Invited review: Genetics and modeling of milk coagulation properties

Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF_t). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a₃₀, mm), and curd-firming time (k₂₀, min). The milk of different ruminant species varies in terms of CF_t pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a₃₀, and samples for which k₂₀ is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a₃₀ are highly correlated, both phenotypically and genetically. This means that the use of a₃₀ data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a₃₀ is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF_t modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.     

Breed of cow and herd productivity affect milk composition and modeling of coagulation,curd firming,and syneresis

Milk coagulation properties (MCP) have been widely investigated in the past using milk collected from different cattle breeds and herds. However, to our knowledge, no previous studies have assessed MCP in individual milk samples from several multi-breed herds characterized by either high or low milk productivity, thereby allowing the effects of herd and cow breed to be evaluated independently. Multi-breed herds (n = 41) were classified into 2 categories based on milk productivity (high vs. low), defined according to the average milk net energy yielded daily by lactating cows. Milk samples were taken from 1,508 cows of 6 different breeds: 3 specialized dairy (Holstein-Friesian, Brown Swiss, Jersey) and 3 dual-purpose (Simmental, Rendena, Alpine Grey) breeds, and analyzed in duplicate (3,016 tests) using 2 lactodynamographs to obtain 240 curd firming (CF) measurements over 60 min (1 every 15 s) for each duplicate. The 5 traditional single-point MCP (RCT, k₂₀, a₃₀, a₄₅, and a₆₀) were yielded directly by the instrument from the available CF measures. All 240 CF measures of each replicate were also used to estimate 4 individual equation parameters: RCT estimated according to curd firm change over time modeling (RCT_eq), asymptotic potential curd firmness (CF_P), curd firming instant rate constant (k_CF), and syneresis instant rate constant (k_SR) and 2 derived traits: maximum curd firmness achieved within 45 min (CF_max) and time at achievement of CF_max (t_max) by curvilinear regression using a nonlinear procedure. Results showed that the effect of herd-date on traditional and modeled MCP was modest, ranging from 6.1% of total variance for k₂₀ to 10.7% for RCT, whereas individual animal variance was the highest, ranging from 32.0% for t_max to 82.5% for RCT_eq. The repeatability of MCP was high (>80%) for all traits except those associated with the last part of the lactodynamographic curve (i.e., a₆₀, k_SR, k_CF, and t_max: 57 to 71%). Reproducibility, taking into account the effect of instrument, was equal to or slightly lower than repeatability. Milk samples collected in farms characterized by high productivity exhibited delayed coagulation (RCT_eq: 18.6 vs. 16.3 min) but greater potential curd firmness (CF_P: 76.8 vs. 71.9 mm) compared with milk samples collected from low-productivity herds. Parity and days in milk influenced almost all MCP. Large differences in all MCP traits were observed among breeds, both between specialized and dual-purpose breeds and within these 2 groups of breeds, even after adjusting for milk quality and yield. Milk quality and MCP of samples from Jersey cows, and coagulation time of samples from Rendena cows were better than in milk from Holstein-Friesian cows, and intermediate results were found with the other breeds of Alpine origin. The results of this study, taking into account the intrinsic limitation of this technique, show that the effects of breed on traditional and modeled MCP are much greater than the effects of herd productivity class, parity, and DIM. Moreover, the variance in individual animals is much greater than the variance in individual herds within herd productivity class. It seems that improvement in MCP depends more on genetics (e.g., breed, selection) than on environmental and management factors.     

Associations between pathogen-specific cases of subclinical mastitis and milk yield,quality, protein composition,and cheese-making traits in dairy cows

