Factors Affecting Pasture Intake and Total Dry Matter Intake
in Grazing Dairy Cows
O. P. Vazquez and T. R. Smith
Department of Dairy Science,
University of Wisconsin-Madison, Madison 53706
ABSTRACT
We investigated the most relevant variables for estimating pasture intake and total dry matter (DM) intake in grazing dairy cows using 27 previously published studies. Variables compared were pasture allowance, days in milk, amount of forage, amount of
concentrate and total supplementation, pasture allowance and supplementation interaction, fat-corrected
milk, body weight (BW), metabolic BW, daily change
in BW, percentage of legumes in pasture, neutral detergent fiber (NDF) contents of pasture, and NDF in
pasture selected. The variables were selected using
stepwise regression analysis for total DM intake and
pasture DM intake. Variables selected in the total DM
intake regression equation (R2 = 0.95) were pasture
allowance, total supplementation, interaction of pasture allowance and supplementation, fat-corrected
milk, BW, daily change in BW, percentage of legumes
and pasture NDF content. Pasture DM intake regression equation (R2 = 0.90) was similar to total DM intake
equation, but supplementation coefficient was negative, showing substitution effect in supplementing
grazing cows.
The intake of NDF as a percentage of BW was higher
than 1.3% when considering NDF content of the pasture allowance. Low pasture allowance groups had values higher than 1.3%.
(Key words: pasture, intake, grazing, dairy cows)
Abbreviation key: BWNDF = intake of NDF as percentage of BW, IVDMD = in vitro DM digestibility,
IVOMD = in vitro OM digestibility, PA = pasture allowance, PDMI = pasture DMI, NDFp = NDF in pasture available, NDFs = NDF of pasture selected.
mating DMI compared with confinement feeding systems. Several models have been developed to predict
DMI based on animal characteristics alone (5, 7, 29)
or with feed nutritive factors (5, 7, 23, 32, 45, 46).
However, most of these models have been developed
under confinement feeding conditions, where forage
selection is limited.
Mertens (23) proposed a model to estimate DMI, in
which DMI was a function of the energy requirements
of the cattle and the physical filling capacity of the
feed. The filling intake capacity was expressed as NDF
intake capacity and was approximately 1.2% of BW of
the animal.
The estimation of pasture DMI (PDMI) has been a
subject of much research, and many factors have been
identified that influence intake, including pasture allowance (PA) (2, 7, 21, 39, 40, 41, 43), herbage mass
(5, 39, 40, 41, 43), supplementation (21, 30, 31, 33, 34,
38, 39, 40), herbage digestibility (5, 7, 41), and animal
BW, among others. Unfortunately, most of these studies have been limited to a narrow range of production
and management conditions and conducted with relatively low producing cows.
The objective of this study was to analyze the existing literature for grazing dairy systems to develop
DMI and PDMI prediction equations and determine
the influence of PA, amount and type of supplementation, animal characteristics, NDF content of pasture,
and type of pasture on DMI and PDMI estimates. The
intake of NDF as a percentage of BW (BWNDF) was
also examined to evaluate the applicability of the value
proposed by Mertens (24) under various grazing conditions.
MATERIALS AND METHODS
INTRODUCTION
The prediction of DMI is a fundamental parameter
in determining the performance of dairy cows. Grazing
systems for dairy cows increase the difficulty in esti-
Received September 27, 1999.
Accepted March 15, 2000.
Corresponding author: O. P. Vazquez; e-mail: [email protected].
2000 J Dairy Sci 83:2301–2309
The data set used in this study consisted of 163
observations from 27 published studies of grazing
dairy cows (1, 2, 3, 4, 6, 8, 10, 11, 13, 14, 17, 18, 19,
21, 25, 26, 27, 28, 30, 31, 33, 34, 38, 39, 40, 41, 43).
These studies were conducted across a wide range of
locations and conditions (Australia, New Zealand,
United States, Great Britain, and The Netherlands)
and represented a period from 1979 to 1992. The characteristics of the experiments and the mean values
2301
2302
VAZQUEZ AND SMITH
Table 1. Summary of the experiments analyzed in this study.1
Ref
Trt
BW
Herbage
mass
Milk
CBW
Suppl.
NEL of
Suppl.
kg of
Suppl.
