Milk production and somatic cell counts: a cow-level analysis.

The objectives of this study were to quantify the relationship between 24-h milk loss and lactation milk loss due to mastitis at the cow level. For the year 2009, individual cow test-day production records from 2,835 Ontario dairy herds were examined. Each record consisted of 24-h milk and component yields, stage of lactation (days in milk, DIM), somatic cell count (SCC, ×10(3) cells/mL) and parity. The modeling was completed in 2 stages. In stage 1, for each animal in the study, the estimated slope from a linear regression of 24-h milk yield (kg), adjusted for DIM, the quadratic effect of DIM, and the 24-h fat yield (kg) on ln(SCC) was determined. In stage 2, the estimated slope were modeled using a mixed model with a random component due to herd. The fixed effects included season (warm: May to September, cool: October to April), milk quartile class [MQ, determined by the rank of the 24-h average milk yield (kg) over a lactation within the herd] and parity. The estimated slopes from the mixed model analysis were used to estimate 24-h milk loss (kg) by comparing to a referent healthy animal with an SCC value of 100 (×10(3) cells/mL) or less. Lactation milk loss (kg) was then estimated by using estimated 24-h milk loss within lactation by means of a test-day interval method. Lactation average milk loss (kg) and SCC were also estimated. Lastly, lactation milk loss (kg) was modeled on the log scale using a mixed model, which included the random effect of herd and fixed effects, parity, and the linear and quadratic effect of the number of 24-h test days within a lactation where SCC exceeded 100 (×10(3) cells/mL; S100). The effect of SCC was significant with respect to 24-h milk loss (kg), increasing across parity and MQ. In general, first-parity animals in the first MQ (lower milk yield animals) were estimated to have 45% less milk loss than later parity animals. Milk losses were estimated to be 33% less for animals in first parity and MQ 2 through 4 than later parity animals in comparable MQ. Therefore, the relative level of milk production was found to be a significant risk factor for milk loss due to mastitis. For animals with 24-h SCC, values of 200 (×10(3) cells/mL), 24-h milk loss ranged from 0.35 to 1.09 kg; with 24-h SCC values of 2,000 (×10(3) cells/mL), milk loss ranged from 1.49 to 4.70 kg. Lactation milk loss (kg) increased significantly as lactation average SCC increased, ranging from 165 to 919 kg. The linear and quadratic effect of S100 was a significant risk factor for lactation milk loss (kg), where greatest losses occurred in lactations with 5 or more 24-h test days where SCC exceeded 100 (×10(3) cells/mL).

Short communication: Bulk milk somatic cell penalties in herds enrolled in Dairy Herd Improvement programs.

The objective of this study was to determine the effect of somatic cell count (SCC) monitoring at the cow level through Dairy Herd Improvement (DHI) programs on the risk of bulk tank SCC (BTSCC) penalties. For the year 2009, BTSCC for all producers in Ontario were examined, for a total of 2,898 DHI herds, 1,186 non-DHI herds, and 48,250 BTSCC records. Two penalty levels were examined, where BTSCC exceeded 499,000 (P500) and 399,000 (P400) cells/mL. Data were modeled first to determine the odds of a BTSCC exceeding a set penalty threshold and second to determine the odds of incurring a penalty under the Ontario Milk Act. All data were modeled as a generalized mixed model with a binary link function. Random effects included herd, fixed effects included season of BTSCC (summer, May to September, and winter, October to April), total milk shipped per month (L), fat paid per month (kg), protein paid per month (kg), and participation or not in the DHI program. The likelihood of a BTSCC exceeding a penalty threshold in a non-DHI herd compared with a DHI herd was significantly greater than 1 at both penalty levels, where the odds ratios were estimated to be 1.42 [95% confidence interval (CI): 1.19 to 1.69] and 1.38 (95% CI: 1.25 to 1.54) for P500 and P400, respectively. The likelihood of incurring a BTSCC penalty (where 3 out of 4 consecutive BTSCC exceeded penalty thresholds) was not significantly different at P500; however, it was significantly different for P400, where the odds ratio was estimated to be 1.42 (95% CI: 1.12 to 1.81).

Genetic analysis of return over feed in Canadian Holsteins.