The aim of this study was to investigate associations between pathogen-specific cases of subclinical mastitis and milk yield, quality, protein composition, and cheese-making traits. Forty-one multibreed herds were selected for the study, and composite milk samples were collected from 1,508 cows belonging to 3 specialized dairy breeds (Holstein Friesian, Brown Swiss, and Jersey) and 3 dual-purpose breeds of Alpine origin (Simmental, Rendena, and Grey Alpine). Milk composition [i.e., fat, protein, casein, lactose, pH, urea, and somatic cell count (SCC)] was analyzed, and separation of protein fractions was performed by reversed-phase high performance liquid chromatography. Eleven coagulation traits were measured: 5 traditional milk coagulation properties [time from rennet addition to milk gelation (RCT, min), curd-firming rate as the time to a curd firmness (CF) of 20 mm (k₂₀, min), and CF at 30, 45, and 60 min from rennet addition (a₃₀, a₄₅, and a₆₀, mm)], and 6 new curd firming and syneresis traits [potential asymptotical CF at an infinite time (CF_P, mm), curd-firming instant rate constant (k_CF, % × min^?1), curd syneresis instant rate constant (k_SR, % × min^?1), modeled RCT (RCT_eq, min), maximum CF value (CF_max, mm), and time at CF_max (t_max, min)]. We also measured 3 cheese yield traits, expressing the weights of total fresh curd (%CY_CURD), dry matter (%CY_SOLIDS), and water (%CY_WATER) in the curd as percentages of the weight of the processed milk, and 4 nutrient recovery traits (REC_PROTEIN, REC_FAT, REC_SOLIDS, and REC_ENERGY), representing the percentage ratio between each nutrient in the curd and milk. Milk samples with SCC > 100,000 cells/mL were subjected to bacteriological examination. All samples were divided into 7 clusters of udder health (UH) status: healthy (cows with milk SCC < 100,000 cells/mL and uncultured); culture-negative samples with low, medium, or high SCC; and culture-positive samples divided into contagious, environmental, and opportunistic intramammary infection (IMI). Data were analyzed using a linear mixed model. Significant variations in the casein to protein ratio and lactose content were observed in all culture-positive samples and in culture-negative samples with medium to high SCC compared to normal milk. No differences were observed among contagious, environmental, and opportunistic pathogens, suggesting an effect of inflammation rather than infection. The greatest impairment in milk quantity and composition, clotting ability, and cheese production was observed in the 2 UH status groups with the highest milk SCC (i.e., contagious IMI and culture-negative samples with high SCC), revealing a discrepancy between the bacteriological results and inflammatory status, and thus confirming the importance of SCC as an indicator of udder health and milk quality.     

Milk protein fractions strongly affect the patterns of coagulation,curd firming,and syneresis

The aim of this study was to assess the role of milk protein fractions in the coagulation, curd firming, and syneresis of bovine milk. Analyses were performed on 1,271 individual milk samples from Brown Swiss cows reared in 85 herds classified into 4 types of farming systems, from the very traditional (tied cows, feed manually distributed, summer highland pasture) to the most modern (loose cows, use of total mixed rations with or without silage). Fractions α_S1-casein (CN), α_S2-CN, β-CN, κ-CN, β-lactoglobulin (LG), and α-lactalbumin (LA) and genotypes at CSN2, CSN3, and BLG were obtained by reversed-phase HPLC. The following milk coagulation properties were measured with a lactodynamograph, with the testing time extended to 60 min: rennet coagulation time (RCT, min), curd firming time (min), and curd firmness at 30 and 45 min (mm). All the curd firmness measures recorded over time (total of 240 observations/sample) were used in a 4-parameter nonlinear model to obtain parameters of coagulation, curd firming, and syneresis: RCT estimated from the equation (min), asymptotic potential curd firmness (mm), the curd firming and syneresis instant rate constants (%/min), and the maximum curd firmness value (CF_max, mm) and the time taken to reach it (min). All the aforementioned traits were analyzed with 2 linear mixed models, which tested the effects of the protein fractions expressed in different ways: in the first, quantitative model, each protein fraction was expressed as content in milk; in the second, qualitative model, each protein fraction was expressed as a percentage of total casein content. Besides proteins, additional nuisance parameters were herd (included as a random effect), daily milk production (only for the quantitative model), casein content (only for the qualitative model), dairy system, parity, days in milk, the pendulum of the lactodynamograph, and the CSN2, CSN3, and BLG genotypes. Both α_S1-CN and β-CN showed a clear and favorable effect on CF_max, where the former effect was almost double the latter. Milk coagulation ability was favorably affected by κ-CN, which reduced both the RCT and RCT estimated from the equation, increased the curd firming and syneresis instant rate constants, and allowed a higher CF_max to be reached. In contrast, α_S2-CN delayed gelation time and β-LG worsened curd firming, both resulting in a low CF_max. The results of this study suggest that modification of the relative contents of specific protein fractions can have an enormous effect on the technological behavior of bovine milk.     

