The objective of this study was to estimate heritabilities and repeatabilities for milk coagulation traits [milk coagulation time (RCT) and curd firmness (E(30))] and genetic and phenotypic correlations between milk yield and composition traits (milk fat percentage and protein percentage, urea, somatic cell count, pH) in first-lactation Estonian Holstein dairy cattle. A total of 17,577 test-day records from 4,191 Estonian Holstein cows in 73 herds across the country were collected during routine milk recordings. Measurements of RCT and E(30) determined with the Optigraph (Ysebaert, Frepillon, France) are based on an optical signal in the near-infrared region. The cows had at least 3 measurements taken during the period from April 2005 to January 2009. Data were analyzed using a repeatability animal model. There was substantial variation in milk coagulation traits with a coefficient of variation of 27% for E(30) and 9% for the log-transformed RCT. The percentage of variation explained by herd was 3% for E(30) and 4% for RCT, suggesting that milk coagulation traits are not strongly affected by herd conditions (e.g., feeding). Heritability was 0.28 for RCT and 0.41 for E(30), and repeatability estimates were 0.45 and 0.50, respectively. Genetic correlation between both milk coagulation traits was negligible, suggesting that RCT and E(30) have genetically different foundations. Milk coagulation time had a moderately high positive genetic (0.69) and phenotypic (0.61) correlation with milk pH indicating that a high pH is related to a less favorable RCT. Curd firmness had a moderate positive genetic (0.48) and phenotypic (0.45) correlation with the protein percentage. Therefore, a high protein percentage is associated with favorable curd firmness. All reported genetic parameters were statistically significantly different from zero. Additional univariate random regression analysis for milk coagulation traits yielded slightly higher average heritabilities of 0.38 and 0.47 for RCT and E(30) compared with the heritabilities of the repeatability model.

The objective of this study was to estimate the effect of composite β-κ-CN genotypes on milk coagulation and composition traits, and on the additive genetic variation of these traits in Estonian Holstein dairy cattle. A total of 23,970 milk samples, repeated measurements from the first to third lactation from 2,859 Estonian Holstein cows from 78 herds across the country, were analyzed for milk yield, milk fat and protein percentages, somatic cell count, and milk coagulation properties (milk coagulation time and curd firmness). Each cow had at least 3 measurements per lactation. Two single-trait random regression animal models were fitted for the traits studied. The first model considered fixed effects of year-season of sampling and year-season of calving, calving age (nested within lactation), sample age (only for milk coagulation traits) and days in milk, and random herd, additive genetic, and permanent environmental effects. The animal and permanent environmental effects were modeled over the lactation period by using Legendre polynomials. The second model had the additional fixed β-κ-casein effect in the form of a third-order Legendre polynomial. The 2 most frequent β-κ-casein composite genotypes were A2A2AA and A1A2AA, both with prevalence greater than 20%. Percentages of the remaining 31 genotypes were less than 8%, including 20 genotypes with percentages less than 1%. The β-κ-casein genotype-specific lactation curves were significantly different for milk coagulation traits and milk protein percentage. The B variant of κ-casein showed a favorable effect on both milk coagulation traits, whereas the IB haplotype had an increasing effect on curd firmness and protein percentage. Inclusion of the β-κ-casein genotype effects in the model resulted in decreases in the mean additive genetic variations for milk coagulation time and curd firmness of 12.9 and 51.1%, respectively.

Milk coagulation properties (MCP) analysis is performed using a wide range of methodologies in different countries and laboratories, using different instruments, coagulant activity in the milk, and type of coagulant. This makes it difficult to compare results and data from different research. The aims of this study were to propose a method for the transformation of values of rennet coagulation time (RCT) and curd firmness (a(30)) and to predict the noncoagulation (NC) probability of milk samples analyzed using different methodologies. Individual milk samples were collected during the morning milking in October 2010 from each of 165 Holstein-Friesian dairy cows in 2 freestall barns in Italy, and sent to 3 laboratories for MCP analysis. For each laboratory, MCP analysis was performed using a different methodology: A, with a computerized renneting meter instrument using 0.051 international milk clotting units (IMCU)/mL of coagulant activity; B, with a Lattodinamografo (Foss-Italia, Padova, Italy) using 0.051 IMCU/mL of coagulant activity; and C, with an Optigraph (Ysebaert, Frépillon, France) using 0.120 IMCU/mL of coagulant activity. The relationships between MCP traits were analyzed with correlation and regression analyses for each pair of methodologies. For each MCP trait, 2 regression models were applied: model 1 was a single regression model, where the dependent and independent variables were the same MCP trait determined by 2 different methodologies; in model 2, both a(30) and RCT were included as independent variables. The NC probabilities for laboratories with the highest number of NC samples were predicted based on the RCT and a(30) values measured in the laboratories with lower number of NC samples using logistic regression and receiver operating characteristic analysis. The percentages of NC samples were 4.2, 11.5, and 0.6% for A, B, and C, respectively. The transformation of MCP traits was more precise with model 1 for RCT (R(2): 0.77-0.82) than for a(30) (R(2): 0.28-0.63). The application of model 2 was needed when the C measurements were transformed into the other scales. The analyses of NC probabilities of milk samples showed that NC samples from one methodology were well distinguishable (with an accuracy of 0.972-0.996) based on the rennet coagulation time measured with the other methodology. A standard definition for MCP traits analysis is needed to enable reliable comparisons between MCP traits recorded in different laboratories and in different animal populations and breeds.