ArticlePDF Available

Natural climate solutions for the United States

Authors:

Abstract and Figures

Limiting climate warming to <2°C requires increased mitigation efforts, including land stewardship, whose potential in the United States is poorly understood. We quantified the potential of natural climate solutions (NCS)—21 conservation, restoration, and improved land management interventions on natural and agricultural lands—to increase carbon storage and avoid greenhouse gas emissions in the United States. We found a maximum potential of 1.2 (0.9 to 1.6) Pg CO 2 e year ⁻¹ , the equivalent of 21% of current net annual emissions of the United States. At current carbon market prices (USD 10 per Mg CO 2 e), 299 Tg CO 2 e year ⁻¹ could be achieved. NCS would also provide air and water filtration, flood control, soil health, wildlife habitat, and climate resilience benefits.
Content may be subject to copyright.
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
1 of 14
ENVIRONMENTAL STUDIES
Natural climate solutions for the United States
Joseph E. Fargione1*, Steven Bassett2, Timothy Boucher3, Scott D. Bridgham4, Richard T. Conant5,
Susan C. Cook-Patton3,6, Peter W. Ellis3, Alessandra Falcucci7, James W. Fourqurean8,
Trisha Gopalakrishna3, Huan Gu9, Benjamin Henderson10, Matthew D. Hurteau11,
Kevin D. Kroeger12, Timm Kroeger3, Tyler J. Lark13, Sara M. Leavitt3, Guy Lomax14,
Robert I. McDonald3, J. Patrick Megonigal6, Daniela A. Miteva15, Curtis J. Richardson16,
Jonathan Sanderman17, David Shoch18, Seth A. Spawn13, Joseph W. Veldman19,
Christopher A. Williams9, Peter B. Woodbury20, Chris Zganjar3, Marci Baranski21, Patricia Elias3,
Richard A. Houghton17, Emily Landis3, Emily McGlynn22, William H. Schlesinger23,
Juha V. Siikamaki24, Ariana E. Sutton-Grier25,26, Bronson W. Griscom3
Limiting climate warming to <2°C requires increased mitigation efforts, including land stewardship, whose poten-
tial in the United States is poorly understood. We quantified the potential of natural climate solutions (NCS)—21
conservation, restoration, and improved land management interventions on natural and agricultural lands—to
increase carbon storage and avoid greenhouse gas emissions in the United States. We found a maximum potential
of 1.2 (0.9 to 1.6) Pg CO2e year−1, the equivalent of 21% of current net annual emissions of the United States. At
current carbon market prices (USD 10 per Mg CO2e), 299 Tg CO2e year−1 could be achieved. NCS would also pro-
vide air and water filtration, flood control, soil health, wildlife habitat, and climate resilience benefits.
INTRODUCTION
Limiting global warming below the 2°C threshold set by the Paris
Climate Agreement is contingent upon both reducing emissions and
removing greenhouse gases (GHGs) from the atmosphere (1,2).
Natural climate solutions (NCS), a portfolio of discrete land steward-
ship options (3), are the most mature approaches available for car-
bon conservation and uptake compared to nascent carbon capture
technologies (4) and could complement increases in zero-carbon
energy production and energy efficiency to achieve needed climate
change mitigation. Within the United States, the maximum and
economically viable mitigation potentials from NCS are unclear.
Here, we quantify the maximum potential for NCS in the United
States and the portion of this maximum that could be achieved at
several price points. We consider 21 distinct NCS to provide a con-
sistent and comprehensive exploration of the mitigation potential
of conservation, restoration, and improved management in forests,
grasslands, agricultural lands, and wetlands (Fig.1), carefully defined
to avoid double counting (details in the Supplementary Materials).
We estimate the potential for NCS in the year 2025, which is the
target year for the United States’ Nationally Determined Contribution
(NDC) under the Paris Agreement to reduce GHG emissions by 26
to 28% from 2005 levels. Our work refines a coarser-resolution
global analysis (3) and updates and expands the range of options
considered in previous analyses for the United States (58).
For each NCS opportunity (Fig.1 and the Supplementary Materials),
we estimate the maximum mitigation potential of GHGs measured
in CO2 equivalents (CO2e), given the below constraints. We then
estimate the reductions obtainable for less than USD 10, 50, and 100
per Mg CO2e. Current carbon markets pay around USD 10 (9). The
social cost of carbon in 2025 is approximately USD 50, using a 3%
discount rate (10). However, a price of at least USD 100 is thought
to be needed to keep the 100-year average temperature from warm-
ing more than 2.5°C (11), and an even higher price may be needed
to meet the Paris Agreement <2°C target. Many NCS also generate
co-benefits, which, even without a price on carbon, provide incen-
tives to invest in NCS implementation. We identified co-benefits
generated by each NCS in four categories of ecosystem services: air,
biodiversity, water, and soil (Fig.1 and table S2).
To avoid conflicts with other important societal goals for land use,
we constrain our maximum estimate to be compatible with human
needs for food and fiber (Supplementary Materials). Within these
constraints, 5.1 Mha of cropland can be restored to grasslands, for-
ests, and wetlands, equal to the area that has left the Conservation
Reserve Program (CRP) since 2007 (8) and less than half the land
currently dedicated to corn ethanol. We also estimate that 1.3 Mha
of pasture could be reforested without affecting livestock produc-
tion, assuming recent improvements in efficiency continue (see the
Supplementary Materials). We assume that timber production can
temporarily decrease by 10%, which maintains timber production
1The Nature Conservancy, Minneapolis, MN 55415, USA. 2The Nature Conservancy,
Santa Fe, NM 87501, USA. 3The Nature Conservancy, Arlington, VA 22203, USA. 4In-
stitute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.
5Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO
80523, USA. 6Smithsonian Environmental Research Center, Edgewater, MD 21037,
USA. 7Food and Agriculture Organization, Rome, Italy. 8Marine Sciences Program,
Florida International University, North Miami, FL 33181, USA. 9Graduate School of
Geography, Clark University, Worcester, MA 01610, USA. 10Trade and Agriculture
Directorate, Organization for Economic Cooperation and Development, Paris 75016,
France. 11Department of Biology, University of New Mexico, Albuquerque, NM 87131,
USA. 12Woods Hole Coastal and Marine Science Center, United States Geological
Survey, Woods Hole, MA 02543, USA. 13Center for Sustainability and the Global En-
vironment, University of Wisconsin-Madison, Madison, WI 53726, USA. 14The Nature
Conservancy, Oxford OX1 1HU, UK. 15Department of Agricultural, Environmental
and Development Economics, Ohio State University, Columbus, OH 43210, USA.
16Duke University Wetland Center, Nicholas School of the Environment, Durham, NC
27708, USA. 17Woods Hole Research Center, Falmouth, MA 02540, USA. 18TerraCarbon
LLC, Charlottesville, VA 22903, USA. 19Department of Ecosystem Science and Manage-
ment, Texas A&M University, College Station, TX 77843, USA. 20College of Agricul-
ture and Life Sciences, Cornell University, Ithaca, NY 14853, USA. 21U.S. Department
of Agriculture, Washington, DC 20250, USA. 22Department of Agriculture and Re-
source Economics, University of California, Davis, Davis, CA 95616, USA. 23Cary
Institute of Ecosystem Studies, Millbrook, NY 12545, USA. 24International Union for
Conservation of Nature, Washington, DC 20009, USA. 25The Nature Conservancy,
Bethesda, MD 20814, USA. 26Earth System Science Interdisciplinary Center, Univer-
sity of Maryland, College Park, MD 20740, USA.
*Corresponding author. Email: jfargione@tnc.org
Copyright © 2018
The Authors, some
rights reserved;
exclusive licensee
American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
License 4.0 (CC BY).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
2 of 14
levels within the historic range of variation and enables managed
forests and plantations to transition to longer harvest rotations (see
the Supplementary Materials). We assume that extensive natural
forests on private lands can all undergo harvest extension, with
the temporary loss of timber supply replaced by reforestation and
thinning for fire risk reduction (12) or with thinning or select har-
vest practices that still provide timber but maintain carbon levels
(Supplementary Materials) (13,14). We further constrain our analy-
sis to avoid impacts on biodiversity. This biodiversity constraint pre-
cludes both the conversion of natural habitat to energy crops and
the afforestation of native grasslands.