PDMI
13
6
43
41
40
38
39
21
11
30
31
14
10
27
26
25
28
18
17
19
2
8
1
33
34
4
3
6
3
8
8
4
4
4
12
9
12
7
3
4
5
2
3
2
6
6
6
12
3
10
3
3
8
8
kg
398−403
479
327−421
427
515−549
505−522
482−506
575−579
370−475
576
575
505
370−425
424
447
477
540
514−571
542−605
560
367−388
375
396−423
637
637
389−410
304−356
kg DM/d
6.7−21.2
14.4−43.1
20.6−76.1
14.9−26.6
16−24
4.2−21.4
13.6−20.5
15.8−25.7
12.1−64.2
10.8−14.6
3.6−12.8
47.6
21.7−26.3
29.2−45.3
32.0
30.0−40.0
9.0−18.0
23.8−43.3
12.9−20.0
20.3−50.7
13.0−54.0
13.5−52.7
18.0−61.0
31.3
N.A.
19.0−55.0
9.6−17.4
kg/d
5.0−10.7
15.5−17.1
9.2−20.4
7.4−11.8
11.4−15
7.3−9.8
17.3−18.7
22.2−25.5
10.5−21.5
14.4−25.6
2.6−12.5
23.4−26.0
17.0−22.3
18.5−20.1
22.3−23.1
13.9−14
20.3−21.3
13.0−20.7
9.8−20.0
11.8−17.0
5.7−18.1
12.8−16.7
9.3−20.8
27.2−28.3
36.7−36.8
13.9−19.5
0.0
kg/d
N.A.2
N.A.
N.A.
−0.4−1.0
N.A.
0.1−0.6
0.2−0.6
N.A.
N.A.
0.0−0.5
0.0−1.0
0.2−0.5
N.A.
0.4−0.8
0.0−0.1
0.5−0.6
−0.6−0.3
0.0−0.3
0.0−0.4
−0.9−0.1
−0.2−0.7
−0.3−0.7
−0.2−0.7
−0.1−0.1
−0.8−0.7
−0.9−0.8
0.6−1.0
N
N
N
C
C
C
C
C
N
C,CF
C,CF
C
N
F,CF
F
F
F
N
F
N
N
N
N
C,CF
C,CF
N
N
Mcal/kg DM
N
N
N
2.01
1.92
1.92
1.92
1.90
N
1.65−1.89
1.57−1.83
1.95−2.41
N
1.27−1.48
1.33
1.39−1.43
1.40
N
1.28
N
N
N
N
1.70−1.72
1.70−1.72
N
N
kg/d
N
N
N
3
2
2
2
5
N
5
7
2
N
2
2
2
2
N
1
N
N
N
N
2
2
N
N
kg/d
6.5−10.4
11−12.9
9.5−14.3
6.5−10.6
10.8−15.1
12.0−16.9
11.0−15.5
10.6−15.1
7.1−21.3
8.7−13.8
2.6−12.5
15.6−17.0
13.8−14.8
9.4−16.3
9.6−10.1
8.1−16.2
4.3−7.6
11.5−14.9
12.5−14.8
9.0−13.5
6.2−12.5
9.6−16.3
11.5−17.1
14.5
15.8−16.6
7.5−14.9
6.7−8.5
1
Ref = Reference number; Trt = no. of treatments; PA = pasture availability; CBW = change in BW; Suppl
= type of supplement (N = no supplementation, F = forage, C = concentrate, CF = concentrate and forage);
PDMI = pasture DMI.
2
Not available.
for BW, milk yield, and stage of lactation for each
experiment are summarized in Table 1.
Animals, Pastures, and Supplements
The breeds of cattle included Friesian, Jersey, Holstein, and Friesian-Jersey crossbreed. The animals
were predominantly multiparous cows across stages
of lactation and dry cows.
Pasture composition was variable and pasture species ranged from grasses only to all legumes. The pastures were mainly mixed, but grasses were more predominant than legumes (mean 18% legumes in pasture). Perennial ryegrass (Lolium perenne) was the
most frequently reported species in the pastures.
Other grasses utilized included paspalum (Paspalum
dilatatum), cocksfoot or orchardgrass (Dactylis glomerata), and Kentucky bluegrass (Poa pratensis). The
most prevalent legumes reported were white clover
(Trifolium repens) and red clover (Trifolium pratense).
Pasture allowance, herbage mass, and PDMI ranges
are reported in Table 1.
