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Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements. For macrominerals, the R 2 ranged from 0.59 to 0.78. Promising predictability was achieved for Cu and B (R 2  = 0.66 and 0.74, respectively) and more moderate ones for Fe, Mn, Zn, and Al (R 2 from 0.48 to 0.58). These results provide a reliable basis for a rapid and cost-effective quantification of these traits, serving as a resource for dairy farmers seeking to enhance the quality of milk production and optimize cheese properties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Milk and dairy products are important in the human diet not only for the macro nutrients, such as proteins and fats, that they provide, but also for the supply of essential micronutrients, such as minerals. Minerals are present in milk in soluble form in the aqueous phase and in colloidal form associated with the macronutrients of the milk. These 2 forms affect the nutritional functions of the minerals and their contribution to the technological properties of milk during cheese-making. The aim of the present work was to study and compare the detailed mineral profiles of dairy foods (milk, whey, and cheese) obtained from cows, buffaloes, goats, ewes and dromedary camels, and to analyze the recovery in the curd of the individual minerals according to a model cheese-making procedure applied to the milk of these 5 dairy species. The detailed mineral profile of the milk samples was obtained by inductively coupled plasma - optical emission spectroscopy (ICP - OES). We divided the 21 minerals identified in the 3 different matrices into essential macro- and micro-minerals, and environmental micro-minerals, and calculated the recovery of the individual minerals in the cheeses. The complete mineral profiles and the recoveries in the cheeses were then analyzed using a linear mixed model with Species and Food, and their interaction included as fixed effects, and Sample within Species as a random effect. The mineral profiles of each food matrix were then analyzed separately with a general linear model in which only the fixed effect of Species was included. The results showed that the species could be divided into 2 groups: those producing a more diluted milk characterized by a higher content of soluble minerals (in particular K), and those with a more concentrated milk with a higher colloidal mineral content in the skim of the milk (such as Ca and P). The recoveries of the minerals in the curd were in line with the initial content in the milk, and also highlighted the fact that the influence of the brine was not limited to the Na content but to its whole mineral makeup. These results provide valuable information for the evaluation of the nutritional and technological properties of milk, and for the uses made of the byproducts of cheese making from the milk of different species., (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/).)

Cheese presents extensive variability in physical, chemical, and sensory characteristics according to the variety of processing methods and conditions used to create it. Relationships between the many characteristics of cheeses are known for single cheese types or by comparing a few of them, but not for a large number of cheese types. This case study used the properties recorded on 1,050 different cheeses from 107 producers grouped into 37 categories to analyze and quantify the interrelationships among the chemical and physical properties of many cheese types. The 15 cheese traits considered were ripening length, weight, firmness, adhesiveness, 6 different chemical characteristics, and 5 different color traits. As the 105 correlations between the 15 cheese traits were highly variable, a multivariate analysis was carried out. Four latent explanatory factors were extracted, representing 86% of the covariance matrix: the first factor (38% of covariance) was named Solids because it is mainly linked positively to fat, protein, water-soluble nitrogen, ash, firmness, adhesiveness, and ripening length, and negatively to moisture and lightness; the second factor (24%) was named Hue because it is linked positively to redness/blueness, yellowness/greenness, and chroma, and negatively to hue; the third factor (17%) was named Size because it is linked positively to weight, ripening length, firmness, and protein; and the fourth factor (7%) was named Basicity because it is linked positively to pH. The 37 cheese categories were grouped into 8 clusters and described using the latent factors: the Grana Padano cluster (characterized mainly by high Size scores); hard mountain cheeses (mainly high Solids scores); very soft cheeses (low Solids scores); blue cheeses (high Basicity scores), yellowish cheeses (high Hue scores), and 3 other clusters (soft cheeses, pasta filata and treated rind, and firm mountain cheeses) according to specific combinations of intermediate latent factors and cheese traits. In this case study, the high variability and interdependence of 15 major cheese traits can be substantially explained by only 4 latent factors, allowing us to identify and characterize 8 cheese type clusters., (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/).)

