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The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm -1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R 2 VAL ) and the root mean squared error (RMSE VAL ) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R 2 VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSE VAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits., (The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Costs of production have deeply increased each year in the last decades, breeders are continuously looking for more cost effective and more efficient ways to produce milk. Despite the major signs of progress in productivity, it is fundamental to optimize rather than maximize the performances of the dairy cows. Mastitis is still a highly prevalent disease in the dairy sector which causes several economic losses and environmental effect. Its accurate and early diagnosis is crucial to improve profitability of dairy cows and contribute to a more sustainable dairy industry. Among mastitis reduction strategies, there is the urgent need to implement breeding objectives to select cows displaying mastitis resistance by investigating the genetic mechanisms at the base of the inflammatory response. Therefore, in this study we aimed to further understand the genetic background of the differential somatic cell count (DSCC), which provides thorough insights on the actual inflammatory status of the mammary glands. The objectives of this study were to estimate on a cohort of 20,215 Italian Simmental cows over a 3-yr period: (1) the heritability and repeatability values of somatic cell score (SCS) and DSCC, (2) the genetic and phenotypic correlations between these 2 traits and milk production and milk composition traits, (3) the heritability and repeatability values of SCS and DSCC within class of udder health status. Heritability was low both for SCS (0.06) and DSCC (0.08), whereas the repeatability values for these traits were 0.43 and 0.36, suggesting that the magnitude of cow permanent environmental effect for these traits is remarkable. The genetic and phenotypic correlation of SCS with DSCC was 0.612 and 0.605, respectively. Because both significantly differed from the unit, we must consider those traits as different ones. This latter aspect corroborates the need to consider the DSCC as a further indicator of inflammatory status which might be implemented in the Simmental breed genetic evaluation. It is worthy to mention that heritability estimates for SCS and DSCC were the highest in healthy cows compared with the other udder health classes. This implies that when the udder health status changes, it is most likely due to environmental factors rather than aspects related to the animal's genetics. In contrast, the highest additive genetic variance and heritability found for SCS and DSCC in the healthy group might reveal the potential to further implement breeding strategies to select for healthier animals., (© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% validation set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as "water" and "fingerprint" regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The aim of this study was to quantify some environmental (individual herds, herd productivity, milking system, and season) and animal factors [individual animals, breed, days in milk (DIM) and parity] on the variability of the log-10 transformation of somatic cell count (LSCC) and differential somatic cell count (DSCC) on individual bovine milk. A total of 159,360 test-day records related to milk production and composition were extracted from 12,849 Holstein-Friesian and 9,275 Simmental cows distributed across 223 herds. Herds were classified into high and low productivity, defined according to the average daily milk net energy output (DMEO) yielded by the cows. Data included daily milk yield (DYM; kg/d), milk fat, protein, lactose, SCC, and DSCC, and information on herds (i.e., productivity, milking system). The daily production of total and differential somatic cells in milk was calculated and then log-10 transformed, obtaining DLSCC and DLDSCC, respectively. Data were analyzed using a mixed model including the effects of individual herd, animal, repeated measurements intra animal as random, and herd productivity, milking system, season, breed, DIM, parity, DIM × parity, breed × season, DIM × milking system and parity × milking system as fixed factors. Herds with a high DMEO were characterized by a lower content of LSCC and DSCC, and higher DLSCC and DLDSCC, compared to the low DMEO herds. The association between milking system and somatic cell traits suggested that the use of the automatic milking systems would not allow for a rapid intervention on the cow, as evidenced by the higher content of all somatic cell traits compared to the other milking systems. Season was an important source of variation, as evidenced by high LSCC and DSCC content in milk during summer. Breed of cow had a large influence, with Holstein-Friesian having greater LSCC, DSCC, DLSCC, and DLDSCC compared to Simmental. With regard to DIM, the variability of LSCC was mostly related to that of DSCC, showing an increase from calving to the end of lactation, and suggesting the higher occurrence of chronic mastitis in cows toward the end of lactation. All the somatic cell traits increased across number of parities, possibly because older cows may have increased susceptibility to intramammary infections., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Dairy cattle breeds have been exposed to intense artificial selection for milk production traits over the last fifty years. In Italy, where over 80% of milk is processed into cheese, selection has also focused on cheese-making traits. Due to a deep-rooted tradition in cheese-making, currently fifty Italian cheeses are marked with the Protected Designation of Origin (PDO) label as they proved traditional land of origin and procedures for milk transformation. This study aimed to explore from a genetic point of view if the presence of such diverse productive contexts in Italy have shaped in a different manner the genome of animals originally belonging to a same breed. We analyzed high density genotype data from 1000 Italian Holstein cows born between 2014 and 2018. Those animals were either farmed in one of four Italian PDO consortia or used for drinkable milk production only. Runs of Homozygosity, Bayesian Information Criterion and Discriminant Analysis of Principal Components were used to evaluate potential signs of genetic divergence within the breed. We showed that the analyzed Italian Holstein cows have genomic inbreeding level above 5% in all subgroups, reflecting the presence of ongoing artificial selection in the breed. Our study provided a comprehensive representation of the genetic structure of the Italian Holstein breed, highlighting the presence of potential genetic subgroups due to divergent dairy farming systems. This study can be used to further investigate genetic variants underlying adaptation traits in these subgroups, which in turn might be used to design more specialized breeding programs.

Driven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCT eq ), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV 80 ). To assess model performance, coefficient of determination (R 2 VAL ) and the root mean squared error of validation were recorded. The R 2 VAL varied between 0.14 and 0.45 (instant curd-firming rate constant and RCT eq , respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV 80 , the maximum R 2 VAL increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCT eq (6%). Further investigation evidenced important variability among farms, with R 2 VAL for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CF t ) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CF t parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CF t parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm -1 . Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R 2 VAL ), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R VAL ), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R 2 and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSE VAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The yield, flavor, and texture of ripened cheese result from numerous interrelated microbiological, biochemical, and physical reactions that take place during ripening. The aims of the present study were to propose a 2-compartment first-order kinetic model of cheese weight loss over the ripening period; to test the variation in new informative phenotypes describing this process; and to assess the effects on these traits of dairy farming system, individual farms within dairy system, animal factors, and milk composition. A total of 1,211 model cheeses were produced in the laboratory using individual 1.5-L milk samples from Brown Swiss cows reared on 83 farms located in Trento Province. During ripening (60 d; temperature 15°C, relative humidity 85%), the weight of all model cheeses was measured, and cheese yield (cheese weight/processed milk weight, %CY) was calculated at 7 intervals from cheese-making (0, 1, 7, 14, 28, 42, and 60 d). Using these measures, a 2-compartment first-order kinetic model (3-parameter equation) was developed for modeling %CY during the ripening period, as follows: [Formula: see text] , where %CY t is the %CY at ripening time t; %CY i and %CY f are the modeled %CY traits at time 0 d (%CY i = initial %CY) and at the end of a ripening period sufficient to reach a constant wheel weight (%CY f = final %CY after 60 d ripening in the case of small model cheeses); k CY is the instant rate constant for cheese weight loss (%/d). Cheese weight and protein and fat losses were calculated as the % difference between the model cheeses at 0 and after 60 d of ripening. The variation in cheese pH was calculated as the % difference between pH at 0 and after 60 d. Dairy system, individual herd within dairy system, and the cow's parity and lactation stage (tested with a linear mixed model) strongly affected almost all the traits collected during model cheese ripening. Milk fat, protein, lactose, pH, and somatic cell score also greatly affected almost all the traits, although k CY was affected only by milk protein. After including milk composition in the linear mixed model, the importance of all the herd and animal sources of variation was greatly reduced for all traits. The proposed model and novel traits could be tested, first, with the aim of establishing new monitoring procedures enabling the dairy industry to improve milk quality-based payment systems at the herd level and, second, with a view to exploring possible