The levels and types of supplementation of pasture
were also highly variable. More than half of the experiJournal of Dairy Science Vol. 83, No. 10, 2000
ments reported no supplementation provided to grazing cows. The supplementation was mainly in the form
of concentrates alone, but some experiments supplemented only forage or both concentrates and forages.
The type of concentrate used was mainly mixtures of
grains along with oilseed meals; some experiments
reported cottonseed and fat supplementation. The supplemental forages were hay, hay crop silage, or corn
silage. Table 1 shows the types and levels of supplementation for each experiment included in the study.
Most of the studies used some type of rotational
grazing system with variable numbers of paddocks and
resting periods. The lengths of the experiments ranged
from 10 to 365 d.
Measurements
The selected experiments measured PA, BW, PDMI,
change in BW, milk yield, milk fat percent, and supplement intake. The chemical composition of the feeds
reported in some cases included NDF and in others
in vitro DM digestibility (IVDMD) (or in vitro OM
digestibility, IVOMD). For comparison purposes, this
study used the NDF content of the pastures. When
2303
FACTORS AFFECTING PASTURE INTAKE
IVDMD was reported instead of NDF, the values of
NDF were estimated using the equations:
ADF = 87.85 – 0.825∗IVDMD (12)
Grasses: NDF = 16.12 + 1.327*ADF (22)
Legumes: NDF = 3.53 + 1.21*ADF (22),
where values are a percentage of DM.
Other papers reported CP, ADF, and crude fiber content of the pasture available. Some reported the chemical composition (NDF, IVDMD, or ADF) of the pasture
consumed by the cattle from samples at approximate
grazing height (38, 39, 40), using the difference between the composition of pasture before and after grazing (17, 19, 27) or using extrusa samples from cows
fistulated at the esophagus (6). The pasture allowance
for the cattle was usually calculated from the herbage
mass estimated by direct cutting from ground level in
most of the studies, but cutting height 2.5 (17, 18, 27)
or 4.0 (21) cm above ground level were also used. The
PDMI was usually estimated as the difference in
herbage mass before and after grazing. Some studies
(2, 10, 17, 18, 19) estimated PDMI with markers (chromic oxide), and others (25, 26, 30, 31) estimated PDMI
based on the energy requirements of the cows and the
metabolizable energy content of the pasture grazed.
The NRC (29) equations were used to estimate energy
requirements of grazing cattle. The NDF content of
the pasture selected (NDFs) was estimated using the
Kristiensen equation (16) as follows:
two levels of production (high and low). High production was defined as greater than 22.3 kg/d of 4% FCM,
the low production was yields 22.3 kg/d or less.
For the analysis of BWNDF, the data were classified
into eight groups, representing the combination of two
levels of PA and four types of supplementation. Pasture allowance was classified as high or low for values
above or below 20 kg of DM/d, respectively. This PA
was considered an upper limit of possible pasture intake based on these data. The groups for type of supplementation were no supplementation, supplementation
with concentrates, supplementation with forages and
concentrates together, and supplementation with forage only. A two-way ANOVA, using 4% FCM, PA, and
amount supplemented as covariables as represented
by the following expression was used:
yijk = µ + PAi + Tj + (PT)ij + b1(xijk – x) + b2(zijk – z)
+ b3(sijk – s) + εijk,
where, yijk is BWNDF; PA (i = 1,2); T is type of supplementation (j = 1,2,3,4); PT is the interaction between
PA and type of supplementation; b1, b2, and b3 are
the slopes of the covariables; x, z, and s are the covariables 4% FCM, PA, and amount supplemented, respectively, and ε is the error term. This design was unbalanced for the number of data in each cell, thus a type
III analysis for ANOVA was utilized. The differences
among means were analyzed using an LSD procedure.
Data were analyzed using PROC GLM of SAS (37).
NDFs = (–0.079/(PDMI/PA) + 1.04) × NDFp,
where NDFs is the NDF content of selected pasture,
and NDF in pasture available (NDFp) is the NDF
content of the pasture allowance (% of DM).