This study conducted a seasonal analysis of bulk tank milk from 77 sheep farms to establish relationships between the concentration of major microbial groups and milk coagulation properties. The investigated milk traits included composition (pH, fat, casein, lactose), coagulation properties (curd firmness: A 60 -, rennet clotting time: RCT-, curd firming time: k 20 -, curd yield: CY-), and somatic cell score (SCS). The main microbial groups analyzed were total mesophilic bacteria (SPC), thermodurics (THERMO), psychrotrophs (PSYCHRO), Pseudomonas spp. (PSEUDO), lactic acid bacteria (LAB), catalase-negative gram-positive cocci (GPCNC), Escherichia coli (ECOLI), coliforms other than Escherichia coli (COLI), coagulase-positive staphylococci (CPS), coagulase-negative staphylococci (CNS), and spores of lactate-fermenting Clostridium (BAB). Mixed linear models were used to explore associations between coagulation properties and the aforementioned variables. Results demonstrated that incorporating microbial loads into the models improves their fit and the relative quality of the outcomes. An important seasonality is demonstrated by an increase in CY and A 60 , along with a decrease in RCT and k 20 during autumn and winter, contrasting with spring and summer. BAB concentration resulted in a reduction of A 60 and an increase in RCT, whereas SPC concentration led to an enhancement of A 60 and a reduction in RCT. An increase in GPCNC concentration was associated with an increase in k 20 and a decrease in CY.

The transformation of milk into cheese largely depends on the technological properties of the raw material, with breed being a crucial factor that influences both the composition and coagulation properties of the milk used for cheesemaking. This study uses canonical correlation analysis to explore the relationships between physicochemical traits and coagulation properties in milk from various Spanish breeds, aiming to identify both common and breed-specific patterns that impact milk technological aptitude. A total of 832 milk samples from Manchega, Assaf, Merino de Grazalema, and Merino de Los Pedroches breeds were analyzed. The milk characteristics investigated included pH, composition (fat, protein, lactose, total solids), and coagulation properties (curd firmness-A 60 , rennet clotting time-RCT, curd firming time-k 20 , and individual laboratory curd yield-ILCY). The results reveal a shared correlation structure across breeds and unique covariation patterns in some breeds that deviate from the general trend. While Assaf and Merino de Los Pedroches follow the common correlation pattern, Manchega and Merino de Grazalema exhibit distinct patterns. This research underscores the need for in-depth study and suggests that the dairy industry could benefit from shifting from the traditional focus on maximizing fat and protein for higher curd yields to considering technological traits for selective breeding.

Sheep milk from local breeds is important for the production of high-quality cheeses throughout the Mediterranean region, such as Manchego cheese in Spain. To maintain sustainable and efficient production, it is necessary to reach a better understanding of how the composition and hygiene of the milk affect the coagulation process, with the aim of optimizing production yield. This study implemented a stochastic production frontier function to estimate the potential production of curd and efficiency using data from the four seasons of a study of 77 Manchega sheep farms. The Cobb-Douglas production frontier model was estimated using the maximum likelihood estimation method. The results showed that the content of protein, lactose, and fat exhibited increasing returns to scale, with protein content being the most significant factor for curd production. Approximately half of the inefficiency was due to factors related to the technological properties and the hygiene of the milk. The pH, curd firmness, and concentration of lactic acid bacteria improved the efficiency of coagulation, while the concentration of spores of lactate-fermenting Clostridium spp., Pseudomonas spp., staphylococci, and catalase-negative gram-positive cocci favored the inefficiency of the coagulation process. To date, this is the first study to evaluate the effect of different factors, such as microbial groups, milk composition, and technological properties, on the efficiency of the coagulation process in dairy sheep.