Phenotypic factors affecting coagulation properties of milk from Sarda ewes

In this study, milk-coagulation properties (MCP) were characterized in the Sarda sheep breed. Milk composition and MCP [rennet-coagulation time (RCT), curd-firming time [time to reach a curd firmness of 20 mm (k₂₀)], and curd firmness (a₃₀), (a₄₅), and (a₆₀)] were obtained extending the lactodynamographic analysis from 30 to 60 min from a population of 1,121 ewes from 23 different farms. Managerial characteristics of farms and parity, individual daily milk yields and stage of lactation of ewes were recorded. Data were analyzed using a mixed-model procedure with fixed effects of days in milk, parity, daily milk yield, and flock size and the random effect of the flock/test day nested within flock size. Sampled farms were classified as small (<300 ewes) and medium (300 to 600 ewes), and these were kept by family operations, or as large (>600 ewes), often operated through hired workers. Daily milk yield was, on average, 1.58 ± 0.79 L/d and variability for this trait was very high. The average content of fat, protein, and casein was respectively 6.41, 5.39, and 4.20%. The class of flock size had a significant effect only on curd firmness, whereas days in milk affected RCT and k₂₀. The flock test day, parity, and daily milk yield were important sources of variation for all MCP. The mean value of RCT (8.6 min) and the low occurrence of noncoagulating samples (0.44%) confirmed the excellent coagulation ability of sheep milk compared with cattle milk. A more rapid coagulation was observed in mid-lactating, primiparous, and high-yielding ewes. The k₂₀ was usually reached in less than 2 min after gelation, with the most favorable values at mid lactation. The mean value of curd firmness 30 min after rennet addition (a₃₀) was, on average, 50 mm and decreased to 46 and 42 mm respectively after 45 (a₄₅) and 60 min (a₆₀). The decreasing value of curd-firmness traits was likely to be caused by curd syneresis and whey expulsion. The correlation between RCT and a₃₀ was much lower than in dairy cows and about null for a₄₅ and a₆₀. This means that curd firmness in dairy ewes is almost independent of gelation time and this can provide specific information for this species. In conclusion, this study showed that milk from Sarda sheep is characterized by an earlier gelation, a faster increase in curd firmness with time, and greater curd firmness after 30 min compared with dairy cows. Furthermore, correlations between MCP in sheep are much lower than in bovines and some of the assumptions and interpretations related to cows cannot be applied to sheep.     

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk

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 (a₃₀, 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 a₃₀ 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 a₃₀, 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 a₃₀ 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 a₃₀ 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.     

Pathway-based genome-wide association analysis of milk coagulation properties,curd firmness,cheese yield,and curd nutrient recovery in dairy cattle

It is becoming common to complement genome-wide association studies (GWAS) with gene-set enrichment analysis to deepen the understanding of the biological pathways affecting quantitative traits. Our objective was to conduct a gene ontology and pathway-based analysis to identify possible biological mechanisms involved in the regulation of bovine milk technological traits: coagulation properties, curd firmness modeling, individual cheese yield (CY), and milk nutrient recovery into the curd (REC) or whey loss traits. Results from 2 previous GWAS studies using 1,011 cows genotyped for 50k single nucleotide polymorphisms were used. Overall, the phenotypes analyzed consisted of 3 traditional milk coagulation property measures [RCT: rennet coagulation time defined as the time (min) from addition of enzyme to the beginning of coagulation; k₂₀: the interval (min) from RCT to the time at which a curd firmness of 20 mm is attained; a₃₀: a measure of the extent of curd firmness (mm) 30 min after coagulant addition], 6 curd firmness modeling traits [RCT_eq: RCT estimated through the CF equation (min); CF_P: potential asymptotic curd firmness (mm); k_CF: curd-firming rate constant (% × min^?1); k_SR: syneresis rate constant (% × min^?1); CF_max: maximum curd firmness (mm); and t_max: time to CF_max (min)], 3 individual CY-related traits expressing the weight of fresh curd (%CY_CURD), curd solids (%CY_SOLIDS), and curd moisture (%CY_WATER) as a percentage of weight of milk processed and 4 milk nutrient and energy recoveries in the curd (REC_FAT, REC_PROTEIN, REC_SOLIDS, and REC_ENERGY calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk), milk pH, and protein percentage. Each trait was analyzed separately. In total, 13,269 annotated genes were used in the analysis. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were queried for enrichment analyses. Overall, 21 Gene Ontology and 17 Kyoto Encyclopedia of Genes and Genomes categories were significantly associated (false discovery rate at 5%) with 7 traits (RCT, RCT_eq, k_CF, %CY_SOLIDS, REC_FAT, REC_SOLIDS, and REC_ENERGY), with some being in common between traits. The significantly enriched categories included calcium signaling pathway, salivary secretion, metabolic pathways, carbohydrate digestion and absorption, the tight junction and the phosphatidylinositol pathways, as well as pathways related to the bovine mammary gland health status, and contained a total of 150 genes spanning all chromosomes but 9, 20, and 27. This study provided new insights into the regulation of bovine milk coagulation and cheese ability that were not captured by the GWAS.     

