Jeffrey K. Reneau

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 cows 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.

Compost Dairy Barn Layout and Management Recommendations

Compost barns are a loose housing system that provides excellent cow comfort for dairy cows. Producer experience with well-managed compost barns in Minnesota has generally been positive. Cows are relatively clean, very comfortable, have fewer lameness problems, and in some cases had lower somatic cell counts (SCC) after moving to a compost barn from tie-stall or freestall barns. Current design and management recommendations are based on dairy producer experiences. Compost barns have a concrete feed alley, a bedded pack resting area that is stirred two times a day, and a 1.2-m (4-ft) high wall surrounding the pack. The wall that separates the pack and feed alley has walkways to allow cow and equipment access to the stirred pack area. The stirred pack is sized to provide a minimum stirred bedded pack area of 7.4 m2/cow (80 ft2/cow). Producers use dry fine wood shavings or sawdust for bedding. Fresh bedding is added when the bedded pack becomes moist enough to stick to the cows. The pack is stirred (aerated) at least two times each day to a producer recommended depth of 25 to 30 cm (10 to 12 in.). Stirring aerates and mixes manure and urine on the surface into the pack to provide a fresh surface for cows to lie down on. The pack can provide manure storage for 6 to 12 months. Excellent pack management and pre-milking cow preparation procedures are required. Research on compost barns is needed.

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.

Molecular Subtyping of Mastitis-Associated Klebsiella pneumoniae Isolates Shows High Levels of Diversity Within and Between Dairy Herds

Despite advances in controlling mastitis (inflammation of the mammary gland), udder infections caused by Klebsiella pneumoniae continue to affect dairy cattle. Mastitis caused by K. pneumoniae responds poorly to antibiotic treatment, and as a consequence, infections tend to be severe and long lasting. We sought to determine whether a nonrandom distribution of specific genotypes of K. pneumoniae was associated with mastitis from 6 dairy herds located in 4 different states. A total of 635 isolates were obtained and fingerprinted by repetitive DNA sequence PCR. Significant genetic diversity was observed in 4 of the 6 dairy herds analyzed, and a total of 49 genotypic variants were identified. Within a herd, Simpsons diversity indices were 91.0, 94.1, 91.7, 88.6, 53.3, and 64.3% for dairies A, B, C, D, E, and F, respectively. The association between matrices of genetic similarity and matrices of temporal distance was negative in all the dairies analyzed. Four dairies had a high incidence of K. pneumoniae mastitis during the winter. The majority of genotypes were unique to herds of origin, and only 5 genotypes were detected in more than 2 dairies. Genotype 1 (arbitrary designation) occurred most frequently across dairies and was found in 25.2% of all mastitis cases and among 22.8% of reinfected and culled cows in dairy A. Specific genotypes also tended to be associated with a specific bedding type and dairy location. Analysis of molecular variance showed that 18% of the genetic diversity was due to variation among herds within states, and 82% of the genetic diversity was accounted for by variation of genotypes within herds. The data support the idea that mastitis is caused by a diverse group of K. pneumoniae genotypes and thus has major implications for the diagnosis, prevention, and treatment of udder infections in dairy cows.

Bedding Options for an Alternative Housing System for Dairy Cows: A Descriptive Study

Availability of bedding material for compost bedded pack barns is a concern for dairy producers who use this type of alternative housing system. The material most commonly used in these barns is dry sawdust. The objective of this descriptive study was to evaluate different types of bedding material that could potentially substitute or partially substitute for sawdust in these housing systems. The study was conducted at the West Central Research & Outreach Center in Morris, Minnesota, from November 2006 to March 2007. Materials included: pine sawdust (control) (SD), corn cobs (CC), pine woodchip fines (WC), and soybean straw (SS). Some of these materials were evaluated as mixtures on a 2:1 volume-to-volume ratio. These mixtures included: woodchips/sawdust (WC/SD), woodchips/soybean straw (WC/SS), and soybean straw/sawdust (SS/SD). Experimental bedded packs were used, each with one of the bedding materials, and 16 cows were placed on each pack. Replicated samples of the bedded pack material were collected twice a month and analyzed for dry matter. C:N ratios and pH were analyzed monthly. Temperatures of each pack were measured weekly at various depths (15.2, 30.5, 45.7, and 61.0 cm). Cows were scored for hygiene (1=clean, 5=dirty) twice a month. Moisture content of SD was 59.7; CC, 44.5; WC/SD, 60.6; SS/SD, 58.2; WC/SS, 60.7; and SS, 60.6. SD pH was 8.7; CC, 7.7; WC/SD, 8.6; SS/SD, 8.6; WC/SS, 8.3; and SS, 8.6. C:N ratio of SD was 37.3; CC, 29.2; WC/SD, 47.5; SS/SD, 25.6; WC/SS, 31.0; and SS, 22.8. Hygiene score of cows on SD was 2.4; CC, 2.7; WC/SD, 2.5; SS/SD, 2.9; WC/SS, 2.6; and SS, 2.8. Based on these results and our observations, it appears that any of the bedding materials evaluated in this study would work in this type of housing system if proper bedding management is applied on a consistent basis. It was concluded that ideal bedding material for compost barns should be dry, processed to less than 2.5 cm long, offer structural integrity, and have good water absorption and holding capacity.

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.

Compost barns: an alternative dairy housing system in Minnesota

Compost barns are a loose housing system that provides excellent cow comfort for dairy cows. Producer experience with well-managed compost barns in Minnesota has generally been positive. Cows are relatively clean, very comfortable, have fewer lameness problems, and in some cases had lower somatic cell counts (SCC) after moving to a compost barn. Current design and management recommendations are based on dairy producer experiences. Compost barns have a concrete feed alley, a bedded pack area that is stirred two times a day, and a 1.2-m high wall surrounding the pack. The wall that separates the pack and feed alley has walkways to allow cow and equipment access to the stirred pack area. The stirred pack is sized to provide a minimum stirred bedded pack area of 7.4 m2/cow. Producers use dry fine wood shavings or sawdust for bedding. Fresh bedding is added when the bedded pack becomes moist enough to stick to the cows. The pack is stirred (aerated) at least two times each day to a producer recommended depth of 25 to 30 cm. Stirring aerates and mixes manure and urine on the surface into the pack to provide a fresh surface for cows to lie down on. The pack can provide manure storage for 6 to 7 months. Excellent pack management and pre-milking cow prep is required. Research on compost barns is needed.