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Author(s): Claudia Arndt, Alexander N. Hristov, William J. Price, Shelby C. McClelland, Amalia 
M. Pelaez, Sergio F. Cueva, Joonpyo Oh, Jan Dijkstra, André Bannink, Ali R. Bayat, 
Les A. Crompton, Maguy A. Eugène, Dolapo Enahoro, Ermias Kebreab, Michael 
Kreuzer, Mark McGee, Cécile Martin, Charles J. Newbold, Christopher K. Reynolds, 
Angela Schwarm, Kevin J. Shingfield, Jolien B. Veneman, David R. Yáñez-Ruiz & 
Zhongtang Yu 
 
Title: Full adoption of the most effective strategies to mitigate methane emissions by 
ruminants can help meet the 1.5 °C target by 2030 but not 2050 
Year: 2022 
Version: Published version 
Copyright:   The Authors 2022 
Rights: CC BY-NC-ND 4.0 
Rights url: http://creativecommons.org/licenses/by-nc-nd/4.0/ 
 
Please cite the original version: 
Arndt C., Hristov A.,N., Price W.J., McClelland S.C., Pelaez A.M., Cueva S.F., Oh J. Dijkstra J. Bannink 
A. Bayat A.R., Crompton L.A., Eugène M.A., Enahoro D. Kebreab E. Kreuzer M. McGee M., Martin 
C. Newbold C.J., Reynolds C.K., Schwarm A. Shingfield K.J., Veneman J.B., Yáñez-Ruiz D.R., Yu Z. 
(2022) Full adoption of the most effective strategies to mitigate methane emissions by ruminants 
can help meet the 1.,5 °C target by 2030 but not 2050. Proceedings of the National Academy of 
Sciences PNAS 119(20), e2111294119. https://doi.org/10.1073/pnas.2111294119. 
Full adoption of the most effective strategies to mitigate
methane emissions by ruminants can help meet the 1.5 °C
target by 2030 but not 2050
Claudia Arndta,1 , Alexander N. Hristovb , William J. Pricec, Shelby C. McClellandd , Amalia M. Pelaezb,e , Sergio F. Cuevab, Joonpyo Ohb ,
Jan Dijkstrae , Andr
e Banninke, Ali R. Bayatf , Les A. Cromptong , Maguy A. Eugeneh , Dolapo Enahoroa , Ermias Kebreabi ,
Michael Kreuzerj , Mark McGeek, C
ecile Martinh , Charles J. Newboldl, Christopher K. Reynoldsg , Angela Schwarmm , Kevin J. Shingfieldf,2,
Jolien B. Venemann, David R. Y
a~nez-Ruizo, and Zhongtang Yup
Edited by Akkihebbal Ravishankara, Colorado State University, Fort Collins, CO; received June 25, 2021; accepted February 8, 2022
To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to
30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identi-
fied strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute
(ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP;
weight gain or milk yield). Next, the potential of different adoption rates of one PB or
one ABS strategy to contribute to the 1.5 °C target was estimated. The database
included findings from 430 peer-reviewed studies, which reported 98 mitigation strate-
gies that can be classified into three categories: animal and feed management, diet for-
mulation, and rumen manipulation. A random-effects meta-analysis weighted by
inverse variance was carried out. Three PB strategies—namely, increasing feeding level,
decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—
decreased CH4 per unit meat or milk by on average 12% and increased AP by a median
of 17%. Five ABS strategies—namely CH4 inhibitors, tanniferous forages, electron
sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Glob-
ally, only 100% adoption of the most effective PB and ABS strategies can meet the
1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected
increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and
2050, low- and middle-income countries may not meet their contribution to the 1.5 °C
target for this same reason, whereas high-income countries could meet their contribu-
tions due to only a minor projected increase in enteric CH4 emissions.
methane j meta-analysis j ruminant j enteric j mitigation
Global food systems contribute up to 30% of the worldwide greenhouse gas (GHG)
emissions (1). The goal of the Paris Agreement, to limit global warming to 1.5 °C
above preindustrial levels, is unlikely to be achieved if food systems continue operating
on a business-as-usual (BAU) scenario (1). Among food-related GHG emissions, meth-
ane (CH4) from livestock contributes 30% of the global anthropogenic CH4 emissions
(2), 17% of the global food system GHG emissions, and 5% of global GHG emissions
(2, 3). Of the global livestock CH4 emissions, 88% is contributed by enteric fermenta-
tion (4).
Methane is a short-lived climate pollutant. Given its perturbation lifetime in the
atmosphere of around 12.5 y, CH4 contributes significantly to near-term global warm-
ing (5). Its global warming potential is 84 or 28 for 20- or 100-y time horizons, respec-
tively (5). When evaluating the contribution of global food systems to CH4 emissions
over a 20-y period instead of the commonly used 100-y time period for national GHG
inventories, the contribution of CH4 to food system GHG emissions more than dou-
bles, from 17 to 36% (3, 5).
The realization of nationally determined contributions and 2050 climate neutrality goals
depends upon the reduction of CH4 emissions. Within sectoral reductions of CH4 emis-
sions, technical solutions to decrease CH4 from agricultural production—especially strate-
gies to mitigate CH4 from enteric fermentation by ruminant livestock—are integral to
meeting these climate targets, but quantitative data on mitigation potentials are scarce (6).
Based on 2010 GHG emission levels and different mitigation scenarios to limit global
warming to 1.5 °C, agricultural CH4 emissions need to be decreased by 11 to 30% by
2030 and by 24 to 47% by 2050 (7).
The global population is projected to increase by 23% between 2010 and 2030,
with most of the increase occurring in low- and middle-income countries (LMIC) (8).
Ruminants contribute about half of the animal protein produced by livestock (4). In
Significance
Agricultural methane emissions
must be decreased by 11 to 30%
of the 2010 level by 2030 and by
24 to 47% by 2050 to meet the
1.5 °C target. We identified three
strategies to decrease product-
based methane emissions while
increasing animal productivity and
five strategies to decrease
absolute methane emissions
without reducing animal
productivity. Globally, 100%
adoption of the most effective
product-based and absolute
methane emission mitigation
strategy can meet the 1.5 °C target
by 2030 but not 2050, because
mitigation effects are offset by
projected increases in methane.
On a regional level, Europe but
not Africa may be able to meet
their contribution to the 1.5 °C
target, highlighting the different
challenges faced by high- and
middle- and low-income countries.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
Copyright © 2022 the Author(s). Published by PNAS.
This open access article is distributed under Creative
Commons Attribution-NonCommercial-NoDerivatives
License 4.0 (CC BY-NC-ND).
1To whom correspondence may be addressed. Email:
claudia.arndt@cgiar.org.
2Deceased September 11, 2016.
This article contains supporting information online at
http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.
2111294119/-/DCSupplemental.
Published May 10, 2022.
PNAS 2022 Vol. 119 No. 20 e2111294119 https://doi.org/10.1073/pnas.2111294119 1 of 10
RESEARCH ARTICLE | SUSTAINABILITY SCIENCE OPEN ACCESS
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LMIC, ruminant livestock play a crucial role in food security (9).
Ruminants can convert human-inedible feeds, like those from
pastures and grain commodity by-products produced on marginal
lands or from subsistence agricultural production systems, into
nutritionally dense human-edible foods. Ruminants also provide
other benefits, such as traction and manure for fuel and fertilizer
(10). In addition, human population growth is generally high in
LMIC, while consumption of animal-sourced food is often below
recommended dietary levels or reliant upon ruminant meat and
milk for livelihoods and nutrition security (10, 11). Thus, from a
feed-food competition perspective, ruminant production increases
in LMIC should rely on human inedible feeds (i.e., forage and
by-products). In contrast, in high-income countries (HIC) popu-
lation growth is much lower and the consumption of animal pro-
tein is often above recommended dietary levels (9, 11).
Sustainable strategies for enteric CH4 mitigation that align
with the 1.5 °C target should preferably avoid socioeconomic
and environmental tradeoffs (12) and, ideally, increase produc-
tion yield per unit of input. Reductions in both CH4 emissions
intensity (i.e., emissions per unit of milk and gain [CH4IM and
CH4IG, respectively]) and absolute CH4 emissions are therefore
needed. Strategies that reduce CH4I and increase production
per unit of input could be used to expand food production
from the existing ruminant population without increasing total
CH4 emissions (13–15), and thus contribute to the 1.5 °C
target as well as to sustainable development goals. Several
reviews indicate that animal and feed management, diet formu-
lation, and rumen manipulation strategies could significantly
decrease enteric CH4 emissions (12, 16, 17). However, previ-
ous studies consisted of qualitative reviews (12), examined the
quantitative effects of a single mitigation strategy (18–20), or
compared CH4 yield (CH4Y; CH4 per unit of feed intake)
between multiple mitigation strategies (17). Methane yield is
only one relevant measure, and other major CH4 emission and
animal performance metrics must be considered to determine
the effectiveness and feasibility of mitigation strategies. Only
one recent publication examined the quantitative effects of
multiple mitigation strategies on CH4 emission and animal per-
formance metrics, but the analysis was limited to Latin America
(21). Important CH4 emission metrics include daily CH4 emis-
sions, CH4Y, or CH4-energy conversion factor [Ym; CH4
energy as a proportion of gross energy intake; a component of
the tier 2 calculation for national GHG inventories recom-
mended by the Intergovernmental Panel on Climate Change
(22)], CH4IG, and CH4IM. Important animal performance
metrics include feed intake, nutrient digestibility, and animal
productivity (AP).
