Trend analysis and prediction of seasonal changes in milk composition from a pasture-based dairy research herd.

The composition of seasonal pasture-produced milk is influenced by stage of lactation, animal genetics, and nutrition, which affects milk nutritional profile and processing characteristics. The objective was to study the effect of lactation stage (early, mid, and late lactation) and diet on milk composition in an Irish spring calving dairy research herd from 2012 to 2020 using principal component and predictive analytics. Crude protein, casein, fat, and solids increased from 2012 to 2020, whereas lactose concentration peaked in 2017, then decreased. Based on seasonal data from 2013 to 2016, forecasting models were successfully created to predict milk composition for 2017 to 2020. The diet of cows in this study is dependent upon grass growth rates across the milk production season, which in turn, are influenced by weather patterns, whereby extreme weather conditions (rainfall and temperature) were correlated with decreasing grass growth and increasing nonprotein nitrogen levels in milk. The study demonstrates a significant change in milk composition since 2012 and highlights the effect that seasonal changes such as weather and grass growth have on milk composition of pasture-based systems. The potential to forecast milk composition at different stages of lactation benefits processers by facilitating the optimization of in-process and supply logistics of dairy products.

Processing characteristics of dairy cow milk are moderately heritable.

Milk processing attributes represent a group of milk quality traits that are important to the dairy industry to inform product portfolio. However, because of the resources required to routinely measure such quality traits, precise genetic parameter estimates from a large population of animals are lacking for these traits. Milk processing characteristics considered in the present study-rennet coagulation time, curd-firming time, curd firmness at 30 and 60 min after rennet addition, heat coagulation time, casein micelle size, and milk pH-were all estimated using mid-infrared spectroscopy prediction equations. Variance components for these traits were estimated using 136,807 test-day records from 5 to 305 d in milk (DIM) from 9,824 cows using random regressions to model the additive genetic and within-lactation permanent environmental variances. Heritability estimates ranged from 0.18 ± 0.01 (26 DIM) to 0.38 ± 0.02 (180 DIM) for rennet coagulation time; from 0.26 ± 0.02 (5 DIM) to 0.57 ± 0.02 (174 DIM) for curd-firming time; from 0.16 ± 0.01 (30 DIM) to 0.56 ± 0.02 (271 DIM) for curd firmness at 30 min; from 0.13 ± 0.01 (30 DIM) to 0.48 ± 0.02 (271 DIM) for curd firmness at 60 min; from 0.08 ± 0.01 (17 DIM) to 0.24 ± 0.01 (180 DIM) for heat coagulation time; from 0.23 ± 0.02 (30 DIM) to 0.43 ± 0.02 (261 DIM) for casein micelle size; and from 0.20 ± 0.01 (30 DIM) to 0.36 ± 0.02 (151 DIM) for milk pH. Within-trait genetic correlations across DIM weakened as the number of days between compared intervals increased but were mostly >0.4 except between the peripheries of the lactation. Eigenvalues and associated eigenfunctions of the additive genetic covariance matrix for all traits revealed that at least the 80% of the genetic variation among animals in lactation profiles was associated with the height of the lactation profile. Curd-firming time and curd firmness at 30 min were weakly to moderately genetically correlated with milk yield (from 0.33 ± 0.05 to 0.59 ± 0.05 for curd-firming time, and from -0.62 ± 0.03 to -0.21 ± 0.06 for curd firmness at 30 min). Milk protein concentration was strongly genetically correlated with curd firmness at 30 min (0.84 ± 0.02 to 0.94 ± 0.01) but only weakly genetically correlated with milk heat coagulation time (-0.27 ± 0.07 to 0.19 ± 0.06). Results from the present study indicate the existence of exploitable genetic variation for milk processing characteristics. Because of possible indirect deterioration in milk processing characteristics due to selection for greater milk yield, emphasis on milk processing characteristics is advised.

Cow and environmental factors associated with protein fractions and free amino acids predicted using mid-infrared spectroscopy in bovine milk.