Rotational crossbreeding has not been widely studied in relation to the enteric methane emissions of dairy cows, nor has the variation in emissions during lactation been modeled. Milk infrared spectra could be used to predict proxies of methane emissions in dairy cows. Therefore, the objective of this work was to study the effects of crossbreeding on the predicted infrared proxies of methane emissions and the variation in the latter during lactation. Milk samples were taken once from 1,059 cows reared in 2 herds, and infrared spectra of the milk were used to predict milk fat (mean ± SD; 3.79 ± 0.81%) and protein (3.68 ± 0.36%) concentrations, yield (21.4 ± 1.5 g/kg dry matter intake), methane intensity (14.2 ± 2.0 g/kg corrected milk), and daily methane production (358 ± 108 g/d). Of these cows, 620 were obtained from a 3-breed (Holstein, Montbéliarde, and Viking Red) rotational mating system, and the rest were purebred Holsteins. Milk production data and methane traits were analyzed using a nonlinear model that included the fixed effects of herd, genetic group, and parity, and the 4 parameters (a, b, c, and k) of a lactation curve modeled using the Wilmink function. Milk infrared spectra were found to be useful for direct prediction of qualitative proxies, such as methane yield and intensity, but not quantitative traits, such as daily methane production, which appears to be better estimated (450 ± 125 g/d) by multiplying a measured daily milk yield by infrared-predicted methane intensity. Lactation modeling of methane traits showed daily methane production to have a zenith curve, similar to that of milk yield but with a delayed peak (53 vs. 37 d in milk), whereas methane intensity is characterized by an upward curve that increases rapidly during the first third of lactation and then slowly till the end of lactation (10.5 g/kg at 1 d in milk to 15.2 g/kg at 300 d in milk). However, lactation modeling was not useful in explaining methane yield, which is almost constant during lactation. Lastly, the methane yield and intensity of cows from 3-breed rotational crossbreeding are not greater, and their methane production is lower than that of purebred Holsteins (452 vs. 477 g/d). Given the greater longevity of crossbred cows, and their lower replacement rate, rotational crossbreeding could be a way of mitigating the environmental impact of milk production., (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 problem of the erosion of animal genetic resources is evident in certain local donkey breeds, and their long-term sustainability can be achieved by economically repositioning them. To develop alternative and sustainable commercial programs, the meat and milk production characteristics of Istrian donkey and Littoral Dinaric donkey breeds were investigated. The meat production characteristics were examined in mature males, whose carcasses were dissected, and meat composition was determined using NIT spectrophotometry and gas chromatography. Milk yield and milk composition were determined in jennies in second or subsequent lactations by measuring milk volume and using infrared spectrometry and gas chromatography. Compared to the Littoral Dinaric donkey, the Istrian donkey has a higher carcass weight and dressing percentage ( p < 0.001). The share of boneless meat in relation to live weight was 28.27% in the Istrian donkey and 26.18% in the Littoral Dinaric donkey. The absolute masses of primal cuts of meat in E, I, and II classes were significantly greater in Istrian donkeys than in Littoral Dinaric donkeys ( p < 0.01), although the differences in the proportions of primal cuts were not significant. The breed did not have a significant impact on the color, pH, or meat composition. A significant influence of breed on milk yield, lactose, protein, and the fat content of milk was observed ( p < 0.01). A significant influence of breed on the ratio of n-6/n-3 PUFA fatty acids in donkey milk was observed ( p = 0.002). The values of the atherogenic and thrombogenic indexes were favorable, considering potential beneficial effects of donkey milk and meat on consumer health. The findings of this research suggest that local donkey breeds hold significant potential for meat and milk production, focusing on the uniqueness and quality of their products rather than the quantity of meat and milk they can produce.

The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm -1 ), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.

The study of the complex relationships between milk metagenomics and milk composition and cheese-making efficiency as affected by indoor farming and summer highland grazing was the aim of the present work. The experimental design considered monthly sampling (over 5 mo) of the milk produced by 12 Brown Swiss cows divided into 2 groups: the first remained on a lowland indoor farm from June to October, and the second was moved to highland pastures in July and then returned to the lowland farm in September. The resulting 60 milk samples (2 kg each) were used to analyze milk composition, milk coagulation, curd firming, and syneresis processes, and to make individual model cheeses to measure cheese yields and nutrient recoveries in the cheese. After DNA extraction and Illumina Miseq sequencing, milk microbiota amplicons were also processed by means of an open-source pipeline called Quantitative Insights Into Microbial Ecology (Qiime2, version 2018.2; https://qiime2.org). Out of a total of 44 taxa analyzed, 13 bacterial taxa were considered important for the dairy industry (lactic acid bacteria, LAB, 5 taxa; and spoilage bacteria, 4) and for human (other probiotics, 2) and animal health (pathogenic bacteria, 2). The results revealed the transhumant group of cows transferred to summer highland pastures showed an increase in almost all the LAB taxa, bifidobacteria, and propionibacteria, and a reduction in spoilage taxa. All the metagenomic changes disappeared when the transhumant cows were moved back to the permanent indoor farm. The relationships between 17 microbial traits and 30 compositional and technological milk traits were investigated through analysis of correlation and latent explanatory factor analysis. Eight latent factors were identified, explaining 75.3% of the total variance, 2 of which were mainly based on microbial traits: pro-dairy bacteria (14% of total variance, improving during summer pasturing) and pathogenic bacteria (6.0% of total variance). Some bacterial traits contributed to other compositional-technological latent factors (gelation, udder health, and caseins)., (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/).)

