Modified versus producer milk calibration: mid-infrared analyzer performance validation

Our objective was to determine the validation performance of mid-infrared (MIR) milk analyzers, using the traditional fixed-filter approach, when the instruments were calibrated with producer milk calibration samples vs. modified milk calibration samples. Ten MIR analyzers were calibrated using producer milk calibration sample sets, and 9 MIR milk analyzers were calibrated using modified milk sample sets. Three sets of 12 validation milk samples with all-laboratory mean chemistry reference values were tested during a 3-mo period. Calibration of MIR milk analyzers using modified milk increased the accuracy (i.e., better agreement with chemistry) and improved agreement between laboratories on validation milk samples compared with MIR analyzers calibrated with producer milk samples. Calibration of MIR analyzers using modified milk samples reduced overall mean Euclidian distance for all components for all 3 validation sets by at least 24% compared with MIR analyzers calibrated with producer milk sets. Calibration with modified milk sets reduced the average Euclidian distance from all-laboratory mean reference chemistry on validation samples by 40, 25, 36, and 27%, respectively for fat, anhydrous lactose, true protein, and total solids. Between-laboratory agreement was evaluated using reproducibility standard deviation (s_R). The number of single Grubbs statistical outliers in the validation data was much higher (53 vs. 7) for the instruments calibrated with producer milk than for instruments calibrated with modified milk sets. The s_R for instruments calibrated with producer milks (with statistical outliers removed) was similar to data collected in recent proficiency studies, whereas the s_R for instruments calibrated with modified milks was lower than those calibrated with producer milks by 46, 52, 61, and 55%, respectively for fat, anhydrous lactose, true protein, and total solids.     

Genetic parameters for milk urea nitrogen in relation to milk production traits

The aim of this study was to estimate genetic parameters for test-day milk urea nitrogen (MUN) and its relationships with milk production traits. Three test-day morning milk samples were collected from 1,953 Holstein-Friesian heifers located on 398 commercial herds in the Netherlands. Each sample was analyzed for somatic cell count, net energy concentration, MUN, and the percentage of fat, protein, and lactose. Genetic parameters were estimated using an animal model with covariates for days in milk and age at first calving, fixed effects for season of calving and effect of test or proven bull, and random effects for herd-test day, animal, permanent environment, and error. Coefficient of variation for MUN was 33%. Estimated heritability for MUN was 0.14. Phenotypic correlation of MUN with each of the milk production traits was low. The genetic correlation was close to zero for MUN and lactose percentage (−0.09); was moderately positive for MUN and net energy concentration of milk (0.19), fat yield (0.41), protein yield (0.38), lactose yield (0.22), and milk yield (0.24), and percentage of fat (0.18), and percentage of protein (0.27); and was high for MUN and somatic cell score (0.85). Herd-test day explained 58% of the variation in MUN, which suggests that management adjustments at herd-level can reduce MUN. This study shows that it is possible to influence MUN by herd practice and by genetic selection.     

Genetic analysis of milk urea nitrogen and lactose and their relationships with other production traits in Canadian Holstein cattle

The objective of this research was to estimate heritabilities of milk urea nitrogen (MUN) and lactose in the first 3 parities and their genetic relationships with milk, fat, protein, and SCS in Canadian Holsteins. Data were a random sample of complete herds (60,645 test day records of 5,022 cows from 91 herds) extracted from the edited data set, which included 892,039 test-day records of 144,622 Holstein cows from 4,570 herds. A test-day animal model with multiple-trait random regression and the Gibbs sampling method were used for parameter estimation. Regression curves were modeled using Legendre polynomials of order 4. A total of 6 separate 4-trait analyses, which included MUN, lactose, or both (yield or percentage) with different combinations of production traits (milk, fat and protein yield, fat and protein percentages, and somatic cell score) were performed. Average daily heritabilities were moderately high for MUN (from 0.384 to 0.414), lactose kilograms (from 0.466 to 0.539), and lactose percentage (from 0.478 to 0.508). Lactose yield was highly correlated with milk yield (0.979). Lactose percentage and MUN were not genetically correlated with milk yield. However, lactose percentage was significantly correlated with somatic cell score (−0.202). The MUN was correlated with fat (0.425) and protein percentages (0.20). Genetic correlations among parities were high for MUN, lactose percentage, and yield. Estimated breeding values (EBV) of bulls for MUN were correlated with fat percentage EBV (0.287) and EBV of lactose percentage were correlated with lactation persistency EBV (0.329). Correlations between lactose percentage and MUN with fertility traits were close to zero, thus diminishing the potential of using those traits as possible indicators of fertility.     

