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Effect of the macroalgae Asparagopsis taxiformis on methane production and the rumen microbiome assemblage

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Background: Recent studies using batch-fermentation suggest that the red macroalgae Asparagopsis taxiformis might reduce methane (CH4) emission from beef cattle by up to ~99% when added to rhodes grass hay, a common feed in the Australian beef industry. These experiments have shown significant reductions in methane without compromising other fermentation parameters (i.e. volatile fatty acid production) with A. taxiformis organic matter (OM) inclusion rates of up to 5%. In the study presented here, A. taxiformis was evaluated for its ability to reduce methane production from dairy cattle fed a mixed ration widely utilized in California; the largest milk producer in the US. Results: Fermentation in a semi-continuous in-vitro rumen system suggests that A. taxiformis can reduce methane production from enteric fermentation in dairy cattle by 95% when added at a 5% OM inclusion rate without any obvious negative impacts on volatile fatty acid production. High-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing showed that seaweed amendment effects rumen microbiome communities consistent with the Anna Karenina hypothesis, with increased beta-diversity, over time scales of approximately three days. The relative abundance of methanogens in the fermentation vessels amended with A. taxiformis decreased significantly compared to control vessels, but this reduction in methanogen abundance was only significant when averaged over the course of the experiment. Alternatively, significant reductions of methane in the A. taxiformis amended vessels was measured in the early stages of the experiment. This suggests that A. taxiformis has an immediate effect on the metabolic functionality of rumen methanogens whereas its impact on microbiome assemblage, specifically methanogen abundance, is delayed. Conclusions: The methane reducing effect of A. taxiformis during rumen fermentation makes this macroalgae a promising candidate as a biotic methane mitigation strategy in the largest milk producing state in the US. But its effect in-vivo (i.e. in dairy cattle) remains to be investigated in animal trials. Furthermore, to obtain a holistic understanding of the biochemistry responsible for the significant reduction of methane, gene expression profiles of the rumen microbiome and the host animal are warranted.
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Effect of the macroalgae Asparagopsis taxiformis on methane production and
the rumen microbiome assemblage
Breanna Michelle Roque1 (bmroque@ucdavis.edu), Charles Garrett Brooke1
(cgbrooke@ucdavis.edu), Joshua Ladau2 (jladau@gmail.com), Tamsen Polley1
(tmpolley@ucdavis.edu), Lyndsey Marsh1 (ljmarsh@ucdavis.edu), Negeen Najafi1
(Nnajafi@ucdavis.edu), Pramod Pandey3 (pkpandey@ucdavis.edu), Latika Singh3
(lssingh@ucdavis.edu), Joan King Salwen4 (jsalwen@stanford.edu), Emiley Eloe-Fadrosh2
(eaeloefadrosh@lbl.gov), Ermias Kebreab1 (ekebreab@ucdavis.edu), Matthias Hess1
(mhess@ucdavis.edu)
1Department of Animal Science, University of California, 2251 Meyer Hall, Davis, CA, 95616,
USA
2 Department of Energy Joint Genome Institute, 2800 Mitchell Drive, Walnut Creek, CA, 94598,
USA
3Department of Population Health and Reproduction, School of Veterinary Medicine, One
Shields Avenue, Davis, CA, 95616, USA
4Department of Earth System Science, Stanford University, 450 Serra Mall, Stanford, CA,
94305, USA
Corresponding author: Matthias Hess
2251 Meyer Hall
Department of Animal Science
University of California, Davis
Davis, CA 95616, USA
P: (530) 530-752-8809
F: (530) 752-0175
E: mhess@ucdavis.edu
Subject category: Animal Science, Ruminant Nutrition, Host microbe Interaction
Conflict of interest: The authors declare to have no conflict of interest.
Funding: This work was funded by the College of Agricultural and Environmental Sciences at
the University of California, Davis, the Laboratory Directed Research and Development Program
of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-
AC02-05CH11231, the Hellman Foundation and by ELM Innovations.
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ABSTRACT:
Background: Recent studies using batch-fermentation suggest that the red macroalgae
Asparagopsis taxiformis might reduce methane (CH4) emission from beef cattle by up to ~99%
when added to rhodes grass hay, a common feed in the Australian beef industry. These
experiments have shown significant reductions in methane without compromising other
fermentation parameters (i.e. volatile fatty acid production) with A. taxiformis organic matter
(OM) inclusion rates of up to 5%. In the study presented here, A. taxiformis was evaluated for its
ability to reduce methane production from dairy cattle fed a mixed ration widely utilized in
California; the largest milk producer in the US.
Results: Fermentation in a semi-continuous in-vitro rumen system suggests that A. taxiformis
can reduce methane production from enteric fermentation in dairy cattle by 95% when added at a
5% OM inclusion rate without any obvious negative impacts on volatile fatty acid production.
High-throughput 16S ribosomal RNA (rRNA) gene amplicon sequencing showed that seaweed
amendment effects rumen microbiome communities consistent with the Anna Karenina
hypothesis, with increased beta-diversity, over time scales of approximately three days. The
relative abundance of methanogens in the fermentation vessels amended with A. taxiformis
decreased significantly compared to control vessels, but this reduction in methanogen abundance
was only significant when averaged over the course of the experiment. Alternatively, significant
reductions of methane in the A. taxiformis amended vessels was measured in the early stages of
the experiment. This suggests that A. taxiformis has an immediate effect on the metabolic
functionality of rumen methanogens whereas its impact on microbiome assemblage, specifically
methanogen abundance, is delayed.
Conclusions: The methane reducing effect of A. taxiformis during rumen fermentation makes
this macroalgae a promising candidate as a biotic methane mitigation strategy in the largest milk
producing state in the US. But its effect in-vivo (i.e. in dairy cattle) remains to be investigated in
animal trials. Furthermore, to obtain a holistic understanding of the biochemistry responsible for
the significant reduction of methane, gene expression profiles of the rumen microbiome and the
host animal are warranted.