The objective of this study was to investigate genetic merit of return over feed (ROF), which is a herd profit index defined by CanWest Dairy Herd Improvement as a difference between milk income and feed cost. A multiple-trait (MT) model and random regression model (RRM) were used. The traits analyzed in MT were rearing cost and ROF of the first three lactations. In RRM, a cumulative ROF was fitted as function of age and rearing cost was treated as a correlated trait. Variance components were estimated within a Bayesian framework by Gibbs sampling using a subsample of data. Breeding values were then estimated for 3 041 078 animals using records of 1 951 893 cows. Estimates of heritability for rearing cost from MT and RRM were 0.23 and 0.22, respectively. ROF per lactation and cumulative ROF were negatively correlated with rearing cost. Estimates of heritability of ROF through the first, second and third lactation from MT were 0.27, 0.10 and 0.08, respectively. Estimates of heritability of ROF from RRM increased with age and ranged from 0.08 through 0.31. Estimated breeding values (EBVs) for ROF from MT and RRM were moderately correlated with official EBV for production traits and the Canadian selection index (Lifetime Profit Index). Herd life EBV had -0.07 and 0.19 correlations with EBVs for ROF from MT and RRM, respectively. From both MT and RRM, small favorable correlations were reported between EBVs for ROF and for bone quality and angularity, whereas low unfavorable correlations were reported with EBV for udder depth, front end and chest width. Majority of correlations between EBVs for ROF and for reproduction traits were near 0, with the exception of EBV for gestation length, calf size and calving ease, where small favorable correlations were reported. The ROF is a good indicator of cow profitability despite the fact that it is a simplified profit index that does not account for animal-specific health and reproductive cost. However, because ROF does not account for differences in heritabilities between components of profit, ROF is not recommended to be used for direct selection for profit.

An analysis of the relationship between bulk tank milk quality and wash water quality on dairy farms in Ontario, Canada.

The objective of this study was to identify regions at high risk for bacterial water or milk contamination, as well as risk factors associated with high bacteria counts in raw milk in Ontario, Canada. Between 2003 and 2004, the Dairy Farmers of Ontario (DFO) tested water samples from 5,421 farms in Ontario for the presence of Escherichia coli and coliforms. The water samples were collected as "point-of-use" samples, meaning that each sample was taken from a tap or water hose in the milk house as soon as the water was turned on. Routine, monthly raw milk bacterial counts were determined by DFO using BactoScan (Foss, Hillerød, Denmark). BactoScan data were retrieved from DFO for all of the farms with water test results. The prevalence of samples with E. coli and coliforms in water and elevated bacteria counts in raw milk was 13.6, 53.8, and 2.8%, respectively. The spatial analysis, using a scan statistic, revealed 1 coliform and 3 E. coli clusters of contaminated water, but no clusters of elevated milk bacteria counts in raw milk in southern Ontario. The coliform water contamination cluster was the largest, with a radius of approximately 200 km. Regression analysis indicated that risk factors associated with the occurrence of high levels of bacteria in raw milk were elevated average monthly somatic cell count, increased total milk production, cooler seasons of the year, and the presence of E. coli in wash water.

Milking-to-milking variability for milk yield, fat and protein percentage, and somatic cell count.

The main objectives were to analyze milking-to-milking variability in milk yield, fat and protein percentages, and somatic cell count (SCC). Additional objectives were to investigate the factors that affect variation in milk fat percentage and to study the seasonal variations in milk, fat, and protein yields and SCC. A total of 16 farms (14 milked 2x and 2 milked 3x) across Canada participated in a 5-d milk-sampling study, with 27,328 milk samples collected and analyzed for fat and protein yields and SCC. Descriptive statistics for both 2x and 3x herds for milk yield and fat and protein percentages followed a typical pattern throughout lactation, and the somatic cell linear scores were higher in early lactation for first-lactation cows (4.7 vs. 3.8) but were higher at the end of lactation for cows in second lactation or greater (5.1 vs. 4.9). The 2x herds had higher milk yields in the morning (approximately 17 vs. approximately 14 kg), whereas the 3x herds had the lowest milk yields in the morning, and yields peaked at the evening milking (approximately 9 vs. approximately 11.2 kg). A herd management questionnaire was distributed to participating producers to investigate the relationship between management variables and variations in milk fat percentage over the 5-d sampling period. Data from the questionnaire determined that milking period had a significant effect on milk fat in 2x herds, with fat percentage 1.11% lower in the morning compared with the evening milking period. Seasonal differences in milk, fat, and protein yields were investigated in 910 cows on 3 farms, with 5,517 fat and 5,534 protein samples. The seasonal differences in fat yield [summer = 1.02 +/- 1.05 kg/d (SEM); winter = 1.19 +/- 1.05 kg/d] and protein yield (summer = 0.85 +/- 1.05 kg/d; winter = 0.96 +/- 1.05 kg/d) were significant only for first lactation. Understanding the variability in milk yield, fat and protein percentages, and SCC is important when making management decisions and in milk-recording programs.