Statistical Analysis
Regression analysis was used to develop and test a
prediction model for DMI and PDMI. Stepwise regression and a general linear model procedure (37) were
used to fit the model. The independent variables included in the development of the model were 4% FCM
(kg/d), days since calving, PA (kg DM), NDFp, NDFs,
percentage of legumes in pasture (LEG), quantity of
concentrate supplemented (kg/d), amount of forage
supplemented (kg DM), total supplementation (SUP)
(kg DM), PA and SUP interaction (PASUP), BW (kg),
and BW change (kg DM). Metabolic BW (BW0.75),
amount of forage, and amount of concentrate supplemented and the quadratic terms for SUP and PA were
also tested, but they were not chosen by the selection
procedure. Regression equations were developed for
RESULTS
Table 2 shows the mean values and variances for
some of the variables analyzed from the published
Table 2. Mean values of the variables analyzed.1
Variable
Unit
Mean
s.d.
Minimum
Maximum
PDMI
PA
NDFs
NDFp
DMI
Milk yield
4% FCM
BW
CBW
Days since
calving
SUP
BWNDF
BWNDFs
kg/d
kg DM
% DM
% DM
kg/d
kg/d
kg/d
kg/cow
kg/d
11.6
25.5
50.2
57.3
13.4
15.9
16.4
474.0
0.27
2.95
13.61
7.26
6.95
3.76
6.81
6.40
87.9
0.42
2.6
3.61
29.7
36.5
6.2
0.0
0.0
304.0
−1.61
21.3
76.1
62.6
70.1
27.7
37.1
32.0
637.0
1.04
d
kg DM
% of BW
% of BW
131.6
1.87
1.50
1.32
66.5
2.98
0.31
0.25
40
0.0
0.99
0.89
335
13.0
2.97
2.56
1
PDMI = Pasture DMI, PA = pasture allowance, NDFs = NDF of
pasture selected, NDFp = NDF in pasture available, BWNDF = intake
of NDF as percentage of BW, BWNDFs = intake of NDFs as percentage
of BW, CBW = change in BW, SUP = kg of supplement offered.
Journal of Dairy Science Vol. 83, No. 10, 2000
2304
VAZQUEZ AND SMITH
studies considered. The mean PDMI and PA were 11.6
and 25.5 kg DM, respectively, which implies that most
of the experiments offered more pasture to the animals
than they were able to consume (maximum reported
pasture intake was 21.3 kg DM). However, the ranges
of PDMI and PA were large (Table 2). The average
DMI was 13.4 kg/d, which was consistent with the sum
of PDMI and supplementation.
Average actual milk and 4% FCM yields were 15.9
(±6.8) and 16.4 (±6.4) kg/d, respectively. These yields
are low compared with 22.3 kg/d for an average lactation yield in Wisconsin (44). The cause of those differences is that most of the experiments analyzed were
conducted in Australia, New Zealand, and Great Britain, and the breeds of cattle used had lower milk yields
than did US Holsteins. The average days since calving
was 131.6 (±66.3), which was close to midlactation,
while the range included values higher than 305 d due
to some dry cow experiments. The average BW was
474 (±87.9) kg, generally lower than an average Holstein cow, indicating greater use of lower BW breeds
such as Jersey and Friesian. The mean total DMI as
percentage of BW was 2.82%. Mean change in BW was
0.27 (±0.42) kg/d with a range from –1.61 to 1.04 kg/d.
Mean NDF in available pasture was 57.3 (±7.0)% of
DM, which is high compared with most forages used
in Wisconsin. Mean BWNDF was 1.50 (±0.31)%, considering the NDF content of the pasture allowance.
When the Kristiensen equation (16) was used to estimate the composition of the pasture selected, the
BWNDF was 1.32% (±0.25). With a t-test, both values
for BWNDF were significantly different from 1.2%,
the value reported by Mertens (23). However, recent
studies (24, 36) showed that this value was variable
during lactation, and at 130 d after calving the
BWNDF was 1.30% for cows in second or later lactation and was not significantly different (P < 0.05) from
the mean BWNDF of selected pasture.
Table 3 shows the correlation coefficients for some
of the variables analyzed. Dry matter intake had a
high positive correlation with amount of supplement
fed, PDMI, 4% FCM, and BW (correlation coefficients
higher than 0.5). Pasture DMI had the highest correlation with PA and 4% FCM. Fat-corrected milk production was highly correlated with DMI, PDMI, days since
calving, and NDF concentrations of available and selected pasture.
Equations 1, 2, 3, and 4 in Table 4 were the bestfit regressions models for estimating DMI based on
animal, management, and pasture characteristics.