Key words: 16S rRNA community profiling, Asparagopsis taxiformis, feed supplementation,
greenhouse gas mitigation, in-vitro rumen fermentation, macroalgae, rumen microbiome
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BACKGROUND
Methane (CH4) is a major greenhouse gas with a global warming potential 28-fold greater than
that of carbon dioxide (CO2) on a 100-year scale [1] and it accounts for approximately 11% of
the greenhouse gas (GHG) emissions in the US [2]. Enteric fermentation from ruminant animals
alone accounts for approximately 25% of the total CH4 emissions in the US, representing the
largest anthropogenic source of CH4 [3]. Increasing emphasis on reducing GHG emissions from
the livestock industry requires advanced methods for reducing and controlling CH4 production.
Identifying efficient strategies to lower enteric CH4 production could result in a significantly
reduced carbon footprint from animal production and provide the cattle industry with a way to
meet legislative requirements, requiring a reduction of CH4 emission of ~40% by 2030.
The biological production of CH4 in the rumen is the product of symbiotic relationships between
fiber degrading bacteria, hydrogen (H2) producing protozoa and methanogenic archaea [4, 5].
Besides being converted into CH4, metabolic H2 may also be incorporated into volatile fatty
acids (VFA), such as acetate, propionate, and butyrate which are then used as energy by the
ruminant animal. Theoretically, inhibiting methanogenesis could free molecular hydrogen for use
in pathways that produce metabolites (i.e. VFAs) that are more favorable to the host animal, thus
creating potential for increased feed efficiency. Since production of enteric CH4 can account for
up to 12% of the total energy consumed by the animal [6, 7] even a small reduction of CH4
production and redirection of carbon molecules into more favorable compounds has the potential
to result in significantly more economically and ecologically sustainable production practices in
the ruminant industry.
Extensive research has been performed on the effectiveness of feed supplements to reduce
enteric CH4 emissions through inhibition of microbial methanogenesis within the rumen system
[8]. Results have been reported for a number of feed supplements including inhibitors,
ionophores, electron receptors, plant bioactive compounds, dietary lipids, exogenous enzymes,
and direct-fed microbials indicating reductions on CH4 production [9]. While several of these
compounds have been shown to inhibit ruminal methanogenesis, some have been shown to
decrease VFA production [10] which decreases overall nutrient availability to the animal and is
therefore a non-desirable side effect.
Algae are a stable component of the human diet in some cultures [11] and have also been used as
feed for agricultural products such as abalone [12] and shrimp [13] The ability of algae to
promote well-being and health is mediated to a great extent by highly bioactive secondary
metabolites [14, 15, 16] that are synthesized by some of the algal species [17]. In addition to the
health promoting properties of macroalgae, some of the brown and red macroalgae have shown
to inhibit microbial methanogenesis when tested in-vitro [18] and a similar response of the
animal microbiome has been proposed. These findings suggest that macroalgae could promote
higher growth rates and feed conversion efficiencies in ruminants [19, 20]. Previous findings
also suggest macroalgal feed supplements work as highly potent mitigation strategy to reduce
CH4 production during enteric fermentation [10, 18, 21, 22). Macroalgae feed supplementation
therefore may be an effective strategy to simultaneously improve profitability and sustainability
of dairy operations.
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Various types of algae have antibacterial, antiviral, antioxidant, anti-inflammatory, and anti-
carcinogenic properties [23, 24, 25, 26]. Most recently, macroalgae has been tested in-vitro and
in-vivo to determine if there are anti-methanogenic properties within selected types of
macroalgae. Asparagopsis taxiformis, a red macroalgae, stands out as the most effective species
of macroalgae to reduce methane production.
In a recent study [18] Asparagopsis taxiformis, a red macroalgae, stood out as the most effective
species of macroalgae to reduce methane production. In this work, the effect of a large variety of
macroalgal species including freshwater, green, red, and brown algae on CH4 production during
in-vitro incubation was compared. Results showed that A. taxiformis amendment yielded the
most significant reduction (~98.9%) of CH4 production. Moreover, Additional in-vitro test with
A. taxiformis supplementation at inclusion rates up to 5% organic matter (OM) revealed methane
reduction by 99%, without significant negative impact on VFA profiles and OM digestibility
[10]. Furthermore, this group sought out to identify the anti-methanogenic properties of A.
taxiformis and found that this particular strain of macroalgae contains an abundance of anti-
methanogenic compounds including: bromoform, dibromocholoromethane, bromochloroacetic
acid, dibromoacetic acid, and dichloromethane [27]. Bromoform, the most abundant compound
found in A. taxiformis, was previously identified as a halomethane and has been shown to inhibit
enzymatic activities by binding to vitamin B12 [28], which chemically resembles coenzyme F430
a cofactor needed for methanogenesis [29]. While it is clear that A. taxiformis contains
antimethanogenic compounds, actual concentrations of these compounds seem to vary and what
causes these variations remain unclear.
In the work presented here, we studied the effect of A. taxiformis (5% OM inclusion rate) on the
rumen microbiome assemblage and function during in-vitro fermentation over the duration of
four days. To obtain a better understanding of how this macroalgae would affect CH4 emission,
specifically from dairy cows fed a diet commonly used California, and therefore providing
insight into the value of an A. taxiformis-based CH4 mitigation strategy for the dairy industry in
California. To the end of obtaining new insights into the effect of A. taxiformis supplementation
on rumen microbiome assemblage, we employed high-throughput 16S rRNA amplicon
sequencing. To our knowledge this is the first time that this highly efficient procedure was
employed to dissect the changes in the rumen microbiome of dairy cattle in response to A.
taxiformis as feed supplement and CH4 mitigator.
RESULTS
In-vitro standard measurements remained stable throughout the experiment
Temperature, pH, and mV remained relatively constant (37°C ±2, 6.8 pH ±0.03, 21 mV ±3)
throughout the entire experiment and between individual vessels.
A. taxiformis contains an elevated mineral profile but less organic matter compared to
SBR.
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A higher OM content for SBR was found (92.8% DM) when compared to A. taxiformis (53%
DM). Crude protein amounts were relatively similar for SBR (20% DM) and A. taxiformis
(17.8% DM). Neutral detergent fiber composition of SBR and A. taxiformis were also similar
with 38.1 and 36.9% DM, respectively. Differences in starch content between SBR and A.
taxiformis were prominent with 12.6 and 0.7% DM, respectively. Lignin content for SBR was
determined with 6% DM and 4.4% DM for A. taxiformis. Total digestible nutrient content (TDN)
for A. taxiformis was approximately half (33.8% DM) of the TDN determined for SBR (66.2%
DM). Asparagopsis taxiformis contained elevated mineral profiles compared to SBR. More
specifically, A. taxiformis exhibited higher calcium, sodium, magnesium, iron, and manganese
concentrations. Zinc was present at 23.7 ppm in both SBR and A. taxiformis. The detailed
composition of SBR and A. taxiformis is shown in Table 1.
