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The effect of the contents of casein (CN) and whey protein fractions on curd yield (CY) and composition was estimated using 964 individual milk samples. Contents of α S1 -CN, α S2 -CN, β-CN, γ-CN, glycosylated κ-CN (Gκ-CN), unglycosylated κ-CN, β-LG, and α-LA of individual milk samples were measured using reversed-phase HPLC. Curd yield and curd composition were measured by model micro-cheese curd making using 25 mL of milk. Dry matter CY (DMCY) was positively associated with all casein fractions but especially with α S1 -CN and β-CN. Curd moisture decreased at increasing β-CN content and increased at increasing γ-CN and Gκ-CN content. Due to their associations with moisture, Gκ-CN and β-CN were the fractions with the greatest effect on raw CY, which decreased by 0.66% per 1-standard deviation (SD) increase in the content of β-CN and increased by 0.62% per 1-SD increase in the content of Gκ-CN. The effects due to variation in percentages of the casein fractions in total casein were less marked than those exerted by contents. A 1-SD increase in β-CN percentage in casein (+3.8% in casein) exerted a slightly negative effect on DMCY (β = -0.05%). Conversely, increasing amounts of α S1 -CN percentage were associated with a small increase in DMCY. Hence, results suggest that, at constant casein and whey protein contents in milk, the DMCY depends to a limited extent on the variation in the α S1 -CN:β-CN ratio. κ-Casein percentage did not affect DMCY, indicating that the positive relationship detected between the content of κ-CN and DMCY can be attributed to the increase in total casein resulting from the increased amount of κ-CN and not to variation in κ-CN relative content. However, milk with increased Gκ-CN percentage in κ-CN also shows increased raw CY and produces curds with increased moisture content. Curd yield increased at increasing content and relative proportion of β-LG in whey protein, but this is attributable to an improved capacity of the curd to retain water. Results obtained in this study support the hypothesis that, besides variation in total casein and whey protein contents, variation in protein composition might affect the cheese-making ability of milk, but this requires further studies., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to compare the prediction accuracy of 92 infrared prediction equations obtained by different statistical approaches. The predicted traits included fatty acid composition (n = 1,040); detailed protein composition (n = 1,137); lactoferrin (n = 558); pH and coagulation properties (n = 1,296); curd yield and composition obtained by a micro-cheese making procedure (n = 1,177); and Ca, P, Mg, and K contents (n = 689). The statistical methods used to develop the prediction equations were partial least squares regression (PLSR), Bayesian ridge regression, Bayes A, Bayes B, Bayes C, and Bayesian least absolute shrinkage and selection operator. Model performances were assessed, for each trait and model, in training and validation sets over 10 replicates. In validation sets, Bayesian regression models performed significantly better than PLSR for the prediction of 33 out of 92 traits, especially fatty acids, whereas they yielded a significantly lower prediction accuracy than PLSR in the prediction of 8 traits: the percentage of C18:1n-7 trans-9 in fat; the content of unglycosylated κ-casein and its percentage in protein; the content of α-lactalbumin; the percentage of α S2 -casein in protein; and the contents of Ca, P, and Mg. Even though Bayesian methods produced a significant enhancement of model accuracy in many traits compared with PLSR, most variations in the coefficient of determination in validation sets were smaller than 1 percentage point. Over traits, the highest predictive ability was obtained by Bayes C even though most of the significant differences in accuracy between Bayesian regression models were negligible., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objectives of this study were to estimate, for the Italian Simmental cattle population, genetic parameters for 92 traits and their infrared predictions (IP) and to investigate the genetic relationship between measured traits (MT) and IP. Data for milk fat fatty acid composition (n = 1,040), detailed protein composition (n = 3,337), lactoferrin (n = 558), pH (n = 3,438), coagulation properties (n = 3,266), curd yield and composition obtained by a micro-cheese making procedure (n = 1,177), and content of Ca, P, Mg, and K (n = 689) were obtained using reference laboratory analysis. Infrared prediction for all the investigated traits was performed using 143,198 spectra records belonging to 17,619 Italian Simmental cows. (Co)variance components for MT and their IP were estimated in a set of bivariate animal model REML analyses and genetic correlations between MT and IP were estimated using all IP obtained at the population level. A significant positive relationship was observed between the coefficient of determination of the infrared prediction models and the phenotypic and genetic variation of the IP. The decrease in the estimated genetic variance of IP compared with MT was on average 64%. For traits exhibiting calibration models with coefficients of determination in cross-validation (R 2 CV ) greater than 0.9, the decrease in the genetic variance ranged from approximately 20 to 50%. Most traits (88 out of 92) exhibited lower heritability estimates for IP than for the corresponding MT. The estimated genetic correlations between IP and MT (r a ) were in general very high. A positive relationship (r = 0.57) between R 2 CV of calibration models and the estimated r a has been detected. For calibration models exhibiting R 2 CV higher than 0.75, r a were greater than 0.9. The variability in the estimated