Performance evaluation of an enzymatic spectrophotometric method for milk urea nitrogen

Our objective was to determine the within and between laboratory performance of an enzymatic spectrophotometric method for milk urea nitrogen (MUN) determination. This method first uses urease to hydrolyze urea into ammonia and carbon dioxide. Next, ammonia (as ammonium ions) reacts with 2-oxoglutarate, in the presence of reduced nicotinamide-adenine dinucleotide phosphate (NADPH) and the enzyme glutamate dehydrogenase (GlDH), to form l-glutamic acid, water, and NADP⁺. The change in light absorption at 340 nm due to the conversion of NADPH to NADP+ is stoichiometrically a function of the MUN content of a milk sample. The relative within (RSD_r) and between (RSD_R) laboratory method performance values for the MUN enzymatic spectrophotometric method were 0.57% and 0.85%, respectively, when testing individual farm milks. The spectrophotometric MUN method demonstrated better within and between laboratory performance than the International Dairy Federation differential pH MUN method with a much lower RSD_r (0.57 vs. 1.40%) and RSD_R (0.85 vs. 4.64%). The spectrophotometric MUN method also had similar method performance statistics as other AOAC International official validated chemical methods for primary milk component determinations, with the average of all RSD_r and RSD_R values being <1%. An official collaborative study of the enzymatic spectrophotometric MUN method is needed to achieve International Dairy Federation, AOAC International, and International Organization for Standardization official method status.     

Lipolysis and proteolysis of modified and producer milks used for calibration of mid-infrared milk analyzers

Our objective was to determine if lipolysis or proteolysis of calibration sets during shelf life influenced the mid-infrared (MIR) readings or calibration slopes and intercepts. The lipolytic and proteolytic deterioration was measured for 3 modified milk and 3 producer milk calibration sets during storage at 4°C. Modified and producer milk sets were used separately to calibrate an optical filter and virtual filter MIR analyzer. The uncorrected readings and slopes and intercepts of the calibration linear regressions for fat B, fat A, protein, and lactose were determined over 28 d for modified milks and 15 d for producer milks. It was expected that increases in free fatty acid content and decreases in the casein as a percentage of true protein of the calibration milks would have an effect on the MIR uncorrected readings, calibration slopes and intercepts, and MIR predicted readings. However, the influence of lipolysis and proteolysis on uncorrected readings was either not significant, or significant but very small. Likewise, the amount of variation accounted for by day of storage at 4°C of a calibration set on the calibration slopes and intercepts was also very small. Most of the variation in uncorrected readings and calibration slopes and intercepts were due to differences between the optical filter and virtual filter analyzers and differences between the pasteurized modified milk and raw producer milk calibration sets, not due to lipolysis or proteolysis. The combined impact of lipolysis and proteolysis on MIR predicted values was <0.01% in most cases.     

Comparison of analytical methods and the influence of milk components on milk urea nitrogen recovery

The objectives of this study were to compare analytical instruments used in independent laboratories to measure milk urea nitrogen (MUN) and determine whether any components in milk affect the recovery of MUN. Milk samples were collected from 100 Holstein cows fed one ration in a commercial dairy herd with a rolling herd average of 9500 kg. Half of each sample was spiked with 4 mg/dL of urea N, while the other half was not, to determine recovery. Both milk samples (spiked and not spiked) were sent to 14 independent laboratories involved in the MUN Quality Control Program through National Dairy Herd Improvement Association and analyzed for MUN, fat, protein, lactose, somatic cell count (SCC), and total solids. The laboratories analyzed MUN using CL-10 (n = 3), Skalar (n = 2), Bentley (n = 3), Foss 4000 (n = 3) or Foss 6000 (n = 3) systems. When recovery of MUN was evaluated among the 5 analytical methods, the mean recoveries for the Bentley, Foss 6000, and Skalar systems were 92.1 (SE = 2.76%), 95.4 (SE = 10.1%), and 95.1% (SE = 7.61%), respectively, and did not differ from each other. However, MUN recovery was 85.0% (SE = 2.8%) for the CL-10 system and 47.1% (SE = 9.9%) for the Foss 4000 system, both of which differed from the other 3 systems. Recoveries from Foss 4000, Foss 6000, and Skalar varied among laboratories using the same instrument. As initial MUN concentration increased, recovery decreased using the Bentley and CL-10 systems. Increasing milk fat resulted in a decrease in recovery using the Foss 6000 system. For 4 of the 5 methods, recovery of MUN was not associated with specific milk components. Recovery of MUN was inconsistent for laboratories using the Foss 4000 and the Foss 6000 method and using these systems may result in an overestimation or underestimation of MUN.     