The objective of this study was to conduct a comprehensive
meta-analysis of enteric CH4 mitigation strategies published in
peer-reviewed journals by examining their quantitative effect on
the aforementioned in vivo CH4 emissions and animal perfor-
mance metrics and to estimate their potential to contribute to the
1.5 °C target. As outlined above, there is an urgent need for strat-
egies that can effectively mitigate enteric CH4 emissions without
negatively affecting AP by focusing exclusively on strategies that
decouple CH4 emissions from animal production (23). Mitigation
effects were quantified on a global level as well as on a regional
level. The African and European regions were selected to represent
LMIC and HIC, respectively.
Results and Discussion
The meta-analysis included 98 mitigation strategies reported in
430 peer-reviewed journal publications (SI Appendix, Table S1).
Mitigation strategies were classified into three main categories:
animal and feed management, diet formulation, or rumen manip-
ulation strategies. Of the strategies included, 63 did not signifi-
cantly (adjusted P ≥ 0.05) decrease daily CH4 emissions; the
remaining 35 strategies decreased daily CH4 emissions by on aver-
age 18% (ranging from 5 to 43%). These strategies were classified
as “effective” in decreasing product-based CH4 (PB strategies) if
they significantly decreased CH4IM or CH4IG and CH4Y while
significantly (adjusted P < 0.05) increasing AP. Strategies were
classified as effective in decreasing absolute CH4 emissions (ABS
strategies) if they significantly decreased daily CH4 emissions,
CH4IM or CH4IG, and CH4Y without decreasing AP (weight
gain of growing animals or milk yield of lactating dairy animals)
when productivity data were present.
A summary of the studied mitigation strategies is presented
in Fig. 1, and the full list of the studied mitigation strategies
and their effects on enteric CH4 emission and animal perfor-
mance metrics are presented in SI Appendix, Table S2 and
Dataset S1, respectively. Effective mitigation strategies and their
impact on CH4IM, CH4IG, daily CH4, CH4Y, as well as their
relevance for different systems (feedlot, mixed, and grassland)
are presented in Fig. 2 from highest to lowest efficacy in reduc-
ing CH4IM. All other strategies that were not classified as effec-
tive but had a significant effect on CH4, CH4Y, Ym, CH4IG, or
CH4IM are presented in SI Appendix, Figs. S1–S3.
The meta-analysis identified three effective PB strategies,
namely: increasing feeding level, decreasing grass maturity, and
decreasing dietary forage-to-concentrate ratio. These PB strate-
gies decreased CH4I by on average 12% (range 9 to 17%) and
increased AP by a median of 17% (range 9 to 162%). Further-
more, there were five effective ABS strategies, namely: CH4
inhibitors, tanniferous forages, electron sinks, oils and fats, and
oilseeds (only for lactating animals, since oilseed supplementa-
tion significantly decreased weight gain in growing animals).
These ABS strategies decreased CH4I by on average 17%
(ranging from 12 to 32%) and daily CH4 emissions by on
average 21% (ranging from 12 to 35%) without negatively
affecting AP.
Several mitigation strategies were excluded from the present
evaluation or classified as ineffective because of insufficient
publications. These include breeding low-CH4–emitting ani-
mals and improving animal health. However, modeling studies
have shown that strategies that improve animal health may sig-
nificantly increase AP and reduce CH4I (24). In the subsequent
section, the effects of mitigation strategies are reported in
parenthesis as mean, 95% CI, and the number of treatment
comparisons (n). Reported differences were significant (adjusted
P < 0.05) unless indicated otherwise.
Strategies that Decrease PB CH4 Emissions and Increase
Production. Increasing feeding level (mean = 58%, 95% CI =
47 to 71%, n = 47) decreased CH4IM (17%, 9 to 23%, n = 5).
No data were available for CH4IG. Fiber digestibility was
decreased (7%, 2 to 12%, n = 18), likely due to increased rumen
passage rates (25). Increasing feed intake resulted in increased
weight gain (162%, 38 to 398%, n = 7) and milk yield (17%,
10 to 25%, n = 8). Increasing feed intake to improve AP signifi-
cantly decreases CH4IG and CH4IM (12, 26) as well as the overall
carbon footprint of animal-sourced food (27) when diet composi-
tion remains unchanged. This strategy directs energy for CH4
toward animal production (28) but also decreases energy require-
ments for maintenance relative to milk production and reduces
the time to slaughter for growing animals. Potential effects of this
practice on manure CH4 emissions, as a result of decreased fiber
2 of 10 https://doi.org/10.1073/pnas.2111294119 pnas.org
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digestibility, need to be evaluated. The practice is applicable to
feedlots, mixed, and grassland systems, but particularly the latter
and especially in certain climatic regions where animals are under-
fed due to insufficient or low quality forage (29).
Decreasing grass maturity decreased CH4IM (13%, 7 to
18%, n = 6), did not affect feed intake, but increased milk
yield (9%, 1 to 18%, n = 6). Furthermore, it improved fiber
digestibility (15%, 9 to 21%, n = 9), which can potentially
decrease manure CH4 emissions (22). The positive effect of
decreasing grass maturity on milk yield is likely attributed to
greater digestible energy and protein content. Increased protein
content, however, can lead to increased nitrogen intake and
excretion (30). Thus, possible tradeoffs associated with direct
and indirect manure nitrous oxide emissions require further
evaluation. Decreasing grass maturity is applicable to all pro-
duction systems. Although this strategy increases the overall
efficiency of dietary nutrient use for milk production (kg milk
unit of feed intake1), it was not deemed to be cost-effective in
The Netherlands; however, it was more cost-effective than sup-
plementation with nitrate or linseed (31).
Decreasing dietary forage-to-concentrate ratio decreased
CH4IM (9%, 4 to 14%, n = 19) and CH4IG (9%, 3 to 15%,
n = 16). It increased feed intake (9%, 5 to 14%, n = 85),
which led to an increase in weight gain (21%, 13 to 29%, n =
32) and milk yield (17%, 10 to 24%, n = 26) but did not
increase absolute CH4 emissions or reduce fiber digestibility.
Reduced CH4Y (13%, 10 to 16%, n = 69) was the result of
increased feed intake, which most likely resulted in a shift in
rumen fermentation patterns and a decrease in rumen pH,
which inhibits methanogens (32). However, the supplementa-
tion of grain-based concentrate needs to be limited because
overfeeding can lead to subacute ruminal acidosis. Subacute
ruminal acidosis is a nutritional disease that is mostly found in
the feedlot and high-yielding dairy cattle. It is associated with
perturbation of rumen fermentation, decreased fiber digestibil-
ity, milk fat content, and animal health (33). In addition, the
promotion of increased use of (food-quality) grain-based con-
centrate in ruminant diets will likely intensify feed-food compe-
tition. In contrast, if concentrate-rich diets are mainly based on
food industry by-products, the feed-food competition may be
avoided. The cost-effectiveness of this strategy will depend on
forage and concentrate costs as well as associated increases in
animal production and the price of animal products (meat and
milk).
Strategies that Decrease Absolute CH4 Emissions. Rumen
manipulation by feeding CH4 inhibitors effectively decreased
CH4IM (32%, 21 to 40%, n = 2) without affecting feed intake
or milk yield. Of the CH4 inhibitors, 3-nitrooxypropanol (3-
NOP) acts on a key enzyme of the methanogenesis pathway
that is used by methanogenesis to produce CH4 (34). Insuffi-
cient data were available in the database to evaluate its effect on
weight gain or fiber digestibility in this analysis. However, in
recent studies, 3-NOP did not show adverse effects on weight
gain of growing beef cattle (35) or fiber digestibility in early-
lactation dairy cows (36) and decreased daily CH4 emissions
throughout a 15-wk experiment (37). A recent meta-analysis
showed that 3-NOP decreased daily CH4 emissions in a dose-
dependent manner, that its mitigation effect was greater for
dairy than beef cattle, and that its effectiveness decreased with
increasing dietary fiber content (18). In its current form,
3-NOP can only be used in confinement systems because it is
more effective when fed continuously (36, 38), but ongoing
research is developing mechanisms for its application under
grazing conditions (39). Supplementation of 3-NOP increased
milk fat content in dairy cattle (29) and feed efficiency in feed-
lot cattle (40), which may help offset its cost and stimulate
adoption. A limitation of 3-NOP is that its use as a feed addi-
tive requires regulatory approval by various countries. Another
CH4 inhibitor strategy is supplementation with seaweed (e.g.,
Asparagopsis taxiformis), which can decrease daily CH4 emis-
sions by up to 80% (41). However, more research is warranted
on dietary inclusion levels, effects on animal feed intake and
production (42), the implications and safety of feeding bromo-
form (43), its main active compound (44), the extremely high
iodine content of Asparagopsis species (which limits how much
can be fed in many countries), as well as the environmental
effects of cultivating seaweed (45) before it can be recom-
mended as a mitigation strategy.
