Content uploaded by hélène Soyeurt
Author content
All content in this area was uploaded by hélène Soyeurt on Jan 05, 2016
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
Animals 2015,5, 643-661; doi:10.3390/ani5030377 OPEN ACCESS
animals
ISSN 2076-2615
www.mdpi.com/journal/animals
Article
Predictions of Daily Milk and Fat Yields, Major Groups of Fatty
Acids, and C18:1 cis-9 from Single Milking Data without a
Milking Interval
Valérie M. R. Arnould 1,2, Romain Reding 1, Jeanne Bormann 3, Nicolas Gengler 2and
Hélène Soyeurt 4,*
1CONVIS s.c., Zone Artisanale et Commerciale, 4, Ettelbruck, L-9085, Luxembourg;
E-Mails: valerie.arnould@convis.lu (V.M.R.A.); romain.reding@convis.lu (R.R.)
2Gembloux Agro Bio-Tech, Agriculture, Bio-engineering and Chemistry Department Animal Science
and Nutrition Unit, University of Liège, Passage des Déportés 2, Gembloux B-5030, Belgium;
E-Mail: nicolas.gengler@ulg.ac.be
3Administration des Services Techniques de l’Agriculture ASTA, Luxembourg L-1019, Luxembourg;
E-Mail: jeanne.bormann@asta.etat.lu
4Gembloux Agro Bio-Tech, Department of Agricultural Science, Statistics, Informatics, and Applied
Modeling Unit, University of Liège, Passage des Déportés 2, Gembloux B-5030, Belgium
*Author to whom correspondence should be addressed; E-Mail: hsoyeurt@ulg.ac.be.
Academic Editor: Marina von Keyserlingk
Received: 27 December 2014 / Accepted: 14 July 2015 / Published: 31 July 2015
Simple Summary: Reducing the frequency of milk recording decreases the costs of official
milk recording. However, this approach can negatively affect the accuracy of predicting daily
yields. Equations to predict daily yield from morning or evening data were developed in this
study for fatty milk components from traits recorded easily by milk recording organizations.
The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields
were estimated from the morning (evening) milkings. The simplicity of the proposed models
which do not include the milking interval should facilitate their use by breeding and milk
recording organizations.
Abstract: Reducing the frequency of milk recording would help reduce the costs of
official milk recording. However, this approach could also negatively affect the accuracy
of predicting daily yields. This problem has been investigated in numerous studies. In
addition, published equations take into account milking intervals (MI), and these are often
Animals 2015,5644
not available and/or are unreliable in practice. The first objective of this study was to propose
models in which the MI was replaced by a combination of data easily recorded by dairy
farmers. The second objective was to further investigate the fatty acids (FA) present in
milk. Equations to predict daily yield from AM or PM data were based on a calibration
database containing 79,971 records related to 51 traits [milk yield (expected AM, expected
PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat
yield (expected AM, expected PM, and expected daily; g/day); levels of seven different
FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the
corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and
expected daily; g/day)]. These equations were validated using two distinct external datasets.
The results obtained from the proposed models were compared to previously published
results for models which included a MI effect. The corresponding correlation values ranged
from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and
ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings.
The simplicity of these proposed models should facilitate their use by breeding and milk
recording organizations.
Keywords: milk recording; fatty acid groups; prediction model; single milking
1. Introduction
According to Arnould et al. [1], milk yield, and, particularly, milk fat composition, may facilitate the
development of strategies to prevent and monitor milk production dysfunction in dairy cattle, and may
improve the sustainability of dairy production systems. Correspondingly, various milk fatty acids (FA)
have shown a relationship with methane production in dairy cattle. For example, positive correlations
between saturated FA (SFA) and methane output has been observed (r = 0.87–0.91) [2]. Another example
involves ketosis detection. In reports by van Haelst et al. and Gross et al. [3,4]), a high proportion of long
chain FA (LCFA; especially if combined with a lower proportion of medium chain FA (MCFA)), and
especially a high proportion of C18:1 cis-9, in milk fat were found to be good predictors of subclinical
ketosis. Therefore, a regular quantification of FA in milk is relevant.
Recent studies have demonstrated that mid-infrared spectrometry (MIR) has the potential to quantify
the FA content of milk [5–7]). Therefore, the creation of spectral databases represents valuable resources
for determining the FA profile of test-day samples collected from lactating cows that are routinely
monitored using specific MIR calibration equations. For instance, this is currently realized by the
Belgian (Walloon Breeding Association, Ciney, Belgium) and Luxembourg (CONVIS s.c., Ettelbruck,
Luxembourg) milk recording organizations. Thanks to the easy acquisition of spectral data, other
countries will realize the same work in a near future.
To develop robust management tools, the used phenotypic data should be homogenous. However,
the uses of different sampling methods can bring heterogeneity. Milk recording organizations in many
countries use more and more often an alternate morning (AM) and evening (PM) testing scheme since
it is less expensive than analyzing one milk sample per cow that includes 50% of a representative AM
Animals 2015,5645
milking fraction and 50% of a representative PM milking fraction. Since the 1970s, numerous equations
have been evaluated for their capacity to estimate total daily yields for traditional production traits
(i.e., milk, fat, and protein) from alternate protocols. For example, Lee and Wardrop [8] studied the
effects of milking interval (MI; the duration between two consecutive milkings, expressed in h or min;
AM or PM) and stage of lactation on daily milk, fat, and protein yields, and fat and protein content.
In 1986, adjustment factors for daily milk, fat, and protein yields were reported by Delorenzo and
Wiggans [9], and these remain the most widely used factors based on their ability to take into account
heterogeneous means and variances between MIs and classes of days in milk (cDIM). In 2000, this
model was modified [10], and the changes were approved by the International Committee for Animal
Recording [11]. At our knowledge, nothing is done currently about FA. The general aim of this paper
is therefore to develop equations to estimate the daily yields of the major FAs present in milk, including
SFA, unsaturated FA (UFA), mono-unsaturated FA (MUFA), short-chain FA (SCFA), MCFA, and LCFA
from a single milking. In addition, C18:1 cis-9 was also studied because this FA is interesting for
management purposes [1].
Most of the studies mentioned in the above paragraph included an MI parameter in their predictive
models. However, such information might be difficult to collect on a farm since the time and duration of
milking is often inconsistent. In a previous report [12], it is mentioned that changes in milk composition
can occur according to the MI primarily due to a dilution effect. Thus, a high volume of milk produced
during one milking would be predicted to contain less fat and protein compared to a smaller volume of
milk. Based on this concept, MIs could affect the levels of detected milk components. Therefore, an
additional aim of the present study was to compare the results obtained using the models of Liu [10] and
Berry [13] that include an MI effect for milk, fat, and protein yields with the results obtained from models
that include only factors related to milk composition and production. Potentially, such models could
provide a straightforward prediction of daily yields for production traits from more readily available
information (i.e., fat and protein content and other MIR predicted traits).