RESULTS
We find a maximum additional NCS mitigation potential of 1.2 Pg
CO2e year−1 [95% confidence interval (CI), 0.9 to 1.6 Pg CO2e year−1]
in the year 2025 (Fig.1 and table S1). This is 21% of the 5794.5 Tg
CO2e of net emissions in 2016 (15). The majority (63%) of this po-
tential comes from increased carbon sequestration in plant bio-
mass, with 29% coming from increased carbon sequestration in soil
and 7% coming from avoided emissions of CH4 and N2O. At the
USD 10, 50, and 100 price points, 25, 76, and 91%, respectively, of
the maximum mitigation would be achieved. This means that 1.1 Pg
CO2e year−1 are available at USD 100 per Mg CO2e, which equals
the emission reductions needed to meet the U.S. NDC under the
Paris Agreement (see the Supplementary Materials). If NCS were
pursued in combination with additional mitigation in the energy
sector, then it would therefore enable the United States to exceed its
current NDC ambition. This is important because, globally, current
NDCs (7 to 9 Pg CO2e year−1) would need to be dramatically in-
creased (by an additional 10 to 16 Pg CO2e year−1) to limit warming
below 2°C (16).
This estimate of maximum NCS potential is similar to or higher
than several previous syntheses of mitigation opportunities in the
land sector. For example, the United States Mid-Century Strategy
for Deep Decarbonization estimated a potential land sink of 912 Tg
CO2e year−1, 30% lower than our estimate (5). While other efforts
have focused on the forest sector (7) or the agricultural sector (6),
this analysis presents a comprehensive and up-to-date synthesis of
NCS opportunities in the United States. For example, this analysis
considers potential additional mitigation from tidal wetlands and
seagrass (“blue carbon”), which has been comprehensively analyzed
for its current status in the United States (17), but not its potential
for additional mitigation.
Fig. 1. Climate mitigation potential of 21 NCS in the United States. Black lines indicate the 95% CI or reported range (see table S1). Ecosystem service benefits linked
with each NCS are indicated by colored bars for air (filtration), biodiversity (habitat protection or restoration), soil (enrichment), and water (filtration and flood control).
See the Supplementary Materials for detailed findings and sources.
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
3 of 14
Reforestation has the single largest maximum mitigation poten-
tial (307 Tg CO2e year−1). The majority of this potential occurs in
the northeast (35%) and south central (31%) areas of the United States
(fig. S1). This mitigation potential increases to 381 Tg CO2e year−1
if all pastures in historically forested areas are reforested. Previous
estimates of reforestation potential range widely from 208 to 1290
Tg CO2e year−1 (7). Higher estimates than ours can be obtained by
reforesting or afforesting areas that we excluded (e.g., productive
crop and pasture lands and natural grasslands) and/or by using rates
of carbon sequestration from plantation systems rather than from
natural regenerating forests [e.g., (7)].
Natural forest management of privately held forests has the sec-
ond largest maximum mitigation potential (267 Tg CO2e year−1).
This maximum mitigation is achieved by extending harvest cycles.
Mitigation can also be achieved through forest management prac-
tices such as reduced impact logging and improved silvicultural prac-
tices that release suppressed forest growth (1820), although often
at lower sequestration rates than extending harvest cycles. These
management practices can be implemented at low or no net cost
(21,22) and do not require a change in business-as-usual (BAU)
land use or ownership rights.
Another promising opportunity associated with forests is fire
management (18 Tg CO2e year−1; fig. S6). Fire management entails
restoring frequent, low-intensity, understory fires in fire-prone forest
ecosystems to reduce the potential for high-severity wildfires (23).
The primary carbon benefit from fire management is avoiding de-
creased net ecosystem production from tree-killing wildfire. In the
absence of improved fire management, climate change is expected
to continue to increase the frequency of high-severity fires and com-
promise the ability of forests to regenerate following these fires (24).
The high uncertainty associated with the climate mitigation bene-
fits of fire management would be reduced by additional research to
quantify the carbon storage benefits of prescribed fire across a di-
versity of forest types, including the length of time that prescribed
fire reduces the risk of subsequent high-severity fires.
Avoided conversion protects carbon stored in extant forests and
grasslands from ongoing losses. More than two-thirds of the avoided
forest conversion potential (38 Tg CO2e year−1) occurs in the Southern
and Pacific Northwest regions (table S14 and fig. S9). Many of the
most intensive areas of rapid forest conversion were located near
urban zones, with additional hot spots in recent agricultural expan-
sion zones (such as California’s Central Valley) and semi-arid re-
gions of the West. Avoided conversion of grassland to cropland
prevents emissions from soils and root biomass (107 Tg CO2e year−1;
fig. S12). The emissions from grassland conversion exceed the emis-
sions from forest conversion because both the rate of conversion
and the per hectare emissions are higher (table S1). Cropland ex-
pansion is a major cause of conversion that affects grasslands much
more than forests (25). The higher rate of emissions occurs because
the conversion of grasslands to croplands results in a 28% loss of
soil carbon from the top meter of soil (26). This generates 125 Mg
CO2e ha−1 in emissions, comprising 81% of the emissions from grass-
land conversion (see the Supplementary Materials). Because research
shows conflicting conclusions regarding the impact of forest con-
version in the United States on soil carbon, we do not include the
soil carbon pool in our estimate of emissions from forest conversion
(see the Supplementary Materials).
Carbon sequestration opportunities in croplands include the use of
cover crops and improved cropland nutrient management. Cover
crops, grown when fields are normally bare, provide additional car-
bon inputs to soils. Growing cover crops on the 88 Mha of the five
primary crops in United States not already using cover crops presents
a substantial opportunity for mitigation (103 Tg CO2e year−1). Cover
crops are increasingly used by U.S. farmers to improve soil health,
yields, and yield consistency (27). Improved management of nitro-
gen fertilizers reduces N2O emissions and avoids fossil fuel emis-
sions associated with fertilizer production (52 Tg CO2e year−1).
Fertilizer rates can be reduced while maintaining yields by using
precision agriculture to apply only the amount required in each part
of the field and by splitting fertilizer applications to match the timing
and supply of fertilizer with crop demand (see the Supplementary
Materials). Emissions can also be reduced by switching from anhy-
drous fertilizer to urea, which has lower N2O emission (6).
The agronomic practices of biochar incorporation (95 Tg CO2e
year−1) and alley cropping (planting widely spaced trees interspersed
with a row crop; 82 Tg CO2e year−1) also have high maximum poten-
tial. However, current adoption is negligible due to a variety of cul-
tural, technological, and cost barriers that would need to be overcome
if these practices were to achieve their mitigation potential (28,29).
Tidal wetland restoration is the largest wetland NCS (12 Tg CO2e
year−1). Roughly 27% of U.S. salt marshes are disconnected from the
ocean and subject to freshwater inundation. This results in a large
increase in CH4 emissions from these “freshened” salt marshes. Re-
connecting salt marshes with the ocean, such as via culverts under
roads or other barriers, can avoid these CH4 emissions (30).
The 10 opportunities described above account for 90% (1082 Tg
CO2e year−1) of the maximum NCS mitigation potential across all
21 opportunities. An additional 11 opportunities, which sum to 122
Tg CO2e year−1, account for just 10% of the maximum potential.
However, these NCS may offer optimal ecological and economic
opportunities at local scales (Fig.1 and Supplementary Materials).
For example, peatland restoration offers a high per hectare mitiga-
tion benefit, especially in regions of the United States with warm
temperate climates (8.2 Mg CO2e ha−1 year−1).
Lower-cost opportunities represent particularly promising areas
for increased near-term investment. We identified 299 Tg CO2e year−1
of NCS opportunities that could be realized for USD 10 Mg CO2e−1
or less (table S1), a price that is in line with many current carbon
markets (9). The two largest lower-cost opportunities are improved
management practices: cover crops (100 Tg CO2e year−1) and im-
proved natural forest management (64 Tg CO2e year−1). Both of
these practices, along with planting windbreaks (5 Tg CO2e year−1)
and legumes in pastures (3 Tg CO2e year−1), have the potential to
increase yields (21,22,27) and therefore to generate additional rev-
enue for landowners. Improved manure management can also provide
low-cost mitigation (12 Tg CO2e year−1) (8). In addition, lower- cost
NCS include increased efficiencies (cropland nutrient management,
28 Tg CO2e year−1; grazing optimization, 6 Tg CO2e year−1) and
avoided conversion (avoided forest conversion, 37 Tg CO2e year−1;
avoided grassland conversion, 24 Tg CO2e year−1).
