The aim of the present study was to derive individual
methane (CH4) emissions in ewes separated in CH4 respiration and eructation
traits. The generated longitudinal CH4 data structure was used to estimate
phenotypic and genetic relationships between ewe CH4 records and energy
efficiency indicator traits from same ewes as well as from their lambs
(intergenerational perspective). In this regard, we recorded CH4 emissions
via mobile laser methane detector (LMD) technique, body weight (EBW),
backfat thickness (BFT) and body condition score (BCS) from 330 ewes (253
Merinoland (ML), 77 Rhön sheep (RH)) and their 629 lambs (478 ML, 151 RH). The interval between repeated measurements (for ewe traits and lamb
body weight (LBW)) was 3 weeks during lactation. For methane
concentration (µL L-1) determinations in the exhaled air, we
considered short time measurements (3 min). Afterwards, CH4 emissions
were portioned into a respiration and eructation fraction, based on a double
normal distribution. Data preparation enabled the following CH4 trait
definitions: mean CH4 concentration during respiration and eructation
(CH4r+e), mean CH4 concentration during respiration (CH4r), mean CH4
concentration during eructation (CH4e), sum of CH4 concentrations per minute
during respiration (CH4rsum), sum of CH4 concentrations per minute during
eructation (CH4esum), maximal CH4 concentration during respiration
(CH4rmax), maximal CH4 concentration during eructation (CH4emax), and
eructation events per minute (CH4event). Large levels of ewe CH4 emissions
representing energy losses were significantly associated with lower LBW
(P<0.05), lower EBW (P<0.01) and lower BFT (P<0.05). For genetic parameter estimations, we
applied single- and multiple-trait animal models. Heritabilities and additive
genetic variances for CH4 traits were small, i.e., heritabilities in the
range from <0.01 (CH4r+e, CH4r, CH4rmax, CH4esum) to 0.03
(CH4rsum). We estimated negative genetic correlations between CH4 traits and
EBW in the range from -0.44 (CH4r+e) to -0.05 (CH4rsum). Most of the CH4
traits were genetically negatively correlated with BCS (-0.81 for CH4esum)
and with BFT (-0.72 for CH4emax), indicating same genetic mechanisms for CH4
output and energy efficiency indicators. Addressing the intergenerational
aspect, genetic correlations between CH4 emissions from ewes and LBW ranged
between -0.35 (CH4r+e) and 0.01 (CH4rsum, CH4rmax), indicating that
breeding on reduced CH4 emissions (especially eructation traits) contribute
to genetic improvements in lamb weaning performance.
Introduction
Methane (CH4) is a by-product of microbial fermentation processes in
ruminants (Henderson et al., 2015) and a potential greenhouse gas.
Furthermore, CH4 emissions reflect an unused proportion of gross energy
intake (Johnson and Ward, 1996; Baker, 1999). Fodder is the major cost
factor in sheep production systems (Ellison et al., 2017). Hence, there is
an increasing interest to breed animals with improved productivity and feed
efficiency (i.e., feed intake in relation to body weight gain), possibly via
selection on low individual CH4 emissions (Paganoni et al., 2017). Pickering
et al. (2015) and Paganoni et al. (2017) indicated genetic variation and
small to moderate heritabilities for CH4 traits in dairy cows and sheep, and
Rösler et al. (2018) described an individual variation in enteric CH4
emissions in female goats. Furthermore, the economic benefits from selection
scenarios including CH4 traits (Robinson and Oddy, 2016) suggest
consideration of CH4 or of CH4 indicator traits into overall sheep breeding
goals.
In this regard, respiration chamber calorimetry is the “golden standard”
to determine CH4 emissions in sheep. Nevertheless, respiration chamber
measurements imply strong efforts regarding logistics, associated with a
substantial cost component. In consequence, only a small number of sheep can
be phenotyped for CH4 using the respiration chamber technique. In addition,
respiration chambers reflect an artificial environment, which is not
representative of sheep kept in pasture-based production systems. Animals
might show abnormal behavior (e.g., reduced dry matter intake, DMI) in the
chamber, possibly influencing a CH4 emission pattern (Kabreab et al., 2006;
Bickell et al., 2014). Thus, Knapp et al. (2014) and Huhtanen et al. (2015)
requested alternative reliable and cost-efficient methods for CH4 recording,
especially under field conditions. In such a context, approaches based on
feed supplements were unsuitable under grazing conditions (Baker, 1999).
