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RESEARCH ARTICLE
Milk fatty acids estimated by mid-infrared spectroscopy and milk yield
can predict methane emissions in dairy cows
Stefanie W. Engelke
1
&Gürbüz Daş
1
&Michael Derno
1
&Armin Tuchscherer
2
&Werner Berg
3
&Björn Kuhla
1
&
Cornelia C. Metges
1
Accepted: 5 April 2018
#The Author(s) 2018
Abstract
Ruminant enteric methane emission contributes to global warming. Although breeding low methane-emitting cows
appears to be possible through genetic selection, doing so requires methane emission quantification by using elaborate
instrumentation (respiration chambers, SF
6
technique, GreenFeed) not feasible on a large scale. It has been suggested
that milk fatty acids are promising markers of methane production. We hypothesized that methane emission can be
predicted from the milk fatty acid concentrations determined by mid-infrared spectroscopy, and the integration of
energy-corrected milk yield would improve the prediction. Therefore, we examined relationships between methane
emission of cows measured in respiration chambers and milk fatty acids, predicted by mid-infrared spectroscopy, to
derive diet-specific and general prediction equations based on milk fatty acid concentrations alone and with the addi-
tional consideration of energy-corrected milk yield. Cows were fed diets differing in forage type and linseed supple-
mentation to generate a large variation in both CH
4
emission and milk fatty acids. Depending on the diet, equations
derived from regression analysis explained 61 to 96% of variation of methane emission, implying the potential of milk
fatty acid data predicted by mid-infrared spectroscopy as novel proxy for direct methane emission measurements. When
data from all diets were analyzed collectively, the equation with energy-corrected milk yield (CH
4
(L/day) = −1364 +
9.58 × energy-corrected milk yield + 18.5 × saturated fatty acids + 32.4 × C18:0) showed an improved coefficient of
determination of cross-validation R
2
CV
=0.72 compared to an equation without energy-corrected milk yield
(R
2
CV
=0.61). Equations developed for diets supplemented by linseed showed a lower R
2
CV
as compared to diets
without linseed (0.39 to 0.58 vs. 0.50 to 0.91). We demonstrate for the first time that milk fatty acid concentrations
predicted by mid-infrared spectroscopy together with energy-corrected milk yield can be used to estimate enteric
methane emission in dairy cows.
Keywords Dairy cows .Enteric methane emission .Methane proxy .Methane prediction equation .Mid-infrared spectroscopy .
Milk fatty acids .Linseed supplementation
1 Introduction
Livestock contributes approximately 18% to the global an-
thropogenic greenhouse gas emission (Hristov et al. 2013).
Methane (CH
4
) mitigation through feeding and genetic selec-
tion has been reported to range from 2.5 to 19% in cattle
(Knapp et al. 2014; Pickering et al. 2015). Genetic selection
of low methane-emitting cows requires quantifying individual
methane emission in large populations. However, in vivo
methane quantification via respiration chambers, the SF
6
tech-
nique, and the GreenFeed system are not feasible on a large
scale (Hammond et al. 2016). Biochemical relationships
*Cornelia C. Metges
metges@fbn–dummerstorf.de
1
Institute of Nutritional Physiology, Leibniz Institute for Farm Animal
Biology (FBN), Wilhelm-Stahl-Allee 2,
18196 Dummerstorf, Germany
2
Institute of Genetics and Biometry, Leibniz Institute for Farm Animal
Biology (FBN), Wilhelm-Stahl-Allee 2,
18196 Dummerstorf, Germany
3
Department of Technology Assessment and Substance Cycles,
Leibniz Institute for Agricultural Engineering and Bioeconomy,
Max-Eyth-Allee 100, 14469 Potsdam, Germany
Agronomy for Sustainable Development (2018) 38:27
https://doi.org/10.1007/s13593-018-0502-x
among rumen fermentation pathways, CH
4
production, and
milk fatty acid composition suggest the usefulness of milk
fatty acid profiles to predict methane emission (liters/day)
(Knapp et al. 2014; van Lingen et al. 2014)(Fig.1). There
have been several reports on the relationship between milk
fatty acids and methane emission as well as on equations
predicting methane yield defined as liters of CH
4
per kilogram
dry matter intake and methane intensity defined as liters of
CH
4
per kilogram energy-corrected milk yield (Castro-
Montoya et al. 2017; van Lingen et al. 2014). Besides dry
matter intake, the dietary composition, especially forage type
and fat supplements, affects milk fatty acid profile and influ-
ences methane prediction equations (Dijkstra et al. 2016;Rico
et al. 2016; van Lingen et al. 2014). Thus, diet-specific and
general equations for estimation of methane emission are im-
portant for different farming conditions. Although gas chro-
matography is the reference technique to measure milk fatty
acids, it is unsuitable for routine milk fatty acid determination.