By itself, the marginal abatement cost gives an incomplete pic-
ture of the potential for implementation of NCS, in part because NCS
provide a variety of co-benefits (Fig.1 and table S2). The values of
these co-benefits are not captured in our marginal abatement costs
yet may drive NCS implementation. For example, investments in fire
management are needed to avoid impacts on air quality and drink-
ing water provision; urban forestry provides human health, aesthetic,
and direct temperature reduction benefits; nutrient management is
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
4 of 14
needed to improve water quality and avoid toxic algal blooms (table S2).
Further, NCS can help provide resilience to climate change impacts
on nature and people. For example, building soil carbon increases
the resilience of cropland (31); protecting coastal wetlands can pro-
vide coastal defense against storms (32); and fire management can
help avoid damaging wildfires (23).
We have restricted our analysis to those opportunities where the
literature conclusively demonstrates the potential for mitigation.
This suggests that new research may reveal additional opportunities
for NCS, which would increase the potential identified here. At the
same time, substantial uncertainties exist in some NCS opportuni-
ties (Fig.1 and table S1), highlighting the need for implementation
to be coupled with monitoring and assessment of NCS.
DISCUSSION
The United States is the largest cumulative emitter of carbon dioxide
from fossil fuels (33). Despite the immense size of U.S. GHG emissions
from fossil fuel use, we find that NCS have the potential to generate
mitigation equivalent to 21% of net annual emissions. This reveals
the important contribution to climate mitigation that the land sec-
tor can make, even in developed countries such as the United States.
Globally, current NCS efforts receive only 0.8% of public and
private climate financing (34), despite offering roughly 37% of po-
tential mitigation needed through 2030 (3). One concern that may
have limited the adoption of NCS to date includes competition with
other land uses such as food and bioenergy production. A growing
body of literature suggests that future global food demand can be
met via investments in yield increases, closing yield gaps, diet shifts,
aquaculture, and biofuel policy, without the need to further expand
cropland into natural areas (35,36). In the United States, marginal
cropland, much of which is unprofitable (37), could be restored to
grassland or forests with net societal benefits (38). Similarly, NCS
may compete with bioenergy production. However, this conflict can
be reduced or avoided depending on the form of bioenergy produc-
tion or NCS. Some forms of biomass production, such as residues
and wastes, or high-yielding methods, such as algae, do not require
productive land (39). Our grassland restoration pathway could pro-
duce a limited amount of additional biomass while maintaining carbon
sequestration in soils if low-productivity croplands are converted to
perennial energy grasses (40). Further, NCS based on improved
management of existing land uses do not create land use conflict and
can even increase productivity within that land use (e.g., fire manage-
ment or cover crops). However, aggressive expansion of dedicated
bioenergy crops, given the large land requirement of both first- and
second-generation bioenergy crops (41), would be likely to reduce the
mitigation potential available through NCS, notably via reforesta-
tion, avoided grassland conversion, and natural forest management.
A second concern is that ecosystems have a limited ability to store
additional carbon. For each pathway, we quantified the duration of
time for which mitigation is expected to occur at the rates we esti-
mate, before saturation effects decrease this rate (table S1). We note
that carbon can continue to accumulate in forests for hundreds
of years and in soils for centuries or millennia (table S1 and the
Supplementary Materials). Further, four of our NCS opportunities
(cropland nutrient management, tidal wetland restoration, manure
management, and improved rice management) are based on avoided
emissions of CH4 and N2O, which are benefits that do not saturate.
The mitigation potential of avoided conversion of habitat is limited
by the total carbon contained in the habitat. Our analysis assumes
that rates of conversion persist at current levels in a BAU scenario,
which would represent a continuing source of emissions for at least
67 years for each habitat considered here before reaching “saturation”
when the total area has been lost. However, the long-term benefit of
avoided conversion depends on assumed future BAU conversion rates.
The permanence of the ~2270 Pg C currently stored globally in
biomass (42) and soils to 1 m (26) is a significant concern, because
unmitigated climate change is likely to cause feedbacks that may
increase disturbances such as fire or pest outbreaks (43) or limit net
ecosystem productivity or forest regeneration (24). While NCS would
marginally increase this large carbon pool, putting some additional
carbon at risk, rapid and widespread implementation of NCS would
reduce the overall risk of impermanence to the terrestrial biosphere
that unmitigated climate change is likely to cause.
Another challenge is that avoiding conversion in one area can
cause conversion to shift to other areas, often referred to as “leakage.”
Large-scale sectoral and landscape approaches to land use planning
and policies will be needed to realize the NCS opportunities identi-
fied here. These approaches can and should be designed to buffer
risks of leakage associated with individual projects (44).
Reducing carbon-intensive energy consumption is necessary but
insufficient to meet the ambitious goals of the Paris Agreement.
Comprehensive mitigation efforts that include fossil fuel emission
reductions coupled with NCS hold promise for keeping warming
below 2°C. Beyond providing meaningful climate mitigation, NCS
investment can increase other important ecosystem services. The
conservation, restoration, and improved management of lands in
the United States represent a necessary and urgent component of
efforts to stabilize the climate.
MATERIALS AND METHODS
Below, we provide a brief overview of methods for each of the 21 NCS
that we quantified. Full methodological details are provided in the
Supplementary Materials.
Reforestation: Additional carbon sequestration in above- and
belowground biomass and soils gained by converting nonforest (<25%
tree cover) to forest [>25% tree cover (45)] in areas of the contermi-
nous United States where forests are the native cover type. We ex-
cluded areas with intensive human development, including all major
roads (46), impervious surfaces (47), and urban areas (48). To elimi-
nate double counting with the peatland restoration pathway, we re-
moved Histosol soils (49). To safeguard food production, we removed
most cropland and pasture. We discounted the carbon sequestra-
tion mitigation benefit in conifer-dominated forests to account for
albedo effects.
Natural forest management: Additional carbon sequestration in
above- and belowground biomass gained through improved manage-
ment in forests on private lands under nonintensive timber manage-
ment. The maximum mitigation potential was quantified on the basis
of a “harvest hiatus” scenario starting in 2025, in which natural for-
ests are shifted to longer harvest rotations. This could be accom-
plished with less than 10% reduction in timber supply with new
timber supply from thinning treatments for fuel risk reduction until
new timber from reforestation is available in 2030.
Fire management: Use of prescribed fire to reduce the risk of
high-intensity wildfire. We considered fire-prone forests in the west-
ern United States. We assume that treatment eliminates the risk of
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
5 of 14
subsequent wildfire for 20 years, but only on the land that was di-
rectly treated. We assume that 5% of lands are treated each year, and we
calculated the benefits that accrue over 20 years, finding that the ini-
tial increase in emissions associated with prescribed fire treatment
is more than offset over time by the avoided impacts of wildfires. We
report the average annual benefit across these 20 years. The impact
of wildfires includes both direct emissions from combustion and
suppression of net ecosystem productivity following wildfires.
Avoided forest conversion: Emissions of CO2 avoided by avoiding
anthropogenic forest conversion. Most forest clearing is followed by
forest regeneration rather than conversion to another land use. To
estimate the rate of persistent conversion (i.e., to another land use),
we first calculated forest clearing in the conterminous United States
from 2000 to 2010 and then used the proportion of forest clearing
that historically was converted to another land use to estimate con-
version rates in 2000 to 2010. We used estimates of avoided carbon
emissions from above- and belowground biomass that are specific
to each region and forest type. We did not count forest loss due to
fire to avoid double counting with the improved fire management
opportunity. We did not count forest loss due to pests because it
is unclear whether this loss can be avoided. We reduced the benefit
of avoided conversion in conifer-dominated forests to account for
their albedo effects.
Urban reforestation: Additional carbon sequestration in above-
and belowground biomass gained by increasing urban tree cover.
We considered the potential to increase urban tree cover in 3535
cities in the conterminous United States. We considered the poten-
tial for additional street trees, and for those cities not in deserts, we
also considered the potential for park and yard tree plantings. The
potential percent increase in tree cover was estimated on the basis of
high-resolution analysis of 27 cities, which excluded sports fields,
golf courses, and lawns (50).