correlations increased when R 2 CV decreased, and for calibration models of moderate predictive ability, estimates of r a ranged from 0.2 to 1. Genetic parameter estimates suggested that IP can be used as indicator traits in breeding programs for the enhancement of fine composition and technological properties of milk. The genetic gain achievable selecting for IP is expected to be high for fatty acid composition, minerals, and for technological properties of milk, whereas it will be low for casein and whey protein composition and for the content of lactoferrin., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to compare the common method of exploiting infrared spectral data in animal breeding; that is, estimating the breeding values for the traits predicted by infrared spectroscopy, and an alternative approach based on the direct use of spectral information (direct prediction, DP) to predict the estimated breeding values (EBV). Traits were pH, milk coagulation properties, contents of the main casein and whey protein fractions, cheese yield measured by micro-cheese making, lactoferrin, Ca, and fat composition. For the DP method, the number of spectral variables was reduced by principal components analysis to 8 latent traits that explained 99% of the original spectral variation. Restricted maximum likelihood was used to estimate variance components of the latent traits. (Co)variance components of the original spectral traits were obtained by back-transformation and EBV of all derived milk traits were then predicted as traits correlated with the genetic information of the spectra. The rank correlation between the EBV obtained for the infrared-predicted traits and those obtained from the DP method was variable across traits. Rank correlations ranged from 0.07 (for the content of saturated fatty acids expressed as g/100 g of fat) to 0.96 (for dry matter cheese yield, %) and, for most traits, was <0.5. This result can be explained by the nature of the principal components analysis: it does not take into account the covariance between the spectral variables and the reference traits but produces latent traits that maximize the spectral variance explained. Thus, the direct approach is more likely to be effective for traits more related to the main sources of spectral variation (i.e., protein and fat). More research is required to study spectral genetic variation and to determine the best way to choose spectral regions and the type and number of considered latent traits for potential applications., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to evaluate the ability of mid-infrared predictions of fine milk composition and technological traits to serve as a tool for large-scale phenotyping of the Italian Simmental population. Calibration equations accurately predicted the fatty acid profile of the milk, but we obtained moderate or poor accuracy for detailed protein composition, coagulation properties, curd yield and composition, lactoferrin, and concentration of major minerals. To evaluate the role of infrared predictions as indicator traits of fine milk composition in indirect selective breeding programs, the genetic parameters of the traits predicted using mid-infrared spectra need to be estimated., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this work was to test the applicability of Fourier-transform mid-infrared spectroscopy (FT-MIR) for the prediction of the contents of casein (CN) and whey protein fractions in buffalo milk. Buffalo milk samples spectra were collected using a MilkoScan FT2 (Foss, Hillerød, Denmark) over the spectral range from 5,000 to 900 wavenumber × cm(-1). Contents of protein fractions, as well as CSN1S1 and CSN3 genotypes, were assessed by reversed phase HPLC. The highest coefficients of determination in cross-validation (1 - VR) were obtained for the contents (g/L of milk) of total protein and CN (1 - VR=0.92), followed by the content of β-CN, total whey protein, and αS2-CN (1 - VR of 0.87, 0.77, and 0.63, respectively). Conversely, contents of αS1-CN, γ-CN, glycosylated-κ-CN, total κ-CN, and whey protein fractions were predicted with poor accuracy (1 - VR <0.51). When protein fractions were expressed as percentages to total protein, 1 - VR values were never greater than 0.61 (β-CN). Only 56 and 70% of the observations were correctly classified by discriminant analysis in each of 2 groups of CSN1S1 and CSN3 genotypes, respectively. Results showed that FT-MIR spectroscopy is not applicable when prediction of detailed milk protein composition with high accuracy is required. Predictions may play a role as indicator traits in selective breeding, if the genetic correlation between FT-MIR predictions and measures of milk protein composition are high enough and predictions of protein fraction contents are sufficiently independent from the predicted total protein content., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aims of this study were to investigate genetic and nongenetic variation in the degree of glycosylation of κ-casein (κ-CN) and to estimate the effects of glycosylated (G-κCN) and unglycosylated (U-κCN) κ-CN contents on milk coagulation properties of Simmental cows. Measures of contents of the main casein fractions, G-κCN, and U-κCN, and assessment of genotypes at CSN2, CSN3, and BLG were obtained by reversed-phase HPLC analysis of 2,015 individual milk samples. Content of total κ-CN (κ-CNtot, g/L) was the sum of G-κCN and U-κCN, and the glycosylation degree of κ-CN (GD) was measured as the ratio of G-κCN to κ-CNtot. Rennet coagulation time (RCT) and curd firmness were measured by using a computerized renneting meter. Measures of curd firmness were adjusted for RCT before statistical analysis. Variance components of κ-CNtot, G-κCN, U-κCN, and GD were estimated by Bayesian procedures and univariate linear models that