genetic improvements to dairy cow populations., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aims of this study were to explore the variability of milk composition, coagulation properties, and cheese-making traits of the Sarda goat breed, and to investigate the effects of animal and farm factors, and the geographic area (Central-East vs. South-West) of an insular region of Italy, Sardinia. A total of 570 Sarda goats reared in 21 farms were milk-sampled during morning milking. Individual milk samples were analyzed for composition, traditional milk coagulation properties (MCP), modeled curd-firming over time parameters (CF t ), and cheese-making traits (cheese yield, %CY; recovery of nutrients, %REC; daily cheese yield, dCY). Farms were classified into 2 categories based on milk energy level (MEL; high or low), defined according to the average net energy of milk daily produced by the lactating goats. Milk yield and composition were analyzed using a mixed model including the fixed effects of MEL, geographic area, days in milk, and parity, and the random effect of farm within MEL and geographic area. Data about MCP, CF t , and the cheese-making process were analyzed using the same model, with the inclusion of the effects of animal and pendulum of the lactodynamograph instrument, allowing the measure of repeatability of these traits. Results showed that animal had greater influence on coagulation and cheese-making traits compared with farm effect. Days in milk influenced milk composition, whose changes partly reflected the modifications of %CY traits. Moreover, large differences were observed between primiparous and multiparous goats: primiparous goats produced less milk of better quality (higher fat, lower somatic cell and bacterial counts) and less cheese, but with higher recovery of fat and protein in the curd, compared with multiparous goats. The repeatability was very high, for both coagulation (84.0 to 98.8%) and cheese-making traits (89.7 to 99.9%). The effect of MEL was significant for daily productions of milk and cheese, coagulation time, and recovery of protein in the curd, which were better in high-MEL farms. As regards geographic area, milk composition and percentage cheese yield were superior in the Central-East area, whereas daily milk and cheese production and MCP were better in the South-West. This result was explainable by the phenomenon of crossbreeding Sarda goats with Maltese bucks, which occurred with greater intensity in the South-West than in the Central-East area of the island. The results provided by this study could be of great interest for the goat dairy sector. Indeed, the methods described in the present study could be applicable for other farming methods, goat breeds, and geographic areas. The collection of a wide range of phenotypes at individual animal level is fundamental for the characterization of local populations and can be used to guarantee breed conservation and the persistence of traditional farming systems, and to increase farmers' profit., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Milk samples from 1264 cows in 85 farms were authenticated for different farming-systems using a 10-fold cross-validated linear-discriminant-analysis using Fourier-transform infrared spectra (FTIRS) and gas-chromatographic fatty-acid (FA) profiles. FTIRS gave correct classification greater than FAs (97.4% vs. 81.1%) during calibration, but slightly worse in validation (73.5% vs 77.3%) and their combination improved the results. All milk samples were processed into ripened model-cheeses, and analyzed by near-infrared-spectrometry (NIRS), by proton-transfer-reaction time-of-flight mass-spectrometry for their volatile organic compound (VOCs) fingerprint and by panel sensory profiling (SENS). Farming-system authentication on cheese samples was less efficient than on milk, but still possible. The instrumental methods yielded similar validation results, better than SENS, and their combination improved the correct classification rate. The efficiency of the different technics was affected by specific farming systems. In conclusion, dairy products could be discriminated for farming-systems with acceptable accuracy, but the methods tested differ in sampling procedure, rapidity and costs., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In dairy goats, very little is known about the effect of the 2 most important indirect indicators of udder health [somatic cell count (SCC) and total bacterial count (TBC)] on milk composition and cheese yield, and no information is available regarding the effects of lactose levels, pH, and NaCl content on the recovery of nutrients in the curd, cheese yield traits, and daily cheese yields. Because large differences exist among dairy species, conclusions from the most studied species (i.e., bovine) cannot be drawn for all types of dairy-producing animals. The aims of this study were to quantify, using milk samples from 560 dairy goats, the contemporary effects of a pool of udder health indirect indicators (lactose level, pH, SCC, TBC, and NaCl content) on the recovery of nutrients in the curd (%REC), cheese yield (%CY), and daily cheese yields (dCY). Cheese-making traits were analyzed using a mixed model, with parity, days in milk (DIM), lactose level, pH, SCC, TBC, and NaCl content as fixed effects, and farm, breed, glass tube, and animal as random effects. Results indicated that high levels of milk lactose were associated with reduced total solids recovery in the curd and lower cheese yields, because of the lower milk fat and protein contents in samples rich in lactose. Higher pH correlated with higher recovery of nutrients in the curd and higher cheese yield traits. These results may be explained by the positive correlation between pH and milk fat, protein, and casein in goat milk. High SCC were associated with higher recovery of solids and energy in the curd but lower recovery of protein. The higher cheese yield obtained from milk with high SCC was due to both increased recovery of lactose in the curd and water retention. Bacterial count proved to be the least important factor affecting cheese-making traits, but it decreased daily cheese yields, suggesting that, even if below the legal limits, TBC should be considered in order to monitor flock management and avoid economic losses. The effect of NaCl content on milk composition was linked with lower recovery of all nutrients in the curd during cheese-making. In addition, high milk NaCl content led to reductions in fresh cheese yield and cheese solids. The indirect indicators of the present study significantly affected the cheese-making process. Such information should be considered, to adjust the milk-to-cheese economic value and the milk payment system., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to assess the influence of the amounts of the α S1 -, α S2 -, β-, and κ-casein (CN) and the α-lactalbumin and β-lactoglobulin protein fractions on the efficiency of the cheese-making process independently of their genetic polymorphisms. The study was carried out on milk samples from 1,271 Brown Swiss cows from 85 herds classified into 4 categories according to management, feeding, and housing characteristics (traditional and modern systems). To assess the efficiency of the cheese-making process, we processed the milk samples according to a laboratory cheese-making procedure (1,500 mL/sample) and obtained the following measures: (1) 3 percentage cheese yields (%CY curd , %CY solids , %CY water ), (2) 2 daily cheese yields obtained by multiplying %CY (curd and total solids) by daily milk yields (dCY curd , dCY solids ), (3) 4 measures of nutrient recovery in the curd (REC fat , REC protein , REC solids , REC energy ), and (4) 2 measures of cheese-making efficiency in terms of the ratio between the observed and theoretical %CY (Ef-%CY curd , Ef-%CY solids ). All the aforementioned traits were analyzed by fitting 2 linear mixed models with protein fractions as fixed effects expressed as percentage in the milk (model M-%milk) and as percentage of the total casein content (model M-%cas) together with the effects of total casein content (only in model M-%cas), daily milk yield (only in model M-%milk; not for dCY traits), dairy system, herd (random effect), days in milk, parity, and vat. The efficiency of overall cheese yield (Ef-%CY curd ) was mostly positively associated with β-CN content in the milk, whereas Ef-%CY solids was greater with higher amounts of κ-CN and α S1 -CN (M-%milk) due to the strong influence of both fractions on the recovery rate of milk components in the curd (fat and total solids, protein with α S1 -CN only) when expressed as percentage of milk and of total casein; only β-CN was more important for REC protein . In contrast, we found β-lactoglobulin to be highly negatively related to all the traits related to the cheese-making process and to the daily cheese yield per cow, whereas α-lactalbumin was positively associated with the latter traits. Additional research on this topic is needed, with particular focus on the genetic and genomic aspects of the role of protein fractions in the cheese-making process and on the associations between genetic polymorphisms in milk protein and milk nutrient recovery in the curd., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Traditional milk coagulation properties are used to predict the suitability of milk for cheese-making. In bovine and ovine species, the introduction of the concept of curd firming over time, continuously recorded by a lactodynamograph during prolonged tests, provides additional information about milk coagulation, curd-firming, and syneresis processes. The aims of present study were (1) to test the adaptability of a 4-parameter curd-firming model in the assessment of goat milk (also comparing published data of other species); (2) to describe variability of coagulation, curd firming, and syneresis processes among individual goat milk samples; (3) to quantify the effects of farm and animal factors (breed, parity, and stage of lactation); and (4) to compare 6 goat breeds for their model parameters. Milk samples from 1,272 goats reared in 35 farms were collected. Goats were of 6 breeds: Saanen and Camosciata delle Alpi for the Alpine type; and Murciano-Granadina, Maltese, Sarda, and Sarda Primitiva for the Mediterranean type. During a lactodynamographic analysis (60 min), 240 measures of curd firmness (mm) were recorded for each milk sample. The modeling of curd firming allowed us to achieve the rennet coagulation time estimated on the basis of all the data points (min); the curd firming and the curd syneresis instant rate constants; the asymptotical potential value of curd firming; the actual maximum