Equation 1 used NDF concentration of selected pasture and equation 2 used NDF concentration of available pasture. Equations 1 and 2 show that the use of
NDF concentration of selected pasture did not improve
Journal of Dairy Science Vol. 83, No. 10, 2000
the ability of the model to predict DMI from pasture.
Model 2 explained 95% of the variation, compared with
93% explained with equation 1. The other variables
included in the model were 4% FCM, BW, change in
BW, PA, and interaction of amount of supplement and
PA, all of which are variables measurable by established methods.
None of the prediction equation coefficients were
significantly different when two levels of production
(4% FCM higher and lower than 22.3 kg/d) were considered; therefore, the combined data set was used.
Since many of the studies did not report change in
BW, that variable was not included in equation 3. The
use of NDF of available pasture improved the fit (R2
= 0.87) more than NDF of selected pasture. In equations 1, 2, and 3, DMI was positively correlated with
4% FCM, BW, change in BW, amount of supplement,
PA, and interaction of amount of supplement and PA,
and negatively correlated with NDF in available pasture and percent legumes in pasture. In all cases, the
amount of supplement had a coefficient that was not
significantly different from zero, but it was included
in the equation because the interaction of amount of
supplement and PA was significant.
Equation 4 showed that using animal characteristics alone explained 71% of the total variation in DMI.
This equation included change in BW. In contrast to
other methods of predicting DMI, days since calving
was not selected for inclusion in the regression model,
probably because of its high correlation with 4% FCM.
Table 4 shows the best-fit regression models to estimate PDMI. Equations 5 and 6 are similar, except
equation 6 does not include change in BW. Both are
therefore equivalent to the equations 2 and 3, respectively, but for PDMI instead of DMI. In both cases the
use of NDF in available pasture in the model improved
the R2 more than NDF in selected forage. The results
showed that the only difference between equations 2
and 3 and 5 and 6 were the coefficients for amount of
supplement, which were negative and different from
zero (P < 0.05) for PDMI, indicating that supplementation depressed PDMI as supplement intake was substituted for PDMI.
The effects of type of supplementation and PA on
BWNDF are presented in Table 5. The analysis of the
data showed significant differences among types of
supplementation and PA, but the interaction term was
not significant. The BWNDF was higher for greater
defined PA (1.33 vs. 1.65% of BW for NDF). Intake of
NDF (% of BW) showed significant differences among
types of supplementation: forage supplementation resulted in the highest values (1.78% of BW) and concentrate the lowest values (1.38% of BW). The values for
forage plus concentrate supplementation and no sup-
2305
FACTORS AFFECTING PASTURE INTAKE
Table 3. Correlation coefficients for the variables analyzed.1
PDMI
DMI
SUP
PA
4% FCM
CBW
BW
Days since calving
NDFp
DMI
SUP
0.64
1.00
−0.18
0.63
1.00
PA
0.54
0.28
−0.22
1.00
4% FCM
0.47
0.68
0.42
0.32
1.00
CBW
BW
NS
0.03
0.10NS
0.04NS
0.09NS
−0.25
1.00
0.26
0.59
0.51
−0.25
0.45
−0.19
1.00
Days
since
calving
−0.35
−0.34
−0.10NS
−0.23
−0.66
0.34
−0.07NS
1.00
NDFp
NDFs
−0.24
−0.36
−0.23
−0.08NS
−0.44
0.10NS
−0.00NS
0.50
1.00
−0.25
−0.31
−0.13NS
−0.58
−0.48
0.02NS
−0.24
0.44
0.81
1
PDMI = Pasture DM intake, PA = pasture allowance, CBW = change in BW, NDFs = NDF of pasture
selected, NDFp = NDF in pasture available, SUP = kg of supplement offered. NS indicates correlation
coefficient not different from zero at P ≤ 0.05.
plementation (1.57 and and 1.51% of BW, respectively)
were not different (P < 0.05). The number of studies
with mixed and forage supplementation were small
and may influence the results and the interpretation.
DISCUSSION
The data used in this study attempted to cover a
broad range of situations used in dairy grazing systems. However, based on the average values for the
data set, production systems with low yielding cows
and little or no supplementation predominated. The
PA assigned to the cows was generous compared with
the PDMI, characteristic of low grazing efficiencies
(average of 45.5% of the PA was grazed). Another area
of concern was the method used to estimate PA. Most
of the studies measured PA from ground level, while
some of the studies measured as the total herbage
mass above 2.5 or 4 cm clipping height, which would
result in different estimates of PA and grazing efficiencies.