included the class effects of the herd-test-day, parity, days in milk, genotypes at milk protein genes, and animal. These class effects, those of G-κCN, U-κCN, and content of other caseins, and the linear effect of milk pH were accounted for by models investigating the influence of κ-CN glycosylation on coagulation properties. The GD ranged from 22 to 76%, indicating that variation in G-κCN depends on the variation both in κ-CNtot and in the efficiency of κ-CN glycosylation. Genotype CSN3 BB exhibited high G-κCN and U-κCN relative to that of CSN3 AA. Heritability of G-κCN, U-κCN, and GD was high and ranged from 0.46 to 0.56. A large proportion of the additive genetic variation in G-κCN and U-κCN was attributable to influence of CSN and BLG, but these genes did not affect variation in GD, and across-genotypes differences in the trait were small or trivial. Average RCT of the milk class having the highest G-κCN was, on average, 2min (standard deviation 0.5) shorter than that of the lowest class. Conversely, U-κCN and content of other caseins were not associated with any effect on RCT, except for a slight delay in coagulation when U-κCN was very high. Curd firmness increased when the contents of both κ-CN fractions and other caseins increased. This study provides evidence that the positive association between RCT and κ-CN content is exclusively attributable to the glycosylated fraction of the protein. Because exploitable additive genetic variation in G-κCN exists, improvement of κ-CN composition through selective breeding might be an effective way to enhance milk coagulation properties., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to investigate the effects exerted by the content of casein and whey protein fractions on variation of pH, rennet-coagulation time (RCT), curd-firming time (K20), and curd firmness of Mediterranean buffalo individual milk. Measures of milk protein composition and assessment of genotypes at CSN1S1 and CSN3 were obtained by reversed-phase HPLC analysis of 621 individual milk samples. Increased content of αS1-casein (CN) was associated with delayed coagulation onset and increased K20, whereas average pH, RCT, and K20 decreased when β-CN content increased. Milk with low κ-CN content exhibited low pH and RCT relative to milk with high content of κ-CN. Increased content of glycosylated κ-CN was associated with unfavorable effects on RCT. Effects of milk protein composition on curd firmness were less important than those on pH, RCT, and K20. Likely, this occurred as a consequence of the very short RCT of buffalo milk, which guaranteed a complete strengthening of the curd even in the restricted 31 min time of analysis of coagulation properties and for samples initially showing soft curds. Effects of CSN1S1-CSN3 genotypes on coagulation properties were not to be entirely ascribed to existing variation in milk protein composition associated with polymorphisms at CSN1S1 and CSN3 genes. Although the role of detailed milk protein composition in variation of cheese yield needs to be further investigated, findings of this study suggest that modification of the relative content of specific CN fractions can relevantly influence the behavior of buffalo milk during processing., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of the study was to investigate the effect of composite CSN1S1-CSN3 [α(S1)-κ-casein (CN)] genotype on milk protein composition in Mediterranean water buffalo. Content of α(S1)-CN, α(S2)-CN, β-CN, γ-CN, κ-CN, glycosylated and unglycosylated κ-CN, α-lactalbumin, and β-lactoglobulin was measured by reversed-phase HPLC using 621 individual milk samples. Genotypes at CSN1S1 and CSN3 were also obtained by reversed-phase HPLC. Two alleles were detected at CSN1S1 (corresponding to the A and B variants, O62823: p.Leu193Ser,) and at CSN3 (corresponding to the X1 and X2 variants, CAP12622.1: p.Ile156Thr). Increased proportions of α(S1)-CN in total casein (TCN) were associated with genotypes carrying CSN1S1 A. Genotypes associated with a marked decrease of the proportion of α(S1)-CN in TCN (composite genotypes AB-X1X1 and BB-X1X2) were associated with marked increases in the proportion of α(S2)-CN. In addition, composite genotypes carrying the X1 allele at CSN3 were associated with a greater proportion of α(S2)-CN in TCN relative to those carrying CSN3 X2. Composite genotypes greatly affected also the variability of ratios of κ-CN to TCN, with genotypes carrying the X1 allele at CSN3 being associated with decreased ratios. The decreased content of glycosylated κ-CN associated with CSN3 X1 was responsible for the overall lower content of total κ-CN in milk of X1-carrying animals. Increasing the frequency of specific genotypes might be an effective way to alter milk protein composition, namely the proportion of α(S1)-CN, α(S2)-CN, and κ-CN in TCN, and the degree of glycosylation of κ-CN., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The effects of some nongenetic factors on milk protein fraction contents and relative proportions were estimated in 606 individual milk samples of Mediterranean water buffalo. Content of α(S1)-casein (CN), α(S2)-CN, β-CN, γ-CN, κκ-CN, glycosylated κ-CN (glyco-κ-CN), α-lactalbumin, and β-lactoglobulin was measured by reversed-phase HPLC. Relative contents of α(S1)-CN%, α(S2)-CN%, β-CN%, and κ-CN% were, respectively, 32.1, 17.1, 34.5, and 15.7%, whereas γ-CN% accounted for 0.6% of total casein content. Increasing total casein content in milk would result in a greater proportion of β-CN% at the expense of all of the other major casein fractions, especially of κ-CN%. Values of α(S2)-CN%, β-CN%, and γ-CN% tended to decrease with parity, although their variations were not significant, whereas α(S1)-CN% and glyco-κ-CN% showed the opposite trend. Contents of most protein fractions showed the typical trends