Cheeses are produced by many different procedures, giving rise to many types differing in ripening time, size, shape, chemical composition, color, texture, and sensory properties. As the first step in a large project, our aim was to characterize and quantify the major sources of variation in cheese characteristics by sampling 1050 different cheeses manufactured by over 100 producers and grouped into 37 categories (16 with protected designation of origin, 4 traditional cheese categories, 3 pasta filata cheese categories, 5 flavored cheese categories, 2 goat milk categories, and 7 other categories ranging from very fresh to very hard cheeses). We obtained 17 traits from each cheese (shape, height, diameter, weight, moisture, fat, protein, water soluble nitrogen, ash, pH, 5 color traits, firmness, and adhesiveness). The main groups of cheese categories were characterized and are discussed in terms of the effects of the prevalent area of origin/feeding system, species of lactating females, main cheese-making technologies, and additives used. The results will allow us to proceed with the further steps, which will address the interrelationships among the different traits characterizing cheeses, detailed analyses of the nutrients affecting human health and sensorial fingerprinting.

Meat flavor is an important aspect of meat quality that also influences consumer demand, and is therefore very important for the meat industry. Volatile organic compounds (VOCs) contribute in large part to the flavor of meat, and while increasing numbers of articles are published on this topic, reviews of these articles are very scarce. Therefore, our aim was to perform a bibliometric analysis of the scientific publications on VOCs in meat over the period 2000-2020. We selected 611 scientific sources from the Scopus database related to VOCs in meat (seafood excluded). The bibliometric information retrieved included journals, authors, countries, institutions, keywords, and citations. From this analysis, we drew up a list of the most important journals, authors, countries, and institutions, and the trends in VOC research on meat. We conducted a social network analysis (SNA) to identify the collaborations among the many authors and countries, and a keyword analysis to generate a network map of the authors' keywords. We also determined which meat species were most frequently chosen as research subjects, traced the evolution of the various methods/instruments used, and explored the research tendencies. Finally, we point out the need for further research in defining meat quality, improving meat flavor, identifying adulterants, and certifying the authenticity of meat.

At the global level, the quantity of goat milk produced and its gross production value have increased considerably over the last 2 decades. Although many scientific papers on this topic have been published, few studies have been carried out on bulk goat milk samples. The aim of the present study was to investigate in the field the effects of farming system, breed type, individual flock, and stage of production on the composition, coagulation properties (MCP), curd firming over time parameters (CF t ), predicted cheese yield (CY%), and nutrient recovery traits (REC) of 432 bulk milk samples from 161 commercial goat farms in Sardinia, Italy. We found that the variance due to individual flock was of the same order as the residual variance for almost all composition and cheesemaking traits. With regard to the fixed effects, the effect of farming system on bulk milk variability was not highly significant for the majority of traits (it was lower than individual flock), whereas the effects of breed type and stage of production were much higher. More specifically, the intensive farms produced milk with the best concentrations of almost all constituents, whereas extensive farms exhibited faster rennet coagulation times, a slower rate of curd firming, lower potential curd firmness, and lower percentages of fat and energy recoveries in the fresh curd. Farms rearing the local breed, Sarda, alone or together with the Maltese breed, produced milk with the best concentrations of fat and protein, superior curd firmness, and better predicted percentage of fresh curd (CY CURD ) and recovery traits. The results show the potential of both types of breed, either for their quantitative (specialized breeds) or their qualitative (local breeds) attributes. As expected, the concentrations of fat, protein fractions, and lactose were influenced by the stage of production, with samples collected in the early stage of production (in February and March) having a greater quantity of the main constituents. Somatic cells reached the highest levels in the late stage of production, which corresponds to the goats' advanced stage of lactation (June-July), although no differences were present in the logarithmic bacterial counts between the early and late stages. Regarding cheesemaking potential, bulk milk samples of the late stage were characterized by delayed rennet coagulation and curd firming times, the lowest values of curd firmness, and a general reduction in CY%, and REC traits. In conclusion, we highlight several issues regarding the effects of the most important sources of variation on bulk goat milk, and point to some critical factors relevant for improving dairy goat farming and milk production., (© 2022, 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/).)

To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (α S1 -CN, α S2 -CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the β-CN, κ-CN, and β-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the β-LG B variant was associated with a lower concentration of β-LG compared with the β-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the β-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health., (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/).)