Improved plantations: Additional carbon sequestration gained in
above- and belowground tree biomass by extending rotation lengths
for a limited time in even-aged, intensively managed wood production
forests. Rotation lengths were extended from current economic opti-
mal rotation length to a biological optimal rotation length in which
harvest occurs when stands reach their maximum annual growth.
Cover crops: Additional soil carbon sequestration gained by
growing a cover crop in the fallow season between main crops. We
quantified the benefit of using cover crops on all of the five major
crops in the United States (corn, soy, wheat, rice, and cotton) that
are not already growing cover crops (27), using the mean sequestra-
tion rate quantified in a recent meta-analysis (51).
Avoided conversion of grassland: Emissions of CO2 avoided by
avoiding conversion of grassland and shrubland to cropland. We
quantified avoided emissions from soil and roots (for shrubs, we also
considered aboveground biomass) based on the spatial pattern of
conversion from 2008 to 2012. We used spatial information on lo-
cation of recent conversion and variation in soil carbon and root
biomass to estimate mean annual emission rate from historic con-
version. We estimated a 28% loss of soil carbon down to 1 m (26).
We modeled spatial variation in root biomass based on mean annual
temperature and mean annual precipitation using data from (52).
Biochar: Increased soil carbon sequestration by amending agri-
cultural soils with biochar, which converts nonrecalcitrant carbon
(crop residue biomass) to recalcitrant carbon (charcoal) through
pyrolysis. We limited the source of biochar production to crop resi-
due that can be sustainably harvested. We assumed that 79.6% of
biochar carbon persists on a time scale of >100 years (53,54) and that
there are no effects of biochar on emissions of N2O or CH4 (55,56).
Alley cropping: Additional carbon sequestration gained by plant-
ing wide rows of trees with a companion crop grown in the alley-
ways between the rows. We estimated a maximum potential of alley
cropping on 10% of U.S. cropland (15.4 Mha) (57).
Cropland nutrient management: Avoided N2O emissions due to
more efficient use of nitrogen fertilizers and avoided upstream emis-
sions from fertilizer manufacture. We considered four improved
management practices: (i) reduced whole-field application rate, (ii)
switching from anhydrous ammonia to urea, (iii) improved timing
of fertilizer application, and (iv) variable application rate within
field. We projected a 4.6% BAU growth in fertilizer use in the United
States by 2025. On the basis of these four practices, we found a maxi-
mum potential of 22% reduction in nitrogen use, which leads to
a 33% reduction in field emissions and a 29% reduction including
upstream emissions.
Improved manure management: Avoided CH4 emissions from
dairy and hog manure. We estimated the potential for emission
reductions from improved manure management on dairy farms
with over 300 cows and hog farms with over 825 hogs. Our calcu-
lations are based on improved management practices described
by Pape etal. (8).
Windbreaks: Additional sequestration in above- and belowground
biomass and soils from planting windbreaks adjacent to croplands
that would benefit from reduced wind erosion. We estimated that
windbreaks could be planted on 0.88 Mha, based on an estimated
17.6 Mha that would benefit from windbreaks, and that windbreaks
would be planted on ~5% of that cropland (8).
Grazing optimization: Additional soil carbon sequestration due
to grazing optimization on rangeland and planted pastures, derived
directly from a recent study by Henderson etal. (58). Grazing opti-
mization prescribes a decrease in stocking rates in areas that are
overgrazed and an increase in stocking rates in areas that are under-
grazed, but with the net result of increased forage offtake and live-
stock production.
Grassland restoration: Additional carbon sequestration in soils
and root biomass gained by restoring 2.1 Mha of cropland to grass-
land, equivalent to returning to the 2007 peak in CRP enrollment.
Grassland restoration does not include restoration of shrubland.
Legumes in pastures: Additional soil carbon sequestration due
to sowing legumes in planted pastures, derived directly from a re-
cent global study by Henderson etal. (58). Restricted to planted
pastures and to where sowing legumes would result in net seques-
tration after taking into account potential increases in N2O emis-
sions from the planted legumes.
Improved rice management: Avoided emissions of CH4 and
N2O through improved practices in flooded rice cultivation. Prac-
tices including mid-season drainage, alternate wetting and drying,
and residue removal can reduce these emissions. We used a U.S. En-
vironmental Protection Agency (EPA) analysis that projects the po-
tential for improvement across U.S. rice fields, in comparison with
current agricultural practices (59).
Tidal wetland restoration: In the United States, 27% of tidal wet-
lands (salt marshes and mangroves) have limited tidal connection
with the sea, causing their salinity to decline to the point where CH4
emissions increase (30). We estimated the potential for reconnect-
ing these tidal wetlands to the ocean to increase salinity and reduce
CH4 emissions.
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
6 of 14
Peatland restoration: Avoided carbon emissions from rewetting
and restoring drained peatlands. To estimate the extent of restor-
able peatlands, we quantified the difference between historic peat-
land extent [based on the extent of Histosols in soil maps (60)] and
current peatland extent. Our estimate of mitigation potential ac-
counted for changes in soil carbon, biomass, and CH4 emissions,
considering regional differences, the type of land use of the converted
peatland, and whether the peatland was originally forested.
Avoided seagrass loss: Avoided CO2 emissions from avoiding sea-
grass loss. An estimated 1.5% of seagrass extent is lost every year (61).
We assumed that half of the carbon contained in biomass and sedi-
ment from disappearing seagrass beds is lost to the atmosphere (62).
Seagrass restoration: Increased sequestration from restoring the
estimated 29 to 52% of historic seagrass extent that has been lost
and could be restored (61). We estimated the average carbon se-
questration rate in the sediment of seagrass restorations based on
data from six seagrass restoration sites in the United States (63).
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/4/11/eaat1869/DC1
Supplementary Materials and Methods
Fig. S1. Mapped reforestation opportunity areas in the lower 48 states.
Fig. S2. Conceptual framework for improved forest management carbon accounting.
Fig. S3. MAC for carbon sequestration through forest management and aging, after
Golub et al. (99).
Fig. S4. MAC for natural forest management after Latta et al. (98) and best-fit functions.
Fig. S5. MAC curves for improved plantations.
Fig. S6. Fire management analysis area.
Fig. S7. Regions used for reporting avoided forest conversion results.
Fig. S8. Forest conversion from 1986 to 2000.
Fig. S9. Potential carbon emissions from areas at high risk of forest conversion.
Fig. S10. Cities included in the urban reforestation analysis.
Fig. S11. Calibration of remote sensing data for forest cover estimation in urban areas.
Fig. S12. Avoided grassland conversion map.
Fig. S13. MAC curve for avoided grassland conversion.
Fig. S14. Nitrogen fertilizer use in the United States.
Fig. S15. Marginal abatement cost curve for reducing N fertilizer rate.
Fig. S16. Marginal abatement cost curve for applying variable rate technology fertilizer
application.
Fig. S17. Grazing optimization map.
Fig. S18. Legumes in pastures map.
Fig. S19. Grassland restoration map.
Fig. S20. MAC curve for grassland restoration.
Fig. S21. Break-even prices for GHG abatement from rice production.
Fig. S22. MAC curve for salt marsh restoration.
Fig. S23. MAC of avoided GHG emissions from seagrass.
Table S1. Mitigation potential of NCS in 2025.
Table S2. Co-benefits of NCS.
Table S3. Literature MAC estimates for reforestation of agricultural lands.
Table S4. Literature estimates of reforestation costs used to estimate MAC of reforesting
natural ecosystems.
Table S5. Estimated marginal abatement cost of fire management by major forest region.
Table S6. Forest disturbance rates by source.
Table S7. Mean annual forest hectares cleared per year from 1986 to 2000.
Table S8. Mean annual forest hectares cleared per year from 2001 to 2010.
Table S9. Mean annual forest hectares converted per year from 1986 to 2000.
Table S10. Proportion of areas cleared from 1986 to 2000 that had not regenerated to forest
by 2010.
Table S11. Mean predisturbance dry biomass (kg m−2) in forest areas converted from 1986
to 2000.
Table S12. Mean predisturbance dry biomass (kg m−2) in forest areas converted from 2001
to 2010.
Table S13. Carbon emissions (Mg C year−1) from estimated forest conversion from 2001
to 2010.