Predictions of CH4 via deterministic modeling usually require a large amount
of input data, e.g., DMI, dietary or milk components, which are difficult to
record (Kabreab et al., 2006; Yin et al., 2015). Further indirect methods
for CH4 emission predictions based on the ruminal microbiome composition
but associations between CH4 production and microbiome characteristics were
inconsistent (Shi et al., 2014; Ellison et al., 2017). The portable
handheld laser methane detector (LMD) was suitable for CH4 recording in
dairy cattle under field conditions, with low inter-observer variability
(Chagunda et al., 2009b). In validations, correlations between LMD CH4 and
CH4 measurements from the respiration calorimetric chamber were large
(Chagunda and Yan, 2011).
With regard to associations between CH4 output and other breeding goal
traits, Zetouni et al. (2018) estimated negative genetic correlations
between CH4 production (g d-1) and body conformation traits in Danish
Holstein cows. Nevertheless, there is a gap of knowledge addressing “across
generation studies”, i.e., association analyses between indicators for
energy balances of ewes (including CH4 emissions) and body weights of their
lambs (LBW; also characterizing productivity of the ewe).
The objective of the present study was to focus on such intergenerational
aspects, considering CH4 measurements from ewes (recorded via LMD) as energy
balance indicators. The CH4 databases were used (i) to define and to evaluate
different CH4 measurement characteristics, (ii) to estimate genetic
parameters for CH4 measurements, and (iii) to correlate phenotypically and
genetically ewe CH4 measurements with other breeding goal traits from a
within- and transgenerational perspective.
Materials and methodsEthics statement
The housing and treatment of the animals were carried out in accordance with
national and international laws. The study was restricted to routine
on-farm observations. All presented methods were non-invasive. Therefore,
they did not cause the included animals pain, suffering or harm, in
compliance with the German Animal Welfare Act §7. Nevertheless,
the presented procedures have been approved for a subsample of ewes that
were used for additional blood parameter analyses by the regional board of
Giessen (V 54-19 c 20 15 h 01 GI 18/14 Nr. G 62/2017).
Production system
For trait recording, we focused on sheep from the University of Giessen
research station “Oberer Hardthof”, reflecting a mixture of grazing
(spring to fall) and high-input (fall to spring) sheep production system.
The farm is located 200 m above sea level in the federal state Hesse in the
middle of Germany. The average annual temperature is 8.8 ∘C, and
the average precipitation amount is 695 mm per year. The farm comprises 70 ha for a flock including 630 ewes, 7 rams and 98 hoggets of Merinoland
(ML) and Rhön sheep (RH). During the lambing season, the flock was fed
hay ad libitum. The hay quality was as follows: 90.3 % dry matter (DM),
40.2 % crude fiber (CF), 6.8 % crude protein (CP), 1.3 % crude lipid
(EE) and 7.8 MJ metabolizable energy (ME) per kg in DM. Ewes within the last
third of gestation received additional concentrates up to 1 kg d-1. The
concentrates were composed of barley, wheat, rapeseed meal extract, wheat bran and
triticale (6.8 % CF, 18 % CP, 2.6 % EE, 10.8 ME MJ per kg DM). The
calculated daily ration for a twin-suckling ewe with an average body weight
of 85 kg contained 1.8 kg hay and 900 g concentrates (21.84 MJ ME per ewe
and day). Lambs had ad libitum access to concentrates at an age of 21 to 28 d. They were weaned group-wise at a mean age of 65.35±5.35 d
with an average body weight of 26.10±4.91 kg.
Animals and traits
Data recording spanned a period from 2017 to 2018. The study considered 330 ewes (253 ML, 77 RH) and their purebred 629 lambs (478 ML, 151 RH). The age
of ewes ranged from 22.1 to 96.8 months (mean =51.3±18.2 months).