In contrast to gas chromatography, mid-infrared spectroscopy
can inexpensively predict major milk fatty acids and groups of
fatty acids but prediction quality differs among fatty acids (De
Marchi et al. 2014; Soyeurt et al. 2011). A Belgian group
predicted CH
4
emission by directly using the global milk
mid-infrared spectra with partial least squares regression and
compared results with multiple correlations between milk fat-
ty acids and CH
4
(Dehareng et al. 2012). These authors con-
cluded that global milk mid-infrared spectra could be more
feasible than predicted milk fatty acids to estimate CH
4
emis-
sion (Dehareng et al. 2012). The methane prediction derived
from global mid-infrared milk spectra by Dehareng et al.
(2012) was further improved by including information on lac-
tation stage (Vanlierde et al. 2015; Vanlierde et al. 2016). Dry
matter intake is the main determinant for CH
4
emission
(Hristov et al. 2013; Knapp et al. 2014), but individual cow
intake information is not available on farms. In contrast, indi-
vidual records of energy-corrected milk yield, which is highly
related to dry matter intake (Hristov et al. 2013), can readily be
calculated from milk testing data. Thus, we hypothesized that
CH
4
emission can be predicted from milk fatty acid concen-
trations determined by mid-infrared spectroscopy, and that the
integration of energy-corrected milk yield improves predic-
tion. Our objective was to develop equations that predict
CH
4
emission on the basis of milk fatty acid composition
estimated by mid-infrared spectroscopy. We further investigat-
ed whether inclusion of energy-corrected milk yield, com-
pared with dry matter intake, could improve the prediction.
The datasets were based on CH
4
emission of dairy cows mea-
sured in respiration chambers fed diets differing in forage type
and linseed supplementation to generate a larger variation in
both CH
4
emission and milk fatty acids.
2 Materials and methods
2.1 Animals, experimental design, and diets
Ethical approval for the study was obtained (permission no.
7221.3-1-014/14). Twenty half-sib German Holstein cows
(106 ± 28 days in milk, 580 ± 57 kg bodyweight, mean ± stan-
dard deviation) in second (15 cows) or third lactation (5 cows)
were used. The required number of cows for this study was
determined through a power test for crossover experimental
designs as described (Rasch et al. 1998) using CADEMO
software (Windows ANOV-Version 4.03 (2000); BioMath
Fig. 1 The physiological
relationship between feeding and
methane emission and between
feeding and milk fatty acid
composition. Our aim was to
generate regression equations to
predict methane emission by
using the milk fatty acid
composition estimated by mid-
infrared spectroscopy
27 Page 2 of 9 Agron. Sustain. Dev. (2018) 38:27
GmbH, Rostock, Germany). Cows were kept in tie stalls and
provided with water and total mixed rations for ad libitum
intake. Two isoenergetic total mixed rations, in which the
major forage component was either corn silage or grass silage
both with or without linseed supplementation, were used
(7 MJ NE
L
/kg dry matter) to generate a wide range of varia-
tion in both methane emissions and milk fatty acid profiles.
Diets contained grass and corn silage at dry matter levels of
130 and 450 g/kg (corn silage-based diets) and 360 and
190 g/kg (grass silage-based diets), respectively. Linseed-
supplemented diets contained 60 g of fat/kg dry matter com-
pared to 30 g of fat in diets without supplementation. The
target nutrient content of the diets was according to recom-
mendations of the German Society of Nutritional Physiology
(Gesellschaft für Ernährungsphysiologie 2001). Randomly se-
lected cows (n=10)werefedcornsilage-baseddietsfor
5 weeks without linseed supplementation and subsequently
for another 5 weeks with linseed supplementation with 1 week
in-between for a stepwise change of diets (11 experimental
weeks per cow in total) in a randomized order (5 cows started
with corn silage without linseed). The other 10 cows were
allocated randomly to grass silage diets without or with lin-
seed supplementation for 5 weeks each (5 cows started with
grass silage without linseed). Feed intake was recorded daily.
Residual feed was removed from the trough before cows were
offered fresh feed at 0730 and 1730 h. Cows were milked at
0630 and 1630 h. Three cows feeding on the grass silage-
based diet supplemented with linseed had to be excluded from
analysis because of illness or feed refusal.
