Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CFt). The traditional MCP are ...rennet coagulation time (RCT, min), curd firmness (a30, mm), and curd-firming time (k20, min). The milk of different ruminant species varies in terms of CFt pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a30, and samples for which k20 is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a30 are highly correlated, both phenotypically and genetically. This means that the use of a30 data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a30 is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CFt modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fourier-transform infrared (FTIR) spectra are used to predict the fat, protein, casein, and lactose contents of milk. These estimates are currently used to predict the individual estimated breeding ...values of animals. The objective of the present study was to estimate the genetic variation and heritabilities of the milk transmittance spectrum at each individual FTIR wave. Milk was sampled once per cow from a total of 1,064 Italian Brown Swiss cows from 30 herds, sired by 50 artificial insemination sires. The FTIR spectra of all samples were collected within 3h of sampling from 25mL of milk. The obtained spectral range comprised wavenumbers 5,000 to 930×cm−1, corresponding to wavelengths 2.00 to 10.76μm and frequencies from 149.9 to 27.9THz, for a total of 1,056 waves. These were acquired using a MilkoScan FT120 FTIR interferometer (Foss Electric A/S, Hillerød, Denmark). Each spectral data point was treated as a single trait and analyzed using an animal model REML method. The results indicated that the transmittance of the bovine milk FTIR spectrum was heritable for most individual waves in the wavenumber interval from 5,000 to 930×cm−1. Moreover, the transmittance of contiguous FTIR waves was much more highly correlated in terms of the average value and phenotypic variation, compared with genetic variation. In the present study, we characterized 5 regions of the FTIR spectrum that were relevant to the analysis of milk; 2 regions, one in the transition area between the short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) divisions of the electromagnetic spectrum (SWIR-MWIR region) and another very short region in the MWIR division (MWIR-2 region), were characterized by very high phenotypic variability in the transmittance of individual milk samples within each wave. This was caused by the absorption peaks of water, which can mask the effects of other important milk components. These regions also showed high genetic variability in transmittance, and the heritability estimates of individual waves were generally very low (with some exceptions). The 3 other identified regions contained many transmittance peaks that represented important chemical bonds; these showed much lower phenotypic and genetic variability in terms of individual waves, but relatively higher and less variable heritability estimates. Among them, the SWIR region (near-infrared) showed a peculiar cyclic pattern of the heritability coefficients of transmittance, the MWIR-1 region was particularly important for the estimation of fat, and the MWIR-LWIR region (also known also as the “fingerprint region”) had 3 areas of relatively high heritability. In summary, we found that the transmittance data from the FTIR spectra of milk have genetic variability that may prove useful for the direct genetic improvement of dairy species, rather than only through indirect phenotypic predictions of individual milk quality and technological traits.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to investigate associations between pathogen-specific cases of subclinical mastitis and milk yield, quality, protein composition, and cheese-making traits. Forty-one ...multibreed herds were selected for the study, and composite milk samples were collected from 1,508 cows belonging to 3 specialized dairy breeds (Holstein Friesian, Brown Swiss, and Jersey) and 3 dual-purpose breeds of Alpine origin (Simmental, Rendena, and Grey Alpine). Milk composition i.e., fat, protein, casein, lactose, pH, urea, and somatic cell count (SCC) was analyzed, and separation of protein fractions was performed by reversed-phase high performance liquid chromatography. Eleven coagulation traits were measured: 5 traditional milk coagulation properties time from rennet addition to milk gelation (RCT, min), curd-firming rate as the time to a curd firmness (CF) of 20 mm (k20, min), and CF at 30, 45, and 60 min from rennet addition (a30, a45, and a60, mm), and 6 new curd firming and syneresis traits potential asymptotical CF at an infinite time (CFP, mm), curd-firming instant rate constant (kCF, % × min−1), curd syneresis instant rate constant (kSR, % × min−1), modeled RCT (RCTeq, min), maximum CF value (CFmax, mm), and time at CFmax (tmax, min). We also measured 3 cheese yield traits, expressing the weights of total fresh curd (%CYCURD), dry matter (%CYSOLIDS), and water (%CYWATER) in the curd as percentages of the weight of the processed milk, and 4 nutrient recovery traits (RECPROTEIN, RECFAT, RECSOLIDS, and RECENERGY), representing the percentage ratio between each nutrient in the curd and milk. Milk samples with SCC > 100,000 cells/mL were subjected to bacteriological examination. All samples were divided into 7 clusters of udder health (UH) status: healthy (cows with milk SCC < 100,000 cells/mL and uncultured); culture-negative samples with low, medium, or high SCC; and culture-positive samples divided into contagious, environmental, and opportunistic intramammary infection (IMI). Data were analyzed using a linear mixed model. Significant variations in the casein to protein ratio and lactose content were observed in all culture-positive samples and in culture-negative samples with medium to high SCC compared to normal milk. No differences were observed among contagious, environmental, and opportunistic pathogens, suggesting an effect of inflammation rather than infection. The greatest impairment in milk quantity and composition, clotting ability, and cheese production was observed in the 2 UH status groups with the highest milk SCC (i.e., contagious IMI and culture-negative samples with high SCC), revealing a discrepancy between the bacteriological results and inflammatory status, and thus confirming the importance of SCC as an indicator of udder health and milk quality.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to propose the survival analysis technique as a statistical approach for the analysis of rennet coagulation time (RCT) able to make use of coagulating and noncoagulating ...(NC) milk information in order to estimate potential sources of variations that affects RCT. A total of 1,025 Italian Holstein-Friesian cows (HF; progeny of 54 sires) and 1,234 Brown Swiss cows (BS; progeny of 58 sires) reared in 34 and 38 herds, respectively, were milk-sampled once. Rennet coagulation time was analyzed with a semiparametric proportional hazard model (i.e., a Cox model), with the NC samples considered as censored records. Furthermore, a different censoring scenario, with a new end point at 18min, was considered after the rearrangement of the time space originally used for the observation of RCT. The percentage of NC samples was almost 10% for HF and 3.5% for BS cows in in the 31min set, whereas it increased to 44 and 24.9%, respectively, in the 18min set. Estimated hazard ratios indicated that the most important factors affecting the coagulation process were herd, days in milk, casein number, and milk acidity (expressed in terms of titratable acidity) for both HF and BS, whereas the SCS was relevant only for BS. The survival model seems to be particularly suitable for this analysis, as it can properly account for censored and uncensored records and appropriately use all available information. Moreover, this methodology allows us to rearrange the time space used for the observation of RCT and to define alternative traits (i.e., RCT with an end point at 18min). Our restriction of the time space and the increased percentage of censored records did not highlight any substantial differences in terms of the risk of coagulating with respect to the traditional 31min testing time. Although further research is needed to investigate the effect of these sources of variation on cheese yield, our results indicate that casein number, acidity, and SCS may be used as indicator traits for enhancing the technological properties of bovine milk.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to investigate the associations between subclinical intramammary infection (IMI) from different pathogens combined with inflammation status and a set of blood biochemical ...traits including energy-related metabolites, indicators of liver function or hepatic damage, oxidative stress, inflammation, innate immunity, and mineral status in 349 lactating Holstein cows. Data were analyzed with a linear model including the following fixed class effects: days in milk, parity, herd, somatic cell count (SCC), bacteriological status (positive and negative), and the SCC × bacteriological status interaction. Several metabolites had significant associations with subclinical IMI or SCC. Increased SCC was associated with a linear decrease in cholesterol concentrations which ranged from −2% for the class ≥50,000 and <200,000 cells/mL to −11% for the SCC class ≥400,000 cells/mL compared with the SCC class <50,000 cells/mL. A positive bacteriological result was associated with an increase in