observed for milk components as lactation progressed, with high contents in early lactation, a minimum in midlactation, followed by a gradual increase toward the latter part of lactation. Values of α(S1)-CN% increased during lactation, whereas α(S2)-CN% decreased. The proportion of β-CN% had its maximum value between 60 and 160 d of lactation, followed by a decrease, whereas κ-CN% had its minimum value in early lactation (<60 d) and remained relatively constant in the period of mid and late lactation. Glyco-κ-CN% and β-lactoglobulin% decreased in the first part of lactation, to reach their minimum values in midlactation, followed by an increase. Milk of top-producing buffaloes, compared with that of low-producing ones, had a significantly greater value of β-CN% and glyco-κ-CN%, and lower proportion of α(S1)-CN%. The possible effect exerted by protein genetic variants in affecting variation of milk protein fraction contents and relative proportions should be further considered to better get insight into buffalo milk protein composition., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to estimate effects of CSN1S1-CSN3 (α(S1)-κ-casein) composite genotypes on milk production traits and milk coagulation properties (MCP) in Mediterranean water buffalo. Genotypes at CSN1S1 and CSN3 and coagulation properties [rennet clotting time (RCT), curd firming time (K₂₀), and curd firmness (A₃₀)] were assessed by reversed-phase HPLC and computerized renneting meter analysis, respectively, using single test-day milk samples of 536 animals. Alternative protein variants of α(S1)-CN and κ-CN were detected by HPLC, and identification of the corresponding genetic variants was carried out by DNA analysis. Two genetic variants were detected at CSN1S1 (A and B variants) and 2 at CSN3 (X1 and X2 variants). Statistical inference was based on a linear model including the CSN1S1-CSN3 composite genotype effect (7 genotypes), the effects of herd-test-day (8 levels), and a combined days in milk (DIM)-parity class. Composite genotype AB-X2X2 was associated with decreased test-day milk yield [-0.21 standard deviation (SD) units of the trait] relative to genotype BB-X2X2. Genotypes did not affect milk protein content, but genotype AB-X1X1 was associated with increased fat content compared with genotype BB-X2X2 (+0.28 SD units of the trait) and AB-X1X1 (+0.43 SD units of the trait). For RCT, the largest difference (+1.91 min; i.e., 0.61 SD units of the trait) was observed between genotype AA-X1X2 and AB-X1X1. Direction of genotype effects on K(20) was consistent with that for RCT. The maximum variation in K₂₀ due to genotype effects (between AA-X1X2 and AB-X1X1 genotypes) was almost 0.9 SD units of the trait. Magnitude of genotype effects was smaller for A₃₀ than for RCT and K₂₀, with a maximum difference of 0.5 SD units of the trait between genotype AA-X1X2 and AA-X1X1. The B allele at CSN1S1 was associated with increased RCT and K₂₀ and with weaker curds compared with allele A. Allele X2 at CSN3 exerted opposite effects on MCP relative to CSN1S1 B. Because of linkage disequilibrium, allele B at CSN1S1 and allele X2 at CSN3 tend to be associated and this likely makes their effects cancel each other. This study indicates a role for casein genes in variation of MCP of buffalo milk. Further studies are necessary to estimate the effects of casein genetic variants on variation of cheese yield., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Mid-infrared (MIR) spectroscopy was used to predict the detailed protein composition of 1,517 milk samples of Simmental cows. Contents of milk protein fractions and genetic variants were quantified by reversed-phase HPLC. The most accurate predictions were those obtained for total protein, casein (CN), α(S1)-CN, β-lactoglobulin (LG), glycosylated κ-CN, and whey protein content, which exhibited coefficients of determination between predicted and measured values in cross-validation (1-VR) ranging from 0.61 to 0.78. Less favorable were results for β-CN (1-VR=0.53), α(S2)-CN, and κ-CN (1-VR=0.49). Neither the content of α-LA nor that of γ-CN was accurately predicted by MIR. Predicting the content of the most common milk protein genetic variants (κ-CN A and B; β-CN A¹, A², and B; and β-LG A and B) was unfeasible (1-VR <0.15 for the content of κ-CN genetic variants and 1-VR <0.01 for the content of β-CN variants). The best predictions were obtained for β-LG A and β-LG B contents (1-VR of 0.60 and 0.44, respectively). Results indicated that MIR is not applicable for predicting individual milk protein composition with high accuracy. However, MIR spectroscopy predictions may play a role as indicator traits in selective breeding to enhance milk protein composition. The genetic correlation between MIR spectroscopy predictions and measures of milk protein composition needs to be investigated, as it affects the suitability of MIR spectroscopy predictions as indicator traits in selective breeding., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of this study was to estimate genetic parameters for milk protein fraction contents, milk protein composition, and milk coagulation properties (MCP). Contents of α(S1)-, α(S2)-, β-, γ-, and κ-casein (CN), β-lactoglobulin (β-LG), and α-lactalbumin (α-LA) were measured by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Milk protein composition was measured as percentage of each CN fraction in CN (α(S1)-CN%, α(S2)-CN%, β-CN%, γ-CN%, and κ-CN%) and as percentage of β-LG in whey protein (β-LG%). Rennet clotting time (RCT) and curd firmness (a(30)) were measured by a computerized renneting meter. Heritabilities for contents of milk proteins ranged from 0.11 (α-LA) to 0.52 (κ-CN). Heritabilities for α(S1)-CN%, κ-CN%, and β-CN% were similar and ranged from 0.63 to 0.69, whereas heritability of α(S2)-CN%, γ-CN%, and β-LG% were 