curd firmness; and the time at which the curd firming maximum level is attained. Modeling parameter data were analyzed using a linear mixed model. Comparison with other dairy species showed several differences: goat milk coagulated later than sheep but earlier than bovine, and curd firming and curd syneresis instant rate constants were greater in small ruminants. Modeling parameters of goat milk were mostly affected by the farm effect (37% of the total variance, on average) compared with the results found for bovine and ovine samples, and this was probably attributable to the marked differences among goat farming systems. Small differences were demonstrated between Alpine and Mediterranean breeds, but the time of maximum curd firmness was lower in Murciano-Granadina compared with Maltese, Sarda, and Sarda Primitiva. Sarda and Sarda Primitiva were very similar and exhibited the most favorable coagulation properties of milk. For almost all the model parameters, the direct effect of breed was increased after correction for milk yield and composition. In conclusion, this approach allowed us to fully depict the effects of the different factors on coagulation of goat milk, and clarified the different renneting pattern among goat breeds, and with other species. Results could be used for the valorization of goat dairy products, also when these are linked to particular local breeds, and to stimulate further studies about relationships between coagulation and cheese-making traits., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Mitigating the dairy chain's contribution to climate change requires cheap, rapid methods of predicting enteric CH 4 emissions (EME) of dairy cows in the field. Such methods may also be useful for genetically improving cows to reduce EME. Our objective was to evaluate different procedures for predicting EME traits from infrared spectra of milk samples taken at routine milk recording of cows. As a reference method, we used EME traits estimated from published equations developed from a meta-analysis of data from respiration chambers through analysis of various fatty acids in milk fat by gas chromatography (FA GC ). We analyzed individual milk samples of 1,150 Brown Swiss cows from 85 farms operating different dairy systems (from very traditional to modern), and obtained the cheese yields of individual model cheeses from these samples. We also obtained Fourier-transform infrared absorbance spectra on 1,060 wavelengths (5,000 to 930 waves/cm) from the same samples. Five reference enteric CH 4 traits were calculated: CH 4 yield (CH 4 /DMI, g/kg) per unit of dry matter intake (DMI), and CH 4 intensity (CH 4 /CM, g/kg) per unit of corrected milk (CM) from the FA GC profiles; CH 4 intensity per unit of fresh cheese (CH 4 /CY CURD , g/kg) and cheese solids (CH 4 /CY SOLIDS , g/kg) from individual cheese yields (CY); and daily CH 4 production (dCH 4 , g/d). Direct infrared (IR) calibrations were obtained by BayesB modeling; the determination coefficients of cross-validation varied from 0.36 for dCH 4 to 0.57 for CH 4 /CM, and were similar to the coefficient of determination values of the equations based on FA GC used as the reference method (0.47 for CH 4 /DMI and 0.54 for CH 4 /CM). The models allowed us to select the most informative wavelengths for each EME trait and to infer the milk chemical features underlying the predictions. Aside from the 5 direct infrared prediction calibrations, we tested another 8 indirect prediction models. Using IR-predicted informative fatty acids (FA IR ) instead of FA GC , we were able to obtain indirect predictions with about the same precision (correlation with reference values) as direct IR predictions of CH 4 /DMI (0.78 vs. 0.76, respectively) and CH 4 /CM (0.82 vs. 0.83). The indirect EME predictions based on IR-predicted CY were less precise than the direct IR predictions of both CH 4 /CY CURD (0.67 vs. 0.81) and CH 4 /CY SOLIDS (0.62 vs. 0.78). Four indirect dCH 4 predictions were obtained by multiplying the measured or IR-predicted daily CM production by the direct or indirect CH 4 /CM. Combining recorded daily CM and predicted CH 4 /CM greatly increased precision over direct dCH 4 predictions (0.96-0.96 vs. 0.68). The estimates obtained from the majority of direct and indirect IR-based prediction models exhibited herd and individual cow variability and effects of the main sources of variation (dairy system, parity, days in milk) similar to the reference data. Some rapid, cheap, direct and indirect IR prediction models appear to be useful for monitoring EME in the field and possibly for genetic/genomic selection, but future studies directly measuring CH 4 with different breeds and dairy systems are needed to validate our findings., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Milk samples were taken once from a total of 1,224 Brown Swiss cows from 83 herds, and 1,500 mL of raw full-fat milk from each cow was processed according to a laboratory-scale model-cheese-making procedure. A sensory panel was assembled and the members trained to evaluate the sensory profile of individual model cheeses. The protocol scorecard was composed of 7 main sensory descriptors related to smell intensity, flavor intensity, taste (salt and sour), and texture (elasticity, firmness, and moisture), and 40 sensory attributes describing smell and flavor profiles. Sensory data were analyzed using a mixed model that included random effects of herd, animal, and panelist, as well as fixed effects of dairy system, days in milk, parity, and order of cheese presentation, and covariates for cheese weight and fat:protein ratio. The sensory profile was not much affected by the dairy farming systems included in the trial, but it was affected by farm within dairy system: cheeses from traditional dairy farms had a greater wood/humus attribute of both smell and flavor than those from modern farm. Of the modern farms, cheeses from those using total mixed rations including silages had a more intense smell of sour milk and a firmer, less moist texture than those using total mixed rations without silages. Moreover, for all the sensory traits, we found less variance related to herd and animals than that related to the panelists and the residuals. Stage of lactation was found to be the most important, whereas parity was not relevant. In particular, cheese smell intensity (and some related attributes) exhibited a quadratic trend with lower values in mid-lactation, whereas flavor and salt descriptors were more intense in the last period of lactation., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The importance of milk coagulation properties for milk processing, cheese yield, and quality is widely recognized. The use of traditional coagulation traits presents several limitations for testing bovine milk and even more for sheep milk, due to its rapid coagulation and curd firming, and early syneresis of coagulum. The aim of this technical note is to test and improve model fitting for assessing coagulation, curd firming, and syneresis of sheep milk. Using milk samples from 87 Sarda ewes, we performed in duplicate lactodynamographic testing. On each of the 174 analyzed milk aliquots, using 180 observations from each aliquot (one every 15 s for 45 min after rennet addition), we compared 4 different curd firming models as a function of time (CF t , mm) using a nonlinear procedure. The most accurate and informative results were observed using a modified 4-parameter model, structured as follows: [Formula: see text] where t is time, RCT eq (min) is the gelation time, CF P (mm) is the potential asymptotical CF at an infinite time, k CF (%/min) is the curd firming rate constant, and k SR (%/min) is the curd syneresis rate constant. To avoid nonconvergence and computational problems due to interrelations among the equation parameters, CF P was preliminarily defined as a function of maximum observed curd firmness (CF max , mm) recorded during the analysis. For this model, all the modeling equations of individual sheep milk aliquots were converging, with a negligible standard error of the estimates (coefficient of determination >0.99 for all individual sample equations). Repeatability of the modeled parameters was acceptable, also in the presence of curd syneresis during the lactodynamographic analysis., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The present study investigated the effect of somatic cell count, lactose, and pH on sheep milk composition, coagulation properties (MCP), and curd firming (CF) parameters. Individual milk samples were collected from 1,114 Sarda ewes reared in 23 farms. Milk composition, somatic cell count, single point MCP (rennet coagulation time, RCT; curd firming time, k 20 ; and curd firmness, a 30 , a 45 , and a 60 ), and CF model parameters were achieved. Phenotypic traits were statistically analyzed using a mixed model to estimate the effects of the different levels of milk somatic cell score (SCS), lactose, and pH, respectively. Additive genetic, herd, and residual correlations among these 3 traits, and with milk composition, MCP and CF parameters, were inferred using a Bayesian approach. From a phenotypic point of view, higher SCS levels caused a delayed gelification of milk. Lactose concentration and pH were significant for many milk quality traits, with a very intense effect on both coagulation times and curd firming. These traits (RCT, RCT estimated using the curd firming over time equation, and k 20 ) showed an unfavorable increase of about 20% from the highest to the lowest level of lactose. Milk samples with pH values lower than 6.56 versus higher than 6.78 were characterized by an increase of RCT (from 6.00 to 14.3 min) and k 20 (from 1.65 to 2.65 min) and a decrease of all the 3 curd firmness traits. From a genetic point of view, the marginal posterior distribution of heritability estimates evidenced a large and exploitable variability for all 3 phenotypes. The mean intra-farm heritability estimates were 0.173 for SCS, 0.418 for lactose content, and 0.206 for pH. Lactose (favorably), and SCS and pH (unfavorably), at phenotypic and genetic levels, were correlated mainly with RCT and RCT estimated using the curd firming over time equation and scarcely with the other curd firming traits. The SCS, lactose, and pH were significantly correlated with each other's. In conclusion, results reported in the present study suggest that SCS, pH, and lactose affect, contemporarily and independently, milk quality and MCP. These phenotypes, easily available during milk recording schemes measured by infrared spectra prediction, could be used as potential indicators traits for improving cheese-making ability of ovine