2. Materials and Methods
2.1. Available Data
2.1.1. Overall Strategy
To develop equations which permit the estimation of FA daily yields from one milking, measurements
of milk yield and milk composition at each milking are needed, as well as milk composition data
from 50% AM and 50% PM milk samples. Unfortunately, separate AM and PM milk samples at
the same test day were never collected by the Luxembourg milk recording (CONVIS s.c., Ettelbruck,
Luxembourg). Therefore, the innovative part of this study was to create a calibration set including AM
and PM expected values estimated using selection index theory from available mixed samples. Then, the
equations developed using these expected phenotypes were validated using real data. Indeed, a sampling
including AM, PM and mixed milk samples was performed on a limited number of cows and herds in
order to create a validation set. More details are given in the following sections.
Animals 2015,5646
2.1.2. Calibration Data
The calibration dataset included milk samples collected in Luxembourg between October 2007 and
April 2013 during routine conventional milk testing (data S). These milk samples were composed of
50% morning milk and 50% evening milk and were collected from 21,582 Holstein cows in 163 herds.
All of the milk samples were analyzed by MIR spectrometry using a Foss MilkoScan FT6000 (Hillerod,
Denmark) at CONVIS s.c. (Ettelbruck, Luxembourg). MIR analysis of the milk samples provided
spectral data and the quantities of major milk components, including fat and protein content. By
applying the updated equations of Soyeurt et al. [7], SFA, MUFA, UFA, SCFA, MCFA, LCFA, and
C18:1 cis-9 content in each milk sample (g/L) were determined. As a result, the ratio of the standard
error of cross-validation to the standard deviation (SD) of gas chromatography FA values used in the
calibration set (referred to as a RPD parameter) greater than five was observed. Table 1shows the
statistical parameters of the calibration equations used. The data used to build the mid-infrared FA
equations were not related to the data used in this study.
Table 1. Estimated statistical parameters for each calibration equation that estimated the
concentration of fatty acids (FAs) in milk (g/dL of milk).
FA N Mean SD SECV R2cv RPD
SFA 1176 2.69 0.79 0.051 0.9958 15.34
MUFA 1180 1.04 0.34 0.047 0.9805 7.18
UFA 1179 1.20 0.39 0.051 0.9828 7.62
SCFA 1185 0.35 0.10 0.020 0.9613 5.10
MCFA 1187 2.06 0.65 0.086 0.9824 7.53
LCFA 1188 1.50 0.52 0.087 0.9718 5.96
C18:1 cis-9 1194 0.71 0.26 0.051 0.9610 5.06
FA = fatty acid; SD = standard deviation; SECV = standard error of cross-validation; R2cv =
cross-validation coefficient of determination; RPD = ratio of standard error of cross-validation to standard
deviation; SFA = saturated fatty acids; MUFA = monounsaturated fatty acids; UFA = unsaturated fatty acids;
SCFA = short chain fatty acids, MCFA = medium chain fatty acids; LCFA = long chain fatty acids.
Records were discarded from the dataset if test-day records were lower or higher than mean ˘three
times the observed SD. Furthermore, only spectral data with known production factors such as DIM, and
parity were kept. After these edits, the final calibration dataset contained 79,971 records.
Data from the S milk recording scheme included observed FAT50{50 , SFA50{50, MUFA50{50, UFA50{50,
SCFA50{50 , MCFA50{50, LCFA50{50, and C18:1 cis-950{50 . This dataset also contained AM, PM, and 24
h milk yields. However, this dataset did not contain records for milk composition related to AM or PM
milkings. Therefore, a method similar to that of the selection index theory was used to calculate expected
values for: SFAAM,SFAPM, MUFAAM,MUFAPM, UFAAM, UFAPM, SCFAAM , SCFAPM, MCFAAM ,
MCFAPM , LCFAAM, LCFAPM , and C18:1 cis-9AM , C181 cis-9PM . This method was based on a linear
combination of phenotypic data and the following two equations:
observed_trait50/50 “0.5 ˆvalueAM `0.5 ˆvalueP M (1)
Animals 2015,5647
exp„ected_traitAM„or„PM “fpmilk_yieldAM„or„PM , f at_yieldAM„or„PMq(2)
Equation (1) assumes that the milk samples contained 50% AM milk and 50% PM milk. In addition,
a non-zero correlation between milk fat composition during the AM or PM milking and the milk and fat
yields during the same milking were also assumed (Equation (2)).
Equations (1) and (2) can then be combined to generate Equation (3):
Animals 2015, 5 647
Equation (1) assumes that the milk samples contained 50% AM milk and 50% PM milk. In
addition, a non-zero correlation between milk fat composition during the AM or PM milking and the
milk and fat yields during the same milking were also assumed (Equation (2)).
Equations (1) and (2) can then be combined to generate Equation (3):
5.25.01
5.05.21
3
1
with
_exp
_exp
_
_exp
_exp
_
5.25.01
5.05.21
3
1
_exp
_exp
_
105.0
015.0
25.125.0
25.025.1
_exp
_exp
_
105.0
015.0
10
01
5.05.0
105
.0
015.0
10
01
5.05.0
_exp
_exp
_
50/50
50/50
50/50
1
50/50
50/50
AA
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
PM
AM
traitected
traitected
traitobserved
i
traitected
traitected
traitobserved
value
value
traitected
traitected
traitobserved
value
value
traitected
traitected
traitobserved
value
value
value
value
traitected
traitected
traitobserved
(3)
where i is the vector that contains the AM and PM values that will be used to build the equations to
predict daily yield from AM or PM data for the trait considered and A is the matrix containing the
coefficients used to combine observed studied_trait50/50 with the expected_traitAM or PM, these values
being equal to b × milk_yieldAM or PM. The b coefficients for each studied traits were calculated based
on regression analyses performed using Statistical Analysis System (SAS) software where the yield of
the studied trait (calculated as content × milk yield) for the AM (PM) milking is the dependent variable
and the milk yield observed after the AM (PM) milking is the independent variable.
The b coefficients were obtained from a second dataset that included 225,890 milk samples
collected between October 2007 and February 2013 during the Luxembourg routine alternative milk
recording (type T) from 31,510 cows (Holstein) in 491 herds (data T). During this milk testing, only
one milk sample was collected per cow at one milking (AM or PM). Therefore, FATAM (FATPM),
SFAAM (SFAPM), MUFAAM (MUFAPM), UFAAM (UFAPM), SCFAAM (SCFAPM), MCFAAM (MCFAPM),
LCFAAM (LCFAPM), and C18:1 cis-9AM (C181 cis-9PM) were available for dataset T. This dataset also
contained the AM or PM milk yield.
Based on this approach, expected AM and PM records were obtained for the dataset S. The daily average
quantities (g/day) for all of the studied traits were estimated as the sum of yields after both milkings
(AM and PM). Therefore, the final calibration dataset contained 79,971 records related to 51 traits [milk yield
(expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and
expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different
FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding
FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)].
(3)
where iis the vector that contains the AM and PM values that will be used to build the equations to predict
daily yield from AM or PM data for the trait considered and A is the matrix containing the coefficients
used to combine observed studied_trait50{50 with the expected_traitAMorPM, these values being equal to
bˆmilk_yieldAMorPM. The b coefficients for each studied traits were calculated based on regression
analyses performed using Statistical Analysis System (SAS) software where the yield of the studied trait
(calculated as content ˆmilk yield) for the AM (PM) milking is the dependent variable and the milk
yield observed after the AM (PM) milking is the independent variable.