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a₃₀) 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 a₃₀ 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 (h²) = 0.240 and h² = 0.210 for HF and BS, respectively] than a₃₀ (h² = 0.148 and h² = 0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h² = 0.103 and h² = 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 (h² = 0.108). A negative genetic correlation, lower than −0.85, was estimated between RCT and a₃₀ 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.     

Real-time milk analysis integrated with stacking ensemble learning as a tool for the daily prediction of cheese-making traits in Holstein cattle

Cheese-making traits in dairy cattle are important to the dairy industry but are difficult to measure at the individual level because there are limitations on collecting phenotypic information. Mid-infrared spectroscopy has its advantages, but it can only be used during monthly milk recordings. Recently, in-line devices for real-time analysis of milk quality have been developed. The AfiLab recording system (Afimilk) offers significant benefits as phenotypes can be collected from each cow at each milking session. The objective of this study was to assess the potential of integrating AfiLab real-time milk analyzer measures with the stacking ensemble learning technique using heterogeneous base learners for the in-line daily monitoring of cheese-making traits in Holstein cattle with a view to developing a precision livestock farming system for monitoring the technological quality of milk. Data and samples for wet-laboratory analyses were collected from 499 Holstein cows belonging to 2 farms where the AfiLab system was installed. The traits of concern were 9 milk coagulation traits [3 milk coagulation properties (MCP), and 6 curd firming traits (CF_t)], and 7 cheese-making traits [3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits]. The near-infrared AfiLab spectral data and on-farm information (days in milk and parity) were used to assess the predictive ability of different statistical methods [elastic net (EN), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), and artificial neural network (ANN)] across different cross-validation scenarios. These statistical methods were considered the base learners, which were then combined in a stacking ensemble learning. Results indicate that including information on the cows (days in milk and parity) in the AfiLab infrared prediction increased its accuracy by 10.3% for traditional MCP, 13.8% for curd firming, 9.8% for CY, and 11.2% for REC traits compared with those obtained from near-infrared AfiLab alone. The statistical approaches exhibited high prediction accuracies (R²) averaged across the cross-validation scenarios for traditional MCP (0.58 for ANN, 0.55 for EN and GBM, 0.52 for XGBoost, and 0.62 for stacking ensemble), CF_t (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble), and similar R² averages for CY and REC (0.55 for ANN, 0.54 for EN and GBM, 0.53 for XGBoost, and 0.61 for stacking ensemble). The ANN approach was more accurate than the other base learners (EN, GBM, and XGBoost) and improved accuracy across cross-validation scenarios on average by 7% for traditional MCP, 5% for CF_t, 8% for CY, and 7% for REC. The stacking ensemble method improved prediction accuracy by 3% to 31% for traditional MCP, 2% to 26% for CF_t, 1% to 38% for CY traits, and 2% to 27% for REC traits compared with the base learners. The prediction accuracies of the different approaches evaluated tended to decrease from the 10-fold cross-validation to the independent validation scenario, although there was a smaller reduction in prediction accuracy with the stacking ensemble learning technique across all the cross-validation scenarios. Our results show that combining in-line on-farm information with stacking ensemble machine learning represents an effective alternative for obtaining robust daily predictions of milk cheese-making traits.     

Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy

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 (a₃₀, 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 a₃₀, respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a₃₀. Repeatabilities of RCT and a₃₀ 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.     