milk., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Garlic and garlic components have recently been proposed as ruminal activity modulators to reduce the enteric methane emissions of ruminants, but little is known of their influence on milk coagulation properties, nutrient recovery, cheese yield, and sensorial and rheological characteristics of milk and cheese. The present study assessed the effects of garlic and diallyl sulfide supplements on dry matter intake (DMI), productive performance, milk coagulation properties, cheese yield, milk and cheese sensory profiles, and rheological characteristics. Four dairy cows were fed a total mixed ration either alone (control) or supplemented with 100 or 400 g/d of garlic cloves or 2 g/d of diallyl sulfide in 4 consecutive experimental periods in a 4 × 4 Latin square design. The diallyl sulfide dose was established to provide approximately the same amount of allyl thiosulfinate compounds as 100 g of fresh garlic cloves. The total mixed ration was composed of 0.29 corn silage, 0.23 corn-barley mixture, 0.17 sunflower-soybean mixture, 0.12 alfalfa hay, 0.12 grass hay, 0.04 sugar beet pulp, and 0.02 other additives, and contained 0.253 starch, 0.130 crude protein, and 0.375 neutral detergent fiber, on a dry matter basis. Each experimental period consisted of 7 d of transition and 14 d of treatment. On d 18 and 21 of each period, milk samples (10 L) were collected from each cow for chemical analysis and cheese-making. The organoleptic properties of the milk and 63-d-ripened cheeses were assessed by a panel of 7 trained sensory evaluators. The experimental treatments had no effects on DMI, milk yield, feed efficiency (milk yield/DMI), milk coagulation properties, nutrient recovery, or cheese yield. Garlic-like aroma, taste, and flavor of milk and cheese were significantly influenced by the treatments, particularly the highest dose of garlic cloves, and we found close exponential relationships between milk and cheese for garlic-like aroma (R 2 = 0.87) and garlic-like flavor (R 2 = 0.79). Diallyl sulfide and 400 g/d of garlic cloves resulted in lower pH, shear force, and shear work of ripened cheeses compared with the other treatments. Garlic cloves and diallyl sulfide had opposite effects on cheese color indices. We conclude that adding 400 g/d of garlic to the feed of lactating dairy cows highly influences the sensory and rheological characteristics of cheese., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

We studied the genetics of cheese-related latent variables (factors; Fs) for application in dairy cattle breeding. In total, 26 traits, recorded in 1264 Brown Swiss cows, were analyzed through multivariate factor analysis (MFA). Traits analyzed were descriptors of milk quality and yield (including protein fractions) and measures of coagulation, curd firmness (CF), cheese yields (%CY) and nutrient recoveries in the curd (REC). A total of 10 Fs (mutual orthogonal with a varimax rotation) were obtained. To assess the practical use of the Fs into breeding, we inferred their genetic parameters using single and bivariate animal models under a Bayesian framework. Heritability estimates (intra-herd) varied between 0.11 and 0.72 (F3: Yield and F7: κ-β-CN, respectively). The Fs underlined basic characteristics of the cheese-making process, milk components and udder health, while retaining 74% of the original variability. The first two Fs were indicators of the CY percentage (F1: %CY) and the CF process (F2: CF t ), and presented similar heritability estimates: 0.268 and 0.295, respectively. The third factor was associated with the yield of milk and solids (F3: Yield) characterized by a low heritability (0.108) and the fourth with the cheese nitrogen (N) (F4: Cheese N) that conversely appeared to be characterized by a high heritability (0.618). Three Fs were associated with the proportion of the basic milk caseins on total milk protein (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN), also highly heritable (0.565, 0.723 and 0.397, respectively) and 1 factor with the phosphorylated form of the as1-CN (F9: as1-CN-Ph; 0.318). Moreover, 1 factor was linked to the whey protein α-LA (F10: α-LA; 0.147). An indicator factor of a cow's udder health (F6: Udder health) was also obtained and showed a moderate heritability (0.204). Although the Fs were phenotypically uncorrelated, considerable additive genetic correlations existed among them, with highest values observed between F10: α-LA and F6: Udder health (-0.67) as well as between F9: as1-CN-Ph and F3: Yield (-0.60). Our results show the usefulness of MFA in dairy cattle breeding. The ability to replace a large number of variables with a few latent indicators of the same biological meaning marks MFA as a valuable tool for developing breeding strategies to improve cow's cheese-related traits.

Little is known about cheese-making efficiency at the individual cow level, so our objective was to study the effects of herd productivity, individual herd within productivity class and breed of cow within herd by producing, then analyzing, 508 model cheeses from the milk of 508 cows of six different breeds reared in 41 multi-breed herds classified into two productivity classes (high v. low). For each cow we obtained six milk composition traits; four milk nutrient (fat, protein, solids and energy) recovery traits (REC) in curd; three actual % cheese yield traits (%CY); two theoretical %CYs (fresh cheese and cheese solids) calculated from milk composition; two overall cheese-making efficiencies (% ratio of actual to theoretical %CYs); daily milk yield (dMY); and three actual daily cheese yield traits (dCY). The aforementioned phenotypes were analyzed using a mixed model which included the fixed effects of herd productivity, parity, days in milk (DIM) and breed; the random effects were the water bath, vat, herd and residual. Cows reared in high-productivity herds yielded more milk with higher nutrient contents and more cheese per day, had greater theoretical %CY, and lower cheese-making efficiency than low-productivity herds, but there were no differences between them in terms of REC traits. Individual herd within productivity class was an intermediate source of total variation in REC, %CY and efficiency traits (10.0% to 17.2%), and a major source of variation in milk yield and dCY traits (43.1% to 46.3%). Parity of cows was an important source of variation for productivity traits, whereas DIM affected almost all traits. Breed within herd greatly affected all traits. Holsteins produced more milk, but Brown Swiss cows produced milk with higher actual and theoretical %CYs and cheese-making efficiency, so that the two large-framed breeds had the same dCY. Compared with the two large-framed breeds, the small Jersey cows produced much less milk, but with greater actual and theoretical %CYs, similar efficiencies and a slightly lower dCY. Compared with the average of the specialized dairy breeds, the three dual-purpose breeds (Simmental and the local Rendena and Alpine Grey) had, on average, similar dMY, lower actual and theoretical %CY, similar fat and protein REC, and slightly greater cheese-making efficiency.

The aim of this study was to apply Bayesian models to the Fourier-transform infrared spectroscopy spectra of individual sheep milk samples to derive calibration equations to predict traditional and modeled milk coagulation properties (MCP), and to assess the repeatability of MCP measures and their predictions. Data consisted of 1,002 individual milk samples collected from Sarda ewes reared in 22 farms in the region of Sardinia (Italy) for which MCP and modeled curd-firming parameters were available. Two milk samples were taken from 87 ewes and analyzed with the aim of estimating repeatability, whereas a single sample was taken from the other 915 ewes. Therefore, a total of 1,089 analyses were performed. For each sample, 2 spectra in the infrared region 5,011 to 925 cm -1 were available and averaged before data analysis. BayesB models were used to calibrate equations for each of the traits. Prediction accuracy was estimated for each trait and model using 20 replicates of a training-testing validation procedure. The repeatability of MCP measures and their predictions were also compared. The correlations between measured and predicted traits, in the external validation, were always higher than 0.5 (0.88 for rennet coagulation time). We confirmed that the most important element for finding the prediction accuracy is the repeatability of the gold standard analyses used for building calibration equations. Repeatability measures of the predicted traits were generally high (≥95%), even for those traits with moderate analytical repeatability. Our results show that Bayesian models applied to Fourier-transform infrared spectra are powerful tools for cheap and rapid prediction of important traits in ovine milk and, compared with other methods, could help in the interpretation of results., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This paper aimed at evaluating the effect of summer transhumance to mountain pastures of dairy cows of different breeds on cheese-making ability of milk. Data were from 649 dairy cows of specialized (Holstein Friesian and Brown Swiss) dual purpose (Simmental) and local (mostly Rendena and Alpine Grey) breeds. The Fourier-Transform Infra-Red Spectra (FTIRS) of their milk samples were collected before and after transhumance in 109 permanent dairy farms, and during transhumance in 14 summer farms (with multi-breeds herds) of the Trento Province, north-eastern Italy. A variety of 18 traits describing milk coagulation, curd firming, cheese yield and nutrients recovery in curd/loss in whey were predicted on the basis of FTIRS collected at the individual cow level. Moving the cows to summer farms improved curd firming traits but reduced cheese yields because of an increase of water and fat lost in the whey. During summer grazing, most of cheese-making traits improved, often non-linearly. The milk from summer farms supplementing cows with more concentrates showed better curd firming and cheese yield, because of lower fat lost in the whey. The breed of cows affected almost all the traits with a worst cheese-making ability for milk samples of Holsteins through all the trial, and interacted with concentrate supplementation because increasing compound feed tended to improve cheese-making traits for all breed, with the exception of local breeds for coagulation time and of Brown Swiss for curd firming time. In general, summer transhumance caused a favourable effect on cheese-making aptitude of milk, even though with some difference according to parity, initial days in milk, breed and concentrate supplementation of cows.