The different methods used to estimate PDMI could
be another important source of error. Most studies
used the method of difference between herbage mass
before and after grazing corrected from grass growth,
but others used chromic oxide or energy balance. Some
studies (10, 11) reported important differences between the PDMI values estimated with chromic oxide
or by difference between herbage masses.
A high correlation was found between days since
calving and 4% FCM. This could explain why days
since calving was not selected in the equations developed to predict PDMI and DMI. This lack of days since
calving effect was one of the main differences between
the equations developed in this study to estimate DMI
and those reported in the literature (32, 45, 46).
The amount of concentrate and forage supplemented
were not chosen by the statistical selection procedure
in spite of the expected different substitution effect
of forages and concentrates on PDMI. This could be
attributed to a low amount of data with forage supplementation and a low level of supplementation. Stockdale (42) did not find significant differences on pasture
intake among forage and concentrate supplementation
for a low level of supplementation and PA.
The use of change in BW to estimate DMI and PDMI
was another difference among some of the equations
reported in the literature (21, 32, 41, 45, 46). Youngblut et al. (45, 46) included change BW in two regression equations to estimate DMI, and had coefficients
of 2.68 and 2.00, which were close to those found in
this study of 2.25, 2.00, and 2.82 from the equations
Table 4. Regression equations to estimate total DMI (kg/d) and pasture DMI (kg/d) based on pasture and animal characteristics.1
Equation
Variable
Intercept
4% FCM
BW
CBW
PA
PASUP
SUP
NDFs
NDFp
LEG
R2
s.d.
N
C.V.
[1]
[2]
[3]
[4]
[5]
[6]
DMI
DMI
DMI
DMI
PDMI
PDMI
2.65
4.47
6.14
−1.84
4.47
6.14
0.17
0.14
0.06
0.38
0.14
0.06
0.025
0.024
0.020
0.018
0.024
0.02
2.25
2.00
...
2.82
2.00
...
−0.003
0.04
0.075
...
0.04
0.075
0.024
0.022
0.018
...
0.022
0.018
−0.04
0.10
0.16
...
−0.90
−0.84
−0.026
...
...
...
...
...
...
−0.13
−0.11
...
−0.13
−0.11
−0.026
−0.037
...
...
−0.037
0.009
0.93
0.95
0.87
0.71
0.91
0.78
1.02
0.90
1.33
2.12
0.90
1.33
90
90
132
117
90
132
7.6
6.7
10.1
15.6
8.3
11.9
1
CBW = Change in BW (kg/d), PA = pasture allowance (kg of DM/d), PASUP = interaction between PA and SUP, SUP = supplement
offered (kg/d), NDFs = NDF content of selected pasture (%), NDFp = NDF content of pasture (%), LEG = percentage of legume in pasture,
PDMI = pasture DMI.
Journal of Dairy Science Vol. 83, No. 10, 2000
2306
VAZQUEZ AND SMITH
Table 5. Intake of NDF as percentage of the BW (BWNDF) for different pasture allowances (PA) and type
of supplementation (TS).
Pasture
Allowance
Low
BWNDF
1.33
a
Type of supplementation
High
None
b
a
1.65
1.51
Forage
a
1.78
Forage and
Concentrate
a
1.57
Contrast
Concentrate
b
1.38
PA
TS
Interaction
P ≤ 0.01
P ≤ 0.01
NS
Values within PA or TS with different superscripts are different (P ≤ 0.01).
a,b
1, 2, and 4, respectively. From these results, between
2.0 and 2.8 kg of DMI are required to increase 1 kg of
BW in milking cows. The NRC (29) states that between
4.92 and 5.12 Mcal of NEL are needed per kilogram of
BW loss or gain, respectively. Roseler et al. (35) also
included change in BW in their equations to predict
DMI, but coefficients (0.12 for multiparous cows, using
kg/wk) were lower than those found in this study.