Calibration of infrared milk analyzers: modified milk versus producer milk

Mid-infrared (MIR) milk analyzers are traditionally calibrated using sets of preserved raw individual producer milk samples. The goal of this study was to determine if the use of sets of preserved pasteurized modified milks improved calibration performance of MIR milk analyzers compared with calibration sets of producer milks. The preserved pasteurized modified milk sets exhibited more consistent day-to-day and set-to-set calibration slope and intercept values for all components compared with the preserved raw producer milk calibration sets. Pasteurized modified milk calibration samples achieved smaller confidence interval (CI) around the regression line (i.e., calibration uncertainty). Use of modified milk calibration sets with a larger component range, more even distribution of component concentrations within the ranges, and the lower correlation of fat and protein concentrations than producer milk calibration sets produced a smaller 95% CI for the regression line due to the elimination of moderate and high leverage samples. The CI for the producer calibration sets were about 2 to 12 times greater than the CI for the modified milk calibration sets, depending on the component. Modified milk calibration samples have the potential to produce MIR milk analyzer calibrations that will perform better in validation checks than producer milk-based calibrations by reducing the mean difference and standard deviation of the difference between instrument values and reference chemistry.     

Short communication: Analytical method and amount of preservative added to milk samples may alter milk urea nitrogen measurements

Milk urea N (MUN) is used by dairy nutritionists and producers to monitor dietary protein intake and is indicative of N utilization in lactating dairy cows. Two experiments were conducted to explore discrepancies in MUN results provided by 3 milk processing laboratories using different methods. An additional experiment was conducted to evaluate the effect of 2-bromo-2-nitropropane-1, 3-diol (bronopol) on MUN analysis. In experiment 1, 10 replicates of bulk tank milk samples, collected from the Pennsylvania State University's Dairy Center over 5 consecutive days, were sent to 3 milk processing laboratories in Pennsylvania. Average MUN differed between laboratory A (14.9 ± 0.40 mg/dL; analyzed on MilkoScan 4000; Foss, Hillerød, Denmark), laboratory B (6.5 ± 0.17 mg/dL; MilkoScan FT + 6000), and laboratory C (7.4 ± 0.36 mg/dL; MilkoScan 6000). In experiment 2, milk samples were spiked with urea at 0 (7.3 to 15.0 mg/dL, depending on the laboratory analyzing the samples), 17.2, 34.2, and 51.5 mg/dL of milk. Two 35-mL samples from each urea level were sent to the 3 laboratories used in experiment 1. Average analyzed MUN was greater than predicted (calculated for each laboratory based on the control; 0 mg of added urea): for laboratory A (23.2 vs. 21.0 mg/dL), laboratory B (18.0 vs. 13.3 mg/dL), and laboratory C (20.6 vs. 15.2 mg/dL). In experiment 3, replicated milk samples were preserved with 0 to 1.35 mg of bronopol/mL of milk and submitted to one milk processing laboratory that analyzed MUN using 2 different methods. Milk samples with increasing amounts of bronopol ranged in MUN concentration from 7.7 to 11.9 mg/dL and from 9.0 to 9.3 mg/dL when analyzed on MilkoScan 4000 or CL 10 (EuroChem, Moscow, Russia), respectively. In conclusion, measured MUN concentrations varied due to analytical procedure used by milk processing laboratories and were affected by the amount of bronopol used to preserve milk sample, when milk was analyzed using a mid-infrared analyzer. Thus, it is important to maintain consistency in milk sample preservation and analysis to ensure precision of MUN results.     

