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 1.1mg/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.

Milk urea nitrogen (MUN) and blood urea nitrogen are correlated with nitrogen balance and nitrogen excretion; however, there is also a genetic component to MUN concentrations that could be associated with differences in urea transport. It was hypothesized that a portion of the variation in MUN concentrations among cows is caused by variation in gastrointestinal and kidney urea clearance rates. Eight lactating cows with varying MUN concentrations while fed a common diet were infused with [ 15 N 15 N]urea to determine urea N entry rate (UER), gastrointestinal entry rate, returned to ornithine cycle, urea N used for anabolism, urea N excretion in feces and urine. Urea clearance rates by the kidneys and gastrointestinal tract were calculated from isotopic enrichment of urea excretion in urine and gut entry rate, respectively, and plasma urea N concentrations (PUN). Over the course of the experiment, animals weighed an average of 506 ± 62 kg and produced 26.3 ± 4.39 kg of milk/d, with MUN concentrations ranging from 11.6 to 17.3 mg/dL (average of 14.9 ± 2.1 mg/dL). Plasma urea N was positively correlated with UER, urea N excretion in urine, and urea N used for anabolism. Plasma urea N and MUN were negatively correlated with gut clearance rates and ratio of gastrointestinal entry rate to UER. This relationship supports the hypothesis that differences in gut urea transport activity among animals causes variation in PUN and MUN concentrations, and that cows with high PUN and MUN are less efficient at recycling PUN to the gastrointestinal tract and thus may be more susceptible to ruminal N deficiencies when fed low RDP diets. Such biological variation in urea metabolism necessitates an adequate safety margin when setting regulations for maximal MUN levels as an indicator of herd N efficiency.

Sustainable production of adequate quantities of food to support a growing human population is a worldwide goal. Under current feeding conditions in the United States, dairy cattle convert dietary nitrogen to milk nitrogen with 25% efficiency. The remaining 75% is excreted, which contributes to air and water quality problems and reduces economic performance of the industry. Efficiency could be improved to 29% if protein was given to just meet current NRC requirements. Additional improvements may be achievable, but only with improved knowledge of amino acid (AA) requirements. The current metabolizable protein requirement model overestimates true requirements due to lack of knowledge of AA supply and requirements and to intrinsic limitations in system data and assumptions. Existing protein supply models based on passage and degradation rates are biased, which undermines predictions of AA supply. The use of an equation driven solely by protein solubility of each ingredient in the diet with no consideration of the effects of passage rate yielded unbiased predictions with significant improvements in precision. However, this still leaves a problem in predicting the AA composition of the ruminally undegraded protein (RUP). Current models generally assume that RUP AA composition equals the parent ingredient composition, but assessments of RUP AA composition indicate that this is false. Thus, bias is being introduced into predictions of the absorbed AA supply, which hampers derivation of estimates of AA digestion and absorption from the small intestine. Emerging isotope-based methods hold promise in allowing assessment of AA availability from individual ingredients in vivo, which will allow construction of a database of true ingredient AA bioavailabilities. These efforts will eventually allow development of more robust predictions of AA supply. On the AA requirement side, numerous data indicate that the efficiency of metabolizable protein use for lactation is variable and maximally 45%, whereas most models assume an efficiency of 65% or greater. The efficiencies of individual AA are centered on the protein efficiency value with those lower in efficiency, likely being provided in large excess. A better representation of the use efficiency of individual AA would allow improvements in overall animal N efficiency. Variable efficiency is driven by regulatory mechanisms that control protein synthesis in response to the supply of energy and individual AA and circulating concentrations of hormones and these drivers act independently and additively. Under this theory, protein synthesis can respond to nutrients other than the one identified as most limiting. Reflecting this regulation in our requirement models will allow better prediction of AA efficiency and enable construction of diets that minimize excess of individual AA by optimizing the energy and hormonal signals to improve N efficiency. Models of such an interacting system have been developed and shown to be superior in performance to models based on current paradigms.

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On pages 6721 and 6723, Figures 2 and 3 were incorrectly matched with their captions. The corrected figures are shown below.On page 6721, the second and third sentences should read (corrected text in bold), "Although flux into the GIT was not correlated with MUN (P = 0.88), the GIT clearance rate was highly correlated (Figure 3a; P = 0.02), as were UUE (P = 0.05) and UUA (P = 0.03). The relationship with UUE appears to be a mass-action relationship, as MUN was not related to kidney transport activity (data not shown; P = 0.33). In addition, kidney clearance rate was only weakly related to PUN (P = 0.13) in simple regressions.On page 6723, the first two sentences should read, "The fractional transfer of UER to the GIT (GER/UER), which averaged 70%, tended to be negatively related to MUN (P = 0.06; Figure 3b), and PUN (P = 0.003; Figure 2b). The GIT urea clearance rates were negatively correlated with PUN and the latter positively related with MUN (P = 0.02; Figure 2a).