Therefore, if the pasture consumed had an average of
1.48 Mcal/kg of DM (approximate average NEL consumed had an average of 1.48 Mcal/kg of DM (approximate average NEL content of the pasture consumed),
3.46 kg of PDMI would be needed per kilogram of BW
gain, reflecting that some of the BW gain of grazing
cows is gut fill, which consists of undigested feed and
water contained in the pasture consumed.
Caird and Holmes (5) developed several equations
to predict total DMI of dairy cows under rotational
grazing conditions. The best fit equation [OM intake
(kg/d) = 0.323 + 0.177 ∗ milk yield (kg/d) + 0.01 ∗ BW
(kg) + 1.636 ∗ CI – 1.008 ∗ Herbage mass + 0.54 ∗ PA –
0.006 ∗ PA2 – 0.048 PA ∗ CI; CI is intake of concentrates
kg/d] and includes the herbage mass variable, but it
does not include change in BW, NDF, and percentage
of legumes compared to equation 5. Figure 1 compares
the equations of Caird and Holmes (5) with equation
2 for either 2 or 4 kg of DM supplementation/d at
different PA. The equation of Caird and Holmes (5)
tended to predict higher values than equation 2, except
for PA higher than 40 kg of DM/d and 4 kg of DM
supplementation. Values estimated using equation 2
were derived with the BW and for milk yield data of
Caird and Holmes (5); NDF from pasture was deduced
from average IVOMD of these data; and change in BW,
and herbage mass were assumed 0.2 kg/d and 3000 kg
of DM/ha. As a consequence of the negative coefficients
for the PA and concentrate interaction and the square
of PA, the Caird and Holmes (5) equation shows a
curvilinear pattern in which maximum DMI is not at
maximum pasture allowance. This effect is associated
with the large substitution rates observed for high PA
(20), which could be attributed to reduced time spent
grazing. Another influential factor could be a higher
quality of the pasture selected, which would increase
Journal of Dairy Science Vol. 83, No. 10, 2000
the nutrient density of the ration. This curvilinear
pattern was not observed with equation 2 because the
interaction term was positive. The differences of the
responses of both equations could be explained by the
type and characteristics of the data set used by Caird
and Holmes (5) with a higher average milk yield and
IVOMD of the pasture than the data set used in this
study.
Equation 4 indicates that animal characteristics
(BW, change in BW, and 4% FCM) accounted for most
of the variance (71%) in DMI. Body weight was selected
versus metabolic BW. This suggests that DMI by cows,
under a grazing system for a given level of production,
can be estimated with reasonable accuracy from animal characteristics assuming pasture is readily available. Equation 4 had coefficients for 4% FCM and BW
similar to those developed by Rayburn and Fox (32).
The utilization of NDF concentration of available
instead of selected pasture in equations 1 and 2 increased the R2 from 0.93 to 0.95. The fact that NDFs
did not add more information than NDF in available
pasture can be explained on the basis of the estimation
of NDFs itself. The NDFs was estimated with the Kris-
Figure 1. Comparison of some equations used to predict DMI.
Caird and Holmes (5) equation for 4 kg of DM supplementation (×)
and for 2 kg of DM supplementation (+), Equation 2 for 4 kg of DM
supplementation (䊏), and for 2 kg of DM supplementation (䊊).
FACTORS AFFECTING PASTURE INTAKE
Figure 2. Comparison of some equations used to predict pasture
DMI. Meijs and Hoekstra (21) equation for 6.2 kg of DM supplementation (䊏) and for 3.5 kg of DM supplementation (×), Equation 5 for
6.2 kg of DM supplementation (䊊), and for 3.5 kg of DM supplementation (+).
tensen equation (16), which required NDF in available
pasture and PA.
Equations 1, 2, and 3 showed than DMI was related
to the level of supplementation, PA, and their interaction. The same relationship was to change for equations 5 and 6 for PDMI. The effect of supplementation
on pasture intake for different PA was also described
by Stockdale and Trigg (43), Stakelum (38), and Meijs
and Hoekstra (21). In one experiment by Stakelum
(34) during the autumn grazing season, no differences
were found between the rate of substitution of concentrates and PA. In a study by Stockdale and Trigg (43),
a reduction in the rate of substitution was found for
high PA but not for low PA.
The principal difference between equations 2 and 3,
versus equations 5 and 6, was the coefficient for level
of supplementation; in equations 5 and 6 the coefficient
was significantly different from zero.