Table 1.
Composition of SBR and Asparagopsis taxiformis
SBR
1)
A. taxiformis
Chemical Composition
% Dry matter
Organic matter 92.8
53
Crude protein 20.0
17.8
Neutral detergent fiber 38.1
36.9
Acid detergent fiber 27.3
11.6
Starch 12.6
0.7
Fat 2.7
0.4
Total digestible nutrients 66.2
33.8
Lignin 6
4.4
Calcium 0.9
3.8
Phosphorus 0.4
0.2
Sodium 0.1
6.6
Magnesium 0.5
0.8
Parts per million
Iron 632.7
6241
Manganese 41.7
112.7
Zinc 23.7
23.7
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Copper 11
8.7
1) Super basic ration
A. taxiformis decreases methane production and increases propionate:acetate ratio
Total gas production (TGP) and CH4 production were significantly affected by the inclusion of
A. taxiformis (p < 0.05, Table 2). Average total gas production for the A. taxiformis treatment
group was 14.81 mL/(g OM) whereas the control group was 28.54 mL/(g OM), representing a
51.8% reduction in TGP with A. taxiformis. Average CH4 production for the A. taxiformis
treatment group was 0.59 mg/(g OM), whereas the control group produced 12.08 mg/(g OM),
representing a 95% reduction of CH4 being synthesized. No significant difference was found in
CO2 production between the A. taxiformis treatment and the control groups. Figure 1 illustrates
how total gas (i.e. CH4 and CO2) was affected over the duration of the experiment. It appears that
A. taxiformis is effective at reducing TGP and CH4 almost immediately, beginning at the 12 hour
time point, and continues to inhibit CH4 production over 24 hrs just prior to when new bioactive
is provided during the feeding process (at 24 hr, 48 hr, and 72 hr). Inhibition of methanogenesis
was also measured just prior to the termination of the experiment (96 hr).
Slightly higher total VFA concentrations were recorded for the control group when compared to
the A. taxiformis treatment group [2332.52 ppm vs. 2105.11 ppm ± 269.20 ppm respectively
(means ± SE),] however this difference was not statistically significant (p = 0.45, Table 2).
Additionally, no significant differences were found when comparing concentrations of acetate,
propionate, butyrate, isobutyrate, valerate, and isovalerate (Table 2) between control and A.
taxiformis treatment group. Although, valerate was not found to be statistically different between
groups (p < 0.05), it was observed that the A. taxiformis treatment group tended to have lowered
concentrations of valerate when compared to the control group (p = 0.06). Statistical differences
were found between groups when comparing the propionate:acetate ratio, with a higher
proportion of propionate to acetate within the A. taxiformis treatment groups (p = 0.001).
Differences observed at each timepoint between control and A. taxiformis treatment groups were
determined to be not significant (Figure 2).
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Table 2.
Effects of A. taxiformis on total gas production and total volatile fatty acid production.
Control A. taxiformis Standard
error p value
Gas Production [mg/(g OM)]
CH
4
12.08
0.59
0.59
<0.0001
CO
2
15.67
14.24
3.82
0.73
Total Volume
1)
28.54
14.81
3.85
0.02
Volatile Fatty Acid Production [ppm]
Total VFA 2332.52
2105.11
269.2
0.45
Acetate 1056.99
856.77
135.08
0.21
Propionate 481.12
490.54
58.36
0.88
Propionate:Acetate
2)
0.48
0.6
0.01
<0.001
Butyrate 394.35
423.01
53.55
0.62
Isobutyrate 84.81
79.83
4.32
0.31
Valerate 212.79
168.72
16.99
0.06
Isovalerate 102.44
86.21
14.49
0.33
1)
ml/(g OM)
2) reported as a ratio of respective VFAs
Sequencing and quality filtering
A total of 1,251,439 reads were generated from a total of 77 samples, with a mean (± SD) of
16,275 (±1,879) reads per sample. After quality filtering, 757,325 (60.5%) high quality
sequences remained. Operational taxonomic units (OTU) based analysis (at 97% sequence
identity) revealed 32,225 unique OTUs across all samples. Singletons contributed 23,043 (3%)
unique reads to the total filtered read count, and were removed prior further analysis. The mean
Goods’ coverage for all samples was 88 ±3%, suggesting that the sequencing effort recovered a
large proportion of the microbial diversity in each of the samples under investigation.
Distribution of the number of OTUs among each condition and time point during the experiment
can be found in Supplementary Table S1.
-diversity measurements show microbial communities diverged slightly over the course of
the experiment.
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The microbial communities of the control and A. taxiformis amended vessels were compared at
each incubation time. Significant differences in the microbial community between the two
conditions appeared transiently at only two time points, the 12 hour time point on the first day of
the experiment and again at the 24 hour time point on the fourth day (96 hrs after the start of the
experiment, AMOVA, p 0.02, and p 0.04 respectively). Comparison of the microbial
communities from the start and end of the experiment within each group suggested that the
microbial communities changed over the course of the experiment (AMOVA, p 0.06 and p
0.05, treatment and control respectively). The divergence of the microbial communities
throughout the experiment was visualized by Principal Coordinate Analysis (PCoA) and is
illustrated in Supplemental Figure S2. The first two axes of the PCoA plot account for a low
amount of the total variation among the samples (13.5%), which coincides with finding that the
two vessel groups were largely similar.
Microbial communities respond to A. taxiformis as a stressor, but recover quickly.
Although the effects of seaweed amendments on methane production were immediate ( 12 hr),
amendments may also affect microbial populations on a longer time scale. Over the duration of
the experiment, the -diversity (turnover) between pairs of control vessels remained constant, but
the -diversity between pairs of treatment vessels and between treatment and control vessels was
variable: -diversity amongst treatment vessels increased and then decreased, peaking at near 72
hours, while -diversity between treatment and control vessels increased essentially
monotonically until the end of the experiment (Figure 3A). These slow shifts in community
composition were evident regardless of the taxonomic level at which beta-diversity was
considered, including at coarse taxonomic resolutions (Figure 3B). Examination of the genus-
level -diversity within vessels across different time lags also indicated that the microbial
communities continued to shift throughout the duration of the experiment (Figure 3C).