In a subset of 177 ewes (133 ML, 44 RH), the whole pattern of traits was
recorded: ewe body weight (EBW) (digital scale: model 703, TRU-TEST Group,
Auckland, New Zealand), ewe body condition score (BCS), ewe backfat
thickness (BFT) in millimeters (mm), and the individual CH4 concentrations
(µL L-1) in the exhaled air. Body condition score was assessed
by palpating the transverse and spinous processes of the lumbar region
around the backbone. Scores ranged on a scale from 1.0 (emaciated) to 5.0
(obese) with increments of 0.5 (Russel et al., 1969). Backfat thickness was
measured on the right side directly behind the 13th rib (Silva et al.,
2006; Gernand and Lenz, 2005), using a mobile ultrasound transducer
(EasiScan ultrasound scanner, 4.5–8.5 MHz linear, BCF Technology Ltd.,
Bellshill, Scotland). Individual CH4 concentrations in the exhaled air were
measured using an LMD (Crowcon LaserMethane Mini, Tokyo Gas Engineering Co
Ltd., Tokyo, Japan). Lamb body weight was recorded from 281 offspring (216 ML, 65 RH). A further subset for genetic analyses considered only EBW and
BCS of an additional 153 ewes (120 ML, 33 RH) and LBW of their 348 lambs (262 ML, 86 RH). We generated a longitudinal data structure, implying ewe trait
and LBW recording on the same days in intervals of 3 weeks from
parturition until weaning.
Laser methane detector method and CH4 data preparation
According to Ricci et al. (2014), the interval between feeding and LMD CH4
recording comprised 3–5 h. Ricci et al. (2014) identified
substantial impact of meteorological data on individual CH4 emissions.
Consequently, we selected a wind-still environment, and we accounted for
temperature and humidity in genetic-statistical modeling. In order to
guarantee standardized trait recording, ewe CH4 measurements were performed
after weighing in the weighing facility, and additionally an assistant
fixated the ewes during CH4 recording. Hence, we always had a distance of
exactly 1 m between the operator (i.e., the LMD) and the sheep's nostrils,
and we avoided noisy data because of an uncontrolled movement (Ricci et al.,
2014; Huhtanen et al., 2015).
Example for a CH4 measurement profile of one ewe recorded
via laser methane detector (LMD) and corrected for background CH4. Values
under the threshold describe CH4 emissions during respiration and values
above the threshold describe CH4 emissions during eructation.
The LMD recorded CH4 concentrations in intervals of 0.5 s in the
exhaled air. Methane concentrations were directly displayed in parts per
million-meter (ppm-m) (Tokyo Gas Engineering Co. Ltd., 2013). Because the
distance between the LMD and the ewe's nostrils was exactly 1 m in the
present study, the CH4 concentration was expressed in microliters per liter
(Ricci et al., 2014). Ongoing CH4 data preparation in R 3.3.2 (R Development
Core Team, 2016) is based on the protocol as suggested by Ricci et al. (2014):
the minimum CH4 concentration of each measurement was set as a background CH4
concentration, i.e., to reflect environmental CH4 influence (overall mean
background CH4= 6.82 µL L-1). Afterwards, background CH4 was
subtracted from the remaining CH4 records. Because the LMD detection is based
on CH4 in the exhaled air, we considered the dynamics of the respiratory
cycle (Chagunda, 2013). In this regard, Fig. 1 illustrates the CH4
measurement profile for one ewe. Every dot represents a detected CH4
concentration in microliters per liter. The CH4 emission profile represents
small increases in CH4 concentration (mini-peaks; solid dots) due to
exhalation or eructation. Before and after one mini-peak, mini-troughs (open
dots) represent small CH4 concentration decreases due to CH4 diffusions.
Only mini-peak data (solid dots) were log-transformed (natural logarithm)
and used for further analyses (Chagunda et al., 2009b). Because mini-peaks
reflect two different possibilities of CH4 excretion – (i) CH4 absorption from
the rumen or lower digestive tract and emission via the lungs (respiration)
and (ii) CH4 emissions directly from the rumen (eructation) (Murray et al.,
1976) – a double normal distribution for mini-peaks was assumed. The dashed
line in Fig. 1 shows the defined threshold at 95 % cumulative probability
(35.87 µL L-1) for the lower normal distribution from all CH4
mini-peak observations. Consequently, all mini-peaks (solid dots) under the
dashed line belong to CH4 emitted during respiration. All mini-peaks (solid
dots) above the threshold represent CH4 concentrations during eructation. A
group of solid dots including more than two mini-peaks above the dashed line
was defined as one eructation event. Each normal distribution (respiration
CH4; eructation CH4) represents a separate CH4 dataset with separate mean
and maximum. Based on the data preparation protocol, the following CH4
traits were defined:
CH4r+e: mean CH4 concentration during respiration and eructation,
CH4r: mean CH4 concentration during respiration,
CH4rsum: sum of CH4 concentrations per minute during respiration,
CH4rmax: maximum CH4 concentration during respiration,
CH4e: mean CH4 concentration during eructation,
CH4esum: sum of CH4 concentrations per minute during eructation,
CH4emax: maximum CH4 concentration during eructation,
CH4event: number of eructation events per minute.