The b coefficients were obtained from a second dataset that included 225,890 milk samples collected
between October 2007 and February 2013 during the Luxembourg routine alternative milk recording
(type T) from 31,510 cows (Holstein) in 491 herds (data T). During this milk testing, only one milk
sample was collected per cow at one milking (AM or PM). Therefore, FATAM (FATPM ), SFAAM
(SFAPM ), MUFAAM (MUFAPM), UFAAM (UFAPM ), SCFAAM (SCFAPM ), MCFAAM (MCFAPM),
LCFAAM (LCFAPM), and C18:1 cis-9AM (C181 cis-9PM ) were available for dataset T. This dataset also
contained the AM or PM milk yield.
Based on this approach, expected AM and PM records were obtained for the dataset S. The daily
average quantities (g/day) for all of the studied traits were estimated as the sum of yields after both
milkings (AM and PM). Therefore, the final calibration dataset contained 79,971 records related to
51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM,
expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day);
Animals 2015,5648
levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk),
and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and
expected daily; g/day)].
2.1.3. Validation Data
The equations to predict daily yields were validated using two distinct external validation datasets that
included data for representative milk samples collected during two successive milkings in Luxembourg
(between February and April 2013) and in the Walloon Region of Belgium (from October 2007 to
June 2012).
The first validation dataset included representative milk samples (50 mL) collected from two
consecutive milkings from 687 dairy cows (Holstein) belonging to 43 herds between February 2013
and April 2013 by CONVIS s.c. (Ettelbruck, Luxembourg; LUX data). This dataset contained observed
yields from consecutive AM and PM milkings. Daily yields were also calculated. These samples were
analyzed by MIR spectrometry using a FOSS Milkoscan FT6000 (Foss, Hillerod, Denmark). FA content
(g/dL of milk) was estimated by applying the MIR calibration equations described in Table 1.
The second validation dataset included milk samples composed of 50% morning milk and 50%
evening milk. These samples were collected from 138,141 Holstein cows belonging to 1291 herds
that participated in the Walloon milk recording system from October 2007 to June 2012.Samples were
collected from all of the cows milked in the herds on a given test day, and these samples were analyzed
using MIR spectrometry (MilkoScan FT6000; FOSS, 2005) according to the normal milk recording
procedure [11]. The final Walloon validation dataset contained 1,079,318 records (WAL data). AM and
PM values were estimated by the same methodology used to create the calibration set.
2.2. Development of Statistical Models for Estimating Daily Yields from AM or PM Milking
Models were developed to investigate whether daily yields can be estimated by replacing the
MI effect [10] with different traits that are easily recorded and that are related to changes in milk
composition. Several variation factors were tested in order to build a robust model that uses information
easily collected by milk recording organizations, including: stage of lactation (DIM), parity, yield traits
(g/milking) during AM and PM milking, and the month of recording [14]. Stage of lactation is known
to be one of the most influential factors affecting milk composition [10,15,16], and a month of recording
was included in order to consider the season effect, and, indirectly, the feeding effect which affects the
FA composition of milk [17,18]. Considering that not all of these influential factors may have statistically
significant effects on all of the traits examined, an appropriate subset of variables for each model was
determined using the stepwise GLMSELECT procedure in the SAS/STAT software package [19]. The
data used to develop the models came from the calibration set. The TEST dataset required by the
GLMSELECT procedure was the LUX validation dataset collected in Luxembourg and including real
observed AM/PM data. The VAL dataset, required by the GLMSELECT procedure, corresponded to the
WAL validation dataset which was collected in the Walloon Region of Belgium.This procedure allowed a
model to be selected from the framework of general linear models. All of the models that were developed
were compared for all of the studied traits: milk yield, FAT, SFA, MUFA, UFA, SCFA, MCFA, LCFA,
and C18:1 cis-9.
Animals 2015,5649
The accuracy of the AM-PM predictions was evaluated using two criteria. First, root mean squared
error (RMSE) was calculated (Equation (4)), which represents the SD of the difference between
observed and estimated daily yields. The model with the smallest RMSE and the highest coefficient
of determination (or correlation) was considered to provide the best fit.
RMSE “dSSE
n´p(4)
where n is the number of observations in the statistical model, p is the number of parameters (including
the intercept), and SSE is the error sum of squares (i.e., the sum of the squared differences between each
observation and its predicted value) for the estimated model.
The second criterion was R2, defined as the coefficient of determination. The square root of this value
is the correlation (Ry,ˆ
y) which represents the relationship between the observed and predicted values.
Statistical parameters were calculated using the GLMSELECT procedure in the SAS/STAT software
package [19].
A validation was applied on the best fitted model using the two available validation sets. The estimated
statistical parameters were RMSE, the standard deviation of prediction (σˆ
y) and Ry,ˆ
y.
3. Results and Discussion
3.1. Available Data
Tables 2–4present descriptive statistics of the traits studied. Daily average values showed the same
direction for the three datasets except for milk production. The origins of each dataset could explain
these differences. For example, the calibration dataset (Table 2) and the first validation set (Table 3)
were obtained from Luxembourg, with the latter including milk samples that were collected over a short
period of time (between February 2013 and April 2013). In contrast, the second validation dataset
(Table 4) was generated from cows recorded in the Walloon Region of Belgium from October 2007 to
June 2012.
Animals 2015,5650
Table 2. Descriptive statistics of the calibration dataset (N = 79,971).
Variable
Collection
of milk
sample
Mean SD Min Max. Mean SD Min Max
Milk
(kg/day)
AM 12.79 4.27 1.20 37.30
PM 13.57 4.41 1.20 39.20
Daily 26.36 8.33 2.40 72.80
g/dL milk g/day
Fat
Expected
AM
4.11 0.71 1.01 7.23 515.31 164.29 22.13 1629.96
Expected
PM
4.55 0.78 1.12 8.00 605.64 191.21 24.48 1822.35
Expected
Daily
4.34 0.75 1.07 7.67 1120.94 340.33 46.61 3345.67
SFA
Expected
AM
2.76 0.56 0.51 6.95 344.33 111.28 14.49 1272.59
Expected
PM
2.95 0.60 0.54 7.44 392.11 127.91 15.51 1268.89
Expected
Daily
2.86 0.58 0.53 7.21 736.41 229.39 30.00 2482.20
MUFA
Expected
AM
1.15 0.24 0.29 3.84 144.14 53.26 6.08 678.89
Expected
PM
1.36 0.29 0.35 4.56 181.38 64.96 7.22 963.86
Expected
Daily
1.26 0.27 0.32 4.22 325.55 114.28 13.31 1562.22
UFA
Expected
AM
1.34 0.27 0.37 4.25 168.57 61.34 7.14 765.02
Expected
PM
1.59 0.32 0.44 5.02 210.87 73.76 8.45 1047.49
Expected
Daily
1.47 0.29 0.40 4.66 379.44 130.42 15.59 1700.99
SCFA
Expected
AM
0.38 0.08 0.10 1.02 47.77 16.50 1.63 200.32
Expected
PM
0.40 0.08 0.10 1.08 53.83 18.69 1.73 188.64
Expected
Daily
0.39 0.08 0.10 1.06 101.57 33.93 3.36 388.96
MCFA
Expected
AM
2.16 0.47 0.06 5.77 268.89 87.51 6.98 975.09
Expected
PM
2.29 0.50 0.06 6.11 302.83 99.40 9.45 937.77
Expected
Daily
2.22 0.49 0.06 5.96 571.72 179.28 16.43 1891.53
Animals 2015,5651
Table 2. Cont.