0.28, 0.18, and 0.34, respectively. Effects of CSN2-CSN3 haplotype and BLG genotype accounted for more than 80% of the genetic variance of α(S1)-CN%, β-CN%, and κ-CN% and 50% of the genetic variance of β-LG%. The genetic correlations among the contents of CN fractions and between CN and whey protein fractions contents were generally low. When the data were adjusted for milk protein gene effects, the magnitude of the genetic correlations among the contents of milk protein fractions markedly increased, indicating that they undergo a common regulation. The proportion of β-CN in CN correlated negatively with κ-CN% (r=-0.44). The genetic relationships between CN and whey protein composition were trivial. Low milk pH correlated with favorable MCP. Genetically, contents and proportions of α(S1)- and α(S2)-CN in CN were positively correlated with RCT. The relative proportion of β-CN in CN exhibited a genetic correlation with RCT of -0.26. Both the content and the relative proportion of κ-CN in CN did not correlate with RCT. Weak curds were genetically associated with increased proportions in CN of α(S1)- and α(S2)-CN, decreased contents of β-CN and κ-CN, and decreased proportion of κ-CN in CN. Negligible effects on the estimated correlations between a(30) and κ-CN contents or proportion in CN were observed when the model accounted for milk protein gene effects. Increasing β-CN and κ-CN contents and relative proportions in CN and decreasing the content and proportions of α(S1)-CN and α(S2)-CN and milk pH through selective breeding exert favorable effects on MCP., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Aims of this study were to propose statistical models for the analysis of rennet coagulation time (RCT) suitable for making use of coagulating and noncoagulating (NC) milk information, to estimate heritabilities and to obtain rank correlations for sire merit. A total of 1,025 Holstein cows (progeny of 54 sires) reared in 34 herds were milk-sampled once. Data were analyzed using 4 alternative models: a standard linear (SLM), a right-censored linear Gaussian (CLM), a survival (SUM), and a threshold (THM) model. Model SLM analyzed coagulated milk records only, whereas analysis with CLM or SUM considered information of NC samples as censored records. Model THM analyzed occurrence of milk coagulation as a dichotomous trait. An artificial censoring scenario with an endpoint at 18 min (SET18) was considered after the rearrangement of the timeframe originally used for the observation of RCT (SET31). Heritabilities ranged from 0.12 to 0.25. Correlations of sire rankings ranged from 0.23 to 0.92. Differences in sire rankings between SLM and CLM or SUM increased when the proportion of NC records increased. Correlations between sire rankings obtained for SET31 and SET18 were high for CLM and SUM, indicating that rankings provided by these models tended to be stable even when a large fraction of samples with observed RCT was re-classified as NC milk. Results indicate that CLM and SUM are more suitable than SLM and THM for the analysis of coagulation ability when data contain NC milk information., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a(30)) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a(30) with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h(2))=0.240 and h(2)=0.210 for HF and BS, respectively] than a(30) (h(2)=0.148 and h(2)=0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h(2)=0.103 and h(2)=0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h(2)=0.108). A negative genetic correlation, lower than -0.85, was estimated between RCT and a(30) for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were those of RCT with pH and titratable acidity in both breeds, ranging from 0.80 to 0.94. Including NC milk information in the data affected the estimated correlations and decreased the uncertainty associated with the estimation process. On the basis of the estimated heritabilities and genetic correlations, enhancement of MCP through selective breeding with no detrimental effects on yield and composition seems feasible in both breeds. Milk acidity may play a role as an indicator trait for indirect enhancement of MCP., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to investigate the effect exerted by the relative content of κ-casein (κ-CN) B in bulk milk κ-CN on coagulation properties and cheese yield of 3 Italian cheese varieties (Montasio, Asiago, and Caciotta). Twenty-four cheese-making experiments were carried out in 2 industrial and 1 small-scale dairy plant. Detailed protein composition of bulk milk of 380 herds providing milk to these dairies was analyzed by reversed-phase HPLC. To obtain 2 experimental milks differing in the relative content of κ-CN B in κ-CN, herds were selected on the basis of bulk milk protein composition and relative content of κ-CN genetic variants. Milk was collected and processed separately for the 2 groups of selected herds. A difference of 20% in the relative content of κ-CN B in κ-CN was obtained for the 2 experimental milks for Montasio and a difference of 15% for Asiago and Caciotta. The 2 experimental milks were of similar protein and CN content, casein number, pH, CN composition, and β-CN genetic composition. For each cheese-making trial, amounts of milk, ranging from 2,000 to 6,000kg, were manufactured. Each vat contained milk collected at least from 4 dairy herds. Cheese yield after brining and at the end of the aging was recorded. Milk with a greater proportion of κ-CN B in κ-CN (HIGHB) exhibited similar coagulation properties and greater cheese yield compared with milk with a lower proportion of κ-CN B in κ-CN (LOWB). The increased cheese yield observed for HIGHB when manufacturing Montasio cheese was ascribed to a greater fat content compared with LOWB. The probability of HIGHB giving a