Descriptive statistics for the defined CH4 traits are given in Table 1.
Descriptive statistics for ewe body weight (EBW), ewe body
condition score (BCS), ewe backfat thickness (BFT) and CH4 traits in the
breeds Merinoland (ML) and Rhön sheep (RH).
*CH4r+e: mean CH4 concentration during respiration and eructation;
CH4r: mean CH4 concentration during respiration; CH4rmax: maximum CH4
concentration during respiration; CH4rsum: sum of CH4 concentrations per
minute during respiration; CH4e: mean CH4 concentration during
eructation; CH4emax: maximum CH4 concentration during eructation; CH4esum: sum of CH4 concentrations per minute during eructation; CH4event: number of eructation events per minute.
Phenotypic associations between ewe CH4 emissions with ewe and
lamb body weight traits
The impact of ewe CH4 emissions on EBW, BFT, BCS and LBW was studied via
mixed model applications as implemented in the software package SAS Studio
Version 3.71 (SAS Institute Inc., 2017). In matrix notation, the statistical
model Eq. (1) was defined as follows:
y=Xb+Zu+e,
where y is a vector of observations for the traits EBW, BFT, BCS
and LBW; b is a vector of fixed effects including the combined
effect of birth type (single, twin, triplet) and sex of the lamb (male,
female), breed (ML, RH), the combined month–of-the-year effect, ewe BCS (1–5)
(apart from the models where BCS and BFT are the traits of interest), and
the fixed regression of the lamb age (0 to 73 d) within breed modeled
with Legendre polynomials of fourth order. Furthermore, vector b
included in consecutive runs the different CH4 traits CH4r+e- (≤25µL L-1; 26–35 µL L-1; ≥36µL L-1), CH4r- (≤15.5µL L-1; 15.6–19.5 µL L-1; > 19.5 µL L-1), CH4rsum- (≤360µL L-1 min-1; 361–439 µL L-1 min-1; ≥440µL L-1 min-1), CH4rmax- (≤34µL L-1; > 34 µL L-1), CH4e- (≤72µL L-1; 73–99 µL L-1; > 99 µL L-1),
CH4esum- (≤310µL L-1 min-1; 311–620 µL L-1 min-1; > 620 µL L-1 min-1),
CH4emax- (≤170µL L-1; 171–315 µL L-1;
> 315 µL L-1), and CH4event class (≤0.96 min-1; > 0.96 min-1). u is a vector for the
random ewe or lamb effect considering up to four repeated measurements per
ewe and lamb, e is a vector of random residual effects, and
X and Z are incidence matrices for b and
u, respectively.
Genetic parameters for ewe CH4 emissions and body weight traits
Genetic (co)variance components for all trait combinations including EBW,
BCS, BFT and LBW were estimated by applying the software package DMU (Madsen
and Jensen, 2013) and using the AI-REML algorithm for multiple-trait animal
models. For the CH4 traits CH4r+e, CH4r, CH4rmax, CH4rsum, CH4e, CH4emax,
CH4esum and CH4event, single-trait animal models were applied. Multiple-trait models converged properly for EBW, BCS, BFT and LBW due to the larger
dataset, but some convergence problems occurred when additionally including
CH4 traits from the smaller subset of phenotyped ewes. This was the reason
for the application of single-trait animal models for CH4 traits.