Dietary inclusion of tanniferous forages decreased CH4IM
(18%, 8 to 26%, n = 7). However, it also decreased fiber
digestibility (7%, 2 to 12%, n = 21), which could potentially
increase manure CH4 emissions (22). Daily CH4 emissions
were also decreased (12%, 7 to 16%, n = 42) and feed intake
Feed processing
Genetic selection
Improving animal
health
Improving pasture
management
Increasing
feeding level
Increasing
forage quality
Optimizing
temperature
TMR feeding
By-products
Decreasing forage-
to-concentrate
ratios
Minerals and salts
Oils and fats
Oilseeds
Increasing
protein
Tanniferous
forages
Urea
Additives
Defaunation
Electron sinks
RUMEN MANIPULATION
DIET FORMULATION
ANIMAL & FEED MANAGEMENT
ENTERIC METHANE
MITIGATION STRATEGIES
Fig. 1. Studied enteric methane mitigation strategies. For a complete list of strategies, see SI Appendix, Table S2.
PNAS 2022 Vol. 119 No. 20 e2111294119 https://doi.org/10.1073/pnas.2111294119 3 of 10
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or animal production were unaffected. There are differences in
efficacy among tannin sources. Sericea lespedeza (Lespedeza
cuneata) and Lotus (Lotus corniculatus and Lotus pedunculatus)
were determined as the most promising tanniferous forage as
they significantly decreased daily CH4 emissions (32% and 8%,
respectively) without affecting feed intake. S. lespedeza (L.
cuneata) decreased daily CH4 emissions (32%, 24 to 39%,
n = 5) without affecting feed intake in goats and it has been
effective in decreasing daily CH4 emissions throughout a
12-wk experiment (46). Other tanniferous forages that may
potentially decrease daily CH4 emissions are Leucaena (8%,
0 to 16%, n = 12, P = 0.10) and Lotus (L. corniculatus and
L. pedunculatus) (8%, 3 to 13%, n = 3). Although this meta-
analysis did not reveal any effect on feed intake, tanniferous
forages have been associated with decreased palatability and
feed intake (47). In addition, tannins can bind to dietary pro-
tein and thus decrease protein digestion and animal production,
especially when dietary protein is limiting. Nevertheless, when
dietary protein is excessive or highly degradable, tannins may
be beneficial because they reduce the excretion of nitrogen in
urine, which decreases ammonia and nitrous oxide emissions
from manure (48). The cost-effectiveness of their supplementa-
tion still needs to be evaluated. Among the identified effective
ABS strategies, dietary inclusion of tanniferous forages is the
only one applicable to grassland besides feedlot and mixed sys-
tems. As 37% of global enteric CH4 emissions from ruminant
livestock is attributed to grazing systems (4), it will be impor-
tant to identify other effective ABS strategies that are applicable
to grassland systems.
Rumen manipulation with electron sinks decreased CH4IM
(13%, 9 to 16%, n = 12) and CH4IG (12%, 2 to 20%, n = 3).
Although they led to small decreases in feed intake (2%, 1 to
3%, n = 49), small increases in milk yield (3%, 1 to 5%,
n = 13) were observed. Electron sinks accept hydrogen that
would otherwise be used by methanogens for CH4 production
in the rumen (32). Of the studied electron sinks (fumaric acid
and nitrate), only nitrate was classified as effective. Nitrate has
been shown to decrease daily CH4 emissions and CH4Y in a
dose-dependent manner with no loss of effectiveness and effec-
tively decreased daily CH4 emissions over the long term (20,
49). Similar to 3-NOP, nitrate was more effective in decreasing
daily CH4 emissions and CH4Y in dairy than in beef cattle
(20). Although nitrate can be toxic, early research on nitrate
supplementation in ruminant diets reported a decrease in feed
intake and no toxicity symptoms; however, toxicity can occur if
animals are not properly acclimatized (50). Acclimatization of
Increasing feeding level
Decreasing dietary forage-to-
concentrate ratio
+9%
+58%
+21%
+162%
+17%
+17%
No Effect
-7%
Decreasing Grass Maturity No Effect No Data+9%+15%
oils & Fats
Oilseeds
Tanniferous forages
ELECTRON SINKS
No Effect No Effect No EffectNo Effect
-6% -4% No Effect
No Effect -13%
No Effect No Effect
No Effect No Effect No Effect
No Effect
-8% No Effect
-2% +3%
-7%
Ch inhibitors4
Lactating animals only
4
4Daily CH -19%
-15%YCHOils & Fats
Oilseeds
Tanniferous forages
Feedlot & mIxed Systems grassland Systems
ELECTRON SINKS
4
4Daily CH -35%
-34%YCH
4
4Daily CH -20%
-14%YCH
4
4Daily CH -17%
-15%YCH
4
4Daily CH -12%
-10%YCH
CH4IG No Data
-32%
CH4IM
CH4IG -22%
-12%
CH4IM
CH4IG No Effect
-12%
CH4IM
CH4IG -12%
-13%
CH4IM
CH4IG No Data
-18%
Lactating animals only
Ch inhibitors4
CH4IM
Increasing feeding level
Decreasing dietary forage-to-
concentrate ratio
CH4IM
CH4IG -9%
-9%
CH4IM
CH4IG No Data
-17%
Decreasing Grass Maturity
CH4IM
CH4IG No Data
-13%
A
B
Fig. 2. Effective mitigation strategies and their effect on methane (CH4) emissions (A) and animal performance metrics (B). CH4IM = CH4 emission intensity
for milk (g CH4 kg of milk
1); CH4IG = CH4 emission intensity for weight gain (g CH4 kg of weight gain for growing animals
1); daily CH4 = daily CH4 emissions
(g animal1 d1); digestibility = apparent digestibility of neutral detergent fiber (%); gain = average daily gain (kg d1); intake = dry matter intake (kg d1);
milk = milk yield (kg d1); when numeric values are shown a significant effect was observed (adjusted P < 0.05) and no effect when adjusted P ≥ 0.05.
4 of 10 https://doi.org/10.1073/pnas.2111294119 pnas.org
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animals to dietary nitrate is required to avoid methemoglobine-
mia, a blood disorder in which too little oxygen is delivered to
the cells. However, this acclimatization can be lost within 3 wk
when nitrate is not fed daily (51). Simultaneous sulfate supple-
mentation has been shown to help protect cattle against nitrate
toxicity (52). Nitrate supplementation may increase enteric and
possibly manure nitrous oxide emissions (53). Studies in France
(54) and The Netherlands (31) found that nitrate supplementa-
tion was not cost-effective.
Dietary inclusion of oil and fat decreased CH4IM (12%, 6 to
18%, n = 24) and CH4IG (22%, 8 to 35%, n = 6); however,
possible effects on manure CH4 emissions due to decreased
fiber digestibility (4%, 2 to 7%, n = 37) need to be evaluated
(22). Weight gain in growing animals or milk production in
dairy animals was unaffected despite decreasing feed intake
(6%, 3 to 8%, n = 58) and fiber digestibility, likely because of
the high energy concentration of lipids compared with the feeds
it replaces in livestock diets. Of the subcategories included in
oil and fat supplementation, only dietary inclusion of predomi-
nantly vegetable oils effectively decreased daily CH4 emissions.
This effect can be attributed to increased supply of nonferment-
able highly digestible energy, decreased feed intake and fiber
digestibility, as well as inhibition of methanogenesis by unsatu-
rated (or medium-chain saturated) fatty acids, which are usually
abundant in vegetable oils. Oil inclusion reportedly decreases
daily CH4 emissions in a dose–response manner (19) and over
the long term (55, 56). The amount of oil that can be included
in ruminant diets, however, is limited and inclusion level
should not be at the expense of healthy rumen fermentation to
avoid negative impact on animal health and productivity (57).