bilirubin (+24%), paraoxonase (+11%), the ratio paraoxonase/cholesterol (+9%), and advanced oxidation protein product concentration (+23%). Increased SCC were associated with a linear decrease in ferric reducing antioxidant power concentrations ranging from −3% for the class ≥50,000 and <200,000 cells/mL to −9% for the SCC class ≥400,000 cells/mL (respect to the SCC class <50,000 cells/mL). A positive bacteriological result was associated with an increase in haptoglobin concentrations (+19%). Increased SCC were also associated with a linear increase in haptoglobin concentrations, which ranged from +24% for the class ≥50,000 and <200,000 cells/mL (0.31 g/L) to +82% for the SCC class ≥400,000 cells/mL (0.45 g/L), with respect to the SCC class <50,000 cells/mL (0.25 g/L). Increased SCC were associated with a linear increase in ceruloplasmin concentrations (+15% for SCC ≥50,000 cells/mL). The observed changes in blood biochemical markers, mainly acute phase proteins and oxidative stress markers, suggest that cows with subclinical IMI may experience a systemic involvement.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The aim of this study was to elucidate the relationships between various cheesemaking-related traits, namely the well-known traditional milk coagulation properties (MCP), the new curd firming and ...syneresis traits, the cheese yield, and the curd nutrient recoveries or whey losses (all measured at the individual level). Data were obtained from 1,167 Brown Swiss cows reared in 85 herds. A 2-L milk sample was collected once from each animal and assessed for 10 phenotypes related to changes in curd firmness (CF) over time, plus 7 cheesemaking traits. The CF-related traits included 4 traditional single-point lactodynamographic properties rennet coagulation time (RCT, min); time to a CF of 20mm, min; and the CF 30 and 45min after rennet addition (a30 and a45, respectively), 4 parameters used to model the 360 CF data recorded over time for each milk sample the potential asymptotic CF at infinite time (CFP, mm); the CF instant rate constant, % × min−1; the syneresis instant rate constant, % × min−1; and the RCT obtained from modeling individual samples, and 2 traits calculated from individual equations the maximum CF(CFmax, mm); and the time at CFmax, min. The cheesemaking traits included 3 cheese yield traits (weights of the fresh curd, curd solids and curd moisture as percent of the weights of the processed milk) and 4milk nutrient recoveries in the curd (calculated as the percent ratios between a given nutrient in the curd versus that in the processed milk). Bayesian methodology-based multivariate analyses were used to estimate the phenotypic, additive genetic, herd/date, and residual relationships between the aforementioned traits, whereas statistical inferences were based on the marginal posterior distributions of the parameters of concern. The a45, CFP, and CFmax traits were genetically associated with all of the percent cheese yield traits (the additive genetic correlations varied from 0.752 to 0.855 for a45; 0.496 to 0.583 for CFP; and 0.750 to 0.801 for CFmax) and the nutrient recovery traits (additive genetic correlations varied from 0.296 to 0.901 for a45; 0.428 to 0.697 for CFP; and 0.412 to 0.941 for CFmax). Moreover, the nutrient recoveries for fat, solids, and energy exhibited large additive genetic correlations with the other coagulation and curd firming traits. In particular, recovery of protein and fat were found to be powerful instruments for understanding the relationships between milk technological properties and cheese quantity or quality. We observed only weak genetic relationships with the milk quality and MCP traits, suggesting that the highly heritable trait of protein recovery should perhaps be included as a genetic index when seeking to improve cheesemaking efficiency at the population level. In contrast, we found that fat recovery exhibited moderate genetic variation and could be improved through the CF over time traits, especially using those recorded during the late phase of the curd firming process. Moreover, our results demonstrated that the traditional MCP have limited relevance for predicting individual cheese yield. Therefore, their use for this purpose in the dairy industry and breeding programs seems questionable.