Post on 28-Feb-2023
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Energy and protein interactions and their effect on nitrogen excretion in dairy cows
E. Kebreab1, A.B. Strathe
1, J. Dijkstra
2, J.A.N. Mills
3, C.K. Reynolds
3, L.A. Crompton
3, T. Yan
4,
and J. France4
1Department of Animal Science, University of California, Davis, Davis CA, USA
2Animal Nutrition Group, Wageningen University, Wageningen, The Netherlands
3Animal Science Research Group, School of Agriculture, Policy and Development, University of
Reading, Whiteknights, PO Box 237, Reading RG6 6AR, UK 4Agri-Food and Biosciences Institute, Hillsborough Co. Down, Northern Ireland BT26 6DR
5Department of Animal and Poultry Science, University of Guelph, Guelph ON, Canada
Abstract
The principal driver of nitrogen (N) losses from the body including excretion and secretion in
milk is N intake. However, other covariates may also play a role in modifying the partitioning of
N. This study tests the hypothesis that N partitioning in dairy cows is affected by energy and
protein interactions. A database containing 470 dairy cow observations was collated from
calorimetry experiments. The data include N and energy parameters of the diet and N utilization
by the animal. Univariate and multivariate meta-analyses that considered both within and
between study effects were conducted to generate prediction equations based on N intake alone
or with an energy component. The univariate models showed that there was a strong positive
linear relationship between N intake and N excretion in faeces, urine and milk. The slopes were
0.28 faeces N, 0.38 urine N and 0.20 milk N. Multivariate model analysis did not improve the fit.
Metabolizable energy intake had a significant positive effect on the amount of milk N in
proportion to faeces and urine N, which is also supported by other studies. Another measure of
energy considered as a covariate to N intake was diet quality or metabolizability (the
concentration of metabolizable energy relative to gross energy of the diet). Diet quality also had
a positive linear relationship with the proportion of milk N relative to N excreted in faeces and
urine. Metabolizability had the largest effect on faeces N due to lower protein digestibility of low
quality diets. Urine N was also affected by diet quality and the magnitude of the effect was
higher than for milk N. This research shows that including a measure of diet quality as a
covariate with N intake in a model of N excretion can enhance our understanding of the effects
of diet composition on N losses from dairy cows. The new prediction equations developed in this
study could be used to monitor N losses from dairy systems.
Introduction
Increasing feed efficiency and reducing environmental pollution, particularly nitrogen (N) has
been the subject of several previous studies. In ruminants, dietary N is partitioned into
proteinaceous products such as milk and meat or excreted in faeces and urine. The amount of N
lost in urine and faeces by the high-producing dairy cow is greater than in any other ruminant or
non-ruminant production system and as a consequence, most studies have been carried out in
dairy cattle (e.g., Castillo et al., 2001a, b). Nitrogen intake has been identified as the principal
driver of N excretion and studies have shown that reducing N intake reduces N in faeces and
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urine. Castillo et al. (2000) and Kebreab et al. (2001) reported a linear relationship between N
intake and N excretion in faeces and N secretion in milk; urine N, however, was exponentially
related to N intake. A limitation of the study was that the authors did not take study effect into
account, and some of the urine N data was calculated by difference and was not directly
measured. A more rigorous statistical analysis is therefore required to establish the relationships
between N intake and excretion.
Another shortcoming of the previous empirical analyses of N utilization was the failure to
investigate the effect of covariates. Reynolds and Firkins (2005) identified that N excretion is
related not only to N intake but to other parameters such as the energy content of the diet. Milk
production, milk N and apparent N utilization have been reported to increase and urinary N
excretion to decrease by supplementing high digestibility, high protein dairy cow diets with
cereal grains such as barley (Cohen et al., 2006). The authors attributed the higher milk
production in supplemented cows to the higher metabolizable energy intakes. The key energy
parameters that may influence N excretion are metabolizability, i.e. feed quality defined as the
proportion of metabolizable energy (ME) to gross energy of feed (AFRC, 1993), and ME intake
(MJ/d).
