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).

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.

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.

Twice-a-day milking is currently the most frequently used milking schedule in Canadian dairy cattle. However, with an automated milking system (AMS), dairy cows can be milked more frequently. The objective of this study was to estimate genetic parameters for milking frequency and for production traits of cows milked within an AMS. Data were 141,927 daily records of 953 primiparous Holstein cows from 14 farms in Ontario and Quebec. Most cows visited the AMS 2 (46%) or 3 (37%) times a day. A 2-trait [daily (24-h) milking frequency and daily (24-h) milk yield] random regression daily animal model and a multiple-trait (milk, fat, protein yields, somatic cell score, and milking frequency) random regression test-day animal model were used for the estimation of (co)variance components. Both models included fixed effect of herd x test-date, fixed regressions on days in milk (DIM) nested within age at calving by season of calving, and random regressions for additive genetic and permanent environmental effects. Both fixed and random regressions were fitted with fourth-order Legendre polynomials on DIM. The number of cows in the multiple-trait test-day model was smaller compared with the daily animal model. Heritabilities from the daily model for daily (24-h) milking frequency and daily (24-h) milk yield ranged between 0.02 and 0.08 and 0.14 and 0.20, respectively. Genetic correlations between daily (24-h) milk yield and daily (24-h) milking frequency were largest at the end of lactation (0.80) and smallest in mid-lactation (0.27). Heritabilities from the test-day model for test-day milking frequency, milk, fat and protein yield, and somatic cell score were 0.14, 0.26, 0.20, 0.21, and 0.20, respectively. The genetic correlation was positive between test-day milking frequency and official test-day milk, fat, and protein yields, and negative between official test-day somatic cell score and test-day milking frequency.

The objective was to examine the potential benefits of using different combinations of multiple quarter milk samples compared with a single sample for diagnosing intramammary infections (IMI) in dairy cattle. Data used in the analyses were derived from 7,076 samples from 667 quarters in 176 cows in 8 herds in 4 locations (Minnesota/Wisconsin, n=4; Prince Edward Island, n=2; Ontario, n=1; New York, n=1). Duplicate quarter milk samples were collected at morning milking for 5 consecutive days. Cows were evenly distributed between early postparturient and mid- to late-lactation cows. All samples were frozen for shipping and storage, thawed once, and cultured in university laboratories using standardized procedures consistent with National Mastitis Council guidelines. The presence of specific pathogens was confirmed and identified using the API identification system (bioMerieux, Marcy l'Etoile, France) in each laboratory. A previously developed gold standard was applied to the first sample from d 1, 3, and 5 to classify infected quarters. The data were analyzed separately for coagulase-negative staphylococci (CNS) and Streptococcus spp. Various combinations of test results from d 2 and 4 were used in the test evaluation. These consisted of single samples (n=4), 2 sets of duplicate samples (2 samples collected on the same day), 2 sets of consecutive samples (2 samples collected 2 d apart), and 2 sets of triplicate samples (2 samples on the same day and a third sample 2 d apart). Series interpretation of duplicate or consecutive samples (i.e., positive=same pathogen isolated from both samples) resulted in the highest specificity (Sp; CNS Sp=92.1-98.1%; Streptococcus spp. Sp=98.7-99.6%), but lowest sensitivity (Se; CNS Se=41.9-53.3%; Streptococcus spp. Se=7.7-22.2%). Parallel interpretation of duplicate or consecutive samples (i.e., positive=pathogen isolated from either) resulted in the highest Se (CNS Se=70.8-80.6%; Streptococcus spp. Se=31.6-48.1%), but lowest Sp (CNS Sp=72.0-77.3%; Streptococcus spp. Sp=89.5-93.3%). The difference in estimates between single and duplicate samples was larger than between single and consecutive samples. Overall, triplicate samples provided the best combination of Se and Sp, but compared with a single sample, provided only a modest gain in Sp and little or no gain in Se.