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In the present study we estimated the genetic parameters of enteric methane emissions (EME) traits predicted from milk fatty acid profile (FA) and those of their predictors in 1,091 Brown Swiss cows ...reared on 85 farms in order to assess the potential of using EME-related phenotypes in selective breeding. Univariate and bivariate genetic models were fitted in a Bayesian framework. The means of the marginal posterior distribution of intra-herd heritability ranged from 0.12 for estimated methane production (g/d/cow) to 0.24 for estimated methane yield (g/kg dry matter intake DMI), with intermediate values for estimated methane intensity, increasingly higher when expressed per kg of corrected milk (0.13), fresh cheese (0.16), or cheese solids (0.20). Regarding the correlations, the milk quality traits and percentage cheese yields were generally moderately correlated with the estimated EME traits, and were variable in terms of sign. Daily milk and cheese yield traits were, as expected, all highly positively correlated with estimated daily methane production. In contrast, they were negatively correlated with estimated methane yield and intensity, the estimates being large in the case of phenotypic and herd correlations, and low in the case of additive genetic and residual correlations. With the exception of the negative correlations with daily methane production, EME traits exhibited trivial correlations with body size and BCS of cows, which, in turn, were negatively correlated with milk yield. Although the results should be validated on a larger population and different breeds, our study demonstrate the presence of additive genetic variation of EME traits, which could be exploited in breeding programmes for the improvement in both milk production and the ecological footprint of dairy farming.
Highlights
Enteric methane emissions (EME) of dairy cows can be estimated on the basis of milk fatty acid profile.
EME exhibited exploitable genetic variation.
Genetic selection could be preferentially based on predicted methane intensity per kg of milk, or per kg of cheese in countries where milk production is used mainly for cheese-making.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The aim of this study was to investigate the associations between differential somatic cell count (DSCC) and milk quality and udder health traits, and for the first time, between DSCC and milk ...coagulation properties and cheesemaking traits in a population of 1,264 Holstein cows reared in northern Italy. Differential somatic cell count represents the combined proportions of polymorphonuclear neutrophils plus lymphocytes (PMN-LYM) in the total somatic cell count (SCC), with macrophages (MAC) making up the remaining proportion. The milk traits investigated in this study were milk yield (MY), 8 traits related to milk composition and quality (fat, protein, casein, casein index, lactose, urea, pH, and milk conductivity), 9 milk coagulation traits 3 milk coagulation properties (MCP) and 6 curd firming (CF) traits, 7 cheesemaking traits, 3 cheese yield (CY) traits, and 4 milk nutrient recovery in the curd (REC) traits. A linear mixed model was fitted to explore the associations between SCS combined with DSCC and the aforementioned milk traits. An additional model was run, which included DSCC expressed as the PMN-LYM and MAC counts, obtained by multiplying the percentage of PMN-LYM and MAC by SCC in the milk for each cow in the data set. The unfavorable association between SCS and milk quality and technological traits was confirmed. Increased DSCC was instead associated with a linear increase in MY, casein index, and lactose proportion and a linear decrease in milk fat and milk conductivity. Accordingly, DSCC was favorably associated with all MCP and CF traits (with the exception of the time needed to achieve maximum, CF), particularly with rennet coagulation time, and it always displayed linear relationships. Differential somatic cell count was also positively associated with the recovery of milk nutrients in the curd (protein, fat, and energy), which increased linearly with increasing DSCC. The PMN-LYM count was rarely associated with milk traits, even though the pattern observed confirmed the results obtained when both SCS and DSCC were included in the model. The MAC count, however, showed the opposite pattern: MY, casein index, and lactose percentage decreased and milk conductivity increased with an increasing MAC count. No significant association was found between PMN-LYM count and MCP, CF, CY, and REC traits, whereas MAC count was unfavorably associated with MCP, CF traits, some CY traits, and all REC traits. Our results showed that the combined information derived from SCS and DSCC might be useful to monitor milk quality and cheesemaking-related traits.