Nitrogen isotopic discrimination as a biomarker of between-cow variation in the efficiency of nitrogen utilization for milk production: A meta-analysis

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 = δ¹⁵N_animal - δ¹⁵N_diet). 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.

A statistical evaluation of associations between reproductive performance and milk composition and animal factors on grazing dairy cows in two New Zealand dairy farms

AIMS

To determine if milk composition, milk yield, live weight, live weight change, breed and heterosis are associated with reproductive performance in dairy cows from two dairy farms under New Zealand grazing conditions.

METHODS

Milk composition was determined in herd tests from 205 Holstein-Friesian (F), 77 Jersey (J) and 351 F × J crossbred cows from two Massey University dairy herds in the 2016 and 2017 production seasons. Mating occurred from October to December in each production season. The start of breeding to first service (SBFS), start of breeding to conception (SBCO), submission rate at 21 days (SR21), pregnancy rate at 21 (PR21) and 42 days (PR42) were calculated for each cow. These traits were analysed using mixed linear models that included fixed effects for herd, production season, regression coefficients of deviation from median calving date, lactation number, proportion of F, F × J heterosis, energy-corrected milk yield (ECMY), percentages of fat, protein and lactose, milk urea nitrogen (MUN), live weight and change in live weight, with random effects for cow and residual error. The variables with binomial distribution were analysed using logistic regression.

RESULTS

Deviation from the herd's median calving date had a significant effect (p < 0.05) on all reproductive traits. Proportion of F was significant (p = 0.022) on PR21, and F × J heterosis effects were significant on PR21 (p = 0.049) and PR42 (p = 0.046). F cows had 17.8% higher PR21 than J cows, and F × J cows had higher reproductive performance than the mean of the two purebreeds. ECMY was negatively associated with SBFS (p = 0.001) and SBCO (p = 0.001) and positively associated with PR21 (p = 0.002) and PR42 (p = 0.001). Protein percentage was positively associated (p < 0.05) with PR21 and PR42, whereas lactose percentage was negatively associated (p < 0.05) with PR21 and PR42. Cows gaining live weight were more likely to become pregnant within 21 days of the start of breeding (p = 0.020). Milk urea nitrogen was negatively associated (p = 0.042) with SR21.

CONCLUSIONS AND CLINICAL RELEVANCE

This study confirms that breed, heterosis, ECMY, protein and lactose percentages, live weight change and calving date are associated with the reproductive performance of grazing cows. Results from this study contrast with the historical antagonism between milk production and reproductive performance in dairy cattle, demonstrating that well managed cows can achieve high levels of production and good reproductive performance. MUN was not associated with reproductive performance traits, except with SR21.