Average methanogen abundance decreased, but not in concert with methane reduction.
Across all samples, one archaeal and 21 bacterial phyla were identified. The ten most abundant
phyla recruited >98% of the reads generated from the microbial communities of both the control
and A. taxiformis amended vessels (Figure 4). Microbiomes throughout the experiment,
regardless of experimental condition or time, were dominated by Bacteroidetes, Firmicutes, and
Proteobacteria. The Bacteroidetes:Firmicutes ratio decreased in both conditions over the course
of the experiment, suggesting influence due to the experimental system (Figure 4). With the
drastic decrease in CH4 in mind, the differences between the two groups were investigated at a
finer resolution by exploring the abundance dynamics of the Archaeal phylum Euryarchaeota,
which include the methanogenic Archaea. Based on the 16S rRNA gene profiles, five genera of
methanogenic Archaea were identified in all stages of the experiment. The five genera:
Methanobrevibacter, Methanosphaera, vadin CA11 of the Methanomassiliicoccacaea family,
Methanoplanus and Methanimicrococcus accounted for all reads recruited by the Euryarchaeota.
Methanobrevibacter and Methanosphaera accounted for >99% of the reads assigned to
methanogens. While CH4 production decreased in the A. taxiformis amended vessels 12 hr after
the first feeding event, abundance of methanogenic Archaea in the two conditions did not differ
significantly at individual time points (Figure 5). However, the average relative abundance of
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Euryarchaeota over the duration of the experiment were lower in the A. taxiformis amended
vessels compared to control vessels (1.38 and 1.79% respectively, p 0.03).
DISCUSSION
A significant reduction in CH4 production was found when evaluating the effects of A. taxiformis
on ruminal fermentation characteristics in-vitro at a 5% OM inclusion rate. Results from the
overall experiment show an approximate decrease in TGP by ~50% and in CH4 production by
~95%, which is similar to the reduction that was reported previously when the effect of A.
taxiformis on CH4 production from beef cattle was investigated [10, 18, 30] Carbon dioxide
production remained similar between the control and A. taxiformis amended vessels. Comparison
of total and individual VFA between vessels did not suggest any difference in VFA production at
any specific time point with the 5% OM inclusion rate. A significant reduction of CH4 was
measured 12 hrs after A. taxiformis amendment (Figure 1), while CO2 production and VFAs
profiles remained unchanged throughout the fermentation process (Figures 1 & 2). This suggests
that the amendment of SBR supplemented with A. taxiformis, inhibits methanogenesis but not
necessarily overall microbial growth, per se. This targeted effect on a specific metabolic function
and hence a functional group within the microbiome was also elucidated from the 16S rRNA
profiles of the in-vitro rumen system. Whereas the overall assemblages of the microbiome
associated with the treatment and control fermentation vessels of the in-vitro rumen system
remained rather similar throughout the duration of the fermentation process (Figure 4), changes
in the relative abundance of members belonging to the Euryarchaeota, the taxonomic group that
encompasses the main rumen methanogens, could be observed as early as 36 hr after the
initiation of the experiment. Although a semi-continuous batch fermentation system, as utilized
for this study, is capable of maintaining more rumen like conditions, mainly through maintaining
adequate pH and nutrient levels, when compared to a simple batch fermentation process, a wash-
out of the more sensitive rumen microbes (i.e. protozoa) is inevitable [31]. It is well known that
there is a mutualistic relationship between protozoa and methanogens [32, 33], and it has been
shown before that the removal of rumen protozoa results in a reduction of the methanogen
population and methanogenesis during enteric fermentation [34, 35]. Hence, the decrease in
relative abundance of Euryarchaeota observed for the control vessels at later time points of the
experiment is most likely an artifact caused by the inability of the in-vitro systems to maintain
protists over an extended period of time.
Propionate:acetate ratio increased in treatment vessels
Over the course of the experiment, the propionate:acetate ratio increased (p < 0.001) in treatment
vs control groups. The first step of the formation of acetate in the rumen releases metabolic
hydrogen which acts as a hydrogen donor to methanogenic archaea and therefore facilitates the
production of CH4 in the rumen [36]. In contrast, propionate acts as a competing hydrogen sink
[4, 37]. The increased propionate:acetate ratio suggest that hydrogen is, at least in some part,
being redistributed to propionate, which may help explain a portion of the methane reduction
seen here. In the context of dairy cattle and milk production, the increased propionate:acetate
ratio seen in vessels amended with A. taxiformis may forecast an altered milk composition in-
vivo. A decreased propionate:acetate ratio is associated with increased milk fat, and total milk
yield is positively associated with butyrate and propionate in the rumen [38]. Under this
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paradigm, A. taxiformis supplementation has the potential to increase total milk yield, however
may also negatively impact milk fat content.
Microbial communities overcame the stress of treatment
We observed that seaweed amendment has effects consistent with the Anna Karenina
Hypothesis, which posits that disturbances act to increase differentiation of microbial
communities [39]. Specifically, we found that communities in treatment vessels differentiated
increasingly from each other up to hour 72, after which they re-converged (Figure 3A). Hence,
the rumen microbial community undergoes changes that are both slow and variable in response
to seaweed amendments. However, these changes do not appear to be associated with variability
in reduction of gas production. While seaweed amendment may pose an initial stress on the
rumen microbial community, measured by the increased differentiation between treatment
vessels, after only 72 hours under recurrent daily stress (feeding), the -diversity between
communities in amended vessels stabilized.
A. taxiformis is a potential mineral supplement
Nutritional analysis of A. taxiformis revealed that the red macroalgae has high levels of
important minerals including calcium, sodium, iron, and manganese (Table 2) suggesting that in
addition to its methane reduction potential, A. taxiformis may also be used to increase mineral
availability to basic rations. In-vivo studies directed towards monitoring mineral transfer from
feed into product should be conducted next to facilitate a better understanding of whether or not
minerals or other compounds present in seaweed can be found in milk or meat of the consuming
animals. While halogen compounds have been reported as important players in the bioactive
process of methane reduction, previous studies using seaweed as a feed supplement found that
iodine, which is abundant in brown algae, is found in the milk of cows to which it is fed [40].