Table S14. Albedo-adjusted carbon emissions equivalent (Mg Ce year−1) from estimated forest
conversion from 2001 to 2010.
Table S15. Urban reforestation maximum potential annual net C sequestration in 2025.
Table S16. Uncertainty in urban reforestation average annual abatement (Tg CO2) by 2025 at a
cost of USD 100 per Mg CO2.
Table S17. Profitability impacts of cover crops for selected crops.
Table S18. Marginal abatement costs of cover crops in the five primary crops.
Table S19. Maximum feasible N2O reduction for multiple nitrogen fertilizer practices.
Table S20. Results from the literature of the potential for reducing N fertilizer rate using
within-field management.
Table S21. Current and projected GHG emissions from nitrogen fertilizer manufacturing in the
United States.
Table S22. Mitigation potential for grazing optimization and legumes in pasture NCS at
different marginal abatement costs.
Table S23. Areas and carbon fluxes for Histosols in the conterminous United States.
Table S24. Peatland restoration mitigation calculations for climate zones within the United
States.
Table S25. 95% CIs for Histosol calculations.
References (64398)
REFERENCES AND NOTES
1. United Nations, United Nations Framework Convention on Climate Change: Adoption of the
Paris Agreement (United Nations, 2015).
2. P. Smith, S. J. Davis, F. Creutzig, S. Fuss, J. Minx, B. Gabrielle, E. Kato, R. B. Jackson,
A. Cowie, E. Kriegler, D. P. van Vuuren, J. Rogelj, P. Ciais, J. Milne, J. G. Canadell,
D. McCollum, G. Peters, R. Andrew, V. Krey, G. Shrestha, P. Friedlingstein, T. Gasser,
A. Grübler, W. K. Heidug, M. Jonas, C. D. Jones, F. Kraxner, E. Littleton, J. Lowe,
J. Roberto Moreira, N. Nakicenovic, M. Obersteiner, A. Patwardhan, M. Rogner, E. Rubin,
A. Sharifi, A. Torvanger, Y. Yamagata, J. Edmonds, C. Yongsung, Biophysical and
economic limits to negative CO2 emissions. Nat. Clim. Change 6, 42–50 (2016).
3. B. W. Griscom, J. Adams, P. W. Ellis, R. A. Houghton, G. Lomax, D. A. Miteva,
W. H. Schlesinger, D. Shoch, J. V. Siikamäki, P. Smith, P. Woodbury, C. Zganjar,
A. Blackman, J. Campari, R. T. Conant, C. Delgado, P. Elias, T. Gopalakrishna,
M. R. Hamsik, M. Herrero, J. Kiesecker, E. Landis, L. Laestadius, S. M. Leavitt,
S. Minnemeyer, S. Polasky, P. Potapov, F. E. Putz, J. Sanderman, M. Silvius, E. Wollenberg,
J. Fargione, Natural climate solutions. Proc. Natl. Acad. Sci. U.S.A. 114,
11645–11650 (2017).
4. C. B. Field, K. J. Mach, Rightsizing carbon dioxide removal. Science 356, 706–707 (2017).
5. The White House, United States Mid-Century Strategy for Deep Decarbonization (The White
House, 2016).
6. A. J. Eagle, L. R. Henry, L. P. Olander, K. Haugen-Kozyra, N. Millar, G. P. Robertson,
Greenhouse Gas Mitigation Potential of Agricultural Land Management in the United States:
A Synthesis of the Literature (Nicholas Institute, Duke University, 2012).
7. C. Van Winkle, J.S. Baker, D. Lapidus, S. Ohrel, J. Steller, G. Latta, D. Birur, US Forest Sector
Greenhouse Mitigation Potential and Implications for Nationally Determined Contributions
(RTI Press, 2017).
8. D. Pape, J. Lewandrowski, R. Steele, D. Man, M. Riley-Gilbert, K. Moffroid, S. Kolansky,
Managing Agricultural Land for Greenhouse Gas Mitigation Within the United States
(U.S. Department of Agriculture, 2016); www.usda.gov/oce/climate_change/
mitigation.htm.
9. World Bank; Ecofys; Vivid Economics, State and Trends of Carbon Pricing 2017
(World Bank, 2017).
10. National Academies of Science Engineering and Medicine, Valuing Climate Damages:
Updating Estimation of the Social Cost of Carbon Dioxide (The National Academies
Press, 2017).
11. W. D. Nordhaus, Evolution of modeling of the economics of global warming: Changes in
the DICE model, 1992–2017. Clim. Change 148, 623–640 (2018).
12. K. E. Skog, P. J. Ince, H. Spelter, A. Kramp, R. J. Barbour, in Woody Biomass Utilization:
Challenges and Opportunities (Forest Products Society, 2008), pp. 3–14.
13. S. C. Davis, A. E. Hessl, C. J. Scott, M. B. Adams, R. B. Thomas, Forest carbon
sequestration changes in response to timber harvest. For. Ecol. Manage. 258,
2101–2109 (2009).
14. C. Hoover, S. Stout, The carbon consequences of thinning techniques: Stand structure
makes a difference. J. For. 105, 266–270 (2007).
15. U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990–2016 (U.S. Environmental Protection Agency, 2018); www.epa.gov/
ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2016.
16. J. Rogelj, M. den Elzen, N. Höhne, T. Fransen, H. Fekete, H. Winkler, R. Schaeffer, F. Sha,
K. Riahi, M. Meinshausen, Paris Agreement climate proposals need boost to keep
warming well below 2°C. Nat. Clim. Change 534, 631–639 (2016).
17. Commission for Environmental Cooperation, North America’s Blue Carbon: Assessing
Seagrass, Salt Marsh and Mangrove Distribution and Carbon Sinks (Commission for
Environmental Cooperation, 2016).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
7 of 14
18. T. D. Lee, S. E. Eisenhaure, I. P. Gaudreau, Pre-logging treatment of invasive glossy
buckthorn (Frangula alnus Mill.) promotes regeneration of eastern white pine
(Pinus strobus L.). Forests 8, 16 (2017).
19. T. M. Schuler, M. Thomas-Van Gundy, J. P. Brown, J. K. Wiedenbeck, Managing
Appalachian hardwood stands using four management practices: 60-year results.
For. Ecol. Manage. 387, 3–11 (2017).
20. S. A. Moss, E. Heitzman, The economic impact of timber harvesting practices on NIPF
properties in West Virginia, in Proceedings of the 18th Central Hardwood Forest Conference,
G. W. Miller, T. M. Schuler, K. W. Gottschalk, J. R. Brooks, S. T. Grushecky, B. D. Spong,
J. S. Rentch, Eds. (U.S. Department of Agriculture, Forest Service, 2013), pp. 129–141.
21. Ruslandi, C. Romero, F. E. Putz, Financial viability and carbon payment potential of
large-scale silvicultural intensification in logged dipterocarp forests in Indonesia.
For. Policy Econ. 85, 95–102 (2017).
22. V. P. Medjibe, F. E. Putz, Cost comparisons of reduced-impact and conventional logging
in the tropics. J. For. Econ. 18, 242–256 (2012).
23. J. Williams, Exploring the onset of high-impact mega-fires through a forest land
management prism. For. Ecol. Manage. 294, 4–10 (2013).
24. C. S. Stevens-Rumann, K. B. Kemp, P. E. Higuera, B. J. Harvey, M. T. Rother, D. C. Donato,
P. Morgan, T. T. Veblen, Evidence for declining forest resilience to wildfires under climate
change. Ecol. Lett. 21, 243–252 (2018).
25. T. J. Lark, J. Meghan Salmon, H. K. Gibbs, Cropland expansion outpaces agricultural and
biofuel policies in the United States. Environ. Res. Lett. 10, 044003 (2015).
26. J. Sanderman, T. Hengl, G. J. Fiske, Soil carbon debt of 12,000 years of human land use.
Proc. Natl. Acad. Sci. U.S.A. 114, 9575–9580 (2017).
27. Conservation Technology Information Center, Sustainable Agriculture Research and
Education, American Seed Trade Association, Annual Report 2016-2017 Cover Crop Survey
(Conservation Technology Information Center, Sustainable Agriculture Research and
Education, American Seed Trade Association, 2017).
28. D. Knowler, B. Bradshaw, Farmers’ adoption of conservation agriculture: A review and
synthesis of recent research. Food Policy 32, 25–48 (2007).