Variable
Collection
of milk
sample
Mean SD Min Max. Mean SD Min Max
LCFA
Expected
AM
1.58 0.35 0.31 5.47 198.89 75.77 6.65 1028.49
Expected
PM
1.87 0.42 0.37 6.48 249.47 92.22 7.88 1352.77
Expected
Daily
1.73 0.39 0.34 6.01 448.39 162.72 14.53 2195.18
C18:1
cis-9
Expected
AM
0.75 0.19 0.07 2.97 94.13 38.48 4.51 501.97
Expected
PM
0.91 0.23 0.09 3.59 120.85 48.53 5.46 777.79
Expected
Daily
0.83 0.21 0.08 3.30 214.98 84.65 9.96 1251.72
Min: minimum; Max: maximum; AM = morning milking; PM = evening milking, Daily = daily content; SFA
= saturated fatty acids; MUFA = monounsaturated fatty acids; UFA = unsaturated fatty acids; SCFA = short
chain fatty acids; MCFA = medium chain fatty acids; LCFA = long chain fatty acids.
In the calibration set, the average milk production between October 2007 and April 2013 (Table 2)
was 26.36 kg/day, with 4.34 g fat/dL milk having a saturated part equal to 65.9%. Based on the WAL
dataset, the average production was 24.11 kg milk/day, with 4.25 g fat/dL milk composed of 68.2%
SFAs (Table 4). These values for fat and SFA content were slightly higher than those observed for the
calibration set (Table 2). Overall, the quantities and content of individual FAs present in the milk samples
and fat were consistent with those previously reported for the Walloon data [20–22]. The milk and fat
yields had similar descriptive statistics compared to the results mentioned by Liu et al. [10] from their
calibration set.
Table 3. Descriptive statistics of the Luxembourg (LUX) validation dataset (N = 687).
Variable
Collection
of milk
sample
Mean SD Min Max Mean SD Min Max
Milk(kg/day)
AM 12.83 4.46 2.40 30.10
PM 15.13 5.18 2.80 33.00
Daily 27.96 9.41 5.20 57.00
g/dL milk g/day
Fat
AM 4.27 0.80 1.05 7.51 537.47 187.33 115.64 1257.44
PM 4.68 0.80 1.59 7.51 695.33 239.95 165.56 1550.10
Daily 4.49 0.71 2.33 7.33 1232.80 404.26 321.01 2656.58
SFA
AM 2.91 0.59 0.74 5.03 364.69 126.28 80.44 801.65
PM 3.14 0.59 1.13 5.90 465.74 159.42 96.15 1075.73
Daily 3.03 0.53 1.50 5.22 830.43 268.59 181.14 1698.26
Animals 2015,5652
Table 3. Cont.
Variable
Collection
of milk
sample
Mean SD Min Max Mean SD Min Max
MUFA
AM 1.18 0.29 0.27 3.67 148.79 61.37 29.71 448.47
PM 1.33 0.30 0.39 3.37 198.17 85.18 52.20 763.26
Daily 1.26 0.27 0.62 3.02 346.97 139.58 111.48 1094.06
UFA
AM 1.40 0.32 0.33 4.08 176.17 70.56 36.82 506.42
PM 1.56 0.34 0.49 3.75 233.58 97.09 61.22 853.54
Daily 1.49 0.30 0.75 3.37 409.75 159.88 128.22 1229.88
SCFA
AM 0.40 0.08 0.10 0.71 51.00 18.47 7.36 112.88
PM 0.44 0.08 0.17 0.79 65.16 23.18 8.80 145.92
Daily 0.42 0.07 0.24 0.71 116.16 39.48 16.16 240.74
MCFA
AM 2.30 0.47 0.56 3.95 288.14 98.17 53.54 622.87
PM 2.47 0.47 0.94 4.11 365.12 121.71 67.72 799.04
Daily 2.39 0.42 1.15 3.78 653.33 206.66 121.27 1314.99
LCFA
AM 1.66 0.41 0.46 5.29 209.24 87.77 48.90 669.326
PM 1.86 0.44 0.55 5.09 278.76 122.40 71.67 1123.44
Daily 1.77 0.39 0.80 4.49 488.00 200.03 147.86 1582.02
C18:1 cis-9
AM 0.81 0.23 0.21 2.85 102.13 46.85 23.20 361.59
PM 0.91 0.24 0.27 2.54 136.21 65.34 35.44 593.22
Daily 0.86 0.22 0.43 2.27 238.35 107.11 75.06 852.25
Min: minimum; Max: maximum; AM = morning milking; PM = evening milking, Daily = daily
content; SFA = saturated fatty acids; MUFA = monounsaturated fatty acids; UFA = unsaturated fatty acids;
SCFA = short chain fatty acids; MCFA = medium chain fatty acids; LCFA = long chain fatty acids.
Table 4. Descriptive statistics of the Walloon (WAL) validation dataset (N = 1,079,318).
Variable Collection of
milk sample
Mean SD Min Max Mean SD Min Max
Milk
(kg/day)
AM 11.41 4.27 0.20 49.00
PM 12.70 4.63 0.40 49.00
Daily 24.11 8.64 3.00 75.40
g/dL milk g/day
Fat
Expected AM 4.00 0.70 0.09 6.61 448.07 166.73 7.21 2730.67
Expected PM 4.47 0.79 0.11 7.40 558.72 204.05 12.86 3418.45
Expected Daily 4.25 0.75 0.10 7.22 1006.79 360.00 30.89 4640.37
SFA
Expected AM 2.78 0.56 0.00 5.35 311.57 119.42 0.62 1590.42
Expected PM 3.00 0.60 0.01 5.79 375.79 142.26 0.42 1854.17
Expected Daily 2.90 0.58 0.00 5.56 687.55 254.50 1.05 2792.34
Animals 2015,5653
Table 4. Cont.