CONCLUSIONS
The methane reducing effect of A. taxiformis during rumen fermentation of feedstuff widely used
in California, makes this macroalgae a promising candidate as a biotic methane mitigation
strategy in the largest milk producing state in the US. The organic matter inclusion required to
achieve such a drastic decrease in methane is low enough to be practically incorporated in the
rations of average dairy operations. Significant limitations to the implementation of A. taxiformis
and potentially other algae include the infrastructure and capital necessary to make these
products commercially available and affordable. Furthermore, our understanding of the host
microbe interactions during seaweed amendment are limited. In order to obtain a holistic
understanding of the biochemistry responsible for the significant reduction of methane, and its
potential long-term impact on ruminants, gene expression profiles of the rumen microbiome and
the host animal are warranted.
ABBREVIATIONS
16S rRNA – 16 Svedberg ribosomal Ribonucleic Acid
AMOVA – Analysis of Molecular Variance
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bp – base pair
C - Celsius
CH4 - Methane
Co - Company
CO2 – Carbon Dioxide
DM – Dry Matter
DNA – Deoxyribonucleic Acid
FID – Flame Ionization Detector
g – Gram
GC – Gas Chromatography
Hrs – Hours
IACUC - Institution of Animal Care and Use Committee
ml – Milliliters
OM – Organic Matter
OTU – Operational Taxonomic Unit
PCoA – Principal Coordinate Analysis
PCR – Polymerase Chain Reaction
PVC – Poly Vinyl Chloride
SBR – Super Basic Ration
SD – Standard Deviation
TDN – Total Digestible Nutrients
TGP – Total Gas Production
VFA – Volatile Fatty Acid
METHODS
Animals, diets and rumen content collection
All animal procedures were performed in accordance with the Institution of Animal Care and
Use Committee (IACUC) at University of California, Davis under protocol number 19263.
Rumen content was collected from two rumen fistulated cows, one Jersey and one Holstein,
housed at the UC Davis Dairy Unit. Animals were fed a dry cow total mixed ration (50% wheat
hay, 25% alfalfa hay/manger cleanings, 21.4% almond hulls, and 3.6% mineral pellet (Table 1).
Three liters of rumen fluid and 60 g of rumen solids were collected 90 min after morning
feeding. Rumen content was collected via transphonation using a perforated PVC pipe, 500 mL
syringe, and Tygon tubing (Saint-Gobain North America, PA, USA). Fluid was strained through
a colander and 4 layers of cheesecloth into two 4L pre-warmed, vacuum insulated containers and
transported to the laboratory.
In-vitro feed and feed additive composition and collection
Due to its wide utilization in the dairy industry for cows during lactation, super basic ration
(SBR) was used as feed in the in-vitro experiment. SBR was composed of 70% alfalfa pellets,
15% rolled corn, and 15% dried distillers’ grains (Table 3). Individual components were dried at
55°C for 72 hours, ground through a 2 mm Wiley Mill (Thomas Scientific, Swedesboro, NJ) and
manually mixed. Asparagopsis taxiformis used as feed additive was provided in kind from the
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Commonwealth Scientific and Industrial Research Organization (CSIRO) Australia. The
macroalgae was in its filamentous gametophyte phase when collected near Humpy Island,
Keppel Bay, QLD (23o13’01”S, 150o54’01”E) by MACRO (Center for Macroalgal Resources
and Biotechnology) of James Cook University (JCU) in Townsville, QLD. The collected
biomass was frozen and stored at -15°C then shipped to Forager Food Co. in Red Hills,
Tasmania, AUS, where it was freeze dried and milled (2-3 mm) to ensure a uniform product.
Chemical composition of SBR and of A. taxiformis were analyzed at Cumberland Analytical
Services (Waynesboro, PA).
Table 3. Composition of dry cow diet and super basic ration (SBR).
Dry Cow Diet SBR
Ingredient
Alfalfa 25% Alfalfa 70%
Wheat 50% Dried distillers grain 15%
Almond hulls 21.40% Rolled corn 15%
Mineral pellets 3.60%
Engineered (in-vitro) rumen system
An advanced semi-continuous fermentation system, with six 1L vessels with peristaltic agitation,
based on the rumen simulation technique (RUSITEC) developed by Czerkawski and
Breckenridge [41] was used to simulate the rumen in the laboratory.
Experimental design
Equilibration (Day 0): Temperature, pH and conductivity of the rumen fluid and solids were
recorded using a mobile probe (Extech Instruments, Nashua, NH). Rumen fluid, 3L, from each
cow were combined with 2L of artificial saliva buffer [42] homogenized and then split into two
3L aliquots. Rumen solids, 15 g, from each animal were sealed in Ankom concentration bags
(Ankom, Macedon, NY) and added to each equilibration vessel (30g of rumen solids per vessel
total). Three concentrate bags containing 10g of SBR each were added to each vessel. One of the
vessels was also inoculated with 1.5 g of A. taxiformis to equilibrate microbial populations to the
treatment prior to the start of the experiment. SBR was ground in a 2 mm Wiley Mill before
being added to each concentrate bag to increase substrate availability and therefore producing
similar particle sizes that which the mastication function in-vivo provides to the animal. The two
vessels were then placed in a 39°C water bath and stirred with a magnetic stir bar for a 24 hour
equilibration period.
Fermentation (Days 1-4): After 24 hours of equilibration, temperature, pH, and conductivity of
the rumen fluid were recorded to determine stability of the vessels and their content. Each of the
6 in-vitro rumen vessels were randomly designated as either treatment or control vessel and filled
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with 750 mL of the corresponding fluid from the equilibration vessels. Location of the vessels
within the in-vitro platform were randomly allocated.
Each vessel received one concentrate bag of SBR from its respective equilibration vessel and one
new concentrate bag. Control concentrate bags contained 10 g SBR. Treatment concentrate bags
contained 10 g SBR plus 5% (OM) A. taxiformis. To simulate rumen retention time, each of the
feedbags were incubated in the allocated fermentation vessel for 48 hours. Temperature, pH, and
conductivity were measured every 24 hours prior to exchanging one of the concentrate bags
(feeding). After each feeding, all vessels were flushed with N2 to maintain anaerobic conditions
within the reactors. Individual reactor vessels of the artificial rumen system were connected to a
reservoir containing artificial saliva buffer. A peristaltic pump delivered 0.39 mL/min of buffer
to each vessel throughout the course of the experiment. Gas bags (Restek, USA) and overflow
vessel were used to continuously collect generated gas and effluent fluid. Effluent vessels were
chilled with ice to mitigate residual microbial activity.