The objective of the present study was to identify the factors associated with both the protein composition and free amino acid (FAA) composition of bovine milk predicted using mid-infrared spectroscopy. Milk samples were available from 7 research herds and 69 commercial herds. The spectral data from the research herds comprised 94,286 separate morning and evening milk samples; the spectral data from the commercial herds comprised 40,260 milk samples representing a composite sample of both the morning and evening milkings. Mid-infrared spectroscopy prediction models developed in a previous study were applied to all spectra. Factors associated with the predicted protein and FAA composition were quantified using linear mixed models. Factors considered in the model included the fixed effects of calendar month of the test, milking time (i.e., morning, evening, or both combined), parity (1, 2, 3, 4, 5, and ≥6), stage of lactation, the interaction between parity and stage of lactation, breed proportion of the cow (Friesian, Jersey, Norwegian Red, Montbéliarde, and other), and both the general heterosis and recombination coefficients of the cow. Contemporary group as well as both within- and across-lactation permanent environmental effects were included in all models as random effects. Total proteins (i.e., total casein, CN; total whey; and total ß-lactoglobulin) and protein fractions (with the exception of α-lactalbumin) decreased postcalving until 36 to 65 days in milk and increased thereafter. After adjusting the statistical model for differences in crude protein content and milk yield separately, irrespective of stage of lactation, younger animals produced more total proteins (i.e., total CN, total whey, and total ß-lactoglobulin) as well as more total FAA, Glu, and Asp than their older contemporaries. The concentration of all protein fractions (except ß-CN) in milk was greatest in the evening milk, even after adjusting for differences in the crude protein content of the milk. Relative to a purebred Holstein cow, Jersey cows, on average, produced a greater concentration of all CN fractions but less total FAA, Glu, Gly, Asp, and Val in milk. Relative to their respective purebred parental average, first-cross cows produced more total CN and more ß-CN. Results from the present study indicate that many cow-level factors, as well as other factors, are associated with protein composition and FAA composition of bovine milk.

Factors associated with milk processing characteristics predicted by mid-infrared spectroscopy in a large database of dairy cows.

Despite milk processing characteristics being important quality traits, little is known about the factors underlying their variability, due primarily to the resources required to measure these characteristics in a sufficiently large population. Cow milk coagulation properties (rennet coagulation time, curd-firming time, curd firmness 30 and 60 min after rennet addition), heat coagulation time, casein micelle size, and pH were generated from available mid-infrared spectroscopy prediction models. The prediction models were applied to 136,807 spectra collected from 9,824 Irish dairy cows from research and commercial herds. Sources of variation were investigated using linear mixed models that included the fixed effects of calendar month of test; milking time in the day; linear regressions on the proportion of Friesian, Jersey, Montbéliarde, Norwegian Red, and "other" breeds in the cow; coefficients of heterosis and of recombination loss; parity; stage of lactation; and the 2-way interaction parity × stage of lactation. Within- and across-parity cow effects, contemporary group, and a residual term were also included as random effects in the model. Supplementary analyses considered the inclusion of either test-day milk yield or milk protein concentration as fixed-effects covariates in the multiple regression models. Milk coagulation properties were most favorable (i.e., short rennet coagulation time and strong curd firmness) for cheese manufacturing in early lactation, concurrent with the lowest values of both pH and casein micelle size. Milk coagulation properties and pH deteriorated in mid lactation but improved toward the end of lactation. In direct contrast, heat coagulation time was more favorable in mid lactation and less suitable (i.e., shorter) for high temperature treatments in both early and late lactation. Relative to multiparous cows, primiparous cows, on average, yielded milk with shorter rennet coagulation time and longer heat coagulation time. Milk from the evening milking session had shorter rennet coagulation time and greater curd firmness, as well as lower heat coagulation time and lower pH compared with milk from the morning session. Jersey cows, on average, yielded milk more suitable for cheese production rather than for milk powder production. When protein concentration was included in the model, the improvement of milk coagulation properties toward the end of lactation was no longer apparent. Results from the present study may aid in decision-making for milk manufacturing, especially in countries characterized by a seasonal supply of fresh milk.

Effectiveness of mid-infrared spectroscopy to predict the color of bovine milk and the relationship between milk color and traditional milk quality traits.