The statistical model Eq. (2) for genetic analyses in matrix notation was
defined as follows:
y=Xb+Za+Wpe+e,
where y is a vector of observations for EBW, BCS, BFT and LBW and
CH4 traits; b is a vector of fixed effects including all effects
as introduced in model (1) and the fixed effects for the temperature class
(≤4, 4.1–8.5, 8.6–11,
> 11 ∘C) and for the humidity class (≤43 %,
44 %–55 %, 56 %–64 %, > 64 %); a is a vector of
random additive genetic effects considering the genetic relationships from
an animal model; pe is a vector of random permanent environmental
effects for repeated measurements; e is a vector of random
residual effects; and X, Z and W are incidence
matrices for b, a and pe, respectively.
Correlations among estimated breeding values (EBVs) for CH4 traits (EBV from
the single-trait models), and between EBV for CH4 traits and EBV for EBW,
BCS, BFT and LBW (EBV from the multiple-trait model) were transformed into
genetic correlations according to Calo et al. (1973):
rg1,2=∑iRi1⋅∑iRi2∑iRi1⋅Ri2⋅rEBV1,EBV2,
where R was the EBV reliability for an individual i in trait j. For the
genetic correlation approximations, we only considered EBV from ewes with
phenotypic records.
Results and discussionStrategies of CH4 trait definitions
The introduced CH4 data preparation strategy is very complex. Nevertheless,
a separation of respiration and eructation CH4 is physiologically
reasonable and considers environmental air movements. We identified a high
agreement between statistically defined eructation events and ewe eructation
during trait recording (own visual inspections of eructation events during
CH4 recording). In our data, during the 3 min recording interval,
95 % of ewes eructated at least once. The eructation probability in the
study by Ricci et al. (2014) was slightly lower (92 %), but they
considered a 2 min recording interval. Hence, a minor disadvantage for
specific CH4 eructation trait definitions is the small percentage of ewes
(5 %) which had to be excluded from data processing. Chagunda et al. (2009b) introduced a further transformation of LMD output data (µL L-1) into daily CH4 production (g d-1) but without distinguishing
into respiration and eructation. The data processing procedure by Chagunda
et al. (2009b) also required complex equations including approximations for,
for example, individual respiratory tidal volume or for the daily animal activity
level. Methane traits as defined in our study reflect “pure” CH4
emissions, whereas CH4 predictions by Chagunda et al. (2009b) depend on body
trait or physiological characteristics. Hence, in quantitative genetic
studies, and following a deterministic CH4 prediction strategy, the
estimated heritability does not fully reflect the individual CH4 genetic
background (Yin et al., 2015). In genome-wide association studies,
Manzanilla-Pech et al. (2016) found an overlap for 19 % of SNP markers
being significantly associated with DMI, body weight and individual CH4
production in dairy cattle. In consequence, they suggested consideration of
residual CH4 emissions that are additionally pre-corrected for CH4 indicator
traits (e.g., for DMI and for body weight).
Moreover, our CH4 trait separation into respiration and eructation provides
deeper insights into the different physiological mechanisms associated with
CH4 output. The CH4 separation strategy allows studying the isolated
influence of either respiration or eructation on ewe body condition traits
and on LBW. Nevertheless, our approach depends on the individual threshold
definition for the two normal distributions (respiration and eructation).
Least-squares means for lamb body weight (LBW) depending
on ewe mean CH4 concentration during respiration (CH4r class; model 1).
Different letters represent significant differences (P<0.01).
Phenotypic impact of CH4 traits on lamb body weight and ewe body
condition
Among all CH4 traits, the inclusion of CH4 mean concentration during
respiration (CH4r) as class effect in model (1) gave the lowest value for
the Akaike information criterion (Akaike, 1973) (Table 2). Hence, CH4r
consideration indicated statistical modeling superiority. The CH4r class
effect significantly influenced LBW (P<0.05) and EBW (P<0.01) (model 1). Ewes with low
mean CH4 emissions during respiration reared heavier lambs than ewes with
high CH4 emissions (P<0.001; Fig. 2). Simultaneously, low mean CH4 emissions during
respiration were associated with larger estimates for EBW during lactation
(P<0.001; Fig. 3). EBW during lactation for ewes from the low CH4rmax class was
significantly higher (0.74 kg, P<0.05) compared to EBW from ewes with high CH4rmax
emissions (CH4rmax class > 34 µL L-1). In cattle,
Johnson and Johnson (1995) identified high CH4 emissions as major
contributors to energy losses, comprising 5 %–12 % of the gross energy
intake. Consequently, limited energy is available for milk production,
explaining the lower LBW of lambs from ewes with high CH4 output during
lactation. Kandel et al. (2017) and Chagunda et al. (2009a) confirmed such
unfavorable associations between CH4 emissions and milk yield in cattle.