A meta-analytical approach is best suited to test the hypothesis that energy variables have an
effect on N utilization and excretion rates. The aim of the present study was to investigate the
effects of diet quality and ME intake on the efficiency of utilizing N intake for milk production
and subsequent excretion of N in faeces and urine.
Material and methods
Database
A database containing N and energy balance data of 470 dairy cow observations was assembled
from calorimetry studies conducted at the Centre for Dairy Research at the University of
Reading, the Agricultural Research Institute for Northern Ireland, Queens University of Belfast
and Grassland Research Institute, Hurley. For details of diet composition, experimental design
and references see Kebreab et al. (2003). Nitrogen intake, faecal and urinary N excretion, milk N
secretion and metabolizable energy intakes were all measured in the experiments.
Statistical analysis
Nitrogen balance data was analyzed in three fundamental ways using N intake as the primary
covariate. First, the response variables faeces, urine and milk N were modelled using univariate
normal mixed models for identifying potential covariates. Secondly, an index of N efficiency
was produced by expressing the ratio of milk N to excreta N and analyzing this as a function of
the potential covariates. Finally, a multivariate analysis was undertaken to generate new
prediction equations for N partitioning in dairy cows. The multivariate approach was chosen
because faeces, urine and milk N are correlated biologically and statistically.
Let yij and xij denote N output for the jth observation from the ith study. Then the following
normal linear mixed model may be fitted:
0 0 1 1 1 2 2 2( ) ( )i i iij ij ij ijy b b x b x e
where 0 1 2, ,T
and 0 1 2, ,i i i
T
b b b are fixed and random effects regression coefficients related
to N intake (x1ij) and metabolizability or ME intake (x2ij). The standard assumptions were
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0 1 2, , ~ 0,i i i
T
b b b N and 2,0~ Neij . The matrix Ω was an unstructured positive-definite
variance-covariance matrix.
If yij denote the ratio of milk to excreta N for the jth observation from the ith study then
the following mixed effect model may be fitted:
0 0 1 1( )i iij ij ijy b b x e
where 0 1,T
and 0 1,i i
T
b b are fixed and random effects regression coefficients related the
covariates metabolizability, and ME intake (xij). The standard assumptions were
0 1, ~ 0,i i
T
b b N and 2,0~ Neij . All univariate models were implemented in the linear
mixed effects function in R (lmer(); Bates and Maechler, 2009). The best performing model was
identified based on the Bayesian information criterion (BIC) as the goodness-of-fit indicator.
The multivariate model can be written in the following way and centring the mean, i.e.,
1 1 2 1 1 3 2 2 1 1
2 4 5 1 1 6 2 2 2 2
3 7 8 1 1 9 2 2 3 3
( ) ( )
( ) ( )
( ) ( )
i
i
i
ij ij ij ij
ij ij ij ij
ij ij ij ij
y x x x x b e
y x x x x b e
y x x x x b e
In the multivariate case, correlations were introduced in both the random effects and residual
variances, i.e. 1 2 3, , ~ 0,i i i
T
b b b N and 1 2 3, , ~ 0,T
ij ij ije e e N where T denotes the
transpose. The matrices Σ and Ω were unstructured positive-definite variance-covariance
matrices. The populations’ effects were denoted by parameters (β1,…, β12). The multivariate
model was implemented in OpenBUGS and the parameters where estimated using a Gibbs
sampling scheme (Lunn et al., 2000; Thomas et al., 2006). The non-informative priors were
specified because the likelihood should dominate the posterior. Convergence was established
using the Gelman-Rubin statistic as the main determinant and running three chains. Convergence
was established after 10,000 samples, i.e. the burn-in period. Inference was based on an
additional 100,000 samples from the posterior distribution where every 10th
sample was used.
The deviance information criterion (DIC) was used to compare models with varying complexity.