Agreement of predicted 305-day milk yields relative to actual 305-day milk weight yields.

The objectives of this study were to compare the multiple trait prediction (MTP) model estimate of 305-d lactation yield with the 305-d daily milk yield data from on-farm automated meters and software and to examine the accuracy of electronic identification (ID). Twenty-four-hour milk and component yields are calculated by using milk weights and samples collected 8 to 10 times/yr by Dairy Herd Improvement (DHI) organizations. Daily milk weights were collected from cows on 20 Canadian farms that used parlor milking systems with electronic ID and that were enrolled in a regular DHI program. A total of 10,175 DHI test days from 1,103 cows with complete 305-d lactation yields were entered into the MTP model, and lactation yields were predicted. Test days were grouped into first, second, and third and greater lactations and within each lactation group, days in milk were categorized in 3 stages (5 to 60, 61 to 120, and 120 to 305 d in milk) for a total of 9 classes. Agreement analysis was used to compare the 305-d sum of daily milk to the MTP 305-d lactation yield predictions by using inputs from test days throughout the lactations. Results indicated that the MTP model overestimated lactation yields across all parity groups, ranging from 310 to 1,552 kg in parity 1, 640 to 2,000 kg in parity 2, and 567 to 1,476 kg in parity 3 and greater. A preliminary examination of electronic ID accuracy was conducted on 4 farms. Two electronic ID systems were examined for cow ID accuracy by verifying the ID number appearing in the parlor with the corresponding ear tag number. There were no ID errors on 3 of 4 farms tested and only a very small number of errors (3/80) on the fourth farm, indicating that the electronic ID systems used in milking parlors identify cows accurately.

A total merit selection index for Ontario organic dairy farmers.

Organic standards require changes in management practices so that health, fertility, and overall fitness are more important than on conventional dairy farms and require different selection objectives. A survey involving 18 (40%) Ontario organic dairy farms was carried out to collect data on their production systems, breeding policies, and concerns. Compared with conventional farms, organic farms had lower milk production, lower replacement rate, higher somatic cell count, and a much higher rate of crossbreeding. Actual culling rate was 21%, and the main causes were fertility, mastitis, feet and legs, production, and old age. The major areas of concern expressed by organic dairy farmers were related to grazing traits, fertility, health, and longevity. An organic total merit index was developed based on the subjective scores for traits with a genetic evaluation in Canada. The relative weights of production to fitness traits (28:72) were substantially different from those in the Canadian Lifetime Profit Index (54:46), but similar to those used in conventional indices in Sweden and Denmark and in the Swiss organic index. The overall weight on health traits was 2.5 times higher in the organic index and, among fitness traits, the emphasis was substantially higher for lactation persistency, somatic cell score, and body capacity. Correlations between the organic index and Lifetime Profit Index were 0.88 for all bulls proven in Canada, 0.70 for the top 1,000, and 0.65 for the top 100, indicating that a different group of bulls would rank at the top of these 2 indices. When the top 100 bulls for either index were compared, those selected for the organic index were about 0.5 standard deviations lower for all yield traits, but were much better for body capacity and somatic cell score, and 0.25 standard deviations higher for herd life, feet and legs, udder conformation, and lactation persistency. Given the small population size, a separate breeding program for an organic management system is not viable in the foreseeable future. However, the organic index would allow producers to rank proven bulls in accordance with their perceived needs.

Short communication: Comparison of protocols to estimate twenty-four-hour fat and protein percentages for herds with a robotic milking system.

Currently, different protocols are used in various countries to estimate the 24-h fat and protein percentages for dairy cows that are milked with an automatic milking system. These protocols include estimating 24-h fat and protein percentages from 1) single samples from all animals in the herd; 2) single samples of fat adjusted for covariates and single samples of protein unadjusted for covariates; or 3) all samples (adjusted and unadjusted) collected on test days where test days vary in length from 10 to 18 h. The accuracy of estimated component percentages in predicting observed percentages was determined via agreement analysis for four protocols. Analysis of the data suggests collecting all samples (unadjusted for covariates) during a sampling period of at least 16 h on test day to be the most accurate protocol when estimating 24-h fat and protein percentages in herds with automatic milking systems.