Variable Collection of
milk sample
Mean SD Min Max Mean SD Min Max
MUFA
Expected AM 1.06 0.24 0.05 3.79 117.76 48.00 1.80 1022.04
Expected PM 1.27 0.29 0.06 4.56 157.73 62.68 3.98 1458.89
Expected Daily 1.17 0.27 0.06 4.18 275.49 107.92 8.62 1943.96
UFA
Expected AM 1.20 0.26 0.03 3.72 134.10 54.00 2.10 1127.60
Expected PM 1.44 0.32 0.04 4.47 179.10 70.40 2.80 1588.10
Expected Daily 1.33 0.29 0.03 4.10 313.10 121.30 5.50 2117.50
SCFA
Expected AM 0.36 0.08 0.01 0.93 40.79 17.00 0.67 191.11
Expected PM 0.38 0.08 0.01 0.99 48.52 19.90 0.57 221.20
Expected Daily 0.37 0.08 0.01 0.96 89.31 36.03 1.25 383.54
MCFA
Expected AM 2.18 0.49 0.00 4.49 243.85 94.77 0.63 1095.67
Expected PM 2.36 0.53 0.01 4.86 294.22 113.42 0.43 1257.14
Expected Daily 2.27 0.51 0.00 4.71 538.08 202.61 1.06 2125.97
LCFA
Expected AM 1.46 0.35 0.04 4.41 163.50 68.96 1.50 1503.15
Expected PM 1.73 0.41 0.04 5.23 215.80 88.64 0.71 2066.22
Expected Daily 1.60 0.38 0.04 4.83 379.30 153.91 2.21 2762.95
C18:1 cis-9
Expected AM 0.75 0.20 0.01 2.67 83.99 36.72 0.31 820.82
Expected PM 0.89 0.23 0.02 3.17 110.90 47.33 0.29 1185.75
Expected Daily 0.83 0.21 0.01 2.97 194.89 82.20 0.60 1585.61
Min: minimum; Max: maximum; AM = morning milking; PM = evening milking, Daily = daily
content; SFA = saturated fatty acids; MUFA = monounsaturated fatty acids; UFA = unsaturated fatty acids;
SCFA = short chain fatty acids; MCFA = medium chain fatty acids; LCFA = long chain fatty acids.
3.2. Phenotypic Correlations
Table 5shows the correlations identified between AM and PM collection times, and for daily contents
and yields, for all of the studied traits. Correlations between AM and PM values varied according to trait
and were lower than one, suggesting that AM and PM records represent two distinct types of traits, and,
therefore, need to have individual equations developed for estimating daily yield and content. Correlation
values between yield traits were higher than those observed between content traits. For both units of
expression, the correlations were lower for the fatty traits than milk yield. Moreover, for both content
and yield traits, the PM milking records showed higher or similar correlation values with daily traits
compared with the AM milking records. The same observation was done also by Berry et al. [13] from
fat content and yield. However, Liu et al. [10] observed globally similar correlations between AM and
PM values with a very slight tendency to have higher correlations for AM values.
Correlations between milking and daily yield traits varied from 92.4% (SFA; AM-DY) to 97.9% (milk
yield; PM-DY). The strong positive correlations between daily and AM or PM yields observed in Table 5
suggest that it may be possible to estimate daily FA yields from AM or PM FA yields.
The FAT content and FAT yield correlation values were similar than those observed by Liu et al. [10].
These authors found correlation values equal to 59.0%, 86.4%, and 85.8% for AM/PM, AM/daily content
Animals 2015,5654
(DC) and PM/DC correlations related to the fat content, respectively. The correlation values for the fat
yield observed by these authors for the AM/PM, AM/daily yield (DY) and PM/DY were 83.9%, 92.7%,
and 92.2%, respectively. Similar results were also obtained for milk yield. Liu et al. [10] found 90.8%,
97.9%, and 97.5% for AM/PM, AM/DY, and PM/DY correlations, respectively. The correlation values
obtained by Berry et al. [13] were often lower than the ones found in this study. For fat content (yield),
these authors calculated AM-PM, AM-DC, and PM-DC correlations equal to 36% (54%), 80% (84%),
and 84% (90%), respectively. As observed in this study, the correlations related to fat yield were higher
compared to the one observed for fat content. The same observation was done also by Liu et al. [10].
For milk yield, Berry et al. [13] found 85%, 97%, 95% for AM-PM, AM-DY, and PM-DY correlation
values, respectively. The milk correlations between AM and PM values and between AM and PM values
were slightly lower than the ones observed in this study but can be both considered as strong positive
correlations. The differences in term of correlation values between Berry et al. [13] and Liu et al. [10]
or our study can be probably explained by differences of herd management (feeding system), milking
interval and milk production.
Table 5. Correlation values among morning (AM), evening (PM), and daily records for each
studied trait expressed in g/dL of milk and kg/day. The values were obtained from LUX data
(i.e., real observations, N = 687).
g/dL of milk kg/day
Studied Trait AM-PM AM-DC PM-DC AM-PM AM-DY PM-DY
Milk 90.4 97.2 97.9
Fat 55.9 86.6 89.3 78.7 93.0 95.8
SFA 58.1 87.5 89.8 76.4 92.4 95.3
MUFA 63.7 88.3 92.1 80.9 93.3 96.6
UFA 63.3 88.3 92.0 81.4 93.6 96.6
SCFA 58.0 87.0 90.2 79.4 93.4 95.9
MCFA 60.8 88.3 90.6 76.4 92.5 95.2
LCFA 63.4 88.2 92.1 80.6 93.2 96.6
C18:1 cis-9 65.9 89.0 92.7 81.8 93.6 96.8
AM = morning milking; PM = evening milking; DC = daily content; DY = daily yield; SFA = saturated fatty
acids; MUFA = monounsaturated fatty acids; UFA = unsaturated fatty acids; SCFA = short-chain fatty acids;
MCFA = medium-chain fatty acids; LCFA = long chain fatty acids.
As also shown by Liu et al. [10] and Berry et al. [13] for milk fat, all correlations considered in
Table 5were lower for fatty traits compared to milk yield. This suggests that the prediction of daily
yield or content from AM-PM records will be less accurate for fatty traits than milk yield.
3.3. Models Selected Using PROC GLMSELECT
Table 6describes the equations that were selected using the GLMSELECT procedure for all of the
studied traits. In other words, the models provided the best fit of data are described in Table 6. These
models showed the smallest RMSE and the highest correlation between observed and estimated values.
Animals 2015,5655
Based on these results, it appears that there were similarities between the effects included in the equations
that used AM records and the ones included in the equations that used PM records for each studied trait.
This observation suggests that PM and AM values had a similar evolution pattern but the differences
came only from a question of scale. Indeed, PM values were always higher than AM values (Tables 2–4).
The PROC GLMSELECT procedure selected always combined effects. There were not individual
effects such as only DIM or only lactation number in the selected equations. Such complexity of
equations was not mentioned in previous studies [9,10,13]. However, Berry et al. [13] mentioned
heterogeneous means and variances for 24-h yield over different parities, season of calving and DIM.
Therefore, they realized 54 subclasses taken into account the parity, DIM and the season of calving.
For all of these subclasses, they estimated the coefficients of regression. The same methodology was
previously used by Liu et al. [10]. Based on the composition of selected equations mentioned in Table 6,
this study confirmed this heterogeneity because separate regression coefficients were estimated following
DIM, parity and month of test.
Table 6. Models selected by PROC GLMSELECT procedure.