Analysis of milk urea nitrogen and lactose and their effect on longevity in Canadian dairy cattle

The aim of this study was to assess the phenotypic level of lactose and milk urea nitrogen concentration (MUN) and the association of these traits with functional survival of Canadian dairy cattle using a Weibull proportional hazards model. A total of 1,568,952 test-day records from 283,958 multiparous Holstein cows from 4,758 herds, and 79,036 test-day records from 26,784 multiparous Ayrshire cows from 384 herds, calving from 2001 to 2004, were used for the phenotypic analysis. The overall average lactose percentage and MUN for Ayrshires were 4.49% and 12.20 mg/dL, respectively. The corresponding figures for Holsteins were 4.58% and 11.11 mg/dL. Concentration of MUN increased with parity number, whereas lactose percentage decreased in later parities. Data for survival analysis consisted of 39,536 first-lactation cows from 1,619 herds from 2,755 sires for Holsteins and 2,093 cows in 228 herds from 157 sires for Ayrshires. Test-day lactose percentage and MUN were averaged within first lactation. Average lactose percentage and MUN were grouped into 5 classes (low, medium-low, medium, medium-high, and high) based on mean and standard deviation values. The statistical model included the effects of stage of lactation, season of production, the annual change in herd size, type of milk-recording supervision, age at first calving, effects of milk, fat, and protein yields calculated as within herd-year-parity deviations, herd-year-season of calving, lactose percentage and MUN classes, and sire. The relative culling rate was calculated for animals in each class after accounting for the remaining effects included in the model. Results showed that there was a statistically significant association between lactose percentage and MUN in first lactation with functional survival in both breeds. Ayrshire cows with high and low concentration of MUN tended to be culled at a higher than average rate. Instead, Holstein cows had a linear association, with decreasing relative risk of culling with increasing levels of MUN concentration. The relationship between lactose percentage and survival was similar across breeds, with higher risk of culling at low level of lactose, and lower risk of culling at high level of lactose percentage.     

Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows

Feed intake is one of the most important components of feed efficiency in dairy systems. However, it is a difficult trait to measure in commercial operations for individual cows. Milk spectrum from mid-infrared spectroscopy has been previously used to predict milk traits, and could be an alternative to predict dry matter intake (DMI). The objectives of this study were (1) to evaluate if milk spectra can improve DMI predictions based only on cow variables; (2) to compare artificial neural network (ANN) and partial least squares (PLS) predictions; and (3) to evaluate if wavelength (WL) selection through Bayesian network (BN) improves prediction quality. Milk samples (n = 1,279) from 308 mid-lactation dairy cows [127 ± 27 d in milk (DIM)] were collected between 2014 and 2016. For each milk spectra time point, DMI (kg/d), body weight (BW, kg), milk yield (MY, kg/d), fat (%), protein (%), lactose (%), and actual DIM were recorded. The DMI was predicted with ANN and PLS using different combinations of explanatory variables. Such combinations, called covariate sets, were as follows: set 1 (MY, BW^0.75, DIM, and 361 WL); set 2 [MY, BW^0.75, DIM, and 33 WL (WL selected by BN)]; set 3 (MY, BW^0.75, DIM, and fat, protein, and lactose concentrations); set 4 (MY, BW^0.75, DIM, 33 WL, fat, protein, and lactose); set 5 (MY, BW^0.75, DIM, 33 WL, and visit duration in the feed bunk); set 6 (MY, DIM, and 33 WL); set 7 (MY, BW^0.75, and DIM); set-WL (included 361 WL); and set-BN (included just 33 selected WL). All models (i.e., each combination of covariate set and fitting approach, ANN or PLS) were validated with an external data set. The use of ANN improved the performance of models 2, 5, 6, and BN. The use of BN combined with ANN yielded the highest accuracy and precision. The addition of individual WL compared with milk components (set 2 vs. set 3) did not improve prediction quality when using PLS. However, when ANN was employed, the model prediction with the inclusion of 33 WL was improved over the model containing only milk components (set 2 vs. set 3; concordance correlation coefficient = 0.80 vs. 0.72; coefficient of determination = 0.67 vs. 0.53; root mean square error of prediction 2.36 vs. 2.81 kg/d). The use of ANN and the inclusion of a behavior parameter, set 5, resulted in the best predictions compared with all other models (coefficient of determination = 0.70, concordance correlation coefficient = 0.83, root mean square error of prediction = 2.15 kg/d). The addition of milk spectra information to models containing cow variables improved the accuracy and precision of DMI predictions in lactating dairy cows when ANN was used. The use of BN to select more informative WL improved the model prediction when combined with cow variables, with further improvement when combined with ANN.     

