 200-ml aliquot of wash solution was serially diluted up to 2,500 times with additional saline solution and inoculated on plates containing tryptose agar with 5% sheep blood. After incubation for 48 hours, number of CFU of S uberis was counted. This procedure was replicated 19 and 16 times for each bedding material in experiments 1 and 2, respectively. RESULTS: Evaluation of Bonferroni 95% confidence intervals revealed significant differences for counts of S uberis calculated on a weight basis between sand and sawdust. CONCLUSIONS AND CLINICAL RELEVANCE: Comparison of counts of S uberis determined on a volume basis for sand and sawdust accentuates to a lesser degree the weight difference of the bedding materials and ensures a more appropriate comparison of number of S uberis.

Health care of Holsteins selected for large or small body size.

Genetic groups of Holsteins selected for large size or small size were compared for health care needs. Two groups were formed from a paired foundation population. Large group was mated to sires with extreme estimates of transmitting ability for tall height and deep and wide bodies. Small group was similarly mated to extreme sires but to those transmitting short height and shallow and narrow bodies. Predicted Differences for milk and fat of sires were above breed average. Actual expenses for veterinary treatment, health supplies and drugs, and value of labor required of animal attendants were evaluated. Large cows required significantly more health care than small cows. Digestive disorders accounted for much of the group difference, and displaced abomasums were more frequent among large cows. Small cows may have economic advantages over large cows of the same breed.

Effective use of dairy herd improvement somatic cell counts in mastitis control.

The single most important factor affecting somatic cell count in milk is mammary gland infection status. In comparison, all other factors are minor. Consideration needs to be given to diurnal effects on Dairy Herd Improvement a.m.-p.m. sampling schemes. Somatic cell count linear score of 5 (283,000) appears to be a good choice of threshold for mastitis control applications. A greater understanding of the nonbacteriological factors affecting somatic cell count is needed so that relative thresholds could be used to improve the clarity of somatic cell count interpretation. Linear score loss estimates are effective educational tools providing motivation for mastitis control implementation. Infection status or milk loss estimates based on single somatic cell count tests on individual cows are weak. A lactational average linear score on individual cows or linear score compilations across a herd provide credible estimates. Treatment of subclinical mastitis based on somatic cell count levels is not economically beneficial and is not recommended. Usefulness of Dairy Herd Improvement somatic cell count data as a mastitis management tool requires measures of mastitis level, new infection rate, and mastitis pattern within the herd over time.

Animal health and management and their impact on economic efficiency.

Relationships between animal health and economic efficiency were examined using data from genetic investigations and management studies. Genetic investigations have indicated that cows bred for high production do require more health care, but that increased costs for health care negate only a small fraction of the greater returns from cows that are genetically superior for yield traits. These same studies have identified age of cow and stage of lactation as important sources of variation in health care costs. Health care costs increase with age and are highest at parturition and immediately thereafter, and decrease to much lower levels as lactation progresses. Animal health issues considered from a management perspective were macro-environment (climate, housing, facilities), nutrition-reproduction complex, replacement management, mastitis and udder health, and herd health preventive medicine programs. Most advances in management of animal health were beneficial, but some are economical only for large herds. Improvement of udder health through continued and expanded research on milking procedures and equipment design is an area of unusual promise. Additional research appears needed to cope with stress and fatigue to legs and feet in modern facilities. Preventive medicine programs become more cost effective as herds become larger and should be used by a larger percentage of dairy producers. The economic efficiency of many management practices is uncertain due to a paucity of data. Animal scientists should plan to incorporate economic comparisons into much more of their research.

Blood glucose and tissue glycogen levels in turkey poults with spontaneous round heart disease and furazolidone-induced cardiomyopathy.

Furazolidone (FZ) at 700 ppm was added to feed mixtures fed turkey poults two weeks posthatching to induce acute experimental cardiomyopathy. Poults in the control pen received the same ration but without FZ. Four of the control poults developed spontaneous round heart disease. From EKG data and blood samples obtained at weekly intervals, poults were selected for sacrifice at 5 weeks of age. Tissue samples from the left myocardial wall, liver, and pectoralis major and tibialis anterior muscles were analyzed for glycogen by biochemical assay. Blood glucose was determined with the Technicon autoanalyzer. Deposition of glycogen increased significantly (p less than 0.05) in the myocardium of all affected poults and in the liver of all FZ-treated poults. Glycogen levels of the pectoralis major and tibialis anterior muscles were not affected by FZ, but a significant increase (p less than 0.05) was apparent in the pectoralis major muscle of spontaneous round heart poults. It was concluded that FZ influences glycogen metabolism, probably by enzyme inhibition, and that it tends to magnify effects seen in the spontaneous round heart syndrome. Glycogen infiltration of tissues such as the heart and white skeletal muscle suggests that the round heart syndrome may be a manifestation of the glycogen storage disease, idiopathic generalized glycogenosis. Lack of significant differences in the blood serum glucose levels of all poults indicates that these levels are not a reliable clinical parameter for monitoring development of the round heart syndrome.