Interestingly, the CH4 eructation traits represent larger CH4 emissions than
the respiration traits (Blaxter and Joyce, 1963),
but only the respiration CH4 traits CH4r and CH4rmax significantly
influenced LBW and EBW. An explanation for the significant impact of
“low-level CH4” (CH4r, CH4rmax) on LBW and EBW might be due to the short
recording interval of only 3 min. For a small recording interval,
the percentage of respiration in relation to eructation is larger, compared
to, for example, accumulate 24 h measurements.
Least-squares means for ewe body weight (EBW) depending
on ewe mean CH4 concentration during respiration (CH4r class; model 1).
Different letters represent significant differences (P<0.01).
Akaike information criterion (AIC) for model (1) with the
dependent traits of lamb body weight (LBW) or ewe body weight (EBW),
considering different CH4 class effects.
*CH4r+e: mean CH4 concentration during respiration and eructation;
CH4r: mean CH4 concentration during respiration; CH4rmax: maximum CH4
concentration during respiration; CH4rsum: sum of CH4 concentrations per
minute during respiration; CH4e: mean CH4 concentration during
eructation; CH4emax: maximum CH4 concentration during eructation; CH4esum: sum of CH4 concentrations per minute during eructation; CH4event: number of eructation events per minute.
Least-squares means for BFT declined with increasing ewe CH4 emissions. In
this regard, ewes representing the medium CH4r+e, CH4emax and CH4esum
class had 0.38 to 0.43 mm less BFT than ewes from the low CH4 classes
(P<0.05) (Fig. 4). An increase of CH4 emissions was associated with inefficient
feed conversion, both contributing to energy deficiency during the early
lactation stage (Hegarty et al., 2007; Paganoni et al., 2017). Hence, for
energy deficiency compensation due to mammary requirements during lactation
(intensified through CH4 emissions), ewes are forced to increase the
mobilization rate of their own body fat depots (Bell, 1995), explaining the
EBW and BFT decline. Such initiated catabolic processes depend on liver
glycogen levels, which represent an important glucose (energy) body
resource. Physiologically, catecholamine and glucagon blood levels are
increasing, initiating the hydrolysis of body fat deposits (triglycerides)
(Lawrence and Fowler, 2002). Ewes from the present study received
concentrates but also responded with a BFT decline during lactation. Weston (1996) indicated the general problem of energy deficiency of lactating ewes,
especially in pasture based production systems. Consequently, we suggest
selection strategies on low CH4 emissions, in order to avoid further energy
losses.
Bielak et al. (2016) suggested plasma levels of non-esterified fatty acids
(NEFAs) as indicators for body fat mobilization. In lactating dairy cows,
Bielak et al. (2016) identified a negative relationship between CH4
production per DMI and NEFA plasma levels. Nevertheless, intensified body
fat mobilization with decreasing CH4 emissions in cows is in contradiction
with the identified associations in the present study for sheep. Summarizing
the phenotypic relationships, low values for CH4r+e, CH4r, CH4rmax,
CH4emax and CH4esum in ewes were favorably associated with maternal body fat
storage during lactation, and with increasing LBW.
Genetic parameters for CH4, ewe body condition traits and lamb
body weight
In previous studies, variation of individual CH4 emissions was due to the
diet composition and feeding system characteristics (Chagunda et al., 2009a;
Pinares-Patiño et al., 2011; Bell et al., 2016), ruminal microbiome (Shi
et al., 2014) and host genetic compositions (Pinares-Patiño et al.,
2013). Genetic variation for CH4 emissions indicates the general
possibilities for genetic selection, but this variation was only detected
for CH4rsum and CH4emax (Table 3). Correspondingly, heritabilities for CH4
traits (Table 3) were close to zero, with the largest estimate for CH4rsum
(0.03). Pickering et al. (2015) and Paganoni et al. (2017) estimated
heritabilities for CH4 in a comparable range from 0.05 to 0.14 in dairy
cattle and sheep, respectively. For CH4 recordings, Paganoni et al. (2017)
used portable accumulation chambers, and they applied the technique to lambs
at post-weaning age and hoggets. Hence, CH4 heritabilities in ruminants are
generally quite low, irrespective of the utilized measurement technology and
the age of animals. Quite large residual variances (as also indicated in
Table 3 for the traits in the present study) due to further environmental
effects, which were not considered in statistical modeling, e.g., the
individual stress level during measurement or the exhalation rate (Wu et
al., 2018), might explain the generally small CH4 heritabilities in sheep.