The analysis of milk components and pathogenic bacteria isolated from bovine raw milk in Korea

Bovine mastitis can be diagnosed by abnormalities in milk components and somatic cell count (SCC), as well as by clinical signs. We examined raw milk in Korea by analyzing SCC, milk urea nitrogen (MUN), and the percentages of milk components (milk fat, protein, and lactose). The associations between SCC or MUN and other milk components were investigated, as well as the relationships between the bacterial species isolated from milk. Somatic cell counts, MUN, and the percentages of milk fat, protein, and lactose were analyzed in 30,019 raw milk samples collected from 2003 to 2006. The regression coefficients of natural logarithmic-transformed SCC (SCCt) on milk fat (−0.0149), lactose (−0.8910), and MUN (−0.0096), and those of MUN on milk fat (−0.3125), protein (−0.8012), and SCCt (−0.0671) were negative, whereas the regression coefficient of SCCt on protein was positive (0.3023). When the data were categorized by the presence or absence of bacterial infection in raw milk, SCCt was negatively associated with milk fat (−0.0172), protein (−0.2693), and lactose (−0.4108). The SCCt values were significantly affected by bacterial species. In particular, 104 milk samples infected with Staphylococcus aureus had the highest SCCt (1.67) compared with milk containing other mastitis-causing bacteria: coagulase-negative staphylococci (n = 755, 1.50), coagulase-positive staphylococci (except Staphylococcus aureus; n = 77, 1.59), Streptococcus spp. (Streptococcus dysgalactiae, n = 37; Streptococcus uberis, n = 12, 0.83), Enterococcus spp. (n = 46, 1.04), Escherichia coli (n = 705, 1.56), Pseudomonas spp. (n = 456, 1.59), and yeast (n = 189, 1.52). These results show that high SCC and MUN negatively affect milk components and that a statistical approach associating SCC, MUN, and milk components by bacterial infection can explain the patterns among them. Bacterial species present in raw milk are an important influence on SCC in Korea.     

Variation in fat globule size in bovine milk and its prediction using mid-infrared spectroscopy

The objectives of this study were to investigate the sources of variation in milk fat globule (MFG) size in bovine milk and its prediction using mid-infrared (MIR) spectroscopy. Mean MFG size was measured in 2,076 milk samples from 399 Ayrshire, Brown Swiss, Holstein, and Jersey cows, and expressed as volume moment mean (D[4,3]) and surface moment mean (D[3,2]). The mid-infrared spectra of the samples and milk performance data were also recorded during routine milk recording and testing. The effects of breed, herd nested within breed, days in milk, season, milking period, age at calving, parity, and individual animal on the variation observed in MFG size were investigated. Breed, herd nested within breed, days in milk, season, and milking period significantly affected mean MFG size. Milk fat globule size was the largest at the beginning of lactation and subsequently decreased. Milk samples with the smallest MFG on average came from Holstein cows, and those with the largest were from Jersey and Brown Swiss cows. Partial least squares regression was used to predict MFG size from MIR spectra of samples with a calibration data set containing 2,034 and 2,032 samples for D[4,3] and D[3,2], respectively. Coefficients of determination of cross validation for D[4,3] and D[3,2] prediction models were 0.51 and 0.54, respectively. The associated ratio of performance deviation values were 1.43 and 1.48 for D[4,3] and D[3,2], respectively. With these models, individual mean MFG size could not be accurately predicted, but results may be sufficient to screen samples for having either small or large MFG on average. Significant but low correlations of D[4,3] and D[3,2] with milk fat yield were estimated (0.16 and 0.21, respectively). Significant and moderate Pearson correlation coefficients for fat percent with D[4,3] and D[3,2] were assessed (0.34 and 0.36, respectively). This correlation was greater between milk fat percentage and predicted MFG size than with measured MFG size with coefficients of 0.47 and 0.49 for D[4,3] and D[3,2], respectively. The MIR prediction equations are potentially overusing the correlation between fat and MFG size and exploiting the strong relationship between the MIR spectra and total milk fat. However, the predictions of MFG size are able to determine variation in mean globule size beyond what would be achieved just by looking at the correlation with fat production.     