Maximum oil inclusion levels in ruminant diets depend on the
animal's physiological stage, lipid and other nutrient composi-
tion of the basal diet, and fatty acid profile of the supplemental
oil (58). Dietary oils and fats are by-products of oilseed produc-
tion. Oilseed production has been associated with a near
doubling of upstream GHG emissions per kg dry matter com-
pared with other concentrate feeds (1.27 vs. 0.70 CO2 equiva-
lents kg dry matter1) (59). Thus, upstream emissions are likely
to increase when concentrate feeds are substituted by oil and
fat. However, enteric fermentation usually contributes substan-
tially more GHG to the carbon footprint of ruminant products
than feed production (60, 61) and the dietary inclusion of oil is
limited. Thus, increases in upstream emissions are unlikely to
offset GHG reduction through the mitigation of enteric CH4
emissions by oils or fats. Nevertheless, exact upstream offsets of
oils should be evaluated. The cost-effectiveness of feeding oils
to decrease CH4I varies by region and country, because the
price of oil, as well as meat and milk, vary considerably therein.
Studies in China (62), France (54), and The Netherlands (31)
found that dietary inclusion of oils, for the purpose of mitigat-
ing enteric CH4 emissions, was not cost-effective, but trade-offs
by concomitant improvements in the fatty acid profile of milk
and meat from a human health perspective might help to sup-
port the adoption of certain oils and oilseeds in animal diets.
Dietary inclusion of oilseeds (cracked or crushed) had similar
effects on CH4IM (12%, 4 to 19%, n = 6) compared with oils
and fat. Their supplementation tended to decrease feed intake
(4%, 1 to 7%, n = 25, P = 0.06) and decreased fiber digestibil-
ity (8%, 6 to 11%, n = 13). Similar to oils, oilseeds had no
effect on milk yield but decreased weight gain in growing ani-
mals (13%, 6 to 20%, n = 8); thus, dietary oilseed inclusion
may only be recommended for lactating animals and not for
growing animals. Likewise, the amount of inclusion of oilseeds
should be limited to avoid negative impacts on rumen
fermentation, animal health, and production. However, as part
of the oil in oilseeds is rumen-protected, dietary oil inclusion lev-
els can be slightly higher than that of pure oils (63). And similar
to oil inclusion, the possible impact to manure CH4 emissions
due to decreased fiber digestibility needs to be evaluated. Oil-
seeds that tended to decrease CH4IM were cottonseed (15%,
2 to 25%, n = 2, P = 0.07) and canola seed (13%, 2 to 23%,
n = 3, P = 0.07).
The Potential of the Identified Strategies to Decrease Enteric
Methane Emissions. The potential of the identified strategies
to decrease enteric CH4 emissions globally, in Africa, and in
Europe between 2012 and 2030 and between 2012 and 2050,
was estimated using three mitigation scenarios. The year 2012
was used as the baseline instead of 2010, because projections
used for demand (64) and human population (65) only had fig-
ures for 2012 and not 2010. The identified strategies in the
current meta-analysis (Fig. 2) and BAU projections for per cap-
ita red meat and dairy food protein demand (64), together with
Food and Agriculture Organization of the United Nations
(FAO) projections for human population growth (65), were
used in the mitigation scenarios. Although international trade
allows livestock products to move across regions, it was
assumed that demand increases in each of the modeled regions
would be met by livestock production within the same region,
an assumption that suggests technological, market, and policy
conditions would allow each region to produce enough to meet
their own livestock protein demand. A sensitivity analysis for
100%, 75%, 50%, and 25% adoption rates of mitigation meas-
ures was performed. The three mitigation scenarios were: 1)
adoption of one PB strategy, 2) adoption of one ABS strategy,
and 3) simultaneous adoption of one PB and one ABS.
Globally, only the 100% adoption of the most effective PB
and ABS strategies (increasing feeding level and inclusion of a
CH4 inhibitor, respectively) decreased enteric CH4 emissions
sufficiently (14%) to meet the 1.5 °C target by 2030 (Fig. 3A)
but not by 2050 (SI Appendix, Fig. S4A). In Africa, which was
chosen to represent LMIC, none of the mitigation scenarios
had the potential to meet the 1.5 °C target by 2030 or 2050
(Fig. 3B and SI Appendix, Fig. S4B). Although the 100% adop-
tion of the most effective PB and ABS strategies was estimated
to mitigate enteric CH4 emissions by 47% and 76% between
2012 and 2030 and 2012 and 2050, respectively, the mitiga-
tion effect was offset by estimated increases in CH4 emissions
in the BAU scenario (87% and 220%, respectively) (Fig. 3B
and SI Appendix, Fig. S4B).
In contrast, in Europe, which was chosen to represent HIC,
projected increases in enteric CH4 emissions between 2012 and
2030 and 2012 and 2050 without mitigation strategy (BAU
scenario) were only 11% (Fig. 3C and SI Appendix, Fig. S4C).
By 2030, Europe could meet the 1.5 °C target under the fol-
lowing mitigation scenarios (Fig. 3C): 1) the simultaneous
100% and 75% adoption of one PB strategy (when assuming
the average or above average mitigation potential of all PB
strategies) and one ABS strategy (when assuming the average or
above average mitigation potential of all ABS strategies); 2) the
simultaneous at least 50% adoption of the most effective PB
and ABS strategy; and 3) the at least 75% adoption of the most
effective ABS strategy. By 2050, Europe could only meet the 1.
5 °C target by the simultaneous 100% adoption of the most
effective PB and ABS strategies (SI Appendix, Fig. S4C).
While technically possible, even with transformative agri-food
sector actions that remove barriers for the simultaneous 100%
adoption of the most effective PB and ABS strategies identified
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in this study is unlikely. Consequently, the identified strategies to
reduce enteric CH4 emissions must be enacted together with
other measures to decrease CH4 emissions: For example, strate-
gies to reduce CH4 emissions from manure handling or pre- or
postfarmgate measures, such as the reduction of food waste and a
shift to a more plant-based diets (11) when per capita protein
consumption is high.
Combining two or more strategies to mitigate enteric CH4
can increase or decrease the efficacy of the strategies. However,
most likely the combination of two or more strategies will give
1
1
11
-40%
-20%
0%
20%
40%
60%
80%
100%
1
C
ha
ng
e
in
en
te
ric
C
H
4
em
is
si
on
s
1
100%Adoption 75% Adoption 50% Adoption 25% Adoption
C Projected change in European emissions between 2012 and 2030 under different scenarios
11
1
-40%
-20%
0%
20%
40%
60%
80%
100%
1
C
ha
ng
e
in
en
te
ric
C
H
4
em
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s
1
-40%
-20%
0%
20%
40%
60%
80%
100%
1
C
ha
ng
e
in
en
te
ric
C
H
4
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s
BAU Product Based Absolute
Product Based &
Absolute
A Projected change in global emissions between 2012 and 2030 under different scenarios
B Projected change in African emissions between 2012 and 2030 under different scenarios
-11 to -30% Emission reductions 
needed to meet 1.5°C target
-11 to -30% Emission reductions 
needed to meet 1.5°C target
-7% -3%
-11 to -30% Emission reductions
needed to meet 1.5°C target
-14%
-11%
-16% -12%
-24%
-18% -12%
-6%
-37%
-28%
-19%
-10%-8% -4%
-22% -16%
-11% -6%
-22%
-15%
-7%
-47%
-36%
-24%
-13%
-29%
-10% -5% -8%
-16%
-31%
-24%-19%
-15%
Fig. 3. Projected change in enteric methane (CH4) emissions between 2012 and 2030 without mitigation strategy under BAU and modeled mitigation sce-
narios (Product Based: adoption of one strategy that reduces product-based CH4 emissions; Absolute: adoption of one strategy that reduces absolute CH4
emissions; and Product Based & Absolute: adoption of one strategy that reduces product-based CH4 emissions and one strategy that reduces absolute CH4
emissions) for enteric CH4 emission changes globally (A), in the African region (B), and in the European region (C). Error bars represent the average mitigation
effect of the least and most effective mitigation strategy. Numbers in squares indicate the percentage of change from BAU.
6 of 10 https://doi.org/10.1073/pnas.2111294119 pnas.org
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a greater reduction than when only one is used. In this study, it
was assumed that the combination of strategies would result in
an additive mitigation effect, as this was observed when lipids
were combined with tannins (66), 3-NOP (67, 68), or nitrates
(69). However, more studies are needed to evaluate the effect
of combining two or more strategies, as combinations of multi-
ple mitigation strategies are likely needed to sufficiently miti-
gate CH4 to limit global warming to 1.5 °C.