29. S. Shackley, G. Ruysschaert, K. Zwart, B. Glaser, Biochar in European Soils and Agriculture:
Science and Practice (Routledge, 2016).
30. K. D. Kroeger, S. Crooks, S. Moseman-valtierra, J. Tang, Restoring tides to avoid methane
emissions in impounded wetlands: A new and potent Blue Carbon climate change
intervention. Sci. Rep. 7162, 1–23 (2017).
31. S. Banwart, S. Banwart, H. Black, Z. Cai, P. Gicheru, H. Joosten, R. Victoria, E. Milne,
E. Noellemeyer, U. Pascual, G. Nziguheba, R. Vargas, A. Bationo, D. Buschiazzo,
D. de-Brogniez, J. Melillo, D. Richter, M. Termansen, M. van Noordwijk, T. Goverse,
C. Ballabio, T. Bhattacharyya, M. Goldhaber, N. Nikolaidis, Y. Zhao, R. Funk, C. Duffy,
G. Pan, N. la Scala, P. Gottschalk, N. Batjes, J. Six, B. van Wesemael, M. Stocking, F. Bampa,
M. Bernoux, C. Feller, P. Lemanceau, L. Montanarella, Benefits of soil carbon: Report on
the outcomes of an international scientific committee on problems of the environment
rapid assessment workshop. Carbon Manage. 5, 185–192 (2014).
32. S. Narayan, M. W. Beck, P. Wilson, C. J. Thomas, A. Guerrero, C. C. Shepard, B. G. Reguero,
G. Franco, J. Carter Ingram, D. Trespalacios, The value of coastal wetlands for flood
damage reduction in the northeastern USA. Sci. Rep. 7, 9463 (2017).
33. T. A. Boden, R. J. Andres, G. Marland, Global, Regional, and National Fossil-Fuel CO2
Emissions (1751-2014) (V. 2017) (U.S. Department of Energy, 2017).
34. B. K. Buchner, C. Trabacchi, F. Mazza, D. Abramskiehn, D. Wang, Global Landscape of
Climate Finance 2015 (Climate Policy Initiative, 2015); www.climatepolicyinitiative.org.
35. K.-H. Erb, C. Lauk, T. Kastner, A. Mayer, M. C. Theurl, H. Haberl, Exploring the biophysical
option space for feeding the world without deforestation. Nat. Commun. 7,
11382 (2016).
36. P. Smith, H. Haberl, A. Popp, K. H. Erb, C. Lauk, R. Harper, F. N. Tubiello,
A. de Siqueira Pinto, M. Jafari, S. Sohi, O. Masera, H. Böttcher, G. Berndes, M. Bustamante,
H. Ahammad, H. Clark, H. Dong, E. A. Elsiddig, C. Mbow, N. H. Ravindranath, C. W. Rice,
C. Robledo Abad, A. Romanovskaya, F. Sperling, M. Herrero, J. I. House, S. Rose, How
much land-based greenhouse gas mitigation can be achieved without compromising
food security and environmental goals? Glob. Chang. Biol. 19, 2285–2302 (2013).
37. E. Brandes, G. S. McNunn, L. A. Schulte, I. J. Bonner, D. J. Muth, B. A. Babcock, B. Sharma,
E. A. Heaton, Subfield profitability analysis reveals an economic case for cropland
diversification. Environ. Res. Lett. 11, 014009 (2016).
38. K. A. Johnson, B. J. Dalzell, M. Donahue, J. Gourevitch, D. L. Johnson, G. S. Karlovits,
B. Keeler, J. T. Smith, Conservation Reserve Program (CRP) lands provide ecosystem
service benefits that exceed land rental payment costs. Ecosyst. Serv. 18, 175–185 (2016).
39. D. Tilman, R. Socolow, J. A. Foley, J. Hill, E. Larson, L. Lynd, S. Pacala, J. Reilly,
T. Searchinger, C. Somerville, R. Williams, Beneficial biofuels—The food, energy, and
environment trilemma. Science 325, 270–271 (2009).
40. D. Tilman, J. Hill, C. Lehman, Carbon-negative biofuels from low-input high-diversity
grassland biomass. Science 314, 1598–1600 (2006).
41. J. E. Fargione, R. J. Plevin, J. D. Hill, The ecological impact of biofuels. Annu. Rev.
Ecol. Evol. Syst. 41, 351–377 (2010).
42. K. H. Erb, T. Kastner, C. Plutzar, A. L. S. Bais, N. Carvalhais, T. Fetzel, S. Gingrich, H. Haberl,
C. Lauk, M. Niedertscheider, J. Pongratz, M. Thurner, S. Luyssaert, Unexpectedly large
impact of forest management and grazing on global vegetation biomass. Nature 553,
73–76 (2018).
43. J. T. Abatzoglou, A. P. Williams, Impact of anthropogenic climate change on wildfire
across western US forests. Proc. Natl. Acad. Sci. U.S.A. 113, 11770–11775 (2016).
44. J. Sayer, T. Sunderland, J. Ghazoul, J.-L. Pfund, D. Sheil, E. Meijaard, M. Venter,
A. Klintuni Boedhihartono, M. Day, C. Garcia, C. van Oosten, L. E. Buck, Ten principles for a
landscape approach to reconciling agriculture, conservation, and other competing land
uses. Proc. Natl. Acad. Sci. U.S.A. 110, 8349–8356 (2013).
45. M. C. Hansen, P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina,
D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini,
C. O. Justice, J. R. G. Townshend, High-resolution global maps of 21st-century forest
cover change. Science 342, 850–853 (2013).
46. Open Street Map, Osm2Shp (2016); http://osm2shp.ru/.
47. G. Xian, C. G. Homer, J. Dewitz, J. Fry, N. Hossain, J. Wickham, The change of impervious
surface area between 2001 and 2006 in the conterminous United States.
Photogramm. Eng. Remote Sens. 77, 758–762 (2011).
48. U.S. Census Bureau, Cartographic Boundary File, Urban Area for United States (U.S. Census
Bureau, 2015); www.census.gov/geo/maps-data/data/cbf/cbf_ua.html.
49. Soil Survey Staff, U.S. General Soil Map (STATSGO2) (U.S. Department of Agriculture, 2017);
https://sdmdataaccess.sc.egov.usda.gov.
50. T. Kroeger, R. I. McDonald, T. Boucher, P. Zhang, L. Wang, Where the people are: Current
trends and future potential targeted investments in urban trees for PM10 and
temperature mitigation in 27 U.S. cities. Landsc. Urban Plan. 177, 277–240 (2018).
51. C. Poeplau, A. Don, Carbon sequestration in agricultural soils via cultivation of cover
crops—A meta-analysis. Agric. Ecosyst. Environ. 200, 33–41 (2015).
52. K. Mokany, R. J. Raison, A. S. Prokushkin, Critical analysis of root: Shoot ratios in
terrestrial biomes. Glob. Chang. Biol. 12, 84–96 (2006).
53. R. S. Dharmakeerthi, K. Hanley, T. Whitman, D. Woolf, J. Lehmann, Organic carbon
dynamics in soils with pyrogenic organic matter that received plant residue additions
over seven years. Soil Biol. Biochem. 88, 268–274 (2015).
54. B. Liang, J. Lehmann, D. Solomon, S. Sohi, J. E. Thies, J. O. Skjemstad, F. J. Luizão,
M. H. Engelhard, E. G. Neves, S. Wirick, Stability of biomass-derived black carbon in soils.
Geochim. Cosmochim. Acta 72, 6069–6078 (2008).
55. X. Song, G. Pan, C. Zhang, L. Zhang, H. Wang, Effects of biochar application on fluxes of
three biogenic greenhouse gases: A meta-analysis. Ecosyst. Health Sustain. 2,
e01202 (2016).
56. J. Wang, Z. Xiong, Y. Kuzyakov, Biochar stability in soil: Meta-analysis of decomposition
and priming effects. Glob. Change Biol. Bioenergy 8, 512–523 (2016).
57. R. P. Udawatta, S. Jose, Carbon sequestration potential of agroforestry practices in
temperate North America, in Carbon Sequestration Potential of Agroforestry Systems, vol. 8
of Advances in Agroforestry, B. M. Kumar, P. K. R. Nair, Eds. (Springer Netherlands, 2011),
pp. 17–42.