Sources of variation in milk urea nitrogen in Ohio dairy herds

The purpose of this study was to estimate the amount of variation in milk urea nitrogen (MUN) concentrations attributable to test-day, individual cow, and herd effects and to describe factors associated with MUN measurements in Ohio dairy herds. The data came from 24 Holstein herds, half of which were classified as low producing (LP) [rolling herd average (RHA) milk production < 7,258 kg] and half as high producing (HP) herds (RHA production > 10,433 kg). MUN concentration was measured from cow's monthly test-day milk samples. The data were analyzed using multilevel modeling technique in MLwiN, separately for LP and HP herds. The unadjusted mean MUN was 13.9 mg/dl for the HP herds and 11.3 mg/dl for the LP herds. The variance structure was different between the two groups. Most of the variability was found at test-day level in the LP herds, but at herd level in HP herds. MUN was lowest during the first month of lactation, and also season was associated with MUN in both groups. Test-day milk yield, milk fat percentage, and SCC were associated with MUN in the HP herds. With significant explanatory variables in the model, proportionally more of the variation was explained at herd level and less at test day level in both groups. Lower variability in MUN between test days in the HP herds may indicate more consistent day-to-day feeding and management within a herd. The great variability between test days should be considered when interpreting MUN and samples should be collected at the same time of the day to minimize day-to-day variability.     

On the use of milk composition measures to predict the energy balance of dairy cows

Milk composition varies with energy status and was proposed for measuring energy balance on-farm, but the accuracy of prediction using monthly samples is not high. With automated sampling and inline milk analysis, a much higher measurement frequency is possible, and thus improved accuracy of energy balance determination may be expected. Energy balance was evaluated using data in which milk composition was measured at each milking. Three breeds (Danish Holstein, Danish Red, and Jerseys) of cows (623 lactations from 299 cows) in parities 1, 2, and 3+ were used. Data were smoothed using a rolling local regression. Energy balance (EBal) was calculated from changes in body reserves (body weight and body condition score). The relationship between EBal and milk measures was quantified by partial least squares regression (PLS) using group means data. For each day in lactation, the within-breed and parity mean EBal and mean milk measures were used. Further PLS was done using the individual cow data. The initial PLS models included 25 combinations of milk measures allowing a range of nonlinear effects. These combinations were as follows: days in milk (DIM); DIM raised to the powers 2, 3, and 4; milk yield; fat content; protein content; lactose content; fat yield; protein yield; lactose yield; fat:protein ratio; fat:lactose ratio; protein:lactose ratio; and milk yield:lactose ratio, together with 10 “diff()” variables. These variables are the current minus the previous value of the milk measure in question. Using group means data, a very high proportion (96%) of the variability in EBal was explained by the PLS model. A reduced model with only 6 variables explained 94% of the variation in EBal. This model had a prediction error of 3.82 MJ/d; the 25-variable model had a prediction error of 3.11 MJ/d. When using individual rather than group means data, the PLS prediction error was 17.3 MJ/d. In conclusion, the mean Ebal of different parities of Holstein, Danish Red, and Jersey cows can be predicted throughout lactation using 1 common equation based on DIM, milk yield, milk fat, and milk protein measures.     

A comparison of instruments and laboratories used to measure milk urea nitrogen in bulk-tank milk samples

The objective of this study was to compare the instruments and laboratories that are currently used for analysis of milk urea nitrogen (MUN) for bulk-tank milk samples. Two replicate samples from each bulk tank on 10 different dairy farms were sent to 12 Dairy Herd Improvement Association (DHIA) laboratories throughout the US for MUN analysis. Two laboratories used 2 different methods for MUN analysis for a total of 14 analyses on 20 samples (n = 280). Values of MUN were analyzed using a random effects model with farm, laboratory, and farm x laboratory variance components. Greater than 98% of the variance in measured MUN was attributed to farm-to-farm variance for analysis of MUN by the Bentley, CL 10, Foss 6000, and Skalar instruments. However, for the laboratories using the Foss 4000 system, <60% of the variance in MUN was attributed to farm-to-farm variance. Laboratories using the Bentley, CL 10, Foss 6000, and Skalar instruments provided slightly different results for MUN analysis, but >95% of sample measurements fell within 1.75 mg/ dL of each other. The laboratories using Foss 4000 differed from each other, and 95% of samples fell within 5 mg/dL of the CL 10 measurement. Laboratories using the Foss 4000 instrument did not consistently provide measurements of MUN that were similar to each other or to the measurements of the other instruments.     

