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ABSTRACT: Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

ABSTRACT: Estimating the efficiency of N utilization for milk production (MNE) of individual cows at a large scale is difficult, particularly because of the cost of measuring feed intake. Nitrogen isotopic discrimination (Δ15N) between the animal (milk, plasma, or tissues) and its diet has been proposed as a biomarker of the efficiency of N utilization in a range of production systems and ruminant species. The aim of this study was to assess the ability of Δ15N to predict the between-animal variability in MNE in dairy cows using an extensive database. For this, 20 independent experiments conducted as either changeover (n = 14) or continuous (n = 6) trials were available and comprised an initial data set of 1,300 observations. Between-animal variability was defined as the variation observed among cows sharing the same contemporary group (CG; individuals from the same experimental site, sampling period, and dietary treatment). Milk N efficiency was calculated as the ratio between mean milk N (grams of N in milk per day) and mean N intake (grams of N intake per day) obtained from each sampling period, which lasted 9.0 ± 9.9 d (mean ± SD). Samples of milk (n = 604) or plasma (n = 696) and feeds (74 dietary treatments) were analyzed for natural 15N abundance (δ15N), and then the N isotopic discrimination between the animal and the dietary treatment was calculated (Δ15n = δ15Nanimal − δ15Ndiet). Data were analyzed through mixed-effect regression models considering the experiment, sampling period, and dietary treatment as random effects. In addition, repeatability estimates were calculated for each experiment to test the hypothesis of improved predictions when MNE and Δ15N measurements errors were lower. The considerable protein mobilization in early lactation artificially increased both MNE and Δ15N, leading to a positive rather than negative relationship, and this limited the implementation of this biomarker in early lactating cows. When the experimental errors of Δ15N and MNE decreased in a particular experiment (i.e., higher repeatability values), we observed a greater ability of Δ15N to predict MNE at the individual level. The predominant negative and significant correlation between Δ15N and MNE in mid- and late lactation demonstrated that on average Δ15N reflects MNE variations both across dietary treatments and between animals. The root mean squared prediction error as a percentage of average observed value was 6.8%, indicating that the model only allowed differentiation between 2 cows in terms of MNE within a CG if they differed by at least 0.112 g/g of MNE (95% confidence level), and this could represent a limitation in predicting MNE at the individual level. However, the one-way ANOVA performed to test the ability of Δ15N to differentiate within-CG the top 25% from the lowest 25% individuals in terms of MNE was significant, indicating that it is possible to distinguish extreme animals in terms of MNE from their N isotopic signature, which could be useful to group animals for precision feeding.

The effect of botanical diversity on supply of polyunsaturated fatty acids (PUFA) to ruminants in vitro, and the fatty acid (FA) composition of muscle in lambs was investigated. Six plant species, commonly grown as part of UK herbal ley mixtures (Trifolium pratense, Lotus corniculatus, Achillea millefolium, Centaurea nigra, Plantago lanceolata and Prunella vulgaris), were assessed for FA profile, and in vitro biohydrogenation of constituent PUFA, to estimate intestinal supply of PUFA available for absorption by ruminants. Modelling the in vitro data suggested that L. corniculatus and P. vulgaris had the greatest potential to increase 18:3n-3 supply to ruminants, having the highest amounts escaping in vitro biohydrogenation. Biodiverse pastures were established using the six selected species, under-sown in a perennial ryegrass-based sward. Lambs were grazed (~50 days) on biodiverse or control pastures and the effects on the FA composition of musculus longissimus thoracis (lean and subcutaneous fat) and musculus semimembranosus (lean) were determined. Biodiverse pasture increased 18:2n-6 and 18:3n-3 contents of m. semimembranosus (+14.8 and +7.2 mg/100 g tissue, respectively) and the subcutaneous fat of m. longissimus thoracis (+158 and +166 mg/100 g tissue, respectively) relative to feeding a perennial ryegrass pasture. However, there was no effect on total concentrations of saturated FA in the tissues studied. It was concluded that enhancing biodiversity had a positive impact on muscle FA profile reflected by increased levels of total PUFA.