Bovines produce about 83% of the milk and dairy products consumed by humans worldwide, the rest represented by bubaline, caprine, ovine, camelid, and equine species, which are particularly important in areas of extensive pastoralism. Although milk is increasingly used for cheese production, the cheese-making efficiency of milk from the different species is not well known. This study compares the cheese-making ability of milk sampled from lactating females of the 6 dairy species in terms of milk composition, coagulation properties (using lactodynamography), curd-firming modeling, nutrients recovered in the curd, and cheese yield (through laboratory model-cheese production). Equine (donkey) milk had the lowest fat and protein content and did not coagulate after rennet addition. Buffalo and ewe milk yielded more fresh cheese (25.5 and 22.9%, respectively) than cow, goat, and dromedary milk (15.4, 11.9, and 13.8%, respectively). This was due to the greater fat and protein contents of the former species with respect to the latter, but also to the greater recovery of fat in the curd of bubaline (88.2%) than in the curd of camelid milk (55.0%) and consequent differences in the recoveries of milk total solids and energy in the curd; protein recovery, however, was much more similar across species (from 74.7% in dromedaries to 83.7% in bovine milk). Compared with bovine milk, the milk from the other Artiodactyla species coagulated more rapidly, reached curd firmness more quickly (especially ovine milk), had a more pronounced syneresis (especially caprine milk), had a greater potential asymptotical curd firmness (except dromedary and goat milk), and reached earlier maximum curd firmness (especially caprine and ovine milk). The maximum measured curd firmness was greater for bubaline and ovine milk, intermediate for bovine and caprine milk, and lower for camelid milk. The milk of all ruminant species can be used to make cheese, but, to improve efficiency, cheese-making procedures need to be optimized to take into account the large differences in their coagulation, curd-firming, and syneresis properties., (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 protein profile of milk includes several caseins, whey proteins, and nonprotein nitrogen compounds, which influence milk's value for human nutrition and its cheesemaking properties for the dairy industry. To fill in the gap in current knowledge of the patterns of these individual nitrogenous compounds throughout lactation, we tested the ability of a parametric nonlinear lactation model to describe the pattern of each N compound expressed qualitatively (as % of total milk N), quantitatively (in g/L milk), and as daily yield (in g/d). The lactation model was tested on a data set of detailed milk nitrogenous compound profiles (15 fractions-12 protein traits and 3 nonproteins-for each expression mode: 45 traits) obtained from 1,342 cows reared in 41 multibreed herds. Our model was a modified version of Wilmink's model, often used for describing milk yield during lactation because of its reliability and ease of parameter interpretation from a biological point of view. We allowed the sign of the persistency coefficient (parameter c) that explained the variation in the long-term milk component (parameter a) to be positive or negative. We also allowed the short-term milk component (parameter b) to be positive or negative, and we estimated a specific speed of adaptation parameter (parameter k) for each trait rather than assumed a value a priori, as in the original model (k = 0.05). These 4 parameters were included in a nonlinear mixed model with cow breed and parity order as fixed effects, and herd-date as random. Combinations of the positive and negative signs of the b and c parameters allowed us to identify 4 differently shaped lactation curves, all found among the patterns exhibited by the nitrogenous fractions as follows: the "zenith" curve (with a maximum peak; for milk yield and 10 other N traits), the "nadir" curve (with a minimum point; for 20 traits, including almost all those expressed in g/L of milk), the "downward" curve (continuously decreasing; for 14 traits, including almost all those in g/d), and the "upward" curve (continuously increasing; only for κ-casein, in % N). Direct estimation of the k parameters specific to each trait showed the large variability in the adaptation speed of fresh cows and greatly increased the model's flexibility. The results indicated that nonlinear parametric mathematical models can effectively describe the different and complex patterns exhibited by individual nitrogenous fractions during lactation; therefore, they could be useful tools for interpreting milk composition variations during lactation., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Natural variations in milk minerals, their relationships, and their associations with the coagulation process and cheese-making traits present an opportunity for the differentiation of milk destined for high-quality natural products, such as traditional specialties or Protected Designation of Origin (PDO) cheeses. The aim of this study was to quantify the effects of the native contents of Ca, P, Na, K, and Mg on 18 traits describing traditional milk coagulation properties (MCP), curd firming over time (CF t ) equation parameters, cheese yield (CY) measures, and nutrient recoveries in the curd (REC) using models that either included or omitted the simultaneous effects of milk fat and casein contents. The results showed that, by including milk fat and casein and the minerals in the statistical model, we were able to determine the specific effects of each mineral on coagulation