Short communication: Fourier-transform mid-infrared spectroscopy to predict coagulation and acidity traits of sheep bulk milk

Sheep milk is mainly transformed into cheese; thus, the dairy industry seeks more rapid and cost-effective methods of analysis to determine milk coagulation and acidity traits. This study aimed to assess the feasibility of Fourier-transform mid-infrared spectroscopy to determine milk coagulation and acidity traits of sheep bulk milk and to classify milk samples according to their renneting capacity. A total of 465 bulk milk samples collected in 140 single-breed flocks of Comisana (84 samples, 24 flocks) and Sarda (381 samples, 116 flocks) breeds located in Central Italy were analyzed for coagulation properties (rennet coagulation time, curd firming time, and curd firmness) and acidity traits (pH and titratable acidity) using standard laboratory procedures. Fourier-transform mid-infrared spectroscopy prediction models for these traits were built using partial least squares regression analysis and were externally validated by randomly dividing the full data set into a calibration set (75%) and a validation set (25%). The discriminant capacity of the rennet coagulation time prediction model was determined using partial least squares discriminant analysis. Prediction models were more accurate for acidity traits than for milk coagulation properties, and the ratio of prediction to deviation ranged from 1.01 (curd firmness) to 2.14 (pH). Moreover, the discriminant analysis led to an overall accuracy of 74 and 66% for the calibration and validation sets, respectively, with greater sensitivity for samples that coagulated between 10 and 20 min and greater specificity to detect early-coagulating (<10 min) and late-coagulating (20–30 min) samples. Results suggest that Fourier-transform mid-infrared spectroscopy has the potential to help the dairy sheep industry identify milk with better coagulation ability for cheese production and thus improve milk transformation efficiency. However, further research is needed before this information can be exploited at the industry level.     

Precalibration evaluation procedures for mid-infrared milk analyzers

The purpose of this paper is to present a detailed account of the precalibration procedures developed and implemented by the USDA Federal Milk Market Administrators (FMMA) for evaluating mid-infrared (MIR) milk analyzers. Mid-infrared analyzers specifically designed for milk testing provide a rapid and cost-effective means for determining milk composition for payment and dairy herd improvement programs. These instruments determine the fat, protein, and lactose content of milk, and enable the calculation of total solids, solids-not-fat, and other solids. All MIR analyzers are secondary testing instruments that require calibration by chemical reference methods. Precalibration is the process of assuring that the instrument is in good working order (mechanically and electrically) and that the readings before calibration are stable and optimized. The main components of precalibration are evaluation of flow system integrity, homogenization efficiency, water repeatability, zero shift, linearity, primary slope, milk repeatability, purging efficiency, and establishment of intercorrection factors. These are described in detail and apply to both filter-based and Fourier transform infrared instruments operating using classical primary and reference wavelengths. Under the USDA FMMA Precalibration Evaluation Program, the precalibration procedures were applied longitudinally over time using a wide variety of instruments and instrument models. Instruments in this program were maintained to pass the criteria for all precalibration procedures. All instruments used similar primary wavelengths to measure fat, protein, and lactose but there were differences in reference wavelength selection. Intercorrection factors were consistent over time within all instruments and similar among groups of instruments using similar primary and reference wavelengths. However, the magnitude and sign of the intercorrection factors were significantly affected by the choice of reference wavelengths.     

