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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).

References

Bates, D. and M. Maechler, 2009. lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-

32. http://CRAN.R-project.org/package=lme4. Accessed March 25, 2010.

Castillo, A.R., E. Kebreab, D.E. Beever and J. France, 2000. A review of efficiency of nitrogen utilisation in dairy

cows and its relationship with the environmental pollution. J. Anim. Feed Sci. 9, 1-32.

Castillo, A.R., E. Kebreab, D.E. Beever, J.H. Barbi, J.D. Sutton, H.C. Kirby and J. France, 2001a. The effect of

energy supplementation on nitrogen utilization in lactating dairy cows fed grass silage diets. J. Anim. Sci. 79,

240-246.

10

Castillo, A.R., E. Kebreab, D.E. Beever, J.H. Barbi, J.D. Sutton, H.C. Kirby and J. France, 2001b. The effect of

protein supplementation on nitrogen utilisation in grass silage diets by lactating dairy cows. J. Anim. Sci. 79,

247-253.

Cherney, D.J.R., J.H. Cherney, and L.E. Chase, 2003. Influence of dietary nonfiber carbohydrate concentration and

supplementation of sucrose on lactation performance of cows fed fescue silage. J. Dairy Sci. 86, 3983-3991.

Cohen, D.C., C.R. Stockdale and P.T. Doyle, 2006. Feeding an energy supplement with white clover silage

improves rumen fermentation, metabolisable protein utilisation, and milk production in dairy cows. Aust. J.

Agric. Sci. 57, 367-375.

Firkins, J. L. and C.K. Reynolds, 2005. Whole-animal nitrogen balance in cattle. In: Pfeffer, E. and A.N. Hristov

(editors), Nitrogen and phosphorus nutrition of cattle. CAB International, Cambridge, MA, USA, 167-186.

Gressley, T.F. and L.E. Armentano, 2005. Effect of abomasal pectin infusion on digestion and nitrogen balance in

lactating dairy cows. J. Dairy Sci. 88, 4028-4044.

Kebreab, E., J. France, D.E. Beever and A.R. Castillo, 2001. Nitrogen pollution by dairy cows and its mitigation.

Nutr. Cycl. Agroecosyst. 60: 275-285.

Kebreab, E., J. France, R.E. Agnew, T. Yan, M.S. Dhanoa, J. Dijkstra, D.E. Beever and C.K. Reynolds, 2003.

Alternatives to linear analysis of energy balance data from lactating dairy cows. J. Dairy Sci. 86, 2904-2913.

Lunn, D.J., A. Thomas, N. Best and D. Spiegelhalter, 2000. WinBUGS - a Bayesian modelling framework:

concepts, structure, and extensibility. Statist. Comp. 10, 325-337.

Mills, J.A.N., L.A. Crompton, A. Bannink, S. Tamminga, J.M. Moorby and C.K. Reynolds, 2009. Predicting

methane emissions and nitrogen excretion from cattle. In: Crompton, L.A. and T.R. Wheeler (editors),

Proceedings of the forty-first meeting of the agricultural research modellers’ group, J. Agric. Sci. 147, 741.

Spiegelhalter D.J., N. Best, B.P. Carlin and A. van der Linde, 2002. Bayesian measures of model complexity and fit

(with discussion). J. Roy. Statist. Soc. B. 64, 583-640.

Thomas, A., B. O’Hara, U. Ligges and S. Sturtz, 2006. Making BUGS Open. R News 6:12-17.

van Vuuren, A.M. and M.C.J. Smits, 1997. Effect of nitrogen and sodium chloride intake on production and

composition of urine in dairy cows. In: Jarvis, S.C. and B.F. Pain (editors), Gaseous nitrogen emissions from

grasslands. CAB International, Wallingford, UK, 95-99.

Weiss, W.P., L.B. Willet, N.R. St-Pierre, D.C. Borger, T.R. Mckelvey and D.J. Wyatt. 2009. Varying forage type,

metabolizable protein concentration, and carbohydrate source affects manure excretion, manure ammonia, and

nitrogen metabolism of dairy cows. J. Dairy Sci. 92, 5607-5619.