Large residual variances and small heritabilities indicate only minor
selection response when aiming on reduced CH4 emissions. Besides, some ewes
did not show any eructation during short time measurements. For the
inclusion of eructation CH4 traits (CH4e, CH4emax, CH4esum, CH4event) into
overall breeding goals, it is imperative to consider repeated measurements
per animal, in order to guarantee at least one eructation per measurement.
Least-squares means for ewe backfat thickness (BFT)
depending on ewe mean CH4 concentration during respiration and eructation
(CH4r+e class), maximal CH4 concentration during eructation (CH4emax
class), and sum of CH4 concentrations per minute during eructation (CH4esum
class) (model 1). Definition of CH4 classes: low CH4 class: CH4r+e≤25µL L-1; CH4emax≤170µL L-1; CH4esum≤310µL L-1 min-1; medium CH4 class: CH4r+e 26–35 µL L-1; CH4emax 171–315 µ L L-1; CH4esum 311–620 µL L-1 min-1; high CH4 class: CH4r+e≥36µL L-1;
CH4emax > 315 µL L-1; CH4esum > 620 µL L-1 min-1. Different letters represent significant
differences (P<0.01).
Heritabilities (h2) with standard errors (SE),
additive genetic variances (σa2), permanent environmental
variances (σpe2) and residual variances (σe2) for lamb body weight (LBW), ewe body weight (EBW), ewe body
condition score (BCS), ewe backfat thickness (BFT) and CH4 traits.
*CH4r+e: mean CH4 concentration during respiration and eructation;
CH4r: mean CH4 concentration during respiration; CH4rmax: maximum CH4
concentration during respiration; CH4rsum: sum of CH4 concentrations per
minute during respiration; CH4e: mean CH4 concentration during
eructation; CH4emax: maximum CH4 concentration during eructation; CH4esum: sum of CH4 concentrations per minute during eructation; CH4event: number of eructation events per minute.
Heritabilities for body condition traits were 0.56 for EBW, 0.37 for BCS,
0.25 for BFT and 0.37 for LBW (Table 3). Pinares-Patiño et al. (2013)
and Borg et al. (2009) estimated similar heritabilities for live weight of
0.46 and 0.38, respectively. Jonker et al. (2018) estimated a heritability
of 0.35 for LBW at 4 to 13 months of age, confirming our estimate of 0.37.
The BFT heritability reflects estimates by Gernand et al. (2008) and Brito
et al. (2017), but in both studies, the authors considered records from
lambs instead of ewe traits.
Genetic correlations between lamb body weight (LBW), ewe
body weight (EBW), ewe body condition score (BCS) and ewe backfat thickness
(BFT) with standard errors (in brackets), and approximated genetic
correlations between LBW, EBW, BCS, BFT and CH4 traits.
*CH4r+e: mean CH4 concentration during respiration and eructation;
CH4r: mean CH4 concentration during respiration; CH4rmax: maximum CH4
concentration during respiration; CH4rsum: sum of CH4 concentrations per
minute during respiration; CH4e: mean CH4 concentration during
eructation; CH4emax: maximum CH4 concentration during eructation; CH4esum: sum of CH4 concentrations per minute during eructation; CH4event: number of eructation events per minute.