Equations 5 and 6 for PDMI showed important differences compared with the equation of Mejis and
Hoesktra (PDMI = –0.61 + 0.981PA + 0.479CI – 0.039
(PA ∗ CI) – 0.014PA2, in kg of OM/d; CI is intake
of concentrates; 21). These authors did not include
variables related to the animal and used a quadratic
term for PA. The coefficients for PA, amount of supplement, and the interaction were 0.981, 0.479, and
−0.039, respectively, in their study (21), compared with
0.075, –0.84, and 0.018, respectively, for equation 6
in this study, demonstrating the influence the other
variables had on the coefficients in our model.
Figure 2 compares the equations from Meijs and
Hoekstra (21) with equation 5 for either 3.5 or 6.2 kg
2307
of DM supplementation/d at different PA. Equation 5
tended to predict higher PDMI values than the equation of Meijs and Hoeskra. The differences increased
for lower PA or supplementation. The line representing equation 5 considered the same change in BW and
the milk yield as the values reported by Meijs and
Hoektra (21) for the range of PA. Those authors did
not report differences between groups for 4% FCM and
change in BW. However, a reduction in change in BW
and milk yield could be expected (41) due to lower PA
and a reduction in PDMI and DMI.
Holden et al. (9) reported total DMI and PDMI of
high producing cows grazing pasture during 6 mo. Figure 3 compares total DMI monthly averages from
Holden et al. (9) and DMI predicted from equation 5
with values for the variables deduced from reported
data. Equation 5 tended to predict higher DMI values
than reported data. The larger difference was found
at the beginning of the experiment, where the authors
found an unexpected low PDMI explained by the adaptation period of the cow to grazing conditions. However, these data could suggest restrictions to the use
of these equations in high producing cows highly supplemented with concentrate.
Mertens (24) proposed that the maximum intake of
NDF was approximately 1.2% of BW. This value had
a range from 0.78 to 1.3, based on the stage of lactation
and lactation number (36). Kolver and Muller (15) observed an NDF intake of 1.5% of BW in grazing dairy
cows consuming only pasture. The results in Table 5
show that NDF intake increased from low to high PA.
The value for low supplementation was not significantly different than 1.3% of BW (the mean value for
Figure 3. A comparison of total DMI of high yielding cows reported
by Holden et al. (9) with values predicted using equation 5.
Journal of Dairy Science Vol. 83, No. 10, 2000
2308
VAZQUEZ AND SMITH
the average stage of lactation of the cows analyzed in
this study). One interpretation of these results may be
the effect of pasture selection when the cow is grazed
under conditions when pasture allowance is high. Rayburn and Fox (32) found that BWNDF was a function
of days since calving, 4% FCM, and NDF of the ration.
However, days since calving, 4% FCM and NDF of
the ration consumed were not significantly different
among groups in the present analysis.
CONCLUSION
Accurate estimations of DMI and PDMI are important to the management of dairy grazing systems.
Many studies have analyzed factors influencing pasture intake, but many of those studies were performed
under specific conditions and environments. This
study summarized 27 previously published studies
and analyzed the results to determine the most relevant variables for estimating PDMI and DMI. The
variables included in a regression equation to estimate
DMI were: PA, level of supplementation, interaction
between PA and supplementation, 4% FCM, BW,
change in BW, percentage of legumes in pasture, and
pasture NDF content. The variables related to the cow
(BW, change in BW, and milk yield) explained 71% of
the total variation in DMI.
The regression equations for pasture intake were
similar to those of total DM intake except for the supplementation term, which was negative, indicating the
substitution effect between pasture intake and supplementation.
The analysis of NDF intake as a percentage of the
BW showed that for low PA, this value was not significantly different from the value of 1.3% proposed by
Mertens (24). When PA was high, intake of NDF as a
percentage of BW calculated from NDF of the available
pasture was significantly higher than 1.3.
Models for estimating DMI and PDMI used in this
study were empirical and only showed the most important, directly estimable factors involved in DMI
estimation in grazing dairy cows. When grazing studies are compared, the differences in the methodology
of measurement of some variables such as PDMI, PA,
or nutritive composition of the pasture must be considered. Further work is necessary to determine the specific mechanisms involved in pasture intake and selection.
ACKNOWLEDGMENTS
Special thanks to D. R. Mertens for his collaboration
and ideas.
Journal of Dairy Science Vol. 83, No. 10, 2000
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