The association between milk urea nitrogen and DHI production variables in western commercial dairy herds

A retrospective observational study was conducted using data from Dairy Herd Improvement monthly tests to investigate the association between milk urea nitrogen (MUN) concentration and milk yield, milk protein, milk fat percentage, SCC, and parity for commercial Holstein and Jersey herds in Utah, Idaho, and Montana. Mean MUN for Holstein cows was 15.5 mg/ dl (5.5 mmol/L) MUN and 14.1 mg/dl (5.0 mmol/L) for Jersey cows. Mean MUN, categorized by 30-d increments of days in milk (DIM), paralleled changes in milk values and followed a curvilinear shape. For Holstein cows, concentrations of MUN were different among lactation groups 1, 2, and 3+ for the first 90 DIM for Holsteins. Overall, concentrations of MUN were lower during for the first 30 DIM compared with all other DIM categories for both Holstein and Jersey cows. Multivariate regression models of MUN by milk protein showed that as the milk protein percentage increased, MUN concentration decreased; however, models for Jersey cows showed that MUN did not decrease significantly until above 3.4% milk protein. Milk fat percentage also decreased as MUN increased, but by only 1 mg/dl MUN over the range of 2.2 to 5.8% milk fat. Somatic cell count showed a negative relationship with MUN. Holstein cows with milk protein percentage >3.2% had lower MUN compared with cows having milk protein <3.2% for milk yields from 27.3 to 54.5 kg/d and lower than cows having a milk protein <3.0% for milk yield of 54.5 to 63.6 kg/d. In Jersey cows, MUN concentrations were not different among milk protein percentage categorized by milk yield. This study found that MUN was inversely associated with milk protein percentage and paralleled change in milk yield over time.     

Cow and herd variation in milk urea nitrogen concentrations in lactating dairy cattle

Milk urea nitrogen (MUN) is correlated with N balance, N intake, and dietary N content, and thus is a good indicator of proper feeding management with respect to protein. It is commonly used to monitor feeding programs to achieve environmental goals; however, genetic diversity also exists among cows. It was hypothesized that phenotypic diversity among cows could bias feed management decisions when monitoring tools do not consider genetic diversity associated with MUN. The objective of the work was to evaluate the effect of cow and herd variation on MUN. Data from 2 previously published research trials and a field trial were subjected to multivariate regression analyses using a mixed model. Analyses of the research trial data showed that MUN concentrations could be predicted equally well from diet composition, milk yield, and milk components regardless of whether dry matter intake was included in the regression model. This indicated that cow and herd variation could be accurately estimated from field trial data when feed intake was not known. Milk urea N was correlated with dietary protein and neutral detergent fiber content, milk yield, milk protein content, and days in milk for both data sets. Cow was a highly significant determinant of MUN regardless of the data set used, and herd trended to significance for the field trial data. When all other variables were held constant, a percentage unit change in dietary protein concentration resulted in a 1.1 mg/dL change in MUN. Least squares means estimates of MUN concentrations across herds ranged from a low of 13.6 mg/dL to a high of 17.3 mg/dL. If the observed MUN for the high herd were caused solely by high crude protein feeding, then the herd would have to reduce dietary protein to a concentration of 12.8% of dry matter to achieve a MUN concentration of 12 mg/dL, likely resulting in lost milk production. If the observed phenotypic variation is due to genetic differences among cows, genetic choices could result in herds that exceed target values for MUN when adhering to best management practices, which is consistent with the trend for differences in MUN among herds.     

The use of mid-infrared spectra to map genes affecting milk composition

The aim of this study was to investigate the feasibility of using mid-infrared (MIR) spectroscopy analysis of milk samples to increase the power and precision of genome-wide association studies (GWAS) for milk composition and to better distinguish linked quantitative trait loci (QTL). To achieve this goal, we analyzed phenotypic data of milk composition traits, related MIR spectra, and genotypic data comprising 626,777 SNP on 5,202 Holstein, Jersey, and crossbred cows. We performed a conventional GWAS on protein, lactose, fat, and fatty acid concentrations in milk, a GWAS on individual MIR wavenumbers, and a partial least squares regression (PLS), which is equivalent to a multi-trait GWAS, exploiting MIR data simultaneously to predict SNP genotypes. The PLS detected most of the QTL identified using single-trait GWAS, usually with a higher significance value, as well as previously undetected QTL for milk composition. Each QTL tends to have a different pattern of effects across the MIR spectrum and this explains the increased power. Because SNP tracking different QTL tend to have different patterns of effect, it was possible to distinguish closely linked QTL. Overall, the results of this study suggest that using MIR data through either GWAS or PLS analysis applied to genomic data can provide a powerful tool to distinguish milk composition QTL.