This can be considered an adaptation of the Akaike Information Criteria to Bayesian models
incorporating prior information, whether through fully specified proper prior distributions or
hierarchical models (Spiegelhalter et al., 2002). The notion that “smaller is better” is preserved in
the DIC.
Results and Discussion
The univariate analysis confirmed the previous reports of a linear relationship between N intake
(NI; g/d) and N excretion (g/d).
N excretion = 30 (SE=20) + 0.67 (SE=0.044)NI (1)
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This is in broad agreement with Kebreab et al. (2001) who reported that N in urine and faeces
accounted for approximately 62% of N intake. There was also a linear relationship between NI
and N in faeces, urine and milk (all in g/d):
N faeces = 10 (SE=9.0) + 0.28 (SE=0.023)NI (2)
N urine = 20 (SE=20) + 0.38 (SE=0.039)NI (3)
N milk = 30 (SE=10) + 0.20 (SE=0.03)NI (4)
Kebreab et al. (2001) also reported a similar positive linear relationship between N intake and
faeces N. However, the authors reported that the slope for N excreted in faeces was 0.16, but the
intercept was higher. For a cow consuming 400 g N/d, equation (2) estimates that 31% would be
excreted in faeces, compared to 35% in Kebreab et al. (2001) equation estimate. The relationship
between N intake and milk N was almost identical with the slope of 0.19. In the analysis of
Figure 1. Univariate relationships between nitrogen intake and nitrogen excretion in faeces, urine
and milk. Excreta are the combination of faecal and urinary nitrogen. The responses have been
adjusted for study effect.
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Castillo et al. (2000), based on data for 91 diets and 580 cows taken from the literature, the
slopes for faeces and milk N were 0.21 and 0.17 of the N intake, respectively. There was also a
positive linear relationship between N intake and urinary N using the univariate analysis method.
The results agree with Weiss et al. (2009) who found that increasing protein concentration in the
diet linearly increased N excretion in faeces and urine. Mills et al. (2009) also reported that the
increase in urine N was positively and linearly related to N intake. However, Castillo et al.
(2000) and Kebreab et al. (2001) reported an exponential relationship between N intake and
urinary N excretion. One explanation of this finding could have been, in cases of positive N
balance, inclusion of data where urine N was not measured directly but calculated by difference
might have led to an overestimation of urinary N excretion.
A multivariate analysis was conducted because the excretions were considered to be
correlated with each other. The only difference from the univariate analysis was a slight change
in the slope of faeces N (from 0.28 to 0.29). Slopes for urine and milk N were unchanged and the
standard deviations were slightly lower for urine N and slightly higher for milk N (Table 1).
Therefore, in the absence of other covariates (as will be discussed later), either univariate or
multivariate models can be used to predict N excretions.
Effect of ME on nitrogen excretion
The effect of ME on N utilization was analyzed by regressing ME intake against the ratio of milk
N to that excreted in faeces and urine (Figure 2). There was a positive relationship indicating that
there was more N secreted in milk as ME intake increased. Cohen et al. (2006) also reported that
milk N increased at the expense of urinary N as ME increased. In the same report, the authors
also found that increasing ME intake did not affect N intake, faeces N excretion, and overall N
balance and microbial protein synthesis. Nitrogen excretion patterns may be affected by the site
of ME supplementation. Urinary N excretion was decreased and faecal N increased with
abomasal infusions of up to 1 kg/d of pectin (Gressley and Armentano, 2005); however, there
were no significant differences in milk yield in dairy cows fed molasses or abomasally infused
with pectin, but milk fat was decreased.
Cohen et al. (2006) cautioned that DMI and milk yield could be suppressed, and any benefits
derived from supplemental carbohydrates overshadowed, if the level of cereal grain
supplementation caused the neutral detergent fibre (NDF) concentration of the diet to reach a
point where digestion was impaired or acidosis prevailed. The authors also reported that the cost
of the supplement and the practices needed to feed the supplement should not outweigh the
marginal responses to supplementation. However, under European Union (EU) circumstances the
level of production per unit area may be limited by environmental constrains. This limitation of
production or income may become more important if the EU ends the milk quota system.