Sample collection and analysis
Liquid and gas sample collections took place at 3 time points every 24 hours for 4 days. Time
point intervals were 4, 12, and 24 hours post-feeding each day. Fluid samples were collected in
1.5 mL tubes, flash frozen in liquid nitrogen, and stored at -20°C until processed. Gas bags were
collected at each time series interval for analysis of total gas production, CO2 and CH4
concentrations. Gas volume was measured with a milligas flow meter (Ritter, Germany) by
manual expulsion of the collection bag.
Volatile fatty acid and greenhouse gas analysis
To determine VFA profiles, Gas Chromatography-Flame Ionization detection (GC-FID) was
used. Fermentation fluid was prepared for VFA analysis by mixing with 1/5th volume 25 %
metaphosphoric acid, and centrifugation. Supernatant was filtered through a 0.22 µm filter and
stored in amber autosampler vials at 4 oC until analysis. The GC conditions were as follows:
analytical column RESTEK Rxi® – 5 ms (30 m × 0.25 mm I.D. × 0.25 µm) film thickness; the
oven temperature was set to 80oC for 0.50 min, and followed by a 20oC/min ramp rate until
200oC, holding the final temperature for 2 min; carrier gas was high purity helium at a flow rate
of 2.0 mL/min, and the FID was held at 250oC. A 1 µL sample was injected through
Split/Splitless Injectors (SSL), with an injector base temperature set at 250oC. Split flow and split
ratio were programmed at 200 and 100 mL/min respectively. To develop calibration curves,
certified reference standards (RESTEK, Bellefonte, PA) were used. All analyses were performed
using a Thermo TriPlus Autosampler and Thermo Trace GC Ultra (Thermo Electron
Corporation, Rodano Milan, Italy).
Methane and CO2 were measured using an SRI Gas Chromatograph (8610C, SRI, Torrance, CA)
fitted with a 3’x1/8” stainless steel Haysep D column and a flame ionization detector with
methanizer (FID-met). The oven temperature was held at 90oC for 5 minutes. Carrier gas was
high purity hydrogen at a flow rate of 30 ml/min. The FID was held at 300oC. A 1 mL sample
was injected directly onto the column. Calibration curves were developed with an Airgas
certified CH4 and CO2 standard (Airgas, USA).
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DNA extraction
DNA extraction was performed using the FastDNA SPIN Kit for Soil (MP Biomedicals, Solon,
OH) with ~500 mg of sample according to the manufacturer’s protocol. DNA was subsequently
purified with a Monarch® PCR & DNA Cleanup Kit (New England Biolabs, Ipswich, MA)
following the manufacturer's instructions. Extracted DNA was stored at -20°C until subsequent
PCR amplification and amplicon sequencing.
PCR amplification, library preparation, and sequencing
The V4-V5 hypervariable region of the 16S rRNA gene was sequenced on Illumina’s MiSeq
platform using the 515yF (3’-GTG YCA GCM GCC GCG GTA A-5’) and 926pfR (3’-CCG
YCA ATT YMT TTR AGT TT-5’) primer pair (Research and Testing, Lubock Texas; [43, 44]
For sequencing, forward and reverse sequencing oligonucleotides were designed to contain a
unique 8 nt barcode (N), a primer pad (underlined), a linker sequence (italicized), and the
Illumina adaptor sequences (bold).
Forward primer: AATGATACGGCGACCACCGAGATCTACAC-NNNNNNNN-
TATGGTAATT-GT-GTGYCAGCMGCCGCGGTAA;
Reverse primer: CAAGCAGAAGACGGCATACGAGAT-NNNNNNNN-AGTCAGTCAG-
GG-CCGYCAATTYMTTTRAGTTT.
Barcode combinations for each sample are provided in Supplementary Table S4. Each PCR
reaction contained 1 Unit Kapa2G Robust Hot Start Polymerase (Kapa Biosystems, Boston,
MA), 1.5 mM MgCl2, 10 pmol of each primer, and 1 L of DNA. The PCR was performed using
the following conditions: 95°C for 2 min, followed by 30 cycles at 95°C for 10 s, 55°C for 15 s,
72°C for 15 s and a final extension step at 72°C for 3 min. Amplicons were quantified using a
Qubit instrument with the Qubit High Sensitivity DNA kit (Invitrogen, Carlsbad, CA). Individual
amplicon libraries were pooled, cleaned with Ampure XP beads (Beckman Coulter, Brea, CA),
and sequenced using a 300 bp paired-end method on an Illumina MiSeq at RTL Genomics in
Lubbock Texas. Raw sequence reads were submitted to NCBI’s Sequence Read Archive under
the SRA ID: SRP152555.
Sequence analysis
Sequencing resulted in a total of 1,251,439 raw reads, which were analyzed using mothur
v1.39.5 [45] using the MiSeq SOP accessed on 3/10/2018 [46]. Using the make.contigs
command, raw sequences were combined into contigs, which were filtered using screen.seqs to
remove sequences that were >420 bp or contained ambiguous base calls to reduce PCR and
sequencing error. Duplicate sequences were merged with unique.seqs, and the resulting unique
sequences were aligned to the V4-V5 region of the SILVA SEED alignment reference v123 [47]
using align.seqs. Sequences were removed if they contained homopolymers longer than 8 bp or
did not align to the correct region in the SILVA SEED alignment reference using screen.seqs. To
further denoise the data, sequences were pre-clustered within each sample allowing a maximum
of 3 base pair differences between sequences using pre.cluster. Finally, chimeric sequences were
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removed using VSEARCH [48].
Quality filtered sequences were grouped into OTUs based on 97% sequence identity and
classified using the Bayesian classifier and the Greengenes database (August 2013 release of
gg_13_8_99) [49] with classify.seqs. Sequences that classified as mitochondria, chloroplasts,
eukaryotic, or of unknown origin were removed using remove.lineage. Samples were rarefied to
6,467 sequences per sample, the smallest number of sequences across all collected samples.