58. B. B. Henderson, P. J. Gerber, T. E. Hilinski, A. Falcucci, D. S. Ojima, M. Salvatore,
R. T. Conant, Greenhouse gas mitigation potential of the world’s grazing lands: Modeling
soil carbon and nitrogen fluxes of mitigation practices. Agric. Ecosyst. Environ. 207,
91–100 (2015).
59. U.S. Environmental Protection Agency, Global Mitigation of Non-CO2 Greenhouse Gases:
2010-2030 (U.S. Environmental Protection Agency, 2013).
60. Soil Survey Staff, Gridded Soil Survey Geographic (gSSURGO) Database for the Conterminous
United States (U.S. Department of Agriculture, 2016); https://gdg.sc.egov.usda.gov/.
61. M. Waycott, C. M. Duarte, T. J. Carruthers, R. J. Orth, W. C. Dennison, S. Olyarnik,
A. Calladine, J. W. Fourqurean, K. L. Heck Jr., A. R. Hughes, G. A. Kendrick, W. J. Kenworthy,
F. T. Short, S. L. Williams, Accelerating loss of seagrasses across the globe threatens
coastal ecosystems. Proc. Natl. Acad. Sci. U.S.A. 106, 12377–12381 (2009).
62. L. Pendleton, D. C. Donato, B. C. Murray, S. Crooks, W. A. Jenkins, S. Sifleet, C. Craft,
J. W. Fourqurean, J. B. Kauffman, N. Marbà, P. Megonigal, E. Pidgeon, D. Herr, D. Gordon,
A. Baldera, Estimating global “blue carbon” emissions from conversion and degradation
of vegetated coastal ecosystems. PLOS ONE 7, e43542 (2012).
63. A. Thorhaug, H. M. Poulos, J. López-Portillo, T. C. W. Ku, G. P. Berlyn, Seagrass blue carbon
dynamics in the Gulf of Mexico: Stocks, losses from anthropogenic disturbance, and
gains through seagrass restoration. Sci. Total Environ. 605–606, 626–636 (2017).
64. J. W. Veldman, G. E. Overbeck, D. Negreiros, G. Mahy, S. Le Stradic, G. W. Fernandes,
G. Durigan, E. Buisson, F. E. Putz, W. J. Bond, Where tree planting and forest expansion are
bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).
65. S. Luyssaert, E. D. Schulze, A. Börner, A. Knohl, D. Hessenmöller, B. E. Law, P. Ciais, J. Grace,
Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).
66. B. E. Law, O. J. Sun, J. Campbell, S. Van Tuyl, P. E. Thornton, Changes in carbon storage
and fluxes in a chronoseuence of ponderosa pine. Glob. Chang. Biol. 4, 510–524 (2003).
67. D. J. Nowak, J. C. Stevens, S. M. Sisinni, C. J. Luley, Effects of urban tree management and
species selection on atmospheric carbon dioxide. J. Arboric. 28, 113–122 (2002).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
8 of 14
68. K. K. McLauchlan, S. E. Hobbie, W. M. Post, Conversion from agriculture to grassland
builds soil organic matter on decadal timescales. Ecol. Appl. 16, 143–153 (2006).
69. E. Mcleod, G. L. Chmura, S. Bouillon, R. Salm, M. Björk, C. M. Duarte, C. E. Lovelock,
W. H. Schlesinger, B. R. Silliman, A blueprint for blue carbon: Toward an improved
understanding of the role of vegetated coastal habitats in sequestering CO2.
Front. Ecol. Environ. 9, 552–560 (2011).
70. J. W. Fourqurean, C. M. Duarte, H. Kennedy, N. Marbà, M. Holmer, M. Angel Mateo,
E. T. Apostolaki, G. A. Kendrick, D. Krause-Jensen, K. J. McGlathery, O. Serrano, Seagrass
ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509 (2012).
71. J. Loisel, J. Loisel, Z. Yu, D. W. Beilman, P. Camill, J. Alm, M. J. Amesbury, D. Anderson,
S. Andersson, C. Bochicchio, K. Barber, L. R. Belyea, J. Bunbury, F. M. Chambers,
D. J. Charman, F. De Vleeschouwer, B. Fiałkiewicz-Kozieł, S. A. Finkelstein, M. Gałka,
M. Garneau, D. Hammarlund, W. Hinchcliffe, J. Holmquist, P. Hughes, M. C. Jones,
E. S. Klein, U. Kokfelt, A. Korhola, P. Kuhry, A. Lamarre, M. Lamentowicz, D. Large,
M. Lavoie, G. MacDonald, G. Magnan, M. Mäkilä, G. Mallon, P. Mathijssen, D. Mauquoy,
J. McCarroll, T. R. Moore, J. Nichols, B. O’Reilly, P. Oksanen, M. Packalen, D. Peteet,
P. J. H. Richard, S. Robinson, T. Ronkainen, M. Rundgren, A. B. K. Sannel, C. Tarnocai,
T. Thom, E.-S. Tuittila, M. Turetsky, M. Väliranta, M. van der Linden, B. van Geel,
S. van Bellen, D. Vitt, Y. Zhao, W. Zhou, A database and synthesis of northern peatland
soil properties and Holocene carbon and nitrogen accumulation. Holocene 24,
1028–1042 (2014).
72. C. Frey, J. Penman, L. Hanle, S. Monni, S. Ogle, Chapter 3: Uncertainties, in 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (Intergovernmental Panel on Climate
Change, 2006), p. 3.1–3.66; www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/
V1_3_Ch3_Uncertainties.pdf.
73. Climate Policy Initiative, California Carbon Dashboard (2017); http://calcarbondash.org/.
74. K. Hamrick, M. Gallant, Unlocking Potential State of the Voluntary Carbon Markets 2017
(Ecosystem Marketplace, 2017).
75. U.S. Bureau of Labor Statistics, Consumer Price Index Inflation Calculator (2017);
www.bls.gov/data/inflation_calculator.htm.
76. United Nations, Convention on Biological Diversity (United Nations, 1992); www.cbd.int/
doc/legal/cbd-en.pdf.
77. Millennium Ecosystem Assessment, Ecosystems and Human Well-being: Synthesis (Island
Press, 2005); www.millenniumassessment.org/en/Synthesis.html.
78. U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990-2015 (U.S. Environmental Protection Agency, 2017).
79. E. G. Brockerhoff, H. Jactel,J. A. Parrotta, C. P. Quine, J. Sayer, Plantation forests and
biodiversity: Oxymoron or opportunity? Biodivers. Conserv. 17, 925–951 (2008).
80. L. L. Bremer, K. A. Farley, Does plantation forestry restore biodiversity or create green
deserts? A synthesis of the effects of land-use transitions on plant species richness.
Biodivers. Conserv. 19, 3893–3915 (2010).
81. LANDFIRE, Biophysical Settings (2014); www.landfire.gov/
NationalProductDescriptions20.php.
82. S. N. Goward, C. Huang, F. Zhao, K. Schleeweis, K. Rishmawi, M. Lindsey, J. L. Dungan,
A. Michaelis, NACP NAFD Project: Forest Disturbance History from Landsat, 1986-2010
(Oak Ridge National Laboratory Distributed Active Archive Center, 2015); http://dx.doi.
org/10.3334/ORNLDAAC/1290.
83. U.S. Department of Agriculture National Agricultural Statistics Service, 2015 Cultivated
Layer (U.S. Department of Agriculture, 2015); www.nass.usda.gov/Research_and_
Science/Cropland/Release/.
84. S. Jin, L. Yang, P. Danielson, C. Homer, J. Fry, G. Xian, A comprehensive change detection
method for updating the National Land Cover Database to circa 2011.
Remote Sens. Environ. 132, 159–175 (2013).
85. U.S. Department of Agriculture National Agricultural Statistics Service, Quick Stats
(U.S. Department of Agriculture, 2017); https://quickstats.nass.usda.gov.
86. J. Smith, L. Heath, K. Skog, R. Birdsey, Methods for Calculating Forest Ecosystem and
Harvested Carbon with Standard Estimates for Forest Types of the United States
(U.S. Department of Agriculture Forest Service, 2006); www.actrees.org/files/Research/
ne_gtr343.pdf.
87. T. O. West, G. Marland, A. W. King, W. M. Post, A. K. Jain, K. Andrasko, Carbon
management response curves: Estimates of temporal soil carbon dynamics.