Although one of the identified mitigation scenarios was
suited to decrease global enteric CH4 emissions to limit global
warming to 1.5 °C by 2030 but not 2050, the 1.5 °C target is
unlikely to be achieved, because 100% of the producers would
need to adopt it. While none of the mitigation scenarios would
allow Africa to meet the 1.5 °C target, multiple scenarios that
did not require a 100% adoption would allow Europe to meet
the 1.5 °C target. The reason for this is that Africa had a greater
projected BAU increase in enteric CH4 emissions compared
with Europe (87 vs. 11%) between 2012 and 2030, as a result
of projected increases in human population (56 vs. 4%) and
per capita demand for red meat and milk protein (18 vs. 5%),
resulting in a greater absolute increase in demand of red meat
and milk protein (84 vs. 9%). In addition, Africa compared
with Europe has an overall higher CH4IM (104 vs. 19 kg CO2
equivalents kg milk protein1) and CH4IG (198 vs. 46 kg CO2
equivalents kg red meat protein1), which leads to proportion-
ally greater increases in enteric CH4 for red meat and milk pro-
tein produced in Africa compared to Europe. Similar reasons
led to the observed differences between 2012 and 2050.
Even though Africa may not be able to meet the 1.5 °C tar-
get, the projected BAU per capita red meat and milk protein
demand in 2030 will still be 51% and 78% smaller, respec-
tively, than that of Europe (3.4 vs. 6.9 g red meat protein cap-
ita1 d1 and 4.3 vs. 19.9 g of milk protein capita1 d1,
respectively). Despite this large disparity in annual animal pro-
tein consumption, annual BAU per capita enteric CH4 emis-
sions for red meat and milk consumed in Africa was 111% and
8% greater, respectively, than that in Europe in 2012 (245 vs.
116 kg CO2 equivalents capitia
1 y1 and 144 vs. 134 kg CO2
equivalents capita1 head y1, respectively) and 161% and
25% greater than that in Europe in 2050 (303 vs. 116 kg CO2
equivalents capita1 y1 and 163 vs. 130 kg CO2 equivalents
capita1 y1, respectively). This shows the need and opportu-
nity to decrease CH4I in Africa and other LMIC where CH4I
is high. In Europe and other HIC, where CH4I and annual per
capita enteric CH4 emissions associated with red meat and
milk protein consumption are low but red meat and milk
demand are high, emissions might be reduced by shifting demand
to plant-based alternatives (11). In addition, red meat and milk
exports from HIC to LMIC could help to reduce enteric CH4
emissions to meet the 1.5 °C target. However, these exports often
do not reach food-insecure regions where the money to buy food
is limited or unavailable and increases in local production are
more likely to meet the demand and recommended levels of die-
tary protein intake.
Future research needs to: 1) develop novel mitigation strate-
gies, especially for pasture-based systems (less than half of the
identified strategies were relevant for pasture systems); 2) increase
the understanding of the mitigation potential of combinations of
enteric fermentation mitigation strategies; 3) investigate the miti-
gation effect of identified strategies on emissions of growing and
nonlactating cattle (only half of the identified strategies had suffi-
cient data available to evaluate CH4IG); 4) estimate offsets of
CH4 mitigation by increases in GHG emissions elsewhere in the
supply chain including in longer supply chains characterized by
international trade; and 5) identify the barriers to wide-scale
adoption of effective mitigation strategies in HIC and LMIC.
Conclusion
This comprehensive meta-analysis identified in a quantitative
and comparative manner three effective PB and five effective
ABS strategies. The three PB strategies decreased product-based
CH4 emissions by on average 12% (ranging from 9 to 17%)
and increased animal production by a median of 17% (ranging
from 9 to 162%). The five ABS strategies reduced product-
based CH4 emissions by an average of 17% (ranging from 12
to 32%) and daily CH4 emissions by an average of 21% (rang-
ing from 12 to 35%). The 100% adoption of only one of the
PB or ABS strategies at a time cannot sufficiently decrease
global enteric CH4 emissions from agriculture by 2030 or 2050
to achieve the 1.5 °C target. However, the simultaneous 100%
adoption of the most effective PB and ABS strategy can suffi-
ciently decrease global enteric CH4 emissions to achieve the
1.5 °C target by 2030 but not 2050. Adoption barriers to the
identified strategies are likely to prohibit them from reaching
their full technical potential. Thus, to ensure meeting the
1.5 °C climate target, it will be crucial that adoption barriers
are identified and removed, and the identified strategies are
implemented. This also needs to be done for strategies that
remove emissions from the supply-and-demand side in the agri-
cultural sector. Furthermore, the mitigation effect of the simul-
taneous implementation of more than two of the identified
strategies should be studied. At a regional level, projected
autonomous increases in enteric CH4 emissions may prevent
meeting the 1.5 °C target in studied mitigation scenarios in
LMIC, such as for Africa. The projected increases in enteric
CH4 in HIC, such as Europe, are relatively small. Multiple
studied scenarios may allow HIC to meet the 1.5 °C target by
2030 and one scenario will also do so for the 2050 target.
Materials and Methods
Literature Search and Classification of Mitigation Strategies. The data-
base for this meta-analysis was compiled using data obtained by searching the
databases of the Commonwealth Agricultural Bureau International (CABI), the
EBSCO Discovery Service, and the Web of Science. Publications from 1964
through 2016 were searched using CABI and EBSCO Discovery Service with the
search terms “rumen” AND “methane” and an additional four searches were
completed in the EBSCO Discovery Service using the term “rumen” in combina-
tion with “methane,” “energy partitioning,” “energy metabolism,” or “energy
balance.” Publications from 2017 through 2018 were searched using CABI and
Web of Science databases. Seven searches were conducted with the search term
“methane” in combination with “beef,” “cattle,” “dairy,” “goat,” “sheep,”
“rumen,” or “ruminant” and three searches with the search term “rumen” in
combination with “energy balance,” “energy metabolism,” or “energy parti-
tioning.” Publications listed in an independently developed database supported
by the AnimalChange project, MitiGate (17), were merged with the database cre-
ated in the current analysis.
The abstracts of the publications found in the search were reviewed, and
based on the abstract content, publications were selected for further consider-
ation if they included in vivo measurement of enteric CH4 emissions, a clearly
defined treatment and control, and multiple replications (at least four or more
animals in continuous design experiments, cross-over design experiments, and
so forth). Publications were excluded if they were not from peer-reviewed litera-
ture or if they were not in English, French, German, Spanish, or Portuguese. Fur-
thermore, publications were excluded if they were based on inappropriate study
design (i.e., experimental period ≤10 d) or measurement technique [e.g., the
“sniffer technique” that is based on CH4-to-CO2 ratio of exhaled breath (70, 71)].
The completed database consisted of 650 publications. From these, only the
publications that had a treatment that could be assigned to one of three main
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mitigation categories, as described below, and reported statistical variance for at
least one of the CH4 emissions emission metrics (e.g., least significant difference,
relative standard deviation, or SEM) were included in the final analysis. WebPlot-
Digitizer (https://automeris.io/WebPlotDigitizer/; accessed 30 October 2019) was
used to determine absolute values for a total of nine metrics in seven publica-
tions where data were reported as figures.
The data were classified into three main mitigation categories: 1) animal and
feed management, 2) diet formulation, and 3) rumen manipulation, each of
which was then further classified into up to five subcategories (SI Appendix, Table
S2). Only the mitigation strategies that each had at least two publications for at
least one CH4 emission metric and two of the remaining CH4 emission or animal
metrics were analyzed within a main category. Treatment effects were assessed rel-
ative to their respective control values for all responses; therefore, closely related
variables and variables with different units were included in the analysis. For
example, CH4IM included daily CH4 emissions per kg of milk and milk corrected
for fixed energy, fat and protein, or milk solids (all milk nonwater components
combined) content as well as milk solids yield. Similarly, for CH4IG, both weight
gain and carcass gain were used. Metrics for feed intake included intakes of dry
matter, gross energy, organic matter, and intake expressed per unit of body
weight or metabolic body weight. Digestibility (of fiber) metrics included only
apparent digestibility of neutral detergent fiber. Where multiple treatments of a
common treatment type were present within an experiment, the treatment means
were averaged, and their respective errors pooled, so that each experiment pro-
duced a single “treatment” and “control” pair of response means and SDs.
The final dataset analyzed in the present study included data from 430 peer-
reviewed publications, of which 66% were of cattle, 31% of small ruminants (sheep
and goats), and 3% of other ruminant species (buffalo, deer, and yak). The com-
plete list of references used in the current analysis is given in SI Appendix, Table
S1 and the database can be found on https://www.datacommons.psu.edu under
the link https://www.datacommons.psu.edu/commonswizard/MetadataDisplay.
aspx?Dataset=6333 and the DOI 10.26208/6em7-k817. The majority of the
publications reported daily CH4 emissions (92%), feed intake (84%), and CH4Y
(71%), but less than half of the publications reported weight gain for all ani-
mal types (growing, lactating, and other adult animals) (49%), Ym (48%), fiber
digestibility (41%), milk yield (29%), CH4IM (21%), or CH4IG (7%) (SI Appendix,
Fig. S5). The final analysis only included weight gain data for growing animals
(106 publications), which led to the exclusion of the weight gain data of half
of the publications (104 publications) that reported weight gain data for lactat-
ing and other adult animals.