Genetic parameters of milk coagulation properties and their relationships with milk yield and quality traits in Italian Holstein cows

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.     

Genetic analysis of coagulation properties,curd firming modeling,milk yield,composition, and acidity in Sarda dairy sheep

Sheep milk is an important source of food, especially in Mediterranean countries, and is used in large part for cheese production. Milk technological traits are important for the sheep dairy industry, but research is lacking into the genetic variation of such traits. Therefore the aim of this study was to estimate the heritability of traditional milk coagulation properties and curd firmness modeled on time t (CF_t) parameters, and their genetic relationships with test-day milk yield, composition (fat, protein, and casein content), and acidity in Sarda dairy sheep. Milk samples from 1,121 Sarda ewes from 23 flocks were analyzed for 5 traditional coagulation properties by lactodynamographic tests conducted for up to 60 min: rennet coagulation time (min), curd-firming time (k₂₀, min), and 3 measures of curd firmness (a₃₀, a₄₅, and a₆₀, mm). The 240 curd firmness observations (1 every 15 s) from each milk sample were recorded, and 4 parameters for each individual sample equation were estimated: rennet coagulation time estimated from the equation (RCT_eq), the asymptotic potential curd firmness (CF_P), the curd firming instant rate constant (k_CF), and the syneresis instant rate constant (k_SR). Two other derived traits were also calculated (CF_max, the maximum curd firmness value; and t_max, the attainment time). Multivariate analyses using Bayesian methodology were performed to estimate the genetic relationships of milk coagulation properties and CF_t with the other traits; statistical inference was based on the marginal posterior distributions of the parameters of concern. The marginal posterior distribution of heritability estimates of milk yield (0.16 ± 0.07) and composition (0.21 ± 0.11 to 0.28 ± 0.10) of Sarda ewes was similar to those often obtained for bovine species. The heritability of rennet coagulation time as a single point trait was also similar to that frequently obtained for cow milk (0.19 ± 0.09), whereas the same trait calculated as an individual equation parameter exhibited larger genetic variation and a higher heritability estimate (0.32 ± 0.11). The other curd firming and syneresis traits, whether as traditional single point observations or as individual equation parameters and derived traits, were characterized by heritability estimates lower than for coagulation time and for the corresponding bovine milk traits (0.06 to 0.14). Phenotypic and additive genetic correlations among the 11 technological traits contribute to describing the interdependencies and meanings of different traits. The additive genetic relationships of these technological traits with the single test-day milk yield and composition were variable and showed milk yield to have unfavorable effects on all measures of curd firmness (a₃₀, a₄₅, a₆₀, CF_P, and CF_max) and t_max, but favorable effects on both instant rate constants (k_CF and k_SR). Milk fat content had a positive effect on curd firmness traits, especially on those obtained from CF_t equations, whereas the negative effects on both coagulation time traits were attributed to the milk protein and casein contents. Finally, in view of the estimated heritabilities and additive genetic correlations, enhancement of technological traits of sheep milk through selective breeding could be feasible in this population.     

Relationships between milk coagulation property traits analyzed with different methodologies

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 (a₃₀) 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 a₃₀ 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 a₃₀ 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 (R²: 0.77-0.82) than for a₃₀ (R²: 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.     

Variation of milk technological properties in sheep milk: Relationships among composition,coagulation and cheese-making traits

The relationships between milk composition, coagulation properties and cheese-making traits in sheep milk were characterised. Ten traits related to milk coagulation (RCT_eq, k_CF, CF_p), cheese yield (%CY_CURD, %CY_SOLIDS, %CY_WATER), and curd nutrients recovery or whey loss (%REC_FAT, %REC_PROTEIN, %REC_SOLIDS, %REC_ENERGY) were recorded. To obtain a measure of the efficiency in terms of %CY, the ratio between the observed and the theoretical %CY (Ef-%CY_CURD, Ef-%CY_SOLIDS) was calculated. Sheep milk showed good qualities for coagulation and cheese production; milk lactose appeared to be the component most linked to gelation, curd firming time and water retained in the curd. In the case of milk protein, an opposite relationship with gelation time was observed. Milk fat and protein positively affected total solids recovery and yield inducing higher %CY_CURD. Relationships with CF_t parameters were limited; curd firming instant rate seems to be the most informative trait to assess the efficiency of the cheese-making process.     