2.2 Determination of methane production and feed
and milk composition
Methane emission of each cow was quantified in separate
respiration chambers in experimental weeks 5 and 11. Cows
entered the chambers 15 h before starting two subsequent 24-h
CH
4
measurements (0700 to 0700 h). The average 24-h CH
4
emission was calculated for each cow (Table 1). In the cham-
bers, the temperature and relative humidity were 15 °C and
60%, respectively, and the light was on from 0600 to 1900 h
(Derno et al. 2009). The CH
4
recovery in the chambers was
99.9%. Analyses of feed were performed according to the
Weender standard analysis (Naumann et al. 1976)withmod-
ifications by van Soest et al. (1991) by the accredited feed
laboratory Landwirtschaftliche Untersuchungs- und
Forschungsanstalt der LMS Agrarberatung GmbH (LUFA,
Rostock, Germany). The energy content was calculated as
recommended (Gesellschaft für Ernährungsphysiologie
2001). During the respiration measurements, milk aliquots
of one evening and the following morning milking were
pooled (0.25% of milk yield per milking). Milk fat and protein
content was analyzed using mid-infrared spectroscopy
(MilkoScan FT6000 and MilkoScan FT+; Foss, Hilleroed,
Denmark) by the state control association Mecklenburg-
Table 1 Performance data and milk fatty acid composition of lactating
cows (N= 20) receiving rations based on grass silage or corn silage with
and without linseed supplementation, and performance of milk fatty acid
prediction by mid-infrared spectroscopy. Dry matter intake, energy-
corrected milk yield, methane emission, and unsaturated fatty acids are
abbreviated as DMI, ECM, CH
4
, and UFA. Performance of mid-infrared
spectroscopy equations to predict milk fatty acids: standard error of
calibration (SE
C
), coefficient of determination of calibration (R
2
C
), root
mean square error (RMSE) of cross-validation, and the cross-validation
coefficient of determination (R
2
CV
)
Variable Mean SD Minimum Maximum SE
C
R
2
C
RMSE R
2
CV
DMI (kg/day) 17.4 3.13 9.3 22.3 ––– –
ECM (kg/day) 26.3 5.36 12.6 36.1 ––– –
Milk fat (g/L) 42.8 6.10 32.1 56.7 ––– –
Methane production
CH
4
(L/day) 525 108 324 729 ––– –
CH
4
/ECM (L/kg) 20.3 3.66 14.7 34.3 ––– –
CH
4
/DMI (L/kg) 30.5 4.73 20.7 50.4 ––– –
Fatty acid (% of total fat)
Saturated 66.6 6.69 54.6 78.4 0.06 0.10 0.07 0.99
Unsaturated 33.4 6.69 21.6 45.4 0.05 0.99 0.05 0.99
Mono- (% UFA) 86.8 1.06 84.2 88.9 0.06 0.98 0.06 0.98
Poly- (% UFA) 13.2 1.06 11.1 15.8 0.02 0.83 0.03 0.81
C16:0 28.2 5.03 18.7 37.2 0.09 0.95 0.10 0.94
C18:0 12.5 2.43 8.8 16.7 0.06 0.91 0.06 0.90
C18:1cis 22.2 4.51 13.9 31.0 0.06 0.97 0.07 0.96
C18:1trans 3.81 1.39 1.9 6.9 0.03 0.86 0.03 0.85
ω-3 FA 0.80 0.13 0.57 1.05 0.01 0.76 0.01 0.74
Agron. Sustain. Dev. (2018) 38:27 Page 3 of 9 27
Western Pomerania (Landeskontrollverband für Leistungs-
und Qualitätsprüfung Mecklenburg-Vorpommern e.V.,
Güstrow, Germany). Energy-corrected milk yield (kg/d) was
calculated as [((0.38 × milkfat %) + (0.21 × milk protein %) +
1.05)/3.28 × milk yield (kg/d)] (Spiekers et al. (2009). Milk fat
and fatty acid compositions were analyzed using mid-infrared
spectroscopy (MilkoScan FT6000) by a commercial laborato-
ry (Comité du Lait, Battice,Belgium). Multivariate calibration
equations using partial least squares regression were used to
derive milk fatty acid concentrations. One milk sample (grass
silage without linseed group) produced invalid results which
were not used in the statistical analysis. Contents of saturated
and unsaturated fatty acids, as well as isomers of C18:1cis and
trans and ω-3 fatty acids, were predicted as groups and were
expressed as the percentage of total milk fat. Mono- and poly-
unsaturated fatty acids were expressed as the percentage of
unsaturated fatty acids in milk fat. Milk palmitic (C16:0) and
stearic acids (C18:0) were estimated individually.