Statistical Analysis. A mixed-model meta-analysis weighted by inverse vari-
ance was carried out considering treatment mean comparisons within the publi-
cations as a random effect. Analyses were run across all ruminant species (cattle,
buffalo, deer, goat, sheep, and yak) and included main mitigation strategies and
their respective subcategories as potential moderator fixed effects. Analyses were
conducted separately for each of the nine response variables (daily CH4, CH4Y,
Ym, CH4IG, CH4IM, feed intake, weight gain for growing animals, milk yield, and
fiber digestibility) using a log ratio of means, namely log(treatment/control), in
order to standardize treatment effects across multiple measures, species, and
outcomes, as well as to allow the expression of treatment differences as relative
percentages (72, 73). Weight gain for growing animals when consuming tannif-
erous plants, however, was assessed based on a standardized relative difference,
[(treatment-control)/SEDiff], due to the presence of negative growth rate
responses in two treatment mean comparisons (73). Computations were carried
out using Comprehensive Meta-Analysis (V. 3.3.070; Biostat). All analyses were
adjusted for multiple comparisons using a step-down Bonferroni procedure to
reduce the risk of type I error (74) (SAS, v9.4; SAS Institute). The effect of a miti-
gation strategy was considered significant for adjusted P < 0.05 and 0.05 ≤
adjusted P ≤ 0.10 was considered as a trend.
Estimation of the Potential for Identified Strategies to Decrease
Methane Emissions. The potential of the identified strategies to decrease
global, LMIC (e.g., countries in the African region), and HIC (e.g., countries in
the European region) enteric CH4 emissions between 2012 and 2030 and
between 2012 and 2050 was estimated using three mitigation scenarios. In the
mitigation scenarios, identified measures to mitigate enteric CH4 from the cur-
rent analysis (Fig. 2) were applied to demand projections under a BAU scenario.
Furthermore, a sensitivity analysis for 100%, 75%, 50%, and 25% adoption rate
of mitigation measures was performed.
The BAU scenario was defined by the FAO (75) as a continuation of historical
trends of food preferences and inclusion of current initiatives to address sustain-
able development goal targets. Annual demands for protein from red meat
(bovine meat, mutton, and goat meat) and milk for 2012, 2030, and 2050 were
projected by using published per capita demand projections by Henchion et al.
(64) and human population projections by the FAO (65). Consistent with the
demand projections by Henchion et al. (64), projections were classified into the
six regions defined by the World Health Organization (WHO; African region,
region of the Americas, Southeast Asia region, European region, eastern Mediter-
ranean region, and western Pacific region) (76). The regional production of red
meat and dairy protein by production system (feedlot, grassland, and mixed)
reported by GLEAM (4) for 2010 was applied to demand projections. As some of
the regional classifications in GLEAM differed from the WHO regions, best judg-
ment was used to match GLEAM regions to WHO regions. The countries/regions
included in each WHO region in our analysis for demand projections, population
projection, and GLEAM are listed in SI Appendix, Table S3. Further, CH4I by ani-
mal production system (feedlot, grassland, and mixed) reported by GLEAM for
2010 were multiplied by projected animal protein demand by animal produc-
tion system to estimate annual enteric CH4 emissions for each system. For this,
the underlying assumption was that demand will be met by increased produc-
tion in each region.
The three mitigation scenarios were: 1) adoption of one PB strategy (increas-
ing feeding level, decreasing grass maturity, or decreasing dietary forage-to-con-
centrate ratio); 2) adoption of one ABS strategy (the inclusion of CH4 inhibitors,
tanniferous forages, electron sinks, oils or fats, or oilseeds); and 3) simultaneous
adoption of one PB and one ABS.
For a 100% adoption rate of one PB strategy, the identified average (average
of all mitigation potentials of strategies applicable to a production system), mini-
mum and maximum (strategies with lowest and highest mitigation potential
applicable to a production system, respectively) reductions of CH4I for a produc-
tion system were used to adjust the CH4I reported by GLEAM (4) for red meat
and dairy protein for each of the projected years. When there were no data for
the CH4IG reduction potential of a strategy, it was assumed that the minimum
reduction potential was 0%, the maximum reduction potential was the one iden-
tified for CH4IM, and the average reduction potential was the average of the min-
imum and maximum reduction potential.
For a 100% adoption rate of one ABS strategy, the identified average, mini-
mum, and maximum reductions of daily CH4 emission for a production system
were used to adjust the projected annual CH4 emissions for all red meat and
dairy protein of a given productions system of the projected years. For a 100%
adoption rate of one PB and one ABS strategy, the reduction for the adoption of
one PB strategy was first projected, and afterward, the adoption of one ABS strat-
egy was projected. Similar calculations were done for the other three assumed
adoption rates (75%, 50%, and 25%).
Data Availability. Data have been deposited in Penn State Data Commons
(http://doi.org/10.26208/6em7-k817).
ACKNOWLEDGMENTS. We thank the GLOBAL NETWORK project for generating
part of the database. The GLOBAL NETWORK project (https://globalresearchalliance.
org/research/livestock/collaborative-activities/global-research-project/; accessed 20
June 2020) was a multinational initiative funded by the Joint Programming Initia-
tive on Food Security, Agriculture, and Climate Change and was coordinated by
the Feed and Nutrition Network (https://globalresearchalliance.org/research/
livestock/networks/feed-nutrition-network/; accessed 20 June 2020) within the
Livestock Research Group of the Global Research Alliance on Agricultural GHG
(https://globalresearchalliance.org; accessed 20 June 2020). We thank MitiGate,
which was part of the AnimalChange project funded by the EU under Grant Agree-
ment FP7-266018 for sharing their database with us (http://mitigate.ibers.aber.ac.
uk/, accessed 1 July 2017). Part of C.A., A.N.H., and S.C.M.’s time in the early
stages of this project was funded by the Kravis Scientific Research Fund (New York)
and a gift from Sue and Steve Mandel to the Environmental Defense Fund.
Another part of C.A.’s work on this project was supported by the National Program
for Scientific Research and Advanced Studies - PROCIENCIA within the framework
of the "Project for the Improvement and Expansion of the Services of the National
8 of 10 https://doi.org/10.1073/pnas.2111294119 pnas.org
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System of Science, Technology and Technological Innovation" (Contract No.
016-2019) and by the German Federal Ministry for Economic Cooperation and
Development (issued through Deutsche Gesellschaft f€ur Internationale Zusamme-
narbei) through the research “Programme of Climate Smart Livestock” (Programme
2017.0119.2). Part of A.N.H.’s work was funded by the US Department of Agricul-
ture (Washington, DC) National Institute of Food and Agriculture Federal Appropri-
ations under Project PEN 04539 and Accession no. 1000803. E.K. was supported
by the Sesnon Endowed Chair Fund of the University of California, Davis.
Author affiliations: aIntegrated Sciences Division, International Livestock Research
Institute (ILRI), 00100 Nairobi, Kenya; bDepartment of Animal Science, The Pennsylvania
State University, University Park, PA 16802; cCollege of Agricultural and Life Sciences,
University of Idaho, Moscow, ID 83844; dDepartment of Soil and Crop Sciences,
Colorado State University, Fort Collins, CO 80523; eAnimal Sciences Group, Wageningen
University and Research, 6708 PB Wageningen, The Netherlands; fNatural Resources
Institute Finland, 00790 Helsinki, Finland; gSchool of Agriculture, Policy and
Development, University of Reading, Reading RG6 6EU, United Kingdom; hInstitut
national de recherche pour l'agriculture, l'alimentation et l'environnement (INRAE),
VetAgro Sup, UMR Herbivores, Universit
e Clermont Auvergne, 63122 Saint-Genes-
Champanelle, France; iCollege of Agricultural and Environmental Sciences, University
of California, Davis, CA 95616; jDepartment of Environmental Systems Science, ETH
Zurich, 8092 Z€urich, Switzerland; kAnimal & Grassland Research and Innovation Centre
(AGRIC), Teagasc, Grange C15 PW93, Ireland; lScotland’s Rural College, Edinburgh EH9
3JG, United Kingdom; mDepartment of Animal and Aquacultural Sciences, Norwegian
University of Life Sciences, 1432 Aas, Norway; nDe Heus Animal Nutrition, 6717 VE Ede,
The Netherlands; oEstaci
on Experimental del Zaid
ın (EEZ), Consejo Superior de
Investigaciones Cient
ıficas (CSIC), 18008 Granada, Spain; and pDepartment of Animal
Sciences, The Ohio State University, Columbus, OH 43210
Author contributions: C.A., A.N.H., J.D., A.B., A.R.B., L.A.C., M.A.E., D.E., E.K., M.K., M.M.,
and K.J.S. designed research; C.A. and A.N.H. performed research; A.B., L.A.C., and
C.J.N. contributed new reagents/analytic tools; C.A., A.N.H., W.J.P., S.C.M., A.M.P., S.F.C.,
J.O., J.D., and A.B. analyzed data; C.A., A.N.H., W.J.P., S.C.M., J.D., A.B., A.R.B., L.A.C.,
M.A.E., M.K., M.M., C.M., C.K.R., A.S., J.B.V., D.R.Y.-R., and Z.Y. wrote the paper; C.A.,
A.N.H., S.C.M., A.M.P., S.F.C., and J.O. compiled database; S.F.C., J.O., J.D., A.B., L.A.C.,
M.A.E., E.K., M.K., M.M., C.M., C.K.R., A.S., K.J.S., J.B.V., and D.R.Y.-R. contributed
materials; and D.E. helped with design of modeling scenarios.