Therefore, costs of manure transport should be taken into account.
The type of energy fed to dairy cows has been reported to have the potential to affect N
excretion patterns. For example, a study by Castillo et al. (2001a) found that feeding a
concentrate with high starch degradability resulted in 30% more N excretion in urine compared
to the other study diets, which the authors attributed to an increased amount of rumen-
fermentable energy supporting higher rates of microbial protein synthesis. Although urinary N
was increased, faecal and milk N remained relatively constant in relation to N intake across all
treatments. Milk yield was not affected by concentrate type, but milk protein concentration was
highest for diets containing low or high degradability starch. Castillo et al. (2001a) concluded it
was possible to achieve lower N losses and improve N use efficiency without adverse effects on
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milk production by using mixtures of energy sources that include low degradability starches and
balancing diets for animal requirements and level of milk production. In cows fed diets differing
in their content of non-fibrous carbohydrates (NFC), cows fed higher levels of NFC had a lower
milk fat content and higher milk protein than the cows receiving low levels of NFC (Cherney et
al., 2003). Faecal N excretion was constant across all treatments. Urinary N excretion, however,
was higher for the low NFC diets, possibly due to less efficient rumen microbial N conversion.
Cows fed the high NFC diets had greater N intake and milk N, as well as a higher N use
efficiency. In the same study, Cherney et al. (2003) also compared diets containing sucrose or
maize. When 10% of maize was replaced with sucrose, there was little impact on milk
production and composition but N use efficiency was lower for the diets containing higher
sucrose. Cherney et al. (2003) concluded that replacing maize with sucrose was not to be
recommended as the decrease in N use efficiency and N retained would represent an increased
environmental impact of dairy farming. Diet also affects the composition of urinary N. For
example, van Vuuren and Smits (1997) showed that changing levels of fermentable organic
matter and hence the yield of microbial protein, may result in changes in the proportion of urea,
uric acid and allantoin-N. Therefore, this may be part of the reason for higher urinary N
excretion observed by Castillo et al. (2001a) when cows were fed diets supplemented with high
degradable starch sources.
Figure 2. The effect of metabolizable energy intake on the proportion of nitrogen in milk and that
excreted in faeces and urine. The responses have been adjusted for study effect.
A multivariate analysis was conducted that included N intake and ME intake as
covariates in predicting N output in faeces, urine and milk. Metabolizable intake had a
significant effect on predicting urinary N excretion and milk N secretion but not faecal N
excretion. It should be noted that ME intake already includes faecal and urinary energy as part of
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the calculation, therefore, correlations are expected. The residual standard deviations from the
multivariate analysis including ME intake as a covariate were 13.8, 27.9 and 15.0 g/d. The
prediction equations for urinary N excretion and milk N secretion (which had significant effect
of ME intake) were:
N urine = 47.8 (SE=20.1) + 0.56 (SE=0.03)NI – 71.4 (SE=12.2) ME (5)
N milk = 2.04 (SE=11.7) + 0.10 (SE=0.023)NI + 45.9 (SE=5.43)ME (6)
Effect of metabolizability on nitrogen excretion
Several studies have highlighted the wide variation in N excretion, particularly urine N. Weiss et
al. (2009) reported that the variation in urinary N was 3.5 times greater than for faecal N
excretion and Mills et al. (2009) also found that there was greater variation in N excretion
relative to milk yield. The residual standard deviations for N losses in faeces, urine and milk in
the present study were estimated to be 14.6, 32.5 and 17.5 g/d, respectively from the univariate
analysis. The higher residual standard deviation for urine N compared to faeces or milk output
presents an opportunity to manipulate diets to reduce urine N excretion and may be, in part,
explained by metabolizability of the diet.
Figure 3. The effect of metabolizability on the proportion of nitrogen in milk and that excreted in
faeces and urine. The responses have been adjusted for study effect.