Performance data of milk fatty acid prediction are given in
Tab le 1.
2.3 Statistical analyses
2.3.1 Development of prediction equations and computing
of correlations
Methane emission and CH
4
intensity were estimated using
multiple linear regression analysis with the stepwise
explanatory-independent variable selection method for each
diet separately. Methane emission was also predicted based
on combined data categorized by basal diet or linseed supple-
mentation, as well as for all available data across four diets to
explore diet dependency of methane prediction equations.
This was done to test different feeding scenarios and to ex-
plore how explanatory milk fatty acids differ in prediction
models for various diets. For the regression analysis, three
datasets with differentindependent explanatory variables were
made available. The first dataset included only the milk fatty
acid concentrations (nine items: groups or individual milk
fatty acids; Table 1), whereas the second set additionally in-
cluded the energy-corrected milk yield, and the third set in-
cluded dry matter intake instead of energy-corrected milk
yield. Prediction models for CH
4
intensity were generated
with datasets 1 and 3. The entry and stay levels of explanatory
variables for the stepwise (forward) variable selection method
were set at P≤0.15. Selection of best models was based on
improvement in the multiple fit parameters as described by
Kaps and Lamberson (2004). According to this, we selected
best models that simultaneously maximized the coefficient of
determination (R
2
Model
) and minimized the conceptual predic-
tive criterion (Cp), implying that the lowest number of vari-
ables that could explain the highest variation was allowed to
enter the equations. In a separate crosscheck, it was also
ensured that best model selection based on the two fit param-
eters (R
2
Model
and Cp) also corresponded to the lowest
Akaike’s Information Criteria and residual sum of squares,
two other commonly used fit parameters. For the sake of clar-
ity, we however report only R
2
Model
as a fit parameter because
it is most commonly used and corresponds to the proportion of
variation explained. Using the predicted milk fatty acid and
measured methane emission data, Pearson correlations were
computed. A Spearman rank correlation analysis was also
performed to quantify whether the cow rankings for CH
4
emission levels were consistent between consecutive periods,
when cows were fed diets with or without linseed in two
different periods.
2.3.2 Cross-validation of prediction equations
To assess the predictive ability of the developed methane pre-
diction models, an internal cross-validation was performed
using SAS software (SAS Institute Inc. 2017). The cross-
validation was based on the above-described datasets and
run through a stepwise approach. Data sub-sets were generat-
ed by omitting data ofone animal at a step, and the emission of
the omitted animal was predicted using the regression function
with the data from remaining animals as described (Moraes
et al. 2014). Thus, methane prediction for a specific animal
was generated using models fitted without the observation of
these specific animals. The root mean square error of cross-
validation (RMSE) and the cross-validation coefficient of de-
termination (R
2
CV
) were then estimated to evaluate the good-
ness of model fit for the cross-validation.
3 Results and discussion
3.1 Methaneemission and milk fatty acid composition
The dataset included a total of 36 observations from 20 cows
to develop methane prediction equations feeding four different
diets varying in basal rations (grass silage or corn silage) with
or without linseed supplementation. The diets provoked a
wide range of methane yield with a minimum of 21 and a
maximum of 50 L/kg dry matter intake, and milk fatty acid
composition ranged from 55 to 78% saturated fatty acids of
total fat (Table 1). Results of studies in which 30 different diets
with and without lipid supplements were fed were used in a
meta-analysis (van Lingen et al. 2014); the methane yield
(21.5 ± 2.46 g/kg of dry matter intake; mean ± SD) was lower
than that observed in our study but milk fat levels of C16:0
(31.3 ± 4.91 g/100 g of total milk fat) and C18:0 (9.8 ± 2.41 g/
100 g of total milk fat) were comparable (Table 1). When diets
based on grass and corn silage were fed, mean methane yields
(24.1 ± 1.87 g/kg of dry matter intake) and milk fat levels of
C16:0 (35.4 ± 2.72 g/100 g of total fatty acids) and C18:0 (7.5
27 Page 4 of 9 Agron. Sustain. Dev. (2018) 38:27
± 1.03 g/100 g of total fatty acids) were reported (van Gastelen
et al. 2017) but variability was smaller than in our and the van
Lingen et al. (2014)study.