cheese yield 5% greater than that of LOWB ranged from 51 to 67% for Montasio cheese, but was less than 21% for Asiago and Caciotta cheeses. Variation in relative content of κ-CN B in κ-CN content did not relevantly affect industrial cheese yield when milks of similar CN composition were processed. An indirect effect due to the increased κ-CN content of κ-CN B milk is thought to explain the favorable effects of κ-CN B on cheese yield reported in the literature., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to investigate the effects of CSN2-CSN3 (beta-kappa-casein) haplotypes, BLG (beta-lactoglobulin) genotypes, content of milk protein fractions, and protein composition on coagulation properties of milk (MCP). Rennet coagulation time (RCT) and curd firmness (a(30)) were measured using a computerized renneting meter, and the contents of major milk protein fractions were quantified by reversed-phase HPLC in individual milk samples of 2,167 Simmental cows. Cow genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Phenotypes for MCP were regressed on CSN2-CSN3 haplotype probabilities using linear models that also included the effects of herd-test-day, parity, days in milk, pH, somatic cell score, renneting meter sensor, sire of the cow, BLG genotype, and content of major protein fractions or, alternatively, protein composition. When the statistical model did not account for protein fraction contents or protein composition, haplotypes carrying CSN3 B were associated with shorter RCT and greater a(30) compared with those carrying CSN3 A. Haplotypes carrying CSN2 B had the effect of decreasing RCT and increasing a(30) relative to haplotype A(2)A. When effects of protein fractions content or protein composition were added to the model, no difference across haplotypes due to CSN3 and CSN2 alleles was observed for MCP, with the exception of the effect of CSN2 B on RCT, which remained markedly favorable. Hence, the effect of CSN3 B on MCP is related to a variation in protein composition caused by the allele-specific expression of kappa-casein, rather than to a direct role of the protein variant on the coagulation process. In addition, the favorable effect exerted by CSN2 B on a(30) was caused by the increased beta-casein content in milk. Conversely, CSN2 B is likely to exert a direct genetic effect on RCT, which does not depend upon variation of beta-casein content associated with CSN2 B. Increased RCT was observed for milk yielded by BLG BB cows, even when models accounted for protein composition. Rennet clotting time was favorably affected by kappa-casein content and percentage of kappa-casein to total casein, whereas a(30) increased when contents and percentages of beta-CN and kappa-CN increased. Changes of milk protein composition and allele frequency at casein and whey protein genes affect variation of MCP., (Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to investigate the effects of CSN2-CSN3 (beta-kappa-casein) haplotypes and BLG (beta-lactoglobulin) genotypes on milk production traits, content of protein fractions, and detailed protein composition of individual milk of Simmental cows. Content of the major protein fractions was measured by reversed-phase HPLC in individual milk samples of 2,167 cows. Protein composition was measured as percentage of each casein (CN) fraction to total CN and as percentage of beta-lactoglobulin (beta-LG) to total whey protein. Genotypes at CSN2, CSN3, and BLG were ascertained by reversed-phase HPLC, and CSN2-CSN3 haplotype probabilities were estimated for each cow. Traits were analyzed by using a linear model including the fixed effects of herd-test-day, parity, days in milk, and somatic cell score class, linear regressions on haplotype probabilities, class of BLG genotype, and the random effect of the sire of the cow. Effects of haplotypes and BLG genotypes on yields were weak or trivial. Genotype BB at BLG and haplotypes carrying CSN2 B and CSN3 B were associated with increased CN content and CN number. Haplotypes including CSN3 B were associated with increased kappa-CN content and percentage of kappa-CN to total CN and with decreased percentages of alpha(S1)- and gamma-CN to total CN. Allele CSN2 B had the effect of increasing beta-CN content and decreasing content of alpha(S1)-CN. Haplotypes including allele CSN2 A(1) exhibited decreased beta-, alpha(S2)-, and gamma-CN concentrations and increased alpha(S1)- and kappa-CN contents, whereas CSN2 I had positive effects on beta-CN concentration and trivial effects on content of other protein fractions. Effects of haplotypes on CN composition were similar to those exerted on content of CN fractions. Allele BLG A was associated with increased beta-LG concentration and percentage of beta-LG to total whey protein and with decreased content of other milk proteins, namely beta-CN and alpha(S1)-CN. Estimated additive genetic variance for investigated traits ranged from 14 to 39% of total variance. Increasing the frequency of specific genotypes or haplotypes by selective breeding might be an effective way to change milk protein composition., (Copyright (c) 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a(30), mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Prediction models for RCT and a(30) based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a(30), respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a(30) were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a(30) ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a(30), mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6 h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a(30), respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a(30). Repeatabilities of RCT and a(30) measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a(30.) Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.