1. M. A. Clark et al., Global food system emissions could preclude achieving the 1.5° and 2°C climate
change targets. Science 370, 705–708 (2020).
2. R. Jackson et al., Increasing anthropogenic methane emissions arise equally from agricultural and
fossil fuel sources. Environ. Res. Lett. 15, 071002 (2020).
3. M. Crippa et al., Food systems are responsible for a third of global anthropogenic GHG emissions.
Nat. Food 2, 198–209 (2021).
4. FAO, GLEAM 2.0—Assessment of greenhouse gas emissions and mitigation potential. (Food and
Agriculture Organization of the United Nations, 2017). https://www.fao.org/gleam/results/en/.
Accessed 27 November 2020.
5. IPCC, "Anthropogenic and natural radiative forcing" in Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, G. Myhre et al., Eds. (Cambridge University Press, Cambridge, UK,
2013), pp. 658–740.
6. United Nations Environment Programme and Climate and Clean Air Coalition, Global Methane
Assessment: Benefits and Costs of Mitigating Methane Emissions (United Nations Environment
Programme, Nairobi, 2021).
7. IPCC, Global Warming of 1.5°C, V. Masson-Delmotte et al., Eds. (IPCC, Geneva, 2018).
8. United Nations,World Population Prospects: The 2015 Revision (United Nations Department of
Economic and Social Affairs, Population Division, 2015).
9. L. Iannotti et al., Livestock-Derived Foods and Sustainable Healthy Diets (UN Nutrition Secretariat
Rome, Italy, 2021).
10. W. J. Ripple et al., Ruminants, climate change and climate policy. Nat. Clim. Chang. 4, 2–5 (2014).
11. W. Willett et al., Food in the Anthropocene: The EAT-Lancet Commission on healthy diets from
sustainable food systems. Lancet 393, 447–492 (2019).
12. A. N. Hristov et al., Special topics—Mitigation of methane and nitrous oxide emissions from animal
operations: I. A review of enteric methane mitigation options. J. Anim. Sci. 91, 5045–5069 (2013).
13. M. Balehegn et al., Improving adoption of technologies and interventions for increasing supply of
quality livestock feed in low- and middle-income countries. Glob. Food Secur. 26, 100372 (2020).
14. P. J. Gerber, T. V. Vellinga, C. Opio, H. Steinfeld, Productivity gains and greenhouse gas emissions
intensity in dairy systems. Livest. Sci. 139, 100–108 (2011).
15. J. Chang et al., The key role of production efficiency changes in livestock methane emission
mitigation. AGU Adv. 2, e2021AV000391 (2021).
16. M. Herrero et al., Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Chang. 6,
452–461 (2016).
17. J. B. Veneman, E. R. Saetnan, A. J. Clare, C. J. Newbold, MitiGate; an online meta-analysis
database for quantification of mitigation strategies for enteric methane emissions. Sci. Total
Environ. 572, 1166–1174 (2016).
18. J. Dijkstra, A. Bannink, J. France, E. Kebreab, S. van Gastelen, Short communication:
Antimethanogenic effects of 3-nitrooxypropanol depend on supplementation dose, dietary fiber
content, and cattle type. J. Dairy Sci. 101, 9041–9047 (2018).
19. M. Eugene, D. Mass
e, J. Chiquette, C. Benchaar, Meta-analysis on the effects of lipid
supplementation on methane production in lactating dairy cows. Can. J. Anim. Sci. 88, 331–337
(2008).
20. X. Y. Feng et al., Antimethanogenic effects of nitrate supplementation in cattle: A meta-analysis.
J. Dairy Sci. 103, 11375–11385 (2020).
21. G. Congio et al., Enteric methane mitigation strategies for ruminant livestock systems in the Latin
America and Caribbean region: A meta-analysis. J. Clean. Prod. 312, 127693 (2021).
22. IPCC, “2006 IPCC guidelines for national greenhouse gas inventories” in Volume 4: Agriculture,
Forestry and Other Land Use, H. S. Eggleston, L. Buendia, K. Miwa, T. Ngara, K. Tanabe, Eds. (IGES,
Hayama, 2006), chap. 10, pp. 1–89.
23. E. H. Bennetzen, P. Smith, J. R. Porter, Decoupling of greenhouse gas emissions from global
agricultural production: 1970–2050. Glob. Change Biol. 22, 763–781 (2016).
24. D. von Soosten, U. Meyer, G. Flachowsky, S. D€anicke, Dairy cow health and greenhouse gas
emission intensity. Dairy 1, 20–29 (2020).
25. P. Huhtanen, M. Rinne, J. Nousiainen, A meta-analysis of feed digestion in dairy cows. 2. The
effects of feeding level and diet composition on digestibility. J. Dairy Sci. 92, 5031–5042 (2009).
26. J. P. Goopy et al., Severe below-maintenance feed intake increases methane yield from enteric
fermentation in cattle. Br. J. Nutr. 123, 1239–1246 (2020).
27. A. N. Hristov et al., Special topics—Mitigation of methane and nitrous oxide emissions from animal
operations: III. A review of animal management mitigation options. J. Anim. Sci. 91, 5095–5113
(2013).
28. FAO; New Zealand Agricultural Greenhouse Gas Research Centre, Low Emissions Development of
the Beef Cattle Sector in Uruguay—Reducing Enteric Methane for Food Security and Livelihoods
(FAO/NZAGGRC, Rome, 2017).
29. R. Demanet, M. L. Mora, M. 
A. Herrera, H. Miranda, J. M. Barea, Seasonal variation of the
productivity and quality of permanent pastures in andisols of temperate regions. J. Soil Sci. Plant
Nutr. 15, 111–128 (2015).
30. F. Montes et al., Special topics—Mitigation of methane and nitrous oxide emissions from animal
operations: II. A review of manure management mitigation options. J. Anim. Sci. 91, 5070–5094
(2013).
31. C. E. Van Middelaar, J. Dijkstra, P. B. M. Berentsen, I. J. M. De Boer, Cost-effectiveness of feeding
strategies to reduce greenhouse gas emissions from dairy farming. J. Dairy Sci. 97, 2427–2439
(2014).
32. E. M. Ungerfeld, Metabolic hydrogen flows in rumen fermentation: Principles and possibilities of
interventions. Front. Microbiol. 11, 589 (2020).
33. N. Abdela, Sub-acute ruminal acidosis (SARA) and its consequence in dairy cattle: A review of past
and recent research at global prospective. Achiev. Life Sci. 10, 187–196 (2016).
34. U. Ermler, W. Grabarse, S. Shima, M. Goubeaud, R. K. Thauer, Crystal structure of methyl-coenzyme
M reductase: The key enzyme of biological methane formation. Science 278, 1457–1462 (1997).
35. S. H. Kim et al., Effects of 3-nitrooxypropanol on enteric methane production, rumen fermentation,
and feeding behavior in beef cattle fed a high-forage or high-grain diet1. J. Anim. Sci. 97,
2687–2699 (2019).
36. S. van Gastelen et al., 3-Nitrooxypropanol decreases methane emissions and increases hydrogen
emissions of early lactation dairy cows, with associated changes in nutrient digestibility and energy
metabolism. J. Dairy Sci. 103, 8074–8093 (2020).
37. A. N. Hristov, A. Melgar, Short communication: Relationship of dry matter intake with enteric
methane emission measured with the GreenFeed system in dairy cows receiving a diet without or
with 3-nitrooxypropanol. Animal 14 (S3), s484–s490 (2020).
38. C. K. Reynolds et al., Effects of 3-nitrooxypropanol on methane emission, digestion, and energy
and nitrogen balance of lactating dairy cows. J. Dairy Sci. 97, 3777–3789 (2014).
39. S. Muetzel et al., “Conference abstract” in Proceedings of the 7th International Greenhouse Gas and
Animal Agriculture Conference. Iguassu Falls, Brazil, A. Berndt, L. G. Pereira Ribeiro, A. L. Abdala,
Eds. (Embrapa Southeast Livestock, S~ao Carlos, Brazil, 2019), p. 81.