Studied trait Milking
moment
Selected models
Milk AM a + b ˆDIM + c ˆmonth of test + d ˆ(milk_AM ˆ
DIM ˆparity ˆmonth of test)
PM a + b ˆDIM + c ˆmonth of test + d ˆ(milk_PM ˆ
DIM ˆparity ˆmonth of test)
Fat AM a + b ˆ(qFAT_AM ˆmilk_AM ˆDIM) + c ˆ
(parity) + d ˆ(milk_AM ˆparity) + eˆ(qFAT_AM
ˆmilk_AM ˆparity)
PM a + b ˆ(milk_PM ˆDIM) + c ˆ(qFAT_PM ˆ
milk_PM ˆDIM) + d ˆparity + e ˆ(qFAT_PM ˆ
milk_PM ˆmonth of test)
SFA AM a + b ˆ(qSFA_AM ˆmilk_AM ˆDIM) + c ˆ
parity + d ˆ(milk_AM ˆparity) + e ˆ(milk_AM ˆ
month of test)
PM a + b ˆ(milk_PM ˆDIM) + c ˆ(qSFA_PM ˆ
milk_PM ˆDIM) + d ˆ(parity) + e ˆmonth of test
MUFA AM a+bˆ(milk_AMˆDIM) + c ˆ(milk_AM ˆparity)
+ d ˆ(qMUFA_AM ˆmilk_AM ˆDIM ˆparity) +
eˆ(qMUFA_AM ˆmilk_AM ˆmonth of test)
PM a + b ˆ(milk_PM ˆDIM) + c ˆparity + d ˆ
(qMUFA_PM ˆmilk_PM ˆDIM ˆparity) + e ˆ
(milk_PM ˆmonth of test)
UFA AM a + b ˆ(milk_AM ˆDIM) + c ˆ(qUFA_AM ˆ
milk_AM ˆDIM) + d ˆparity + e ˆ(milk_AM ˆ
parity)
PM a+bˆ(milk_PM ˆDIM) + c ˆ(qUFA_PM ˆ
milk_PM ˆDIM ˆparity) + d ˆ(milk_PM ˆmonth
of test) + e ˆ(qUFA_PM ˆDIM ˆparity ˆmonth
of test)
Animals 2015,5656
Table 6. Cont.
Studied trait Milking
moment
Selected models
SCFA AM a + b ˆ(qSCFA_AM ˆmilk_AM ˆDIM) + c ˆ
parity + d ˆ(milk_AM ˆparity) + e ˆmonth of test
PM a+bˆ(qSCFA_PM ˆmilk_PM ˆDIM) + c ˆ
(milk_PM ˆparity) + d ˆ(milk_PM ˆmonth of
test) + e ˆ(qSCFA_PM ˆDIM ˆparity ˆmonth of
test)
MCFA AM a+bˆ(qMCFA_AM ˆmilk_AM ˆDIM) + c ˆ
parity + d ˆ(qMCFA_AM ˆmilk_AM ˆmonth of
test) + e ˆ(qMCFA_AM ˆDIM ˆparity ˆmonth
of test)
PM a+bˆ(milk_PM ˆDIM) + c ˆ(qMCFA_PM ˆ
milk_PM ˆDIM) + d ˆ(milk_PM ˆparity) + e ˆ
month of test + f ˆ(qMCFA_PM ˆDIM ˆparity ˆ
month of test)
LCFA AM a+bˆ(milk_AM ˆDIM ˆparity) + c ˆ
(qLCFA_AM ˆmilk_AM ˆDIM ˆparity) + d ˆ
(milk_AM ˆmonth of test) + e ˆ(qLCFA_AM ˆ
milk_AM ˆmonth of test) + f ˆ(qLCFA_AM ˆ
DIM ˆparity ˆmonth of test)
PM a+bˆ(milk_PM ˆDIM) + c ˆ(qLCFA_PM ˆ
milk_PM ˆDIM ˆparity) + d ˆ(milk_PM ˆparity
ˆmonth of test) + e ˆ(qLCFA_PM ˆDIM ˆparity
ˆmonth of test)
C18:1 cis-9 AM a+bˆ(milk_AM ˆDIM) + c ˆ(milk_AM ˆ
parity) + d ˆ(qC18:1 cis9_AM ˆmilk_AM ˆDIM
ˆparity) + e ˆ(qC18:1 cis9_AM ˆmilk_AM ˆ
parity ˆmonth of test) + f ˆ(qC18:1 cis9_AM ˆ
DIM ˆparity ˆmonth of test)
PM a+bˆ(milk_PM ˆDIM) + c ˆ(qC18:1 cis9_PM ˆ
milk_PM ˆDIM ˆparity) + d ˆmonth of test + e ˆ
(qC18:1 cis9_PM ˆmilk_PM ˆmonth of test) + f ˆ
(qC18:1 cis9_PM ˆDIM ˆparity ˆmonth of test)
3.4. Goodness of Fit
Table 7shows the correlation values calculated between the observed and estimated daily yields
(Ry,ˆ
y), RMSE, and SDs of the daily yield predictions (σˆ
y) for each studied trait estimated from the milk
samples collected during the AM or PM milking using the calibration set and the two available validation
sets. The tested models were the models selected by PROC GLMSELECT and described in Table 6.
In order to appreciate the good fitting of a model, Liu et al. [10] indicated that σˆ
y should be close to
the SD of the observed daily yield but must not be greater. In the present study, all of the estimates had
smaller σˆ
y values than the observed SD values (Tables 2–4).
Animals 2015,5657
Except for milk yield, the observed correlations suggested that the estimations of daily yield were
better when PM milking data were used. Indeed, the calibration correlation values were found to range
from 96.4% to 97.6%, and from 96.9% and 98.3%, when estimations were realized from AM or PM
milkings, respectively (Table 7). Except for milk yield, this is not in agreement with the observations
made by Liu et al. [10] and Berry et al. [13]. However, the differences between AM and PM Ry,ˆ
y values
were lower than 0.8%.
Table 7. Calibration and validation statistics (correlation values between true and estimate
daily yield (Ry,ˆ
y), root mean square errors (RMSE) and standard deviation for each studied
predicted trait (σˆ
y)) for the best model selected by PROC GLMSELECT. Ry,ˆ
y were
expressed in % and RMSE and σˆ
y were expressed in kg/day for milk and g/day for the
remaining studied traits.