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 60min: rennet coagulation time (min), curd-firming time (k 20 , min), and 3measures of curd firmness (a 30 , a 45 , and a 60 , 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 (a 30 , a 45 , a 60 , 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., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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 60min (1 every 15 s) for each duplicate. The 5 traditional single-point MCP (RCT, k 20 , a 30 , a 45 , and a 60 ) 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 45min (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 k 20 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., a 60 , 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. 16.3min) but greater potential curd firmness (CF P : 76.8 vs. 71.9mm) 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., (Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Summer transhumance of dairy cows to high Alpine pastures is still practiced in many mountainous areas. It is important for many permanent dairy farms because the use of highland pastures increases milk production and high-priced typical local dairy products often boost farm income. As traditional cheese- and ricotta-making procedures in Alpine pastures are central to this dairy system, the objective of this study was to characterize the quality and efficiency of products and their relationships with the quality and availability of grass during the grazing season. The milk from 148 cows from 12 permanent farms reared on a temporary farm located in Alpine pastures was processed every 2wk during the summer (7 cheesemakings from late June to early September). During each processing, 11 dairy products (4 types of milk, 2 by-products, 3 fresh products, and 2 ripened cheeses) were sampled and analyzed. In addition, 8 samples of fresh forage from the pasture used by the cows were collected and analyzed. At the beginning of the pasture season the cows were at 233±90d in milk, 2.4±1.7 parities, and produced 23.6±5.7kg/d of milk. The milk yield decreased with the move from permanent to temporary farms and during the entire summer transhumance, but partly recovered after the cows returned to the permanent farms. Similar trends were observed for the daily yields of fat, protein, casein, lactose, and energy, as we found no large variations in the quality of the milk, with the exception of the first period of Alpine pasture. The somatic cell counts of milk increased during transhumance, but this resulted from a concentration of cells in a lower quantity of milk rather than an increase in the total number of cells ejected daily from the udder. We noted a quadratic trend in availability of forage (fresh and dry matter weight per hectare), with a maximum in late July. The quality of forage also varied during the summer with a worsening of chemical composition. The evening milk (before and after natural creaming), the whole morning milk, and the mixed vat milk had different chemical compositions, traditional coagulation properties, and curd-firming modeling parameters. These variations over the pasture season were similar to the residual variations with respect to chemical composition, and much lower with respect to coagulation and curd-firming traits. Much larger variations were noted in cream, cheese, and ricotta yields, as well as in nutrient recoveries in curd during the pasture season. The protein content of forage was correlated with some of the coagulation and curd-firming traits, the ether extract of forage was positively correlated with milk fat content and cheese yields, and fiber fractions of forage were unfavorably correlated with some of the chemical and technological traits. Traditional cheese- and ricotta-making procedures showed average cream, cheese, and ricotta yields of 6.3, 14.2, and 4.9%, respectively, and an overall recovery of almost 100% of milk fat, 88% of milk protein, and 60% of total milk solids., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to investigate the consequences of reducing the dietary crude protein content, with or without a supply of protected conjugated linoleic acid (CLA), on the milk fatty acid (FA) yield and recovery in 90d ripened cheese. Twenty mid-lactation Friesian dairy cows were reared for 4 periods of 3wk each in groups of 5, following a 4×4 Latin square design. Cows were fed 4 different rations, consisting of a combination of the 2 dietary crude protein levels [150 (CP15) or 123 (CP12) g of crude protein/kg of dry matter], with or without a conjugated linoleic acid supply (80g/d, providing 5.57 and 5.40g/d of C18:2 cis-9,trans-11 and C18:2 trans-10,cis-12, respectively). Milk yield was recorded. Twice in each period, milk samples were analyzed for protein, fat, and lactose content, and 10 L milk samples (pooled by group) were processed to produce 96 cheeses, which were ripened for 90d. Milk and cheese fat were analyzed for their FA profiles. Milk and cheese FA were expressed as daily yields and relative proportions, and nutrient recoveries were computed. Dietary crude protein reduction had small or no effects on the yield and relative presence of FA in milk and cheese, except for a small increase in mid-chain branched saturated fatty acids. The CLA supply strongly reduced the yield of various categories of FA, and had major effects on short-chain FA of de novo synthesis, leading to changes in the relative proportions of the various FA in milk and cheese. The addition of CLA tended to reduce uniformly the recovery of all milk constituents and of short-, medium-, and long-chain FA groups, but we observed large differences among individual FA with apparent recoveries ranging between 640 and 1,710g/kg. The highest recoveries were found for polyunsaturated long-chain FA, the lowest for saturated or monounsaturated short- or medium-chain FA. A notable rearrangement of these FA components, particularly the minor ones, took place during ripening., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aims of this study were (1) to assess variability in the major mineral components of buffalo milk, (2) to estimate the effect of certain environmental sources of variation on the major minerals during lactation, and (3) to investigate the possibility of using Fourier-transform infrared (FTIR) spectroscopy as an indirect, noninvasive tool for routine prediction of the mineral content of buffalo milk. A total of 173 buffaloes reared in 5 herds were sampled once during the morning milking. Milk samples were analyzed for Ca, P, K, and Mg contents within 3h of sample collection using inductively coupled plasma optical emission spectrometry. A Milkoscan FT2 (Foss, Hillerød, Denmark) was used to acquire milk spectra over the spectral range from 5,000 to 900 wavenumber/cm. Prediction models were built using a partial least square approach, and cross-validation was used to assess the prediction accuracy of FTIR. Prediction models were validated using a 4-fold random cross-validation, thus dividing the calibration-test set in 4 folds, using one of them to check the results (prediction models) and the remaining 3 to develop the calibration models. Buffalo milk minerals averaged 162, 117, 86, and 14.4mg/dL of milk for Ca, P, K, and Mg, respectively. Herd and days in milk were the most important sources of variation in the traits investigated. Parity slightly affected only Ca content. Coefficients of determination of cross-validation between the FTIR-predicted and the measured values were 0.71, 0.70, and 0.72 for Ca, Mg, and P, respectively, whereas prediction accuracy was lower for K (0.55). Our findings reveal FTIR to be an unsuitable tool when milk mineral content needs to be predicted with high accuracy. Predictions may play a role as indicator traits in selective breeding (if the additive genetic correlation between FTIR predictions and measures of milk minerals is high enough) or in monitoring the milk of buffalo populations for dairy industry purposes., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.