Our results for milk fatty acid composition were largely in
line with an earlier study where milk fatty acid contents were
also determined by mid-infrared spectroscopy-based predic-
tions (Soyeurt et al. 2011). It has been shown that milk fatty
acid composition analyzed by mid-infrared spectroscopy as
compared to gas chromatography corresponded well
(Soyeurt et al. 2011). Predictions of milk fatty acid contents
by mid-infrared spectroscopy validated by gas chromatogra-
phy showed high accuracies (R
2
CV
≥0.88) for individual sat-
urated fatty acids, C16:0, C18:0, C18:1trans, and C18:1cis,
and groups of saturated, unsaturated, and monounsaturated
fatty acids, whereas accuracy was R
2
CV
=0.75 for ω-3 fatty
acids, which was similar in our study (Table 1). This agree-
ment between mid-infrared spectroscopy and gas chromatog-
raphy is the prerequisite to use mid-infrared spectroscopy for
routine milk fatty acid recordings.
Furthermore, we calculated rank correlation coefficients of
r=0.78 and r=0.73 (P=0.001) for CH
4
emission and CH
4
yield, respectively (Fig. 2). These results indicate replicable
responses under the effects of either diet with or without lin-
seed, representing different environmental conditions, and
suggested the existence of a genetic component responsible
for the individual methane production level.
3.2 Predicting methane emission
Most studies reporting prediction equations have been based
on milk fatty acid concentrations analyzed by gas chromatog-
raphy (Castro-Montoya et al. 2017;Ricoetal.2016;van
Lingen et al. 2014). Published prediction models based on
global infrared milk spectra have used the model of
Dehareng et al. (2012) or further improvement of the model
by integrating the lactation stage (Vanlierde et al. 2015;
Va n l i e rd e e t a l . 2016). Dehareng et al. (2012)includedmeth-
ane production data of 11 cows and have reported R
2
CV
between 0.68 and 0.79 for grams of CH
4
per day and grams
of CH
4
per kilogram of milk, respectively. Shetty et al. (2017)
developed methane prediction models based on global infra-
red milk spectra with R
2
Validation
= 0.13 and concluded that it is
not feasible to predict methane emission (liters/day) based on
mid-infrared spectra alone. Upon integrating milk yield, the
prediction accuracy was improved (R
2
Validation
= 0.35) (Shetty
et al. 2017). In the present study, three datasets were used to
develop multiple regression equations. When energy-
corrected milk yield was additionally included (dataset 2),
the goodness of fit was improved by up to 25% relative to
the equations based on dataset 1 (Table 2). When energy-
corrected milk yield was replaced by dry matter intake in the
model (dataset 3; Table 2), the goodness of fit was improved
by up to 41% relative to equations derived from dataset 1.
Although dry matter intake explains a large part of the varia-
tion observed in methane emissions (Knapp et al. 2014), this
information is not available on farms. Thus, it cannot be
considered as a variable to be implemented in methane
prediction equations for on-farm applications. In contrast,
energy-corrected milk yield is a readily available cow in-
dividual measure reflecting the dry matter intake (Hristov
et al. 2013) and consequently can be used as a surrogate for
dry matter intake. Using the first dataset, the goodness of
the regression fit for CH
4
emission for individual diets
ranged from R
2
Model
=0.61 to 0.94 (Table 2). Using data
from all four diets collectively, a coefficient of determina-
tion of R
2
Model
=0.70 was obtained.
Measured CH4emission (L/day)
without linseed
r = 0.78
p < 0.001
Measured CH4emission (L/day)
with linseed
Measured CH4 emission per dry
matter intake (L/kg) with linseed
Measured CH4 emission per dry matter
intake (L/kg) without linseed
r = 0.73
p = 0.001
Fig. 2 Correlation of measured
CH
4
emission in liters per day and
measured CH
4
emission per
kilogram dry matter intake
between linseed supplemented
and non-supplemented diets
considering individual animals
(A, B, C, …), and basal ration
(grass silage = green, corn silage
= orange) indicating repeatable
phenotypes under the effect of
either diets with or without
linseed. The shaded area depicts
the confidence ellipse. The
Spearman rank correlation
coefficient (r) and the probability
(p) are given
Agron. Sustain. Dev. (2018) 38:27 Page 5 of 9 27
The equations based on datasets 1 to 3 were validated by an
internal cross-validation (Table 2). The R
2
CV
ranged between
0.39 and 0.91 with a RMSE between 147 and 35 liters/day and
the R
2
CV
was lowest for linseed-supplemented diets. If
linseed-supplemented diets were not considered, the R
2
CV
was never lower than 0.50. When coefficients of determina-
tion were compared between dataset 1 and datasets 2 and 3 for
all four diets collectively, the R
2
CV
was improved from 0.61 to
0.72 by the integration of energy-corrected milk yield (Fig. 3),
and to R
2
CV
= 0.75 by the integration of dry matter intake as
explanatory variables. In previous studies predicting CH
4
emission (g/day) using gas chromatography to determine milk
fatty acid concentrations, improvements of goodness of fit
values by 5 to 12% were achieved when dry matter intake
was considered in the model (Mohammed et al. 2011;Rico
et al. 2016). In a meta-analysis, improved indices of predictive
ability were observed when dry matter intake was included
(Castro-Montoya et al. (2017). Nevertheless, a further cross-
validation of our prediction equation using an independent
validation set of cows fed different diets is desirable.