40. D. Vyas et al., The combined effects of supplementing monensin and 3-nitrooxypropanol on
methane emissions, growth rate, and feed conversion efficiency in beef cattle fed high-forage and
high-grain diets. J. Anim. Sci. 96, 2923–2938 (2018).
41. B. Roque et al., Red seaweed (Asparagopsis taxiformis) supplementation reduces enteric methane
by over 80 percent in beef steers. PLoS ONE 16, e0247820 (2021).
42. H. A. Stefenoni et al., Effects of the macroalga Asparagopsis taxiformis and oregano leaves on
methane emission, rumen fermentation, and lactational performance of dairy cows. J. Dairy Sci.
104, 4157–4173 (2021).
43. US EPA, Bromoform Fact Sheet (US Environmental Protection Agency, 2000). https://www.epa.gov/
sites/default/files/2016-09/documents/bromoform.pdf. Accessed 23 May 2020.
44. L. Machado et al., Identification of bioactives from the red seaweed Asparagopsis taxiformis that
promote antimethanogenic activity in vitro. J. Appl. Phycol. 28, 3117–3126 (2016).
45. M. A. Navarro et al., Airborne measurements of organic bromine compounds in the Pacific tropical
tropopause layer. Proc. Natl. Acad. Sci. U.S.A. 112, 13789–13793 (2015).
46. H. Liu et al., Effects of lespedeza condensed tannins alone or with monensin, soybean oil, and
coconut oil on feed intake, growth, digestion, ruminal methane emission, and heat energy by
yearling Alpine doelings. J. Anim. Sci. 97, 885–899 (2019).
47. D. A. H€aring et al., Tanniferous forage plants: Agronomic performance, palatability and efficacy
against parasitic nematodes in sheep. Renew. Agric. Food Syst. 23, 19–29 (2008).
48. J. Zhang et al., Effect of different tannin sources on nutrient intake, digestibility, performance,
nitrogen utilization, and blood parameters in dairy cows. Animals (Basel) 9, 507 (2019).
49. S. M. van Zijderveld et al., Persistency of methane mitigation by dietary nitrate supplementation in
dairy cows. J. Dairy Sci. 94, 4028–4038 (2011).
50. P. A. Farra, L. D. Satter, Manipulation of ruminal fermentation. III. Effect of nitrate on ruminal
volatile fatty acid production and milk composition. J. Dairy Sci. 54, 1018–1024 (1971).
51. A. R. Alaboudi, G. A. Jones, Effect of acclimation to high nitrate intakes on some rumen
fermentation parameters in sheep. Can. J. Anim. Sci. 65, 841–849 (1985).
52. L. Li et al., Effect of added dietary nitrate and elemental sulfur on wool growth and methane
emission of Merino lambs. Anim. Prod. Sci. 53, 1195–1201 (2013).
53. S. O. Petersen et al., Dietary nitrate for methane mitigation leads to nitrous oxide emissions from
dairy cows. J. Environ. Qual. 44, 1063–1070 (2015).
54. M. Doreau, L. Bamiere, S. Pellerin, M. Lherm, M. Benoit, Mitigation of enteric methane
for French cattle: Potential extent and cost of selected actions. Anim. Prod. Sci. 54, 1417–1422
(2014).
55. H. Liu, V. Vaddella, D. Zhou, Effects of chestnut tannins and coconut oil on growth performance,
methane emission, ruminal fermentation, and microbial populations in sheep. J. Dairy Sci. 94,
6069–6077 (2011).
56. E. Jordan et al., Effect of refined coconut oil or copra meal on methane output and on intake and
performance of beef heifers. J. Anim. Sci. 84, 162–170 (2006).
57. D. J. Schauff, J. H. Clark, Effects of feeding diets containing calcium salts of long-chain fatty acids
to lactating dairy cows. J. Dairy Sci. 75, 2990–3002 (1992).
PNAS 2022 Vol. 119 No. 20 e2111294119 https://doi.org/10.1073/pnas.2111294119 9 of 10
D
ow
nl
oa
de
d 
fro
m
 h
ttp
s:/
/w
w
w
.p
na
s.o
rg
 b
y 
46
.3
0.
13
2.
20
4 
on
 A
ug
us
t 1
5,
 2
02
2 
fro
m
 IP
 ad
dr
es
s 4
6.
30
.1
32
.2
04
.
58. D. L. Palmquist, T. C. Jenkins, A 100-year review: Fat feeding of dairy cows. J. Dairy Sci. 100,
10061–10077 (2017).
59. H. Bonesmo et al., Greenhouse gas emission intensities and economic efficiency in crop
production: A systems analysis of 95 farms. Agric. Syst. 110, 142–151 (2012).
60. A. Horrillo, P. Gaspar, M. Escribano, Organic farming as a strategy to reduce carbon footprint in
dehesa agroecosystems: A case study comparing different livestock products. Animals (Basel) 10,
162 (2020).
61. G. Thoma et al., Greenhouse gas emissions from milk production and consumption in the United
States: A cradle-to-grave life cycle assessment circa 2008. Int. Dairy J. 31, S3–S14 (2013).
62. W. Wang et al., Greenhouse gas mitigation in Chinese agriculture: Distinguishing technical and
economic potentials. Glob. Environ. Change 26, 53–62 (2014).
63. M. Doreau, A. Meynadier, V. Fievez, A. Ferlay, “Ruminal metabolism of fatty acids: Modulation
of polyunsaturated, conjugated, and trans fatty acids in meat and milk” in Handbook of Lipids
in Human Function, R. R. Watson, F. De Meester, Eds. (AOCS Press, 2016), chap. 19,
pp. 521–542.
64. M. Henchion, A. P. Moloney, J. Hyland, J. Zimmermann, S. McCarthy, Review: Trends for meat,
milk and egg consumption for the next decades and the role played by livestock systems in the
global production of proteins. Animal 15 (suppl. 1), 100287 (2021).
65. FAO, The future of food and agriculture – Alternative pathways to 2050. https://www.fao.org/
fileadmin/user_upload/global-perspective/csv/FOFA2050RegionsData_all.csv. Accessed 26
January 2021.
66. S. R. O. Williams, M. C. Hannah, R. J. Eckard, W. J. Wales, P. J. Moate, Supplementing the diet of
dairy cows with fat or tannin reduces methane yield, and additively when fed in combination.
Animal 14 (S3), S464–S472 (2020).
67. R. Gruninger et al., Application of 3-nitrooxypropanol and canola oil to mitigate enteric methane
emissions of beef cattle results in distinctly different effects on the rumen microbial community.
Preprint under consideration at Animal Microbiome. Research Square [Preprint] (2021). https://
doi.org/10.21203/rs.3.rs-391068/v1. Accessed 23 May 2021.
68. X. M. Zhang et al., Combined effects of 3-nitrooxypropanol and canola oil supplementation on
methane emissions, rumen fermentation and biohydrogenation, and total tract digestibility in
beef cattle. J. Anim. Sci. 99, skab091 (2021).
69. J. Guyader et al., Additive methane-mitigating effect between linseed oil and nitrate fed to cattle.
J. Anim. Sci. 93, 3564–3577 (2015).
70. P. Huhtanen, A. R. Bayat, P. Lund, A. L. F. Hellwing, M. R. Weisbjerg, Short communication:
Variation in feed efficiency hampers use of carbon dioxide as a tracer gas in measuring methane
emissions in on-farm conditions. J. Dairy Sci. 103, 9090–9095 (2020).
71. K. J. Hammond et al., Review of current in vivo measurement techniques for quantifying enteric
methane emission from ruminants. Anim. Feed Sci. Technol. 219, 13–30 (2016).
72. M. Borenstein, L. V. Hedges, J. P. T. Higgins, H. R. Othstein, Introduction to Meta-Analysis (John
Wiley & Sons, Chichester, 2009).
73. J. O. Friedrich, N. K. Adhikari, J. Beyene, The ratio of means method as an alternative to mean
differences for analyzing continuous outcome variables in meta-analysis: A simulation study. BMC
Med. Res. Methodol. 8, 32 (2008).
74. S. Holm, A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
75. FAO, The Future of Food and Agriculture—Alternative Pathways to 2050 (Food and Agriculture
Organization of the United Nations, Rome, Italy, 2018).
76. WHO,World Health Statistics 2020: Monitoring Health for the SDGs, Sustainable Development
Goals (World Health Organization, Geneva, Switzerland, 2020).
10 of 10 https://doi.org/10.1073/pnas.2111294119 pnas.org
D
ow
nl
oa
de
d 
fro
m
 h
ttp
s:/
/w
w
w
.p
na
s.o
rg
 b
y 
46
.3
0.
13
2.
20
4 
on
 A
ug
us
t 1
5,
 2
02
2 
fro
m
 IP
 ad
dr
es
s 4
6.
30
.1
32
.2
04
.