The aim of this study was to investigate the relationships between somatic cell count (SCC) in milk and several milk technological traits at the individual cow level. In particular, we determined the effects of very low to very high SCC on traits related to (1) milk yield and composition; (2) coagulation properties, including the traditional milk coagulation properties (MCP) and the new curd firming model parameters; and (3) cheese yield and recovery of milk nutrients in the curd (or loss in the whey). Milk samples from 1,271 Brown Swiss cows from 85 herds were used. Nine coagulation traits were measured: 3 traditional MCP [rennet coagulation time (RCT, min), curd firming rate (k20, min), and curd firmness after 30 min (a30, mm)] and 6 new curd firming and syneresis traits [potential asymptotic curd firmness at infinite time (CFP, mm), curd firming instant rate constant (kCF, % × min(-1)), syneresis instant rate constant (kSR, % × min(-1)), rennet coagulation time estimated using the equation (RCTeq, min), maximum curd firmness achieved within 45 min (CFmax, mm), and time at achievement of CFmax (tmax, min)]. The observed cheese-making traits included 3 cheese yield traits (%CYCURD, %CYSOLIDS, and %CYWATER, which represented the weights of curd, total solids, and water, respectively, as a percentage of the weight of the processed milk) and 4 nutrient recoveries in the curd (RECFAT, RECPROTEIN, RECSOLIDS, and RECENERGY, which each represented the percentage ratio between the nutrient in the curd and milk). Data were analyzed using a linear mixed model with the fixed effects of days in milk, parity, and somatic cell score (SCS), and the random effect of herd-date. Somatic cell score had strong influences on casein number and lactose, and also affected pH; these were traits characterized by a quadratic pattern of the data. The results also showed a negative linear relationship between SCS and milk yield. Somatic cell score influenced almost all of the tested coagulation traits (both traditional and modeled), with the exceptions of k20, CFP, and kSR. Gelation was delayed when the SCS decreased (slightly) and when it increased (strongly) with respect to a value of 2, as confirmed by the quadratic patterns observed for both RCT and RCTeq. The SCS effect on a30 showed a quadratic pattern almost opposite to that observed for RCT. With respect to the CFt parameters, kCF decreased linearly as SCS increased, resulting in a linear decrease of CFmax and a quadratic pattern for tmax. Milk SCS attained significance for %CYCURD, %CYWATER, and RECPROTEIN. As the SCS increased beyond 3, we observed a progressive quadratic decrease of the water retained in the curd (%CYWATER), which caused a parallel decrease in %CYCURD. With respect to RECPROTEIN, the negative effect of SCS was almost linear. Recovery of fat and (consequently) RECENERGY was characterized by a more evident quadratic trend, with the most favorable values associated with an intermediate SCS. Together, our results confirmed that high SCS has a negative effect on milk composition and technological traits, highlighting the nonlinear trends of some traits across the different classes of SCS. Moreover, we report that a very low SCS has a negative effect on some technological traits of milk., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Cheese production is increasing in many countries, and a desire toward genetic selection for milk coagulation properties in dairy cattle breeding exists. However, measurements of individual cheesemaking properties are hampered by high costs and labor, whereas traditional single-point milk coagulation properties (MCP) are sometimes criticized. Nevertheless, new modeling of the entire curd firmness and syneresis process (CFt equation) offers new insight into the cheesemaking process. Moreover, identification of genomic regions regulating milk cheesemaking properties might enhance direct selection of individuals in breeding programs based on cheese ability rather than related milk components. Therefore, the objective of this study was to perform genome-wide association studies to identify genomic regions linked to traditional MCP and new CFt parameters, milk acidity (pH), and milk protein percentage. Milk and DNA samples from 1,043 Italian Brown Swiss cows were used. Milk pH and 3 MCP traits were grouped together to represent the MCP set. Four CFt equation parameters, 2 derived traits, and protein percentage were considered as the second group of traits (CFt set). Animals were genotyped with the Illumina SNP50 BeadChip v.2 (Illumina Inc., San Diego, CA). Multitrait animal models were used to estimate variance components. For genome-wide association studies, the genome-wide association using mixed model and regression-genomic control approach was used. In total, 106 significant marker traits associations and 66 single nucleotide polymorphisms were identified on 12 chromosomes (1, 6, 9, 11, 13, 15, 16, 19, 20, 23, 26, and 28). Sharp peaks were detected at 84 to 88 Mbp on Bos taurus autosome (BTA) 6, with a peak at 87.4 Mbp in the region harboring the casein genes. Evidence of quantitative trait loci at 82.6 and 88.4 Mbp on the same chromosome was found. All chromosomes but BTA6, BTA11, and BTA28 were associated with only one trait. Only BTA6 was in common between MCP and CFt sets. The new CFt traits reinforced the support of MCP signals and provided with additional information on genomic regions that might be involved in regulation of the coagulation process of bovine milk., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to propose and test a new laboratory cheesemaking procedure [9-mL milk cheesemaking assessment (9-MilCA)], which records 15 traits related to milk coagulation, curd firming, syneresis, cheese yield, and curd nutrients recovery or whey loss. This procedure involves instruments found in many laboratories (i.e., heaters and lacto-dynamographs), with an easy modification of the sample rack for the insertion of 10-mL glass tubes. Four trials were carried out to test the 9-MilCA procedure. The first trial compared 8 coagulation and curd firming traits obtained using regular or modified sample racks to process milk samples from 60 cows belonging to 5 breeds and 3 farms (480 tests). The obtained patterns exhibited significant but irrelevant between-procedure differences, with better repeatability seen for 9-MilCA. The second trial tested the reproducibility and repeatability of the 7 cheesemaking traits obtained using the 9-MilCA procedure on individual samples from 60 cows tested in duplicate in 2 instruments (232 tests). The method yielded very repeatable outcomes for all 7 tested cheese yield and nutrient recovery traits (repeatability >98%), with the exception of the fresh cheese yield (84%), which was affected by the lower repeatability (67%) of the water retained in the curd. In the third trial (96 tests), we found that using centrifugation in place of curd cooking and draining (as adopted in several published studies) reduced the efficiency of whey separation, overestimated all traits, and worsened the repeatability. The fourth trial compared 9-MilCA with a more complex model cheese-manufacturing process that mimics industry practices, using 1,500-mL milk samples (72 cows, 216 tests). The average results obtained from 9-MilCA were similar to those obtained from the model cheeses, with between-method correlations ranging from 78 to 99%, except for the water retained in the curd (r=54%). Our results indicate that new 9-MilCA method is a powerful research tool that allows the rapid, inexpensive, and partly automated analysis processing 40 samples per day with 2 replicates each, using 1 lacto-dynamograph, 2 heaters, and 3 modified sample racks, and yields a complete picture of the cheesemaking process (e.g., milk gelation, curd firming, syneresis, and whey expulsion) as well as the cheese yield and the efficiency of energy or nutrients retention in the cheese or loss in the whey., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Milk coagulation properties are used to evaluate the cheesemaking aptitude of milk samples. No international standard procedure exists, although laboratories often mimic the production of a full-fat fresh cheese for milk coagulation properties. Questions have arisen about the predictability of such a procedure for different types of cheese production. The aim of this study was to establish a procedure mimicking the production conditions of a long-ripened hard cheese, taking Protected Designation of Origin Grana Padano as a case study. With respect to the traditional conditions (standard procedure; SP), the Grana Padano procedure (GP) modifications were the use of standardized milk, coagulation lower temperature, previous milk acidification, lysozyme addition, and rennet type. Each modification was tested in turn versus the SP and also all together in the GP. Another 3 tests were carried out: SP on naturally creamed milk, SP with double the quantity of rennet, and a simplified GP on a full-fat milk sample. The 10 procedures were tested on 2 subsamples with 2 replicates each and were repeated using individual milk samples from 15 dual-purpose Simmental cows in 4 sessions for a total of 600 tests. Two Formagraph instruments (Foss Electric A/S, Hillerød, Denmark) measuring curd firmness every 15 s were used, prolonging test duration to 60min to obtain 5 traditional single-point milk coagulation properties and 3 parameters of the curd firming model using all 240 points recorded for each replicate. The 8 traits of each replicate were analyzed according to a mixed model with fixed effects of 4 sessions, 10 treatments, 2 instruments, and 16microvats, and random effects of 15 animals and 300 subsamples. Compared with the SP, the coagulation and curd firming was slowed by low temperature and was accelerated by acidification and by adding a double amount of rennet; natural creaming, fat standardization, and rennet with 5% pepsin affected only some traits, whereas lysozyme addition affected none. Combination of all modifications tended to compensate for each of their effects, resulting in similar average patterns between GP and SP. Modifications to repeatability were found for all traits with the exception of 2. The ability of the SP to predict GP, tested through correlations between procedures, was not very high. Whereas SP is used for both research and in the dairy industry, better results for Grana Padano cheesemaking can only be achieved by adopting specific, more complex, and labor-intensive procedures at the research level or, possibly, by specific calibrations through Fourier-transform infrared spectroscopy at the industry level., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aims of this study were to estimate the genetic variation of traditional milk coagulation properties (MCPs), milk acidity, curd firmness (CF) modeled on time t (CF(t) ; comprising: RCT(eq), rennet coagulation time estimated from the equation; CF(P), the asymptotic potential curd firmness; k(CF), the curd firming instant rate constant; and k(SR), the syneresis instant rate constant) and maximum CF traits (MCF; comprising CF(max), the maximum CF value; and tmax, the time of attainment). Furthermore, we investigated 96 single nucleotide polymorphisms (SNPs) from 54 candidate genes, testing their associations with the above-listed traits. Milk and blood samples were collected from 1271 cows (each sampled once) from 85 herds. Genotyping was performed using a custom Illumina VeraCode GoldenGate approach. A Bayesian linear animal model (including the effects of herd, days in milk, parity and additive polygenic effects) was used to estimate the genetic parameters of the studied traits. The same model with the addition of the SNP genotype effect was used for our association analysis. The heritability estimates of CF t and the MCF traits (RCT(eq)=0.258; k(CF)=0.230; CF(max)=0.191; t(max)=0.278) were similar to those obtained using traditional MCPs (0.187 to 0.267), except for the lower estimates for CF(P) (0.064) and k(SR) (0.077). A total of 13 of the 51 tested SNPs had relevant additive effects on at least one trait. We observed associations between MCPs and SNPs in the genes encoding ATP-binding cassette sub-family G member 2 (ABCG2), chemokine ligand 2 (CCL2), growth hormone 1 (GH1), prolactin (PRL) and toll-like receptor 2 (TLR2). Whereas, CF(t) and the MCF traits were associated with polymorphisms in the α-s1-casein (CSN1S1), β-casein (CSN2), GH1, oxidized low-density lipoprotein receptor 1 (OLR1), phospholipase C β1 (PLCB1), PRL and signal transducer and activator of transcription 5A (STAT5A) genes.