and cheese-making efficiency. In general, about two-thirds of the apparent effects of the minerals on MCP and the CF t equation parameters are actually mediated by their association with milk composition, especially casein content, whereas only one-third of the effects are direct and independent of milk composition. In the case of cheese-making traits, the effects of the minerals were mediated only negligibly by their association with milk composition. High Ca content had a positive effect on the coagulation pattern and cheese-making traits, favoring water retention in the curd in particular. Phosphorus positively affected the cheese-making traits in that it was associated with an increase in CY in terms of curd solids, and in all the nutrient recovery traits. However, a very high P content in milk was associated with lower fat recovery in the curd. The variation in the Na content in milk only mildly affected coagulation, whereas with regard to cheese-making, protein recovery was negatively associated with high concentrations of this mineral. Potassium seemed not to be actively involved in coagulation and the cheese-making process. Magnesium content tended to slow coagulation and reduce CY measures. Further studies on the relationships of minerals with casein and protein fractions could deepen our knowledge of the role of all minerals in coagulation and the cheese-making process., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Different fractions of milk nitrogenous compounds (not only caseins) have different effects on the nutritional value of milk, its coagulation and curd firming properties, and its cheese-making efficiency. To assess different sources of variation, especially the cows' breed and genetic variants of the main protein fractions, milk samples were collected from 1,504 cows belonging to 3 dairy breeds (Holstein-Friesian, Brown Swiss, and Jersey) and 3 dual-purpose breeds (Simmental, Rendena, and Alpine Grey) reared in 41 multibreed herds. Beyond crude protein, casein (CN), and urea, 7 protein fractions were analyzed using HPLC, and 5 other N fraction traits were calculated. All 15 traits were measured qualitatively (% of milk N) and quantitatively (g/L of milk). The HPLC technique allowed us to discriminate between the main genetic variants of β-CN, κ-CN, and β-lactoglobulin and thus to genotype the cows for the CSN2, CSN3, and BLG genes, respectively. Data were analyzed using 2 mixed models, both including the effects of herd-date, breed, parity, and lactation stage, and only one also including the effects of the genotypes of the milk proteins. Breed of cow explained 2 to 36% of phenotypic variability for all the N fractions, with the exception of the urea and total casein contents of milk and the urea and β-CN proportions of total milk N. Lactation stage had a considerable influence on the amount (g/L) of almost all the protein fractions in milk, but neither the nonprotein N fractions nor the percentage of milk N protein profile were affected. The inclusion of the CSN2, CSN3, and BLG genotypes in the model explained a large part of the total variability in all the milk protein and nonprotein fractions except urea. It also reduced the variance explained by breed and residual factors. An exception was shown by the proportion of α S1 -CN variance explained by breed that moved from 13 to 28%. Similarly, for amount (g/L) of β-CN, the effect of breed became significant (12%), whereas it was almost null before inclusion of genotypes. In terms of percentage of milk N, the genotypes of CSN3 notably affected all the casein fractions, whereas the BLG genotypes had a much greater influence on most noncasein traits. The genotypes of the CSN2 gene exerted an appreciable effect on α S2 -CN and not β-CN, as expected. Comparing the 2 models, we were also able to discriminate the effect of the breed on a milk N fraction, both quantitatively and qualitatively, in 2 quotas: the first due to the milk protein polymorphisms (major genes) and the second due to other genetic factors (polygene), after correcting for the effect of herd-date of sampling, parity, and lactation stage. The knowledge about the detailed milk protein profile of different cattle breeds provided by this study could be of great benefit for the dairy industry, providing new tools for the enhancement of milk payment systems and breeding program designs., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Summer transhumance to alpine pastures (ALP) is widespread in dairy systems of alpine regions. This study aimed to investigate the effects of transhumance of Brown Swiss cows to ALP on the yield, composition, and coagulation properties of milk (MCP), and on cheese yield (CY). The study involved 12 multiparous cows kept at a mountain lowland permanent farm (PF), which were divided into two equal groups: One remained at the PF, the other was moved to the ALP (1860 m above sea level) from July to September. Every month (June to October), daily milk yield (MY) and body condition score (BCS) were recorded, and individual milk samples ( n = 60, 2000 mL each) were collected to assess milk composition, MCP, and CY. Compared with PF, ALP cows had a reduced MY and BCS, which was maintained on return to the PF, greater fat and lower protein contents of milk. Neither MCP nor CY were affected by summer transhumance. In conclusion, summer transhumance did not affect the cheese making efficiency of milk but depressed MY and consequently daily cheese yield, which was nearly 2 kg/d lower for the ALP than the PF cows and was only partially recovered after returning to the PF in autumn.

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.)