Table 2 Summary of multiple regression equations predicting methane
emission. Dataset 1 included milk fatty acids (saturated, unsaturated,
mono- and polyunsaturated fatty acids, and ω-3 fatty acids are
abbreviated as SFA, UFA, MUFA, PUFA, and ω3, as well as isomers
of C18:1cis and trans and single fatty acids of C16:0 and C18:0), whereas
datasets 2 and 3 additionally included energy-corrected milk yield (ECM)
or dry matter intake (DMI) as independent variables. Equations were
developed separately for experimental diets containing grass silage
without or with linseed supplementation (GS-L0, GS-LS) and corn
silage without or with linseed supplementation (CS-L0, CS-LS) as well
as for basal rations (GS, CS), rations without or with linseed
supplementation (L0, LS), and all experimental diets collectively. The
R
2
Model
and the P
Model
value of the model, the root mean square error
(RMSE) of cross-validation, and the cross-validation coefficient of
determination (R
2
CV
) are given
CH
4
(L/d) NEquation derived from the models (Y=a+b
1
x
1
+…b
n
x
n
)R
2
Model
P
Model
≤RMSE R
2
CV
Dataset 1
GS-L0 9 (−979.3) + 46.2 × C16:0 0.94 0.001 41.91 0.91
GS-LS 7 1002 + (−19.6) × C18:1cis 0.70 0.019 59.52 0.41
CS-L0 10 (−702.6) + 40.4 × C16:0 0.68 0.004 71.58 0.51
CS-LS 10 1233 + (−27.9) × C18:1cis 0.61 0.008 65.69 0.39
GS 16 (−2217) + 39.7 × PUFA + 46.9 × C16:0 + 70.8 × C18:0 0.82 0.001 65.41 0.68
CS 20 2351 + (−24.8) × C16:0 + 71.7 × C18:0 + (−50.8)
×C18:1cis +(−1093) × ω-3
0.75 0.001 74.08 0.50
L0 19 (−2595) + 22.5 × SFA + 27.4 × C16:0 + 61.5 × C18:0 0.86 0.001 146.9 0.50
LS 17 1099 + (−23.0) × C18:1cis 0.61 0.001 54.78 0.50
All diets 36 (−2534) + 33.8 × SFA + 55.0 × C18:0 + 30.9 × C18:1trans 0.70 0.001 67.94 0.61
Dataset 2
GS-L0 9 (−979.3) + 46.2 × C16:0 0.94 0.001 41.91 0.91
GS-LS 7 1002 + (−19.6) × C18:1cis 0.70 0.019 59.52 0.41
CS-L0 10 (−604.9)+ 18.2 × ECM + 20.8 × C16:0 0.85 0.001 57.39 0.67
CS-LS 10 27.42 + 16.7 × ECM 0.66 0.004 59.53 0.47
GS 16 (−1020) + 10.6 × ECM + 27.8 × C16:0 + 38.8 × C18:0 0.82 0.001 64.67 0.69
CS 20 284.1 + 16.8 × ECM + (−6.58) × UFA 0.77 0.001 59.31 0.66
L0 19 (−558.4) + 10.0 × ECM + 24.3 × C16:0 + 47.9
×C18:0+(−617) × ω-3
0.91 0.001 49.96 0.84
LS 17 721.3 + 7.97 × ECM + (−16.8) × C18:1cis 0.71 0.001 50.24 0.58
All diets 36 (−1364) + 9.58 × ECM + 18.5 × SFA + 32.4 × C18:0 0.79 0.001 57.75 0.72
Dataset 3
GS-L0 9 (−979.3) + 46.2 × C16:0 0.94 0.001 41.91 0.91
GS-LS 7 1002 + (−19.6) × C18:1cis 0.70 0.019 59.52 0.41
CS-L0 10 153.0 + 33.3 × DMI + (−23.1) × C18:1cis + 90.3 × C18:1trans 0.96 0.001 34.46 0.88
CS-LS 10 1233 + (−27.9) × C18:1cis 0.61 0.008 65.69 0.39
GS 16 (−2217) + 39.7 × PUFA + 46.9 × C16:0 + 70.8 × C18:0 0.82 0.001 65.41 0.68
CS 20 211.0 + 26.9 × DMI + 29.8 × C18:0 + (−24.1) × C18:1cis 0.90 0.001 40.27 0.84
L0 19 (−1286) + 12.1 × DMI + 37.4 × C16:0 + 40.4 × C18:0 0.87 0.001 58.13 0.79
LS 17 292.2 + 21.2 × DMI + 25.8 × C18:0 + (−20.9) × C18:1cis 0.78 0.001 50.95 0.58
All diets 36 361.4 + 18.9 × DMI + 28.5 × C18:0 + (−23.6) × C18:1cis 0.82 0.001 53.85 0.75
27 Page 6 of 9 Agron. Sustain. Dev. (2018) 38:27
A further factor which might be important for methane pre-
diction is stage of lactation, because in early lactation, fatty acids
entering milk fat are derived to a larger degree from lipolysis of
lipid resources of cows, which can affect explanatory fatty acids
in methane prediction models (Vanlierde et al. 2015). The pat-
terns of fatty acids from endogenous lipid depots differ from
fatty acids in plant oils (Bayat et al. 2018). Vanlierde et al.
(2015)testedtheeffectofinclusionofdaysinmilkinthepre-
diction model based on milk mid-infrared spectra and showed
that lactation stage-dependent prediction is more meaningful