Milking
moment
studied
trait
σˆ
y RMSE Ry,ˆ
y
Calib LUX WAL Calib LUX WAL Calib LUX WAL
AM
MILK 8.08 8.81 8.48 2.03 2.67 2.25 97.0 96.8 96.5
FAT 328.00 385.88 350.93 90.52 160.62 98.47 96.4 92.7 96.2
SFA 221.70 255.19 248.40 58.93 113.88 66.51 96.6 92.1 96.6
MUFA 110.95 133.37 105.22 27.42 51.30 28.22 97.1 93.2 96.5
UFA 127.50 152.31 118.81 32.40 58.14 32.36 96.9 93.4 96.4
SCFA 32.95 37.34 35.37 8.10 15.90 8.58 97.1 93.1 97.1
MCFA 173.37 198.66 195.92 45.66 89.12 52.81 96.7 92.0 92.7
LCFA 158.29 188.74 151.19 37.72 73.34 38.71 97.3 93.3 96.8
C18:1 82.63 105.13 82.50 18.53 38.66 20.11 97.6 97.0 93.4
PM
MILK 8.00 9.36 8.22 2.26 2.57 2.41 96.5 97.5 97.0
FAT 331.70 405.91 352.63 85.49 124.65 91.27 96.8 95.6 97.1
SFA 222.38 272.71 247.44 56.30 90.49 61.22 96.9 95.0 97.4
MUFA 111.80 144.42 106.43 23.82 38.28 24.33 97.8 96.4 97.8
UFA 127.39 165.01 119.82 27.91 43.56 27.98 97.7 96.5 97.7
SCFA 33.01 40.71 35.09 7.85 13.01 8.06 97.3 95.5 97.7
MCFA 173.77 207.82 198.22 44.13 70.49 49.00 96.9 94.8 97.4
LCFA 159.35 208.85 151.48 32.95 55.78 33.06 97.9 96.4 97.9
C18:1 83.21 109.27 79.94 15.54 27.81 16.41 98.3 96.7 98.1
Calib = calibration set including expected Luxembourg records (N = 79,971); LUX = Validation set
including real collected Luxembourg records (N = 687); WAL = Validation set including expected Walloon
records (N = 1,079,318); AM = morning milking; PM = evening milking; SFA = saturated fatty acids;
MUFA = monounsaturated fatty acids; UFA = unsaturated fatty acids; SFCA = short-chain fatty acids;
MCFA = medium-chain fatty acids; LCFA = long chain fatty acids.
Regarding the estimations of daily milk yield, the calibration correlation values were slightly lower
than those obtained by Liu et al. [10] (e.g., 97.0% vs. 97.7% and 96.5% vs. 97.4% for the AM and PM
milking data, respectively) (Table 7). The σˆ
y and RMSE values were also slightly higher in our study
(for the AM and PM milking data: 8.08 and 8.00 kg/day vs. 7.85 and 7.83 kg/day for the σˆ
y values,
Animals 2015,5658
respectively; and 2.03 and 2.26 kg/day vs. 1.72 and 1.84 kg/day for the RMSE values, respectively)
(Table 7).
For the estimates of daily fat yield, obtained values for Ry,ˆ
y, and RMSE corresponded with a better
fit of the model compared with Liu et al. [10] (for the AM and PM milking data: 96.4% vs. 94.3% and
96.8% vs. 94.0% for Ry,ˆ
y, respectively; and 90.52 vs. 106.0 g/day and 85.49 vs. 109.0 g/day for RMSE,
respectively). The σˆ
y values were slightly higher in the present study (328.0 vs. 301.6 g/day and 331.7
vs. 300.6 g/day, respectively) (Table 7).
Better AM/PM predictions were observed for milk yield compared to fat content and yield. It was
also observed by Liu et al. [10] and Berry et al. [13]. These last authors suggested that factors were
missing in their equations permitting to predict AM/PM values for fat traits. However, in this study, the
differences in terms of Ry, ˆ
y between milk and fat were lower. This is explained by a better fitting of fat
traits in the current study.
Observed AM/PM calibration Ry, ˆ
y values for fatty acid traits were all within the same range and were
higher than 96% suggesting a good prediction.
3.5. Model Validation
As expected, validation Ry,ˆ
y values obtained from the two validation sets were lower than calibration
Ry,ˆ
y values. Validation RMSE values were higher than the observed calibration RMSE values (Table 7).
However, RMSE observed for the LUX validation set (i.e., real observed data) were bigger than the WAL
validation set (i.e., expected daily records). One hypothesis is that these differences were due to the initial
step used to predict AM/PM values for the calibration set. A potential confirmation of this hypothesis
comes from the fact that small differences were observed between the RMSE or Ry,ˆ
y observed from
the first and second validation data sets for the equations predicting daily milk yield whose AM and
PM milk records were always observed. However, the predictability stayed good with Ry,ˆ
y never lower
than 92.0%.
Small differences observed between calibration and WAL validation results (i.e., these results were
predicted using the same methodology as the one used for the calibration set) suggest a good robustness
of the developed equations which was the main interest of the proposed methodology to build the
calibration dataset. Indeed, as the first validation set which was composed of real records, was not
large enough to cover the entire lactation, many parities, herds or cows, the theory of selection index
was used to predict AM–PM records from 50% AM/50% PM collected records. Better results could be
obtained by using only real observations but a large sampling procedure (larger than the one conducted
for the LUX data) should be conducted to present a sufficient variability for DIM, parity, month of test
as well as studied traits. The advantage of the selection index theory applied in this study is to use data
routinely available at large scale to build the predictive models and, therefore, to require a smaller dataset
containing real observations to validate the obtained models.
3.6. Milking Interval
The models proposed in the present study demonstrated that it is possible to estimate milk, fat, and
FA yields without the use of MI recorded on site. To explain this observation, different regressions
including the effects and covariates related to changes in milk were tested in order to estimate MI values
Animals 2015,5659
(Table 8). An R2 value of 0.86% was observed between MI and milk daily yield. Additional covariates
and fixed classification effects can be included in the regression model (such as milk and fat yields
obtained during one milking record) if we assume that the milk composition is also influenced by the MI
due to the dilution effect. To predict daily yields for milk, fat, and protein, Berry et al. [13] introduced
milk yield and fat yields of one milking a day. By using this approach, the obtained R2 increased to
17.6% (Table 8).When the stage of lactation was added, the R2 obtained was 18.2% (Table 8), while
inclusion of the parity effect resulted in an R2 value of 18.4%. All of the effects proposed to describe
variations in MI were significant. Therefore, nearly 20% of the MI variability observed can be explained
by a combination of effects related to milk composition and production. Consequently, we can assume
that the MI effect can be partially replaced by a combination of data that are generally available and
are easily recorded by milk recording organizations. In addition, the accuracy of reported MI can be
problematic because, with increasing herd sizes and milking times, the actual MI for a given cow can
be very different from the reported herd MI. Indirect predictors as used in this study have the advantage
that they will be always known very precisely on an individual level.
Table 8. Regression coefficients (in %) for the regressions explaining the milking interval
(MI) in function of milk production, fat (g/milking or /day), dim, and parity (N = 79,971).
MI R2
Milk daily yield 0.86
Milk daily yield + Milk (AM or PM) yield 17.22
Milk daily yield + Milk (AM or PM) yield + FAT (AM or PM) (g/dL of milk) 17.64
Milk daily yield + Milk (AM or PM) yield + FAT (AM or PM) (g/dL of milk) + DIM 18.23
Milk daily yield + Milk (AM or PM) yield + FAT (AM or PM) (g/dL of milk) + DIM + parity 18.40
MI: milking interval; AM = morning milking; PM = evening milking; DIM = Days in milk.