Singleton abundances were calculated with filter.shared. Chao1 diversity [50], Good’s coverage
[51], Shannon [52], and inverse Simpson indices were calculated using summary.single to
quantify coverage and -diversity.
Alpha-diversity
To estimate the microbial diversity within each group, first, rarefaction analyses were performed
(Supplementary Figures S1) and species richness and diversity indices were calculated
(Supplementary Table S2.). Variance of the microbial community between and among the
different vessels were quantified using a YC distance matrix [53].
Beta-diversity
To investigate slow-acting effects of seaweed addition on microbiome communities, we
computed Bray-Curtis dissimilarity ( -diversity) [54] between pairs of samples, both within
vessels at different time points, and between vessels at identical time points. We also considered
Jaccard dissimilarity which only reflects community composition and not relative abundance, but
found similar results and so only report the results for Bray-Curtis dissimilarity. We
independently computed -diversity at the genus, family, order, class, and phylum level to assess
whether the patterns that we found were dependent on taxonomic resolution. All analyses were
performed using custom-written Java, SQL, and Bash code available at https://github.com/jladau.
Statistical analysis
Analysis of molecular variance (AMOVA) [55] was used to identify significant differences in
community structure between treatment and control vessels using a YC distance matrix for the
amova command in Mothur. The complete results of these statistical tests between each time
interval combination is included in the supplementary data.
Gas, VFA, and Euryarchaeota abundance data were analyzed using the linear mixed-effects
model (lme) procedure using the R statistical software (version 3.1.1) [56]. The statistical model
included treatment, day, time point, treatment×day×time point interactions, treatment×day
interactions, treatment×time point interactions, day×time point interactions and the covariate
term, with the error term assumed to be normally distributed with mean = 0 and constant
variance. Orthogonal contrasts were used to evaluate treatments vs. control, linear, and quadratic
effects of treatments. Significant differences among treatments were declared at p 0.05.
Differences at 0.05 < p 0.10 were considered as trend towards significance.
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ETHICS APPROVAL
All animal procedures were performed in accordance with the Institution of Animal Care and
Use Committee (IACUC) at University of California, Davis under protocol number 19263.
CONSENT FOR PUBLICATION
Not applicable
AVAILABILITY OF DATA AND MATERIAL
Sequence data generated during this study are available through NCBI’s Sequence Read Archive
under the SRA ID SRP152555. Custom-written Java, SQL, and Bash code is available at
https://github.com/jladau. All other data is included in this published article and its
supplementary information files.
COMPETING INTERESTS
The authors declare that they have no competing interests.
FUNDING
This work was supported by the Laboratory Directed Research and Development Program of
Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-
AC02-05CH11231, by ELM Innovations, by the Hellman Foundation, and the College of
Agricultural and Environmental Sciences at UC Davis.
AUTHORS' CONTRIBUTIONS
Designed the experiment: BRoque, CBrooke, EKebreab, JSalwen and MHess; Performed the
experiments: BRoque, CBrooke, MHess and NNajafi; Generated and analyzed the microbiome
data: BRoque, CBrooke, EEloe-Fadrosh, JLadau, MHess and NNajafi. Generated and analyzed
GC data: BRoque, CBrooke, LMarsh, LSingh, MHess, NNajafi, PPandey; Wrote the paper:
BRoque, CBrooke, EEloe-Fadrosh, EKebreab JLadau, JSalwen LMarsh, MHess and TPolley.
ACKNOWLEDGEMENTS
The authors would like to thank Kyra Smart, Susan Parkyn and Ania Kossakowski for their
assistance in maintaining the artificial rumen system.
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SUPPLEMENTARY INFORMATION
Supplementary information is available at BMC Microbiomes’ website.
Supplemental File: A.tax_supplemental_Tables_Figures_20180711
Supplementary Table S1. Quality filtering and OTU distribution at each incubation time.
Supplementary Table S2. Diversity indices at each incubation time.
Supplementary Figures S1A, S1B, S1C. Rarefaction curves of equilibration, control and A.
taxiformis amended vessels respectively.
Supplementary Figure S2. Principle Coordinate Analysis plot.
Supplementary Table S3. OTU table.
Supplementary Table S4. Raw sequence barcodes for archived 16S rRNA gene amplicon data.
Supplementary Table S5. Results of AMOVA and HOMOVA statistical tests.
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FIGURE LEGENDS:
Figure 1. Total gas, CH4, and CO2 production during in-vitro fermentation. Production of
total gas [ml/(g OM)], CH4 [mg/(g OM)] and CO2 [mg/(g OM)] from vessels without (n=3) and
with (n=3) A. taxiformis as additive at 4, 12, and 24 h over the course of the experiment. (A)
Total gas production; (B) CH4 production; (C) CO2 production. Measurement were performed in
triplicates. “**” indicates significant difference (p value 0.05), “*“ indicates trend toward
significance (0.05 > p value 0.1).
Figure 2. Volatile Fatty acid production during in-vitro fermentation. Volatile fatty acid
concentrations [ppm] of fermentation fluid of vessels without (n=3) and with (n=3) A. taxiformis
as additive, determined 4, 12, and 24 h after feeding over 4 days. (A) Acetic acid; (B) Propionic
acid; (C) Isobutyric acid; (D) Butyric acid; (E) Isovaleric acid (F) Valeric acid; (G)
Propionate/Acetate Ratio. Measurement were performed in triplicates.
Figure 3. Long-term effects of seaweed amendments on in-vitro rumen microbial
community. (A) Genus-level -diversity between pairs of vessels at parallel incubation times.
(B) -diversity across multiple taxon ranks measured by the slope of the regression of beta-
diversity on time for each of the 6 vessels. (C) Genus-level -diversity within vessels at pairs of
sampling times.
Figure 4. Relative Abundance of Phyla during in-vitro fermentation. Fermentations were
performed in three in-vitro vessels (n=3). Incubation times annotated with “C” represent control
conditions.
Figure 5. Relative abundance of Euryarchaeota during in-vitro fermentation. Fermentations
were performed in three in-vitro vessels (n=3). Error bars indicate standard error of the mean.