compared to lactation stage-independent prediction of methane
emission although coefficient of determination did not improve.
In a recent study, integrating lactation stage in the model did not
improve methane predictive ability (R
2
Validation
= 0.14 vs. 0.13)
(Shetty et al. 2017). In our study, all cows were in the same
lactation stage which might be limiting the validity of our equa-
tion to cows in established lactation. This suggests that further
research is required on the effect of lactation stage for milk fatty
acid-based methane prediction.
Dietary intake of ω-3 fatty acids with linseed reduces
methane emissions and increases the proportion of polyun-
saturated fatty acids in milk whereas it can inhibit mammary
lipid synthesis by fatty acid intermediates produced during
ruminal bio-hydrogenation (Bauman et al. 2011; Bayat et al.
2018). This complex relationship might play a role in the
variability of methane production and milk fatty acid con-
tents. Notably, the predictions obtained in this study with
linseed-supplemented diets showed smaller goodness of fit
values and cross-validation coefficient of determination
values (Table 2) although the RMSE was highest for L0
diets in dataset 1. However, when energy-corrected milk
yield or dry matter intake was considered as additional ex-
planatory variable (datasets 2 and 3), also in diets with lin-
seed a larger part of variation could be explained. This im-
plies that inclusion of other parameters explaining additional
variation of methane emission should also be considered.
Prediction of CH
4
intensity would be desirable because it
considers the product level. However, in our study, CH
4
in-
tensity was not a robust parameter; prediction equations using
the first dataset produced largely insignificant weak to mod-
erate fit values. The use of dataset 3 did not generate consid-
erable improvements when compared to dataset 1. In line with
this, others found moderate to low R
2
of 0.47 and R
2
Validation
of
0.18 (Castro-Montoya et al. 2017; van Lingen et al. 2014)for
predicted CH
4
emission per kilogram milk based on milk fatty
acid data only.
The explanatory fatty acids in published prediction equa-
tions for methane emission differ considerably between stud-
ies (Castro-Montoya et al. 2017; van Lingen et al. 2014). The
milk fatty acids C16:0 and C18:1cis and trans isomers are
frequently found in prediction models for CH
4
emission
(Castro-Montoya et al. 2017; Chilliard et al. 2009;Rico
et al. 2016). A positive relationship with methane emission
was found for C16:0 in this (r=0.57, P< 0.001) and other
studies (Castro-Montoya et al. 2017; Chilliard et al. 2009;
van Lingen et al. 2014). Palmitic acid is the saturated fatty
acid with the highest concentration in plant feed and is also
synthesized de novo from acetate in animal tissues, and ace-
tate concentration in the rumen is directly associated with
methane production (van Lingen et al. 2014). In contrast,
All diets together
RMSE = 67.9
R2
Cross-validation = 0.61
Predicted CH4emission (L/day) based on
cross-validation of data set 1 (Milk fatty
acids only)
Predicted CH4emission (L/day) based on
cross-validation of data set 2 (Milk fatty
acids plus energy corrected milk yield)
Measured CH4emission (L/day)
All diets together
Measured CH4emission (L/day)
RMSE = 57.8
R2
Cross-validation= 0.72
Fig. 3 Measured and predicted CH
4
emission derived from the cross-
validation of multiple regression equations across all experimental diets
(grass silage without linseed, corn silage without linseed, grass silage with
linseed and corn silage with linseed) based on dataset 1 (milk fatty acids
only) and dataset 2 (milk fatty acids plus energy-corrected milk yield).