Genetic analysis of detailed milk protein composition and coagulation properties in Simmental cattle

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 (a₃₀) 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 a₃₀ 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.     

Milk quality,coagulation properties,and curd firmness modeling of purebred Holsteins and first- and second-generation crossbred cows from Swedish Red,Montbéliarde,and Brown Swiss bulls

The objective of the present study was to investigate how the crossbreeding of Holstein (HO) cows with bulls from Nordic and Alpine European breeds affect milk quality traits, traditional milk coagulation properties (MCP), and curd firmness modeling obtained from individual milk samples. A total of 506 individual milk samples were collected from evening milking at 3 commercial farms located in Northern Italy. Over the past decade, the 3 farms have followed crossbreeding programs in part of their herds, whereas the remainder of the animals consisted of purebred HO. The basic scheme was a 3-breed rotation based on the use of Swedish Red (SR) semen on HO cows (SR × HO), the use of Montbéliarde (MO) semen on first-cross cows [MO × (SR × HO)], and the use of HO semen in the third cross. In all herds, a smaller proportion of purebred HO were mated to M and Brown Swiss (BS) bulls, and these first crosses were mated to SR and MO bulls, respectively. Milk samples were analyzed for milk composition and MCP, and parameters for curd firmness were modeled. Compared with purebred HO, crossbred cows produced less milk with lower lactose content, higher fat and protein content, and a tendency for higher casein content. Crossbred cows generally produced milk with a more favorable curd-firming rate (k₂₀) and curd firmness 30 min after rennet addition, among traditional MCP, and better trends of curd firmness measures as shown by model parameters: estimated rennet coagulation time, asymptotical potential value of curd firmness, and curd-firming instant rate constant. Among crossbred cows, SR × HO presented longer rennet coagulation time compared with MO × HO and BS × HO cows, and MO × HO showed shorter k₂₀ compared with BS × HO cows. Among second-generation cows, those sired by SR bulls showed a lower incidence of noncoagulated samples, higher curd firmness 30 min after rennet addition and asymptotical potential value of curd firmness, and faster curd-firming instant rate constant compared with animals sired by MO bulls. Our results revealed that different sire breeds were characterized by specific technological aptitudes, but that these were not strictly related to other milk quality traits. Furthermore, the favorable characteristics (in terms of the quality and technological properties of milk) could be maintained in the third generation of 3-way crosses without negative effects on milk yield, even though the HO heritage had been reduced from 50 to 25%. Our findings, therefore, suggest that different types of sires can be chosen (depending on the intended use of the milk) to ensure the optimization of farm crossbreeding programs.     

Short communication: Factors affecting coagulation properties of Mediterranean buffalo milk

The aim of this study was to investigate sources of variation of milk coagulation properties (MCP) of buffalo cows. Individual milk samples were collected from 200 animals in 5 herds located in northern Italy from January to March 2010. Rennet coagulation time (RCT, min) and curd firmness after 30 min from rennet addition (a₃₀, mm) were measured using the Formagraph instrument (Foss Electric, Hillerød, Denmark). In addition to MCP, information on milk yield, fat, protein, and casein contents, pH, and somatic cell count (SCC) was available. Sources of variation of RCT and a₃₀ were investigated using a linear model that included fixed effects of herd, days in milk (DIM), parity, fat content, casein content (only for a₃₀), and pH. The coefficient of determination was 51% for RCT and 48% for a₃₀. The most important sources of variation of MCP were the herd and pH effects, followed by DIM and fat content for RCT, and casein content for a₃₀. The relevance of acidity in explaining the variation of both RCT and a₃₀, and of casein content in explaining that of a₃₀, confirmed previous studies on dairy cows. Although future research is needed to investigate the effect of these sources of variation on cheese yield, findings from the present study suggest that casein content and acidity may be used as indicator traits to improve technological properties of buffalo milk.     