The color of milk affects the subsequent color features of the resulting dairy products; milk color is also related to milk fat concentration. The objective of the present study was to quantify the ability of mid-infrared spectroscopy (MIRS) to predict color-related traits in milk samples and to estimate the correlations between these color-related characteristics and traditional milk quality traits. Mid-infrared spectral data were available on 601 milk samples from 529 cows, all of which had corresponding gold standard milk color measures determined using a Chroma Meter (Konica Minolta Sensing Europe, Nieuwegein, the Netherlands); milk color was expressed using the CIELAB uniform color space. Separate prediction equations were developed for each of the 3 color parameters (L*=lightness, a*=greenness, b*=yellowness) using partial least squares regression. Accuracy of prediction was determined using both cross validation on a calibration data set (n=422 to 457 samples) and external validation on a data set of 144 to 152 samples. Moderate accuracy of prediction was achieved for the b* index (coefficient of correlation for external validation=0.72), although poor predictive ability was obtained for both a* and L* indices (coefficient of correlation for external validation of 0.30 and 0.55, respectively). The linear regression coefficient of the gold standard values on the respective MIRS-predicted values of a*, L*, and b* was 0.81, 0.88, and 0.96, respectively; only the regression coefficient on L* was different from 1. The mean bias of prediction (i.e., the average difference between the MIRS-predicted values and gold standard values in external validation) was not different from zero for any of 3 parameters evaluated. A moderate correlation (0.56) existed between the MIRS-predicted L* and b* indices, both of which were weakly correlated with the a* index. Milk fat, protein, and casein were moderately correlated with both the gold standard and MIRS-predicted values for b*. Results from the present study indicate that MIRS data provides an efficient, low-cost screening method to determine the b* color of milk at a population level.

Prediction of individual milk proteins including free amino acids in bovine milk using mid-infrared spectroscopy and their correlations with milk processing characteristics.

The aim of this study was to evaluate the effectiveness of mid-infrared spectroscopy in predicting milk protein and free amino acid (FAA) composition in bovine milk. Milk samples were collected from 7 Irish research herds and represented cows from a range of breeds, parities, and stages of lactation. Mid-infrared spectral data in the range of 900 to 5,000 cm(-1) were available for 730 milk samples; gold standard methods were used to quantify individual protein fractions and FAA of these samples with a view to predicting these gold standard protein fractions and FAA levels with available mid-infrared spectroscopy data. Separate prediction equations were developed for each trait using partial least squares regression; accuracy of prediction was assessed using both cross validation on a calibration data set (n=400 to 591 samples) and external validation on an independent data set (n=143 to 294 samples). The accuracy of prediction in external validation was the same irrespective of whether undertaken on the entire external validation data set or just within the Holstein-Friesian breed. The strongest coefficient of correlation obtained for protein fractions in external validation was 0.74, 0.69, and 0.67 for total casein, total ß-lactoglobulin, and ß-casein, respectively. Total proteins (i.e., total casein, total whey, and total lactoglobulin) were predicted with greater accuracy then their respective component traits; prediction accuracy using the infrared spectrum was superior to prediction using just milk protein concentration. Weak to moderate prediction accuracies were observed for FAA. The greatest coefficient of correlation in both cross validation and external validation was for Gly (0.75), indicating a moderate accuracy of prediction. Overall, the FAA prediction models overpredicted the gold standard values. Near-unity correlations existed between total casein and ß-casein irrespective of whether the traits were based on the gold standard (0.92) or mid-infrared spectroscopy predictions (0.95). Weaker correlations among FAA were observed than the correlations among the protein fractions. Pearson correlations between gold standard protein fractions and the milk processing characteristics of rennet coagulation time, curd firming time, curd firmness, heat coagulating time, pH, and casein micelle size were weak to moderate and ranged from -0.48 (protein and pH) to 0.50 (total casein and a30). Pearson correlations between gold standard FAA and these milk processing characteristics were also weak to moderate and ranged from -0.60 (Val and pH) to 0.49 (Val and K20). Results from this study indicate that mid-infrared spectroscopy has the potential to predict protein fractions and some FAA in milk at a population level.

Prediction of bovine milk technological traits from mid-infrared spectroscopy analysis in dairy cows.