The aim of the study was to estimate the effect of the composite CSN2 and CSN3 genotypes on milk coagulation, quality, and yield traits in Italian Holstein cows. A total of 1,042 multiparous Holstein cows reared on 34 commercial dairy herds were sampled once, concurrently with monthly herd milk recording. The data included the following traits: milk coagulation time; curd firmness; pH and titratable acidity; fat, protein, and casein contents; somatic cell score; and daily milk, fat, and protein yields. A single-trait animal model was assumed with fixed effects of herd, days in milk, parity, composite casein genotype of CSN2 and CSN3 (CSN2-CSN3), and random additive genetic effect of an animal. The composite genotype of CSN2-CSN3 showed a strong effect on both milk coagulation traits and milk and protein yields, but not on fat and protein contents and other milk quality traits. For coagulation time, the best CSN2-CSN3 genotypes were those with at least one B allele in both the CSN2 and CSN3 loci. The CSN3 locus was associated more strongly with milk coagulation traits, whereas the CSN2 locus was associated more with milk and protein yields. However, because of the tight linkage between the 2 loci, the composite genotypes, or haplotypes, are more appropriate than the single-locus genotypes if they were considered for use in selection.

Milk coagulation properties (MCP) are an important aspect in assessing cheese-making ability. Several studies showed that favorable conditions of milk reactivity with rennet, curd formation rate, and curd strength, as well as curd syneresis, have a positive effect on the entire cheese-making process and subsequently on the ripening of cheese. Moreover, MCP were found to be heritable, but little scientific literature is available about their genetic aspects. The aims of this study were to estimate heritability of MCP and genetic correlations among MCP and milk production and quality traits. A total of 1,071 Italian Holstein cows (progeny of 54 sires) reared in 34 herds in Northern Italy were sampled from January to July 2004. Individual milk samples were collected during the morning milking and analyzed for coagulation time (RCT), curd firmness (a30), pH, titratable acidity, fat, protein, and casein contents, and somatic cell count. About 10% of individual milk samples did not coagulate in 31 min, so they were removed from the analyses. Estimates of heritability for RCT and a30 were 0.25 +/- 0.04 and 0.15 +/- 0.03, respectively. Estimates of genetic correlations between MCP traits and milk production traits were negligible except for a30 with protein and casein contents (0.44 +/- 0.10 and 0.53 +/- 0.09, respectively). Estimates of genetic correlations between MCP traits and somatic cell score were strong and favorable, as well as those between MCP and pH and titratable acidity. Selecting for high casein content, milk acidity, and low somatic cell count might be an indirect way to improve MCP without reducing milk yield and quality traits.

This study aimed to estimate genetic parameters for body condition score (BCS), calving interval (CI), somatic cell score (SCS), yield, and linear type traits for the Italian Brown Swiss cattle population. A total of 32,359 records of first-parity lactating cows were collected from 2002 to 2004 in 4,885 dairy herds. The pedigree file included 96,661 animals. Multiple-trait animal models were analyzed using REML to estimate (co)variance components without repeated observations on traits. The estimated heritability was 0.15 for BCS, 0.05 for CI, and 0.06 for SCS, and ranged from 0.09 to 0.14 for test-day yield traits and from 0.07 to 0.32 for linear type traits. The genetic correlations of CI with yield and most linear type traits were positive, whereas the correlation between CI and BCS was negative (-0.35). For type traits, BCS showed, in general, a moderately negative genetic correlation except for strength, pastern, and heel height. The genetic correlation of CI or BCS with SCS was moderately low but favorable (0.19 and -0.26, respectively). The estimated correlations indicated that selection for greater yield and type traits can exert unfavorable effects on the reproductive ability of cows. To counterbalance these effects and to carry out early prediction of breeding values of bulls for fertility, inclusion of BCS in the breeding program is advisable.

The study assessed the fertility and purity of sexed semen used for inseminating Holstein-Friesian heifers in commercial dairy herds. Sex-sorted semen from 4 proven Holstein-Friesian bulls and available under commercial conditions was used on nulliparous Holstein heifers reared on 61 dairy farms of northern Italy. Data from 536 artificial inseminations with pregnancy diagnosis and 258 calvings were analyzed using the logistic regression procedure. The effects of year and season of insemination or calving, age at insemination or calving, heifer inbreeding, and the sperm dose used for insemination on the probability of a positive pregnancy diagnosis or of the birth of a female calf, respectively, were studied. The overall pregnancy rate for sexed semen was 51% and was affected by year of insemination and bull. Heifers inseminated with sexed semen from 2 bulls had lower pregnancy rates than heifers inseminated with sexed semen from other bulls. Purity of the sexed sperm, based on the proportion of female calves, was 87% and this percentage was not affected by explanatory variables included in the logistic regression. The results demonstrate that bulls differ in terms of fertility of their sexed semen. Careful selection of the insemination sires used for sorted semen is advisable for avoiding low fertility inseminations.