4. Conclusions
The main objective of this study was to propose a practical, simple, and robust method for accurately
estimating daily FA yields from a single milking (i.e., AM or PM milking). The obtained results show
the interest to use the theory of the selection index to construct the calibration set in order to have more
robust equations thanks to a large calibration set. With validation Ry,ˆ
y higher than 92% obtained from
observed records for all studied traits, the results are promising, although further studies are needed
to confirm these results by using a larger database. Moreover, the results obtained also shows that it
is possible to replace the MI parameter with a combination of more reliable parameters such as: milk
production and fat content, stage of lactation classes, the test month, and calving month. The application
of the models developed in this study has the potential to reduce the number of collected samples per
test-day (i.e., only one AM or PM sample is necessary instead of the two samples needed for the 50/50
sample), thereby reducing the costs associated with official milk recording (i.e., only one visit of the
milk recorder in the farm), while still maintaining a high accuracy of predicted daily yields.
Animals 2015,5660
Acknowledgments
Funding for this research was provided by an AFR grant (AFR PHD-09-119-RE) from the National
Research Fund, Luxembourg, and from grants F.4552.05, 2.4507.02 F (2), and 2.4623.08 from the FNRS.
The authors also acknowledge their collaboration with the Walloon Breeding Association.
Author Contributions
The study was conceived and managed by Arnould and Soyeurt. Both authors contributed to the
presentation and interpretation of findings. Data collection were undertaken by CONVIS s.c.. All
authors contributed to writing of the manuscript. The authors thank the anonymous referees for helpful
comments improving the quality of the manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
References
1. Arnould, V.M.-R.; Reding, R.; Bormann, J.; Gengler, N.; Soyeurt, H. Review: Milk composition
as management tool of sustainability. Biotechnol. Agron. Soc. Environ. 2013,17, 613–621.
2. Chilliard, Y.; Martin, C.; Rouel, J.; Moreau, M. Milk fatty acids in dairy cows fed whole crude
linseed, extruded linseed, or linseed oil, and their relationship with methane output. J. Dairy Sci.
2009,92, 5199–5211. [CrossRef] [PubMed]
3. Van Haelst, Y.N.T.; Beeckman, A.; van Knegsel, A.T.M.; Fievez, V. Short communication: Elevated
concentrations of oleic acid and long-chain fatty acids in milk fat of multiparous subclinical ketotic
cows. J. Dairy Sci. 2008,91, 4683–4686. [CrossRef] [PubMed]
4. Gross, J.; van Dorland, H.A.; Bruckmaier, R.M.; Schwarz, F.J. Milk fatty acids profile related to
energy balance in dairy cows. J. Dairy Res. 2011,78, 479–488. [CrossRef] [PubMed]
5. Rutten, M.J.M.; Bovenhuis, H.; Hettinga, K.A.; van Vanlenberg, H.J.F.; van Arendonck, J.
Predicting bovine milk fat composition using infrared spectroscopy based on milk samples
collected in winter and summer. J. Dairy Sci. 2009,92, 6202–6209. [CrossRef] [PubMed]
6. Soyeurt, H.; Dardenne, P.; Dehareng, F.; Lognay, G.; Veselko, D.; Marlier, M.; Bertozzi, C.;
Mayeres, P.; Gengler, N. Estimating fatty acid content in cow milk using mid-infrared spectrometry.
J. Dairy Sci. 2006,89, 3690–3695. [CrossRef]
7. Soyeurt, H.; Dehareng, F.; Gengler, N.; McParland, S.; Wall, E.; Berry, D.P.; Coffey, M.;
Dardenne, P. Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production
systems, and countries. J. Dairy Sci. 2011,94, 1657–1667. [CrossRef] [PubMed]
8. Lee, A.J.; Wardrop, J. Predicting daily milk yield, fat percent, and protein percent from morning or
afternoon tests. J. Dairy Sci. 1984,67, 351–360. [CrossRef]
9. Delorenzo, M.A.; Wiggans, G.R. Factors for estimating daily yield of milk, fat, and protein from a
single milking for herds milked twice a day. J. Dairy Sci. 1986,69, 2386–2394. [CrossRef]
Animals 2015,5661
10. Liu, Z.; Reents, R.; Reinhardt, F.; Kuwan, K. Approaches to estimating daily yield from single milk
testing schemes and use of a.m.-p.m. records in test-day model genetic evaluation in dairy cattle.
J. Dairy Sci. 2000,83, 2672–2682. [CrossRef]
11. ICAR. International Committee for Animal Recording, International agreement of recording
practices. Available online: http://www.icar.org/documents/Rules%20and%20regulations/
Guidelines/Guidelines_2012.pdf (accessed on 26 December 2014).
12. Ouweltjes, W. The relationship between milk yield and milking interval in dairy cows. Livest. Prod.
Sci. 1998,56, 193–201. [CrossRef]
13. Berry, D.P.; Olori, V.E.; Cromie, A.R.; Veerkamp, R.F.; Rath, M.; Dillon, P. Accuracy of predicting
milk yield from alternative milk recording schemes. Anim. Sci. 2005,80, 53–60. [CrossRef]
14. Lock, A.L.; Garnsworthy, P.C. Seasonal variation in milk conjugated linoleic acid and
delta9-desaturase activity in dairy cows. Livest. Prod. Sci. 2003,79, 47–59. [CrossRef]
15. Kelsey, J.A.; Corl, A.B.; Collier, R.J.; Bauman, D.E. The effect of breed, parity, and stage of
lactation on conjugated linoleic acid (CLA) in milk from dairy cows. J. Dairy Sci. 2003,86,
2588–2597. [CrossRef]
16. Stoop, W.M.; Bovenhuis, H.; Heck, J.M.L.; van Arendonk, J. Effect oflactation stage and energy
status on milk fat composition of Holstein-Friesian cows. J. Dairy Sci. 2009,92, 1469–1478.
[CrossRef] [PubMed]
17. Arnould, V.M.-R.; Soyeurt, H. Genetic variability of milk fatty acids. J. Appl. Genet. 2009,50,
29–39. [CrossRef] [PubMed]
18. Chilliard, Y.; Ferlay, A.; Mansbridge, R.M.; Doreau, M. Ruminant milk fat plasticity: Nutritional
control of saturated, polyunsaturated, trans and conjugated fatty acids. Ann. Zootech. 2000,49,
181–205. [CrossRef]
19. SAS. SAS/STAT User’s Guide; Version 8. SAS Institute: Cary, NC, USA, 1999; p. 3884.
20. Bastin, C.; Gengler, N.; Soyeurt, H. Phenotypic and genetic variability of production traits and milk
fatty acid contents across days in milk for Walloon Holstein first-parity cows. J. Dairy Sci. 2011,
94, 4152–4163. [CrossRef] [PubMed]
21. Bastin, C.; Berry, D.P.; Soyeurt, H.; Gengler, N. Genetic correlations of days open with production
traits and contents in milk of major fatty acids predicted by mid-infrared spectrometry. J. Dairy
Sci. 2012,95, 1–9. [CrossRef] [PubMed]
22. Soyeurt, H.; Gillon, A.; Vanderick, S.; Mayeres, P.; Bertozzi, C.; Gengler, N. Estimation of
heritability and genetic correlations for the major fatty acids in bovine milk. J. Dairy Sci. 2007,90,
4435–4442. [CrossRef] [PubMed]
© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons.org/licenses/by/4.0/).