Rapid, cost-effective monitoring of milk technological traits is a significant challenge for dairy industries specialized in cheese manufacturing. The objective of the present study was to investigate the ability of mid-infrared spectroscopy to predict rennet coagulation time, curd-firming time, curd firmness at 30 and 60min after rennet addition, heat coagulation time, casein micelle size, and pH in cow milk samples, and to quantify associations between these milk technological traits and conventional milk quality traits. Samples (n=713) were collected from 605 cows from multiple herds; the samples represented multiple breeds, stages of lactation, parities, and milking times. Reference analyses were undertaken in accordance with standardized methods, and mid-infrared spectra in the range of 900 to 5,000cm(-1) were available for all samples. Prediction models were developed using partial least squares regression, and prediction accuracy was based on both cross and external validation. The proportion of variance explained by the prediction models in external validation was greatest for pH (71%), followed by rennet coagulation time (55%) and milk heat coagulation time (46%). Models to predict curd firmness 60min from rennet addition and casein micelle size, however, were poor, explaining only 25 and 13%, respectively, of the total variance in each trait within external validation. On average, all prediction models tended to be unbiased. The linear regression coefficient of the reference value on the predicted value varied from 0.17 (casein micelle size regression model) to 0.83 (pH regression model) but all differed from 1. The ratio performance deviation of 1.07 (casein micelle size prediction model) to 1.79 (pH prediction model) for all prediction models in the external validation was <2, suggesting that none of the prediction models could be used for analytical purposes. With the exception of casein micelle size and curd firmness at 60min after rennet addition, the developed prediction models may be useful as a screening method, because the concordance correlation coefficient ranged from 0.63 (heat coagulation time prediction model) to 0.84 (pH prediction model) in the external validation.

The effect of dietary crude protein and phosphorus on grass-fed dairy cow production, nutrient status, and milk heat stability.

Dietary crude protein (CP) and phosphorus (P) have the potential to alter dairy cow production, nutrient status, and milk heat stability, specifically in early lactation. This study examined the effect of supplementary concentrates with different CP and P concentrations on blood N and P status and on milk yield, composition, and heat stability. The concentrates [4kg of dry matter (DM) concentrate per cow daily] were fed to grazing dairy cows (13kg DM grass) during early lactation. Forty-eight spring-calving dairy cows were allocated to 4 treatments: high CP, high P (HPrHP; 302g/kg DM CP, 6.8g/kg DM P), medium CP, high P (MPrHP; 202g/kg DM CP, 4.7g/kg DM P), low CP, high P (LPrHP; 101g/kg DM CP, 5.1g/kg DM P), and low CP, low P (LPrLP; 101g/kg DM CP, 0.058g/kg DM P), for 8wk. Levels of N excretion were significantly higher in animals fed the HPrHP and MPrHP concentrates; P excretion was significantly lower in animals fed the LPrLP concentrate. Reducing the level of P in the diet (LPrLP concentrate) resulted in a significantly lower blood P concentration, whereas milk yield and composition (fat and protein) were not affected by either CP or P in the diet. The effect of the interaction between treatment and time on milk urea N was significant, reflecting the positive correlation between dietary CP and milk nonprotein N. Increasing supplementary CP and P (HPrHP) in the diet resulted in significantly lower milk heat stability at pH 6.8. The findings show that increasing dietary CP caused a decrease in milk heat stability, which reduced the suitability of milk for processing. The study also found that increasing dietary CP increased milk urea N and milk nonprotein N. Increasing dietary P increased fecal P excretion. These are important considerations for milk processors and producers for control of milk processing and environmental parameters.

The effect of fat content on the microbiology and proteolysis in cheddar cheese during ripening dairy foods.

We investigated the effect of incremental reduction in fat content, in the range 33 to 6% (wt/wt), on changes in the microbiology and proteolysis of Cheddar cheese, over a 225-d ripening period at 7 degrees C. A reduction of fat content resulted in significant increases in contents of moisture and protein and a decrease in the concentration of moisture in nonfat substance. Reduced fat had little effect on the age-related changes in the population of starter cells. The populations of nonstarter lactic acid bacteria decreased with fat content, and counts in the low fat cheese (6% wt/wt) were significantly lower than those in the full fat cheese (33% wt/wt) at ripening times >1 and <180 d. Proteolysis as measured by the percentage of total N soluble at pH 4.6 or in 70% ethanol decreased significantly as the fat content decreased. However, the content of pH 4.6 soluble N per 100 g of cheese was not significantly influenced by fat content. At ripening times >60 d, the content of 70% ethanol soluble N per 100 g of full fat (33% wt/wt) cheese was significantly lower than that in either the half fat (17% wt/wt) or low fat (6% wt/wt) cheeses. The concentration of AA N, as a percentage of total N, was not significantly affected by fat content. However, when expressed as a percentage of total cheese, amino acid N increased significantly with decreasing fat content. Analysis of pH 4.6 soluble N extracts by reverse phase- and gel permeation HPLC revealed that fat content affected the pattern of proteolysis, as reflected by the differences in peptide profiles.