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... For example, CH4 emissions may be reduced through the composition of feed or feed additives, [27] (p. 454), [71,72]. Regarding the management of farm manure, air-and seep-proof storage as well as anaerobic digestion in biogas plants for slurry are available measures for reducing emissions. ...
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Legume silages may also have an advantage over grass silage due to their lower fibre content and the additional benefit of replacing inorganic nitrogen fertilizer. Effective silage preservation will improve forage quality on the farm and reduce GHG emission intensity. Introduction of legumes into grass pastures in warm climate regions may offer a mitigation opportunity, although more research is needed to address the associated agronomic challenges and comparative N2O emissions with equivalent production levels from nitrogen fertilizer. Dietary lipids are effective in reducing enteric CH4 emissions, but the applicability of this practice will depend on its cost and its effects on feed intake, production and milk composition. 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Nevertheless, caution should be exercised so that concentrate supplementation and processing does not compromise digestibility of dietary fibre. In many parts of the world, concentrate inclusion may not be an economically feasible mitigation option. In these situations improving the nutritive value of low-quality feeds in ruminant diets can have a considerable benefit on herd productivity, while keeping the herd CH4 output constant or even decreasing it. Chemical treatment of low-quality feeds, strategic supplementation of the diet, ration balancing and crop selection for straw quality are effective mitigation strategies, but there has been little adoption of these technologies. Nitrates show promise as enteric CH4 mitigation agents, particularly in low-protein diets that can benefit from nitrogen supplementation, but more studies are needed to fully understand their impact on whole-farm GHG emissions, animal productivity and animal health. Adaptation to these compounds is critical and toxicity may be an issue. Through their effect on feed efficiency, ionophores are likely to have a moderate CH4 mitigating effect in ruminants fed high-grain or grain-forage diets. However, regulations restrict the availability of this mitigation option in many countries. In ruminants on pasture, the effect of ionophores is not sufficiently consistent for this option to be recommended as a mitigation strategy. Tannins may also reduce enteric CH4 emissions, although intake and milk production may be compromised. Further, the agronomic characteristics of tanniferous forages must be considered when they are discussed as a GHG mitigation option. There is not sufficient evidence that other plant-derived bioactive compounds, such as essential oils, have a CH4-mitigating effect. 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Most mitigation options for GHG emissions from stored manure, such as reducing the time of manure storage, aeration, and stacking, are generally aimed at decreasing the time allowed for microbial fermentation processes to occur before land application. These mitigation practices are effective, but their economic feasibility is uncertain. Semi-permeable covers are valuable for reducing ammonia, CH4 and odour emissions at storage, but are likely to increase N2O emissions when effluents are spread on pasture or crops. Impermeable membranes, such as oil layers and sealed plastic covers, are effective in reducing gaseous emissions but are not very practical. Combusting accumulated CH4 to produce electricity or heat is recommended. Acidification (in areas where soil acidity is not an issue) and cooling are further effective methods for reducing ammonia and CH4 emissions from stored manure. Composting can effectively reduce CH4 but can have a variable effect on N2O emissions and increases ammonia and total nitrogen losses. Anaerobic digesters are a recommended mitigation strategy for CH4 generate renewable energy, and provide sanitation opportunities for developing countries, but their effect on N2O emissions is unclear. Management of digestion systems is important to prevent them from becoming net emitters of GHG. Some systems require high initial capital investments and, as a result, their adoption may occur only when economic incentives are offered. Anaerobic digestion systems are not recommended for geographic locations with average temperatures below 15 °C without supplemental heat and temperature control. Lowering nitrogen concentration in manure, preventing anaerobic conditions and reducing the input of degradable manure carbon are effective strategies for reducing GHG emissions from manure applied to soil. Separation of manure solids and anaerobic degradation pre-treatments can mitigate CH4 emission from subsurface-applied manure, which may otherwise be greater than that from surface-applied manure. Timing of manure application (e.g. to match crop nutrient demands, avoiding application before rain) and maintaining soil pH above 6.5 may also effectively decrease N2O emissions. Animal husbandry mitigation practices Increasing animal productivity can be a very effective strategy for reducing GHG emissions per unit of livestock product. For example, improving the genetic potential of animals through planned cross-breeding or selection within breeds, and achieving this genetic poten tial through proper nutrition and improvements in reproductive efficiency, animal health and reproductive lifespan are effective and recommended approaches for improving animal productivity and reducing GHG emission intensity. Reduction of herd size would increase feed availability and productivity of individual animals and the total herd, thus lowering CH4 emission intensity. Residual feed intake may be an appealing tool for screening animals that are low CH4 emitters, but currently there is insufficient evidence that low residual feed intake animals have a lower CH4 yield per unit of feed intake or animal product. However, selection for feed efficiency will yield animals with lower GHG emission intensity. Breed difference in feed efficiency should also be considered as a mitigation option, although insufficient data are currently available on this subject. Reducing age at slaughter of finished cattle and the number of days that animals are on feed in the feedlot by improving nutrition and genetics can also have a significant impact on GHG emissions in beef and other meat animal production systems. Improved animal health and reduced mortality and morbidity are expected to increase herd productivity and reduce GHG emission intensity in all livestock production systems. Pursuing a suite of intensive and extensive reproductive management technologies provides a significant opportunity to reduce GHG emissions. Recommended approaches will differ by region and species, but will target increasing conception rates in dairy, beef and buffalo, increasing fecundity in swine and small ruminants, and reducing embryo wastage in all species. The result will be fewer replacement animals, fewer males required where artificial insemination is adopted, longer productive life and greater productivity per breeding animal. Conclusions Overall, improving forage quality and the overall efficiency of dietary nutrient use is an effective way of decreasing GHG emissions per unit of animal product. Several feed supplements have a potential to reduce enteric CH4 emission from ruminants, although their long-term effect has not been well-established and some are toxic or may not be economically viable in developing countries. Several manure management practices have a significant potential for decreasing GHG emissions from manure storage and after application or deposition on soil. Interactions among individual components of livestock production systems are very complex, but must be considered when recommending GHG mitigation practices. One practice may successfully mitigate enteric CH4 emission, but increase fermentable substrate for increased GHG emissions from stored or landapplied manure. Some mitigation practices are synergistic and are expected to decrease both enteric and manure GHG emissions (for example, improved animal health and animal productivity). 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