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Udder health in dairy herds is a very important issue given its implications for animal welfare and the production of high-quality milk. Somatic cell count (SCC) is the most widely used means of ...assessing udder health status. However, differential somatic cell count (DSCC) has recently been proposed as a new and more effective means of evaluating intramammary infection dynamics. Differential SCC represents the combined percentage of polymorphonuclear neutrophils and lymphocytes (PMN-LYM) in the total SCC, with macrophages (MAC) accounting for the remaining proportion. The aim of this study was to evaluate the association between SCC and DSCC and the detailed milk protein profile in a population of 1,482 Holstein cows. A validated reversed-phase HPLC method was used to quantify 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin, which were expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, %N). A linear mixed model was fitted to explore the associations between somatic cell score (SCS) combined with DSCC and the protein fractions expressed quantitatively and qualitatively. We ran an additional model that included DSCC expressed as PMN-LYM and MAC counts, obtained by multiplying the percentages of PMN-LYM and MAC by SCC for each cow in the data set. When the protein fractions were expressed as grams per liter, SCS was significantly negatively associated with almost all the casein fractions and positively associated with the whey protein α-lactalbumin, while DSCC was significantly associated with αS1-CN, β-CN, and α-lactalbumin, but in the opposite direction to SCS. We observed the same pattern with the qualitative data (i.e., %N), confirming opposite effects of SCS and DSCC on milk protein fractions. The PMN-LYM count was only slightly associated with the traits of concern, although the pattern observed was the same as when both SCS and DSCC were included in the model. The MAC count, however, generally had a greater impact on many casein fractions, in particular decreasing both β-CN content (g/L) and proportion (%N), and exhibited the opposite pattern to the PMN-LYM count. Our results show that information obtained from both SCS and DSCC may be useful in assessing milk quality and protein fractions. They also demonstrate the potential of MAC count as a novel udder health trait.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this study we wanted to investigate the associations between naturally occurring subclinical intramammary infection (IMI) caused by different etiological agents (i.e., Staphylococcus aureus, ...Streptococcus agalactiae, Streptococcus uberis, and Prototheca spp.), in combination with somatic cell count (SCC), on the detailed milk protein profile measured at the individual mammary gland quarter. An initial bacteriological screening (time 0; T0) conducted on individual composite milk from 450 Holstein cows reared in 3 herds, was performed to identify cows with subclinical IMI. We identified 78 infected animals which were followed up at the quarter level at 2 different sampling times: T1 and T2, 2 and 6 wk after T0, respectively. A total of 529 quarter samples belonging to the previously selected animals were collected at the 2 sampling points and analyzed with a reversed phase HPLC (RP-HPLC) validated method. Specifically, we identified and quantified 4 caseins (CN), namely αS1-CN, αS2-CN, κ-CN, and β-CN, and 3 whey protein fractions, namely β-lactoglobulin, α-lactalbumin, and lactoferrin (LF), which were later expressed both quantitatively (g/L) and qualitatively (as a percentage of the total milk nitrogen content, % N). Data were analyzed with a hierarchical linear mixed model with the following fixed effects: days in milk (DIM), parity, herd, SCC, bacteriological status (BACT), and the SCC × BACT interaction. The random effect of individual cow, nested within herd, DIM and parity was used as the error term for the latter effects. Both IMI (i.e., BACT) and SCC significantly reduced the proportion of β-CN and αS1-CN, ascribed to the increased activity of both milk endogenous and microbial proteases. Less evident alterations were found for whey proteins, except for LF, which being a glycoprotein with direct and undirect antimicrobial activity, increased both with IMI and SCC, suggesting its involvement in the modulation of both the innate and adaptive immune response. Finally, increasing SCC in the positive samples was associated with a more marked reduction of total caseins at T1, and αS1-CN at T2, suggesting a synergic effect of infection and inflammation, more evident at high SCC. In conclusion, our work helps clarify the behavior of protein fractions at quarter level in animals having subclinical IMI. The inflammation status driven by the increase in SCC, rather the infection, was associated with the most significant changes, suggesting that the activity of endogenous proteolytic enzymes related to the onset of inflammation might have a pivotal role in directing the alteration of the milk protein profile.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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