The objectives of this study were to characterize the variation in curd firmness model parameters obtained from coagulating bovine milk samples, and to investigate the effects of the dairy system, season, individual farm, and factors related to individual cows (days in milk and parity). Individual milk samples (n = 1,264) were collected during the evening milking of 85 farms representing different environments and farming systems in the northeastern Italian Alps. The dairy herds were classified into 4 farming system categories: traditional system with tied animals (29 herds), modern dairy systems with traditional feeding based on hay and compound feed (30 herds), modern dairy system with total mixed ration (TMR) that included silage as a large proportion of the diet (9 herds), and modern dairy system with silage-free TMR (17 herds). Milk samples were analyzed for milk composition and coagulation properties, and parameters were modeled using curd firmness measures (CFt) collected every 15 s from a lacto-dynamographic analysis of 90 min. When compared with traditional milk coagulation properties (MCP), the curd firming measures showed greater variability and yielded a more accurate description of the milk coagulation process: the model converged for 93.1% of the milk samples, allowing estimation of 4 CFt parameters and 2 derived traits [maximum CF (CF(max)) and time from rennet addition to CF(max) (t(max))] for each sample. The milk samples whose CFt equations did not converge showed longer rennet coagulation times obtained from the model (RCT(eq)) and higher somatic cell score, and came from less-productive cows. Among the sources of variation tested for the CFt parameters, dairy herd system yielded the greatest differences for the contrast between the traditional farm and the 3 modern farms, with the latter showing earlier coagulation and greater instant syneresis rate constant (k(SR)). The use of TMR yielded a greater tmax because of a higher instant curd-firming rate constant (k(CF)). Season of sampling was found to be very important, yielding higher values during winter for all traits except k(CF) and k(SR). All CFt traits were affected by individual cow factors. For parity, milk produced by first-lactation cows showed higher k(CF) and k(SR), but delays in achieving CF(max). With respect to stage of lactation, RCT(eq) and potential asymptotic CF increased during the middle of lactation and stabilized thereafter, whereas the 2 instant rate constants presented the opposite pattern, with the lowest (k(CF)) and highest (k(SR)) values occurring in mid lactation. The new challenge offered by prolonging the test interval and individual modeling of milk technological properties allowed us to study the effects of parameters related to the environment and to individual cows. This novel strategy may be useful for investigating the genetic variability of these new coagulation traits., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This study investigated the modeling of curd-firming (CF) over time (CF(t)) of sheep milk. Milk samples from 1,121 Sarda ewes from 23 flocks were analyzed for coagulation properties. Lactodynamographic analyses were conducted for up to 60 min, and 240 CF individual observations from each sample were recorded. Individual sample CFt equation parameters (RCT(eq), rennet coagulation time; CF(P), asymptotic potential value of curd firmness; k(CF), curd-firming instant rate constant; and k(SR), curd syneresis instant rate constant) were estimated, and the derived traits (CF(max), the point at which CF(t) attained its maximum level, and tmax, the time at which CF(max) was attained) were calculated. The incidence of noncoagulating milk samples was 0.4%. The iterative estimation procedure applied to the individual coagulation data showed a small number of not-converged samples (4.4%), which had late coagulation and an almost linear pattern of the ascending part of the CF(t) curve that caused a high value of CF(P), a low value of k(CF), and a high value of k(SR). Converged samples were classified on the basis of their CF(t) curves into no-k(SR) (18.0%), low-k(SR) (72.6%), and high-k(SR) (4.5%). A CF(t) that was growing continuously because of the lack of the syneresis process characterized the no-k(SR) samples. The high-k(SR) samples had a much larger CFP, a smaller k(CF), and an anticipation of tmax, whereas the low-k(SR) samples had a fast k(CF) and a slower k(SR). The part of the average CF(t) curves that showed an increase was similar among the 3 different syneretic groups, whereas the part that decreased was different because of the expulsion of whey from the curd. The traditional milk coagulation properties recorded within 30 min were not able to detect any appreciable differences among the 4 groups of coagulating samples, which could lead to a large underestimation of the maximum CF of all samples (if predicted by a30), with the exception of the no-k(SR) samples. Large individual variability was found and was likely caused by the effects of the dairy system, such as flock size (on CF(max), t(max), and % ewes with no-k(SR) milk), flock within flock size (representing 11 to 43% of total variance for % ewes with no-k(SR) milk and CF(max), respectively), days in milk (on all model parameters and CF(max)), parity (on RCT(eq), k(SR), and CF(max)), daily milk yield (on RCT(eq) and CF(max)), and position of the individual pendulum that significantly affected model parameters and derived traits. In conclusion, the results showed that the modeling of coagulation, curd-firming, and syneresis is a suitable tool to achieve a deeper interpretation of the coagulation and curd-firming processes of sheep milk and also to study curd syneresis., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Traits associated with cheese yield and milk nutrient recovery in curd are used to describe the efficiency of the cheese-making process. This is fundamental for all dairy species, including the Italian Mediterranean buffalo, which is largely used for milk production aimed at the dairy industry. To assess cheese-making traits among buffalo, a model cheese-manufacturing process was tested; it was capable of processing 24 samples per run, using 0.5-L samples of milk from individual buffalo. In total, 180 buffalo reared in 7 herds located in Northeast Italy were sampled once. Briefly, each sample was weighed and heated (35°C for 30min), inoculated with starter culture (90min), and mixed with rennet (51.2 international milk-clotting units/L of milk). After 10min of gelation, the curd was cut; 5min after the cut, the curd was separated from the whey, and the curd was subjected to draining (for 30min) and pressing (18h). The curd and whey were weighed, analyzed for pH and the total solid, fat, lactose, and protein contents, and subjected to estimation of the energy content. Three measures of cheese yield (%CY), %CYCURD, %CYSOLIDS, and %CYWATER, were computed as the ratios between the weight of the curd, the curd dry matter, and the water retained in the curd, respectively, and the weight of the milk processed. These traits were multiplied by the daily milk yield to define the 3 corresponding measures of daily cheese yield (dCY, kg/d). The milk component recoveries (REC) in the curd, RECFAT, RECPROTEIN, and RECSOLIDS, represented the ratios between the weights of the fat, protein, and total solids in the curd, respectively, and the corresponding components in the milk. Finally, energy recovery (RECENERGY) was estimated. The values for %CYCURD, %CYSOLIDS, %CYWATER, RECPROTEIN, RECFAT, RECSOLIDS, and RECENERGY averaged 25.6, 12.7, 12.9, 80.4, 95.1, 66.7, and 79.3%, respectively, indicating that buffalo milk has a higher aptitude to cheese-making than bovine milk. The effect of days in milk was the most important source of variation for %CY, RECPROTEIN, and the overall recoveries (which showed higher values toward the end of lactation), whereas parity did not appear to influence any of the investigated traits. The cheese-making procedure tested allowed us to assess the variability of and relationships among different cheese yield traits, recovery traits, daily milk production traits, and milk components at the individual level., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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 20mm (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, 50mm 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., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to test the modeling of curd-firming (CF) measures and to compare the sheep milk of 3 Alpine breeds supplemented with or without rumen-protected conjugated linoleic acid (rpCLA). Twenty-four ewes of the Brogna, Foza, and Lamon breeds were allotted to 6 pens (2 pens/breed) and fed a diet composed of corn grain, corn silage, dried sugar beet pulp, soybean meal, wheat bran, wheat straw, and a vitamin-mineral mixture. The rpCLA supplement (12 g/d per ewe plus 4 g/d for each lamb older than 30 d) was mixed into the diet of 1 pen per sheep breed (3 pens/treatment) to provide an average of 0.945 and 0.915 g/d per ewe of the cis-9,trans-11 C18:2 and trans-10,cis-12 C18:2 conjugated linoleic acid isomers, respectively. The trial started at 38 ± 23 d after parturition, and individual morning milk samples were collected on d 16, 23, 37, 44, and 59 of the trial. Milk samples were analyzed for composition, and duplicate