Genetic relationships between CH4 traits with ewe body condition
traits and with lamb body weight
For the definition of overall sheep breeding goals including CH4, knowledge
about genetic correlations and covariances with other economically important
traits (e.g., LBW) is imperative. Genetic correlations between CH4 traits and
EBW were slightly (CH4r, CH4rsum) or moderately negative (CH4r+e, CH4rmax,
CH4e, CH4emax, CH4esum, CH4event), indicating that breeding on low CH4
emissions increases EBW and vice versa (Table 4). Generally, genetic
correlation estimates between CH4 traits and EBW were in agreement with the
phenotypic associations from model (1). In contrast to our results,
Pinares-Patiño et al. (2013) estimated a differing genetic correlation
of 0.80 between CH4 production (g d-1) and body weights of lambs at the
age of 8 months. In the present study, BCS was moderately negatively
correlated with all CH4 traits reflecting eructation (CH4e, CH4emax,
CH4esum, CH4event; Table 4). Also, genetic correlations between BCS with
CH4r+e and CH4rmax were negative. Zetouni et al. (2018) estimated a
similar genetic correlation of -0.28 between CH4 production and BCS in
Danish Holstein cows. Regarding BFT, a decline in CH4r+e, CH4e, CH4emax,
CH4esum and CH4event was genetically associated with an incline in BFT. The
genetic correlations between the respiration CH4 traits (CH4r, CH4rmax,
CH4rsum) and BFT were close to zero. In contrast to the phenotypic
associations, genetic relationships between respiration CH4 traits (CH4r,
CH4rsum, CH4rmax) and LBW were quite low in the range from -0.07 to 0.01. On
a genetic basis, among all CH4 traits, CH4r+e had the strongest genetic
correlation (-0.35) with LBW.
Genetic correlations among EBW, BCS, BFT and LBW were positive, indicating
an incline in LBW when selecting heavy ewes with high values for BCS and
BFT. However, a strict selection on increasing EBW, BCS and BFT for indirect
improvements of LBW might be associated with insulin resistance and hormone
dysregulation in the future F1 and F2 generations (Pankey et al., 2017).
Furthermore, adipose ewes were susceptible for dystocia (Peel et al., 2012).
In summary, the CH4 traits CH4r+e, CH4e, CH4esum, CH4emax and CH4event
were genetically favorably correlated with LBW, indicating an increase in
LBW and simultaneously improvements of EBW, BCS and BFT when selecting on
low CH4 emissions, particularly during eructation. Nevertheless, small CH4
heritabilities indicate only slight selection response. Hence, in breeding
goals or selection indices, it is imperative to consider the low
heritability CH4 traits with high economic values (König et al., 2009).
Conclusions
CH4 recording via LMD technique was successfully implemented in sheep under
field conditions. On a longitudinal trait basis, we developed statistical
strategies for distinguishing CH4 emissions in respiration and eructation.
Large ewe CH4 emissions during respiration were associated with lower EBW
as well as with impaired body weight development of their lambs. Additionally,
a significant ewe BCS and BFT decrease after lambing was detected in ewes
with high levels of CH4 emissions during eructation. Heritabilities for CH4
traits were close to zero (h2<0.01 to 0.03). Nevertheless,
the genetic correlations between CH4 traits CH4r+e, CH4e, CH4emax, CH4esum
and CH4event and energy efficiency indicators (e.g., LBW) suggest
consideration of ewe CH4 emissions in overall sheep breeding goals when
aspiring to feed efficiency improvements. We proved that the utilization of
LMD equipment is an appropriate non-invasive method to measure CH4 emissions
in sheep rapidly, easily and cost-efficiently. Furthermore, the
differentiation between respiration and eructation CH4 emissions provides
insights into physiological dynamics of CH4 emissions. Nevertheless,
environmental (e.g., micrometeorology) and physiological (e.g., respiratory
volume, behavior) factors can influence results from the applied CH4
recording technique and should be considered in future statistical
modeling approaches.
Data availability
The data that support the findings of this study are available from the
authors upon reasonable request.
Author contributions
SK, AW and HW designed the experiment and supervised the research. SK
supported JR in writing and data validation. JR was responsible for
phenotyping activities, supported by PE. JR, KB and TY were responsible for
data preparation and genetic statistical analyses. All authors read and
approved the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors are grateful to the H. Wilhelm Schaumann Stiftung for
providing a scholarship to Jessica Reintke.
Financial support
This open-access publication was funded by Justus Liebig University Giessen.
Review statement
This paper was edited by Manfred Mielenz and reviewed by Angela Schwarm and one anonymous referee.
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