There was a positive linear increase in milk N relative to urine and faeces as the
metabolizability of the diet increased from 0.45 to 0.70 (Figure 3). There was a significant effect
of metabolizability when explicitly added as a covariate to the relationship between N intake and
faecal N (Table 1). Lower metabolizability values indicated lower digestibility of energy in the
diet, and therefore, greater hindgut fermentation of fibre or starch, which would increase
microbial protein in faeces. In addition, low quality diets might have lower digestible protein,
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increasing the amount of N excreted in faeces. The effect was not as large but still significant
when metabolizability was considered in the relationship between N intake and urinary N. The
effect of metabolizability appears to be lowest for the relationship between N intake and milk N.
There was an apparent negative linear relationship between diet quality and N excretion, but the
magnitude varied between faeces, urine and milk. Therefore, for prediction of N utilization and
partitioning in excreta, it is recommended that metabolizability be included as a covariate.
Table 1. Effect of covariates (ME intake and metabolizability (q)) on the estimate of nitrogen (N)
excretion in faeces, urine and milk
Response Covariate Estimate (SE) P-value BIC1
Faecal N
Nitrogen intake 0.28 (0.022) <0.001 3496
Nitrogen + ME2 0.27 (0.023) <0.001
ME 1.14 (6.1) 0.85 3546
Nitrogen + q3 0.29 (0.023) <0.001
q -305 (31.5) <0.001 3410
Urinary N
Nitrogen intake 0.38 (0.038) <0.001 4651
Nitrogen + ME 0.56 (0.031) <0.001
ME -71.4 (12.2) <0.001 4606
Nitrogen + q 0.38 (0.037) <0.001
q -174 (80.6) 0.03 4660
Milk N
Nitrogen intake 0.20 (0.031) <0.001 3051
Nitrogen + ME 0.10 (0.024) <0.001
ME 45.9 (5.4) <0.001 2986
Nitrogen + q 0.20 (0.033) <0.001
q -96.1 (41.2) 0.02 3069
1BIC = Bayesian Information Criterion
2Nitrogen + ME = Shows the coefficient for N intake when ME was added as covariate in a
multivariate setting 3Nitrogen + q = Shows the coefficient for N intake when q was added as covariate in a
multivariate setting
A multivariate analysis was conducted that included N intake and metabolizability as
covariates in predicting N output in faeces, urine and milk. Metabolizability had a significant
effect on predicting faecal and urinary N excretion but not milk N secretion when all outputs
were considered to be correlated. The residual standard deviations from the multivariate analysis
including energy as a covariate were 14.3, 32.5 and 17.5 g/d which was an improvement on the
univariate analysis. The DIC for the univariate method of analysis was calculated to be 8876, and
for the multivariate method it was 8800. This indicates a substantial improvement in the fit to
data; therefore, generating prediction equations for whole animal N flows are best accomplished
using multivariate statistical models because these account for correlated random study effects
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and residual variances. The new prediction equations for faecal and urinary N excretion (which
had significant effect of metabolizability) were:
N faeces = 244 (SE=23.3) + 0.25 (SE=0.01)NI – 346 (SE=36.5)q (7)
N urine = 150 (SE=53.6) + 0.46 (SE=0.02)NI – 257 (SE=85.4)q (8)
Figure 4 illustrates the effect of modifying diet quality on faecal N excretion. Although, N intake
is the primary driver for N excretion, the predictive model was improved for faecal and urinary
N by including representation of diet quality.
In conclusion, there is a clear interaction between N and energy and affects the relative
partitioning of N in faeces, urine and milk. Therefore when considering the effect of reducing N
concentration in feed through for example, reduced use of fertilizers, the changes in energy
(either ME content or diet quality) should also be taken into account to avoid overestimating N
availability to the animal.
Figure 4. A 3 dimensional representation of a multivariate relationship between nitrogen intake
(NI), faeces nitrogen excretion (FN) and diet quality or metabolizability (q).
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