samples were assessed for milk coagulation properties (MCP). A total of 180 CF measures for each sample (1 every 15s) were recorded. Model parameters were the rennet coagulation time, the asymptotic potential CF, the CF instant rate constant, the syneresis instant rate constant, the maximum CF achieved within 45 min (CFmax), and the time at achievement of CFmax. The data were analyzed using a hierarchical model that considered the fixed effects of breed, diet, lamb birth, and initial days in milk, which were tested on individual ewe (random) variance; the fixed effect of sampling day, which was tested on the within-ewe sample (random) variance; and the fixed effect of instrument or cuvette position (only for MCP), which was tested on the residual (replicates within samples) variance. The local Alpine sheep breeds displayed similar milk compositions, traditional MCP, and CF modeling parameters. Supplementation with rpCLA triggered changes in milk composition and worsened MCP (e.g., delayed rennet coagulation time, slower CF instant rate constant, and a doubling of syneresis instant rate constant), but did not influence potential CF. Overall, our results indicate that rpCLA supplementation reduced the actual maximum CF (CFmax) but did not modify the interval between rennet addition and CFmax or time to CFmax., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of this study was to investigate 96 single-nucleotide polymorphisms (SNPs) from 54 candidate genes, and test the associations of the polymorphic SNPs with milk yield, composition, milk urea nitrogen (MUN) content and somatic cell score (SCS) in individual milk samples from Italian Brown Swiss cows. Milk and blood samples were collected from 1271 cows sampled once from 85 herds. Milk production, quality traits (i.e. protein, casein, fat and lactose percentages), MUN and SCS were measured for each milk sample. Genotyping was performed using a custom Illumina VeraCode GoldenGate approach. A Bayesian linear animal model that considered the effects of herd, days in milk, parity, SNP genotype and additive polygenic effect was used for the association analysis. Our results showed that 14 of the 51 polymorphic SNPs had relevant additive effects on at least one of the aforementioned traits. Polymorphisms in the glucocorticoid receptor DNA-binding factor 1 (GRLF1), prolactin receptor (PRLR) and chemokine ligand 2 (CCL2) were associated with milk yield; an SNP in the stearoyl-CoA desaturase (SCD-1) was related to fat content; SNPs in the caspase recruitment domain 15 protein (CARD15) and lipin 1 (LPIN1) affected the protein and casein contents; SNPs in growth hormone 1 (GH1), lactotransferrin (LTF) and SCD-1 were relevant for casein number; variants in beta casein (CSN2), GH1, GRLF1 and LTF affected lactose content; SNPs in beta-2 adrenergic receptor (ADRB2), serpin peptidase inhibitor (PI) and SCD-1 were associated with MUN; and SNPs in acetyl-CoA carboxylase alpha (ACACA) and signal transducer and activator of transcription 5A (STAT5A) were relevant in explaining the variation of SCS. Although further research is needed to validate these SNPs in other populations and breeds, the association between these markers and milk yield, composition, MUN and SCS could be exploited in gene-assisted selection programs for genetic improvement purposes.

Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh unprocessed milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the milk. All individual milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and milk nutrient recovery, potentially allowing these traits to be enhanced through selection., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Cheese yield (CY) is an important technological trait in the dairy industry, and the objective of this study was to estimate the genetic parameters of cheese yield in a dairy cattle population using an individual model-cheese production procedure. A total of 1,167 Brown Swiss cows belonging to 85 herds were sampled once (a maximum of 15 cows were sampled per herd on a single test day, 1 or 2 herds per week). From each cow, 1,500 mL of milk was processed according to the following steps: milk sampling and heating, culture addition, rennet addition, gelation-time recording, curd cutting, whey draining and sampling, wheel formation, pressing, salting in brine, weighing, and cheese sampling. The compositions of individual milk, whey, and curd samples were determined. Three measures of percentage cheese yield (%CY) were calculated: %CY(CURD), %CY(SOLIDS), and %CY(WATER), which represented the ratios between the weight of fresh curd, the total solids of the curd, and the water content of the curd, respectively, and the weight of the milk processed. In addition, 3 measures of daily cheese yield (dCY, kg/d) were defined, considering the daily milk yield. Three measures of nutrient recovery (REC) were computed: REC(FAT), REC(PROTEIN), and REC(SOLIDS), which represented the ratio between the weights of the fat, protein, and total solids in the curd, respectively, and the corresponding nutrient in the milk. Energy recovery, REC(ENERGY), represented the energy content of the cheese versus that in the milk. For statistical analysis, a Bayesian animal model was implemented via Gibbs sampling. The effects of parity (1 to ≥4), days in milk (6 classes), and laboratory vat (15 vats) were assigned flat priors; those of herd-test-date, animal, and residual were given Gaussian prior distributions. Intra-herd heritability estimates of %CY(CURD), %CY(SOLIDS), and %CY(WATER) ranged from 0.224 to 0.267; these were larger than the estimates obtained for milk yield (0.182) and milk fat content (0.122), and similar to that for protein content (0.275). Daily cheese yields showed heritability estimates similar to those of daily milk yield. The trait %CY(WATER) showed a highly positive genetic correlation with %CY(SOLIDS) (0.87), whereas their phenotypic correlation was moderate (0.37), and the fat and protein contents of milk showed high genetic correlations with %CY traits. The heritability estimates of REC(PROTEIN) and REC(FAT) were larger (0.490 and 0.208, respectively) than those obtained for the protein and fat contents of milk, and the genetic relationships between REC(PROTEIN) and REC(FAT) with milk protein and fat content were low or moderate; REC(PROTEIN) and REC(FAT) were moderately correlated with the %CY traits and highly correlated with REC(SOLIDS) and REC(ENERGY). Both REC(SOLIDS) and REC(ENERGY) were heritable (0.274 and 0.232), and showed high correlations with each other (0.96) and with the %CY traits (0.83 to 0.97). Together, these findings demonstrate the existence of economically important, genetically determined variability in cheese yield that does not depend solely upon the fat and protein contents of milk, but also relies on the ability of the coagulum to retain the highest possible proportions of the available protein, fat, and water. Exploitation of this interesting genetic variation does not seem to be feasible through direct measurement of the phenotype in cows at the population level. Instead, further research is warranted to examine possible means for indirect prediction, such as through assessing the mid-infrared spectra of milk samples., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The aim of the present study was to compare milk coagulation properties measured through a traditional mechanical device, the Formagraph (FRM; Foss Electric A/S, Hillerød, Denmark), and a near-infrared optical device, the Optigraph (OPT; Ysebaert SA, Frépillon, France). Individual milk samples of 913 Brown Swiss cows from 63 herds located in Trento Province (Italy) were analyzed for rennet coagulation time (RCT, min), curd-firming time (k(20), min), and 2 measures of curd firmness (a(30) and a(45),mm) using the 2 instruments and under identical conditions. The trial was performed in the same laboratory, by the same technician, and following the same procedures. Extending the analysis by either instrument to 90 min permitted RCT and k(20) values to be obtained even for late-coagulating milk samples. Milk coagulation properties measured using the OPT differed considerably from those obtained using the FRM. The average k(20) values varied greatly (8.16 vs. 5.36 min for the OPT and the FRM, respectively), as did the a(45) figures (41.49 vs. 33.66 mm for the OPT and the FRM, respectively). The proportion of noncoagulating samples for which k(20) could be estimated differed between instruments, being less for the OPT. The between-instrument correlation coefficients were either moderate (0.48 for a(30)) or low (0.24 and 0.17 for k(20) and a(45), respectively) when the same traits were compared. The correlations between k(20) and a(45), and milk yield varied among instruments, as did the correlations between k(20), a(30), and a(45) and milk composition, and the correlations between a(45) and pH. The relative influence of days in milk on k(20) and a(45) varied, as did the effect of parity on a(45) and that of the measuring unit of coagulation meter on k(20) and a(30). The RCT estimated by the OPT was the only milk coagulation property to show good agreement with the FRM-derived value, although this was not true for the data from late-coagulating samples., (Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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. [ABSTRACT FROM AUTHOR]