This study aimed to estimate heritability for condition score and heart girth using a test-day model, to investigate the genetic relationships between condition score, heart girth, and milk yield traits and to analyze the genetic relationships of condition score and heart girth measured at different stages of lactation. Cows from 25 dairy herds were scored for body condition and measured for heart girth at 3-mo intervals for 2 yr. Approximately 5000 test-day observations on condition score, heart girth, and milk fat and protein yield from 1344 Italian Friesian cows were analyzed using two approaches: 1) repeated observations for a trait were considered repeated measurements of the same trait; 2) observations for a trait collected in different stages of lactation (dry period, 1 to 75, 76 to 130, 131 to 210, and 211 to 300 DIM) were treated as different traits. (Co)variance components and related parameters were estimated using REML multiple-trait procedures and animal models with unequal design for different traits. Heritability estimates for fat and protein test-day yield and for test-day condition score and heart girth were 0.22, 0.18, 0.29, and 0.33, respectively. Condition score was negatively correlated with yield traits and positively correlated with heart girth, whereas genetic relationships between heart girth and milk yield traits were negligible. Heritability estimates were 0.27 for condition score recorded in the first half of lactation (1 to 75 and 76 to 130 DIM), 0.36 for condition score in the second half of lactation (131 to 210 and 211 to 300 DIM) and 0.32 for condition score recorded on dry cows. Genetic correlations between condition scores measured in different lactation stages were generally high (0.85 or more), with the exception of the relationships between the first and the last stage of lactation (0.74) and between the first half of lactation and the dry period (0.7). Heritability estimates for heart girth in different lactation stages ranged from 0.31 to 0.40, and genetic correlations between high girth measured in different lactation stages were higher than 0.80.

Thirty-six Alpine goats (10 primiparous and 26 multiparous) were used to study the milk yield response induced by recombinant bST administered in a sustained-delivery vehicle. Eighteen goats were injected with recombinant bST at 4-wk intervals from about 7 wk postpartum for 12 wk. Patterns of milk yield were highly affected by injection of recombinant bST. Milk yield of treated goats increased from day of injection, peaked between d 3 to 5 after treatment, and then decreased progressively. This pattern of response was well fitted by a curve containing two exponential terms: Yi = A x [exp(-k1di - exp(-k2di)], where Yi = daily relative response of treated goats to recombinant bST, computed as a deviation of least squares means of milk yield at day i (i = 0,..., 26) of the treatment interval from least squares means of milk yield at d 27 of the treatment interval; A = constant; k1 and k2 = response rate parameters; and di = day from treatment (i = 0,..., 27). Administration of recombinant bST also improved persistency of lactation, and the overall milk yield response to exogenous hormone was 300 g/d (13.9%) higher than the yield of untreated goats. This response was due to both the short-term response immediately after injection (53%) and the medium-term effect on lactation persistency.

Cows from 25 dairy herds were scored for body condition at 3-mo intervals for over 2 yr to study the pattern of changes during lactation and the effects of parity and milk yield capability of cows on changes in body reserves. Data were recorded for 1395 Holstein cows grouped into three parity classes and five classes of mature equivalent milk yield. The analysis model accounted for nearly 70% of the variation in body condition score. Body reserves decreased during early lactation until about 100 DIM and were restored during mid and late lactation. The pattern of change in body condition score varied among lactations and dairy merit of cows. Depletion and subsequent recovery of body reserves were less intense for primiparous cows than for multiparous cows. Loss of body condition scores was higher and more prolonged for cows of higher dairy merit. The minimum body condition score occurred at 3 mo after calving for the worst yielding cows and at 4 mo after calving for the best yielding cows; the highest yielding cows had twice the loss in body condition as the lowest yielding cows. General equations are presented as a proposed guideline to examine changes in body condition score of Holstein cows of different parities and dairy merit.

This study evaluated the adequacy of an alternate a.m.-p.m. testing scheme for milk yield in comparison with the official test method based on weighing two milkings within 24 h. A total of 88,558 single test day milking weights from 15,670 Italian Friesian cows in 610 herds in the province of Milano (Italy) were collected between September 1991 and October 1992. Ratios were computed for daily milk yield to a.m. and p.m. milking weights (direct yield ratios) and ratios of a.m and p.m. milking weights to daily milk yield (inverse yield ratios). Analysis of variance indicated that the milking interval is the most important source of variation for yield ratios. Factors for estimating daily milk yield from single milking weights adjusted for milking intervals were derived through regression analysis of direct and inverse yield ratios on the length of the milking interval. Daily milk yield was estimated more precisely and accurately when adjustment factors were used than when single milking weights were doubled. The doubling of a.m. and p.m. milking weights overestimated and underestimated (5% on average) daily milk yield, respectively. For 305-d lactation yield, accuracies of these methods were comparable. In conclusion, alternate recording of a.m. and p.m. milking weights led to reliable estimates of milk yield. Effects of the alternate a.m.-p.m. testing scheme on estimates of milk components and genetic evaluations of dams of sires should be investigated.

The purpose of this study was to develop a time-dependent model to account for the effects of recombinant bST in a slow-release formulation on short-term (between two consecutive injections) and long-term (baseline of the lactation curve) response of milk yield and composition. Observations on milk yield (n = 8234) and on milk composition (n = 1509) of 196 Holstein Friesian cows were recorded over 1 yr of trial. Treated cows were injected with somidobove at 3- or 4-wk intervals. Data were analyzed according to a model that included effect of treatment, parity, interaction, and the random effect of cow; two time-dependent covariables, obtained by the splitting of DIM, were included in the model to account for the effects of bST on short- and long-term responses of milk traits. Cyclical variations of milk yield, protein, and SCC content in the interval between two consecutive injections were detected. Recombinant bST administration also influenced the persistency of milk yield and protein content. Treatment of cows at 3- and 4-wk intervals increased yield by 20.6 and 12.6% over those of untreated cows. Nearly 60% of this increment was due to the short-term effect of bST. Milk composition response to bST was less noticeable.