The shaded area depicts the confidence ellipse. The cross-validation
coefficient of determination (R
2
Cross-validation
) and the root mean square
error (RMSE) of cross-validation are given
Agron. Sustain. Dev. (2018) 38:27 Page 7 of 9 27
C18:0 (r=−0.34, P= 0.044) and C18:1cis isomers (r=−
0.61, P< 0.001) were negatively related to methane emission
in our study. Vanrobays et al. (2016) have found weak nega-
tive relations for C18:0 and C18:1cis9 analyzed with mid-
infrared spectroscopy, whereas studies using gas chromatog-
raphy have reported no correlation for C18:0, but various
C18:1cis isomers, and the sum of C18 fatty acids was nega-
tively related to methane production (Chilliard et al. 2009;
Mohammed et al. 2011;Ricoetal.2016). Stearic acid is an
end product of C18:3 ω-3 bio-hydrogenation and C18:3 ω-3
is particularly abundant in linseed. Products of bio-
hydrogenation and intermediates were negatively related to
CH
4
emission (van Gastelen and Dijkstra 2016). In our study,
C18:1trans isomers were a factor in the model for CH
4
emis-
sion in the equation of dataset 1 for all diets. They tended to be
negatively related to methane emission (r=−0.31, P=
0.066), in line with findings from other studies (Mohammed
et al. 2011;Ricoetal.2016). Groups of fatty acids appeared to
play a minor role as prediction variables in our models, but the
group of saturated fatty acids was positively associated with
methane emission (r=0.60, P< 0.001) as was also found by
others (van Gastelen and Dijkstra 2016; Vanrobays et al.
2016). No specific pattern for milk fatty acids as explanatory
variables could be identified in the equations. However, it
appears that among the milk fatty acids considered in the
datasets, C16:0 plays a dominant role in the prediction of
methane emission by milk fatty acids.
4 Conclusion
The prediction of methane emission using dataof all four diets
collectively showed moderate predictive accuracies (R
2
CV
be-
tween 0.61 and 0.75). When energy-corrected milk yield or
dry matter intake was additionally considered in the models,
prediction accuracy improved by 18 and 23%, respectively.
Thus, energy-corrected milk yield in prediction equations is a
useful replacement for dry matter intake which cannot be de-
termined on farm. Models developed for diets including lin-
seed seem to be lower in prediction accuracy which deserves
further research. We conclude that methane prediction equa-
tions based onmilk fatty acidsestimated by mid-infrared spec-
troscopy and energy-corrected milk yield could be used as a
screening method for individual methane emission of cows.
Acknowledgements The authors express their gratitude to the teams at
the “Tiertechnikum”and “EAR”of the Leibniz Institute for Farm Animal
Biology (FBN) for excellent technical assistance. We appreciate the help
of Dr. O. Bellmann, institute veterinarian of FBN, with medical care. The
selection of cows by F. Schultz (RinderAllianz GmbH, Woldegk,
Germany) is gratefully acknowledged. We thank DANONE GmbH,
Haar, Germany, for providing milk composition data, and Ms. Krüger
and Ms. Göschl of DANONE GmbH for fruitful discussions.
Funding information This work is part of the project “Innovation poten-
tial to reduce greenhouse gas emissions in the dairy supply chain”(INNO
MilCH4) and was supported by funds from the Federal Ministry of Food
and Agriculture based on a decision of the Parliament of the Federal
Republic of Germany via the Federal Office for Agriculture and Food
under the innovation support program (grant no. 2817501011). The pub-
lication of this article was funded by the Open Access Fund of the Leibniz
Institute for Farm Animal Biology (FBN), Dummerstorf, Germany.
Compliance with ethical standards
Ethical approval for the study was obtained (permission no. 7221.3-1-
014/14).
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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