Effects of β-κ-casein (CSN2-CSN3) haplotypes, β-lactoglobulin (BLG) genotypes, and detailed protein composition on coagulation properties of individual milk of Simmental cows

The aim of this study was to investigate the effects of CSN2-CSN3 (β-κ-casein) haplotypes, BLG (β-lactoglobulin) genotypes, content of milk protein fractions, and protein composition on coagulation properties of milk (MCP). Rennet coagulation time (RCT) and curd firmness (a₃₀) 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 a₃₀ compared with those carrying CSN3 A. Haplotypes carrying CSN2 B had the effect of decreasing RCT and increasing a₃₀ relative to haplotype A²A. 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 κ-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 a₃₀ was caused by the increased β-casein content in milk. Conversely, CSN2 B is likely to exert a direct genetic effect on RCT, which does not depend upon variation of β-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 κ-casein content and percentage of κ-casein to total casein, whereas a₃₀ increased when contents and percentages of β-CN and κ-CN increased. Changes of milk protein composition and allele frequency at casein and whey protein genes affect variation of MCP.     

Selection for milk coagulation properties predicted by Fourier transform infrared spectroscopy in the Italian Holstein-Friesian breed

Milk coagulation is based on a series of physicochemical changes at the casein micelle level, resulting in formation of a gel. Milk coagulation properties (MCP) are relevant for cheese quality and yield, important factors for the dairy industry. They are also evaluated in herd bulk milk to reward or penalize producers of Protected Designation of Origin cheeses. The economic importance of improving MCP justifies the need to account for this trait in the selection process. A pilot study was carried out to determine the feasibility of including MCP in the selection schemes of the Italian Holstein. The MCP were predicted in 1,055 individual milk samples collected in 16 herds (66 ± 24 cows per herd) located in Brescia province (northeastern Italy) by means of Fourier transform infrared (FTIR) spectroscopy. The coefficient of determination of prediction models indicated moderate predictions for milk rennet coagulation time (RCT = 0.65) and curd firmness (a₃₀ = 0.68), and poor predictions for curd-firming time (k₂₀ = 0.49), whereas the range error ratio (8.9, 6.9, and 9.5 for RCT, k₂₀, and a₃₀, respectively) indicated good practical utility of the predictive models for all parameters. Milk proteins were genotyped and casein haplotypes (α_S1-, β-, α_S2-, and κ-casein) were reconstructed. Data from 51 half-sib families (19.9 ± 16.4 daughters per sire) were analyzed by an animal model to estimate (1) the genetic parameters of predicted RCT, k₂₀, and a₃₀; (2) the breeding values for these predicted clotting variables; and (3) the effect of milk protein genotypes and casein haplotypes on predicted MCP (pMCP). This is the first study to estimate both genetic parameters and breeding values of pMCP, together with the effects of milk protein genotypes and casein haplotypes, that also considered k₂₀, probably the most important parameter for the dairy industry (because it indicates the time for the beginning of curd-cutting). Heritability of predicted RCT (0.26) and k₂₀ (0.31) were close to the average heritability described in literature, whereas the heritability of a₃₀ was higher (0.52 vs. 0.27). The effects of milk proteins were statistically significant and similar to those obtained on measured MCP. In particular, haplotypes including uncommon variants showed positive (B-I-A-B) or negative (B-A¹-A-E) effects. Based on these findings, FTIR spectroscopy-pMCP is proposed as a potential selection criterion for the Italian Holstein.     

Effects of Somatic Cell Counts and Milk Composition on the Coagulating Properties of Milk

Forty-two Holstein cows were selected to provide monthly milk samples with varying SCC for 1 yr. Coagulating properties of samples measured as rennet clotting time, rate of curd firming, and curd firmness at cutting were determined by a formagraph. Milk samples were analyzed for fat, protein, lactose, total solids, casein, individual caseins, urea, SCC, and pH. Least squares analyses of data, after adjustments were made for the effect of milk composition, indicated that elevated SCC were associated with a significant increase in rennet clotting time and slower rate of curd firming. An increase of SCC from 100,000 to 500,000 SCC/ml resulted in an increase of approximately 2.1 and 2.2% in RCT and K20, respectively. A further increase of SCC to above 1,000,000/ml resulted in an overall increase of 20.7 and 13.84% in RCT and K20, respectively. Regression analyses indicated that K20 was decreased by 5.42 min and curd firmness at cutting was increased by 12.92 mm for every percentage in milk casein. Rennet clotting time, rate of curd firming, and curd firmness at cutting were increased by 3.52, 3.41, min and decreased by 9.45 mm, respectively, for every unit increase in milk pH.