Environ. Manage. 33, 507–518 (2004).
88. B. Ruefenacht, M. V. Finco, M. D. Nelson, R. Czaplewski, E. H. Helmer, J. A. Blackard,
G. R. Holden, A. J. Lister, D. Salajanu, D. Weyermann, K. Winterberger, Coterminous U.S.
and Alaska forest type mapping using forest inventory and analysis data.
Photogramm. Eng. Remote Sens. 11, 1379–1388 (2008).
89. K. Naudts, Y. Chen, M. J. McGrath, J. Ryder, A. Valade, J. Otto, S. Luyssaert, Europe’s forest
management did not mitigate climate warming. Science 351, 597–600 (2016).
90. L. S. Heath, P. E. Kauppi, P. Burschel, H.-D. Gregor, R. Guderian, G. H. Kohlmaier,
S. Lorenz, D. Overdieck, F. Scholz, H. Thomasius, M. Weber, Contribution of
temperate forests to the world’s carbon budget. Water Air Soil Pollut. 70,
55–69 (1993).
91. J. Creyts, A. Derkach, S. Nyquist, K. Ostrowski, J. Stephenson, Reducing U.S. Greenhouse
Gas Emissions: How Much at What Cost? (McKinsey & Company, 2007).
92. R. W. Gorte, “U.S. tree planting for carbon sequestration” (Technical Report R40562,
Congressional Research Service, 2009).
93. P. Potapov, L. Laestadius, S. Minnemeyer, Global Map of Potential Forest Cover (World
Resources Institute, 2011); www.wri.org/resources/maps/atlas-forest-and-landscape-
restoration-opportunities/data-info.
94. V. A. Sample, Potential for Additional carbon sequestration through regeneration of
nonstocked forest land in the United States. J. For. 115, 309–318 (2016).
95. U.S. Environmental Protection Agency, Greenhouse Gas Mitigation Potential in U.S. Forestry
and Agriculture (U.S. Environmental Protection Agency, 2005).
96. R. Alig, G. Latta, D. Adams, B. McCarl, Mitigating greenhouse gases: The importance of
land base interactions between forests, agriculture, and residential development in the
face of changes in bioenergy and carbon prices. For. Policy Econ. 12, 67–75 (2010).
97. D. Haim, E. M. White, R. J. Alig, Agriculture afforestation for carbon sequestration under
carbon markets in the United States: Leakage behavior from regional allowance
programs. Appl. Econ. Perspect. Policy 38, 132–151 (2015).
98. G. Latta, D. M. Adams, R. J. Alig, E. White, Simulated effects of mandatory versus voluntary
participation in private forest carbon offset markets in the United States. J. For. Econ. 17,
127–141 (2011).
99. A. Golub, T. Hertel, H.-L. Lee, S. Rose, B. Sohngen, The opportunity cost of land use and
the global potential for greenhouse gas mitigation in agriculture and forestry.
Resour. Energy Econ. 31, 299–319 (2009).
100. M. M. Atkinson, S. A. Fitzgerald, Successful Reforestation: An Overview (Oregon State
University, 2002); https://archive.extension.oregonstate.edu/sorec/sites/default/files/
sucsessful.pdf.
101. T. Kroeger, F. J. Escobedo, J. L. Hernandez, S. Varela, S. Delphin, J. R. B. Fisher, J. Waldron,
Reforestation as a novel abatement and compliance measure for ground-level ozone.
Proc. Natl. Acad. Sci. U.S.A. 111, E4204–E4213 (2014).
102. J. Sessions, P. Bettinger, R. Buckman, M. Newton, J. Hamann, Hastening the return of
complex forests following fire: The consequences of delay. J. For. 102, 38–45 (2004).
103. J. A. Stanturf, S. H. Schoenholtz, C. J. Schweitzer, J. P. Shepard, Achieving restoration
success: Myths in bottomland hardwood forests. Restor. Ecol. 9, 189–200 (2001).
104. H. E. Garrett, W. D. Walter, L. D. Godsey, Alley Cropping: A relic from the past or a bridge
to the future? Inside Agrofor. 19, 1–12 (2011).
105. Board of Governors of the Federal Reserve System, 10-Year Treasury Constant Maturity
Rate (DGS10) (Federal Reserve System, 2017); https://fred.stlouisfed.org/series/DGS10.
106. S. N. Oswalt, W. B. Smith, P. D. Miles, S. A. Pugh, “Forest Resources of the United States,
2012: A technical document supporting the Forest Service 2015 update of the RPA
Assessment” (General Technical Report WO-91, U.S. Department of Agriculture, Forest
Service, 2014).
107. B. Zeide, Thinning and growth: A full turnaround. J. For. 99, 20–25 (2001).
108. M. J. Schelhaas, K. Kramer, H. Peltola, D. C. van der Werf, S. M. J. Wijdeven, Introducing
tree interactions in wind damage simulation. Ecol. Modell. 207, 197–209 (2007).
109. R. Goodnow, J. Sullivan, G. S. Amacher, Ice damage and forest stand management.
J. For. Econ. 14, 268–288 (2008).
110. C. Kuehne, A. R. Weiskittel, S. Fraver, K. J. Puettmann, Effects of thinning-induced changes
in structural heterogeneity on growth, ingrowth, and mortality in secondary coastal
Douglas-fir forests. Can. J. For. Res. 45, 1448–1461 (2015).
111. S. M. Hood, S. Baker, A. Sala, Fortifying the forest: Thinning and burning increase
resistance to a bark beetle outbreak and promote forest resilience. Ecol. Appl. 26,
1984–2000 (2016).
112. M. G. Ryan, D. Binkley, J. H. Fownes, Age-related decline in forest productivity: Pattern
and Process. Adv. Ecol. Res. 27, 213–262 (1997).
113. U.S. Environmental Protection Agency, Inventory of Greenhouse Gas Emissions and Sinks:
1990–2006 (U.S. Environmental Protection Agency, 2008).
114. U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990–2013 (U.S. Environmental Protection Agency, 2015).
115. L. S. Heath, J. E. Smith, K. E. Skog, D. J. Nowak, C. W. Woodall, Managed forest carbon
estimates for the US greenhouse gas inventory, 1990-2008. J. For. 109,
167–173 (2011).
116. P. B. Woodbury, J. E. Smith, L. S. Heath, Carbon sequestration in the U.S. forest sector
from 1990 to 2010. For. Ecol. Manage. 241, 14–27 (2007).
117. R. A. Birdsey, G. M. Lewis, “Carbon in U.S. forests and wood products, 1987-1997:
State-by-state estimates” (General Technical Report NE-310, U.S. Department of
Agriculture Forest Service, 2003).
118. Y. Pan, R. A. Birdsey, J. Fang, R. Houghton, P. E. Kauppi, W. A. Kurz, O. L. Phillips,
A. Shvidenko, S. L. Lewis, J. G. Canadell, P. Ciais, R. B. Jackson, S. W. Pacala, A. D. McGuire,
S. Piao, A. Rautiainen, S. Sitch, D. Hayes, A large and persistent carbon sink in the world’s
forests. Science 333, 988–993 (2011).
119. C. D. Oliver, B. C. Larson, Forest Stand Dynamics: Updated Edition (CAB Direct, 1996);
www.cabdirect.org/cabdirect/abstract/19980604521.
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
9 of 14
120. J. S. Nunery, W. S. Keeton, Forest carbon storage in the northeastern United States:
Net effects of harvesting frequency, post-harvest retention, and wood products.
For. Ecol. Manage. 259, 1363–1375 (2010).
121. A. W. D’Amato, J. B. Bradford, S. Fraver, B. J. Palik, Forest management for mitigation and
adaptation to climate change: Insights from long-term silviculture experiments.
For. Ecol. Manage. 262, 803–816 (2011).
122. S. Pugh, “RPA Data Wiz users guide, version 1.0” (General Technical Report NC-242,
U.S. Department of Agriculture Forest Service, 2012); www.nrs.fs.fed.us/pubs/1950.
123. T. G. Johnson, J. W. Bentley, M. Howell, T. G. Johnson, J. W. Bentley, The South’s Timber
Industry—An Assessment of Timber Product Output and Use, 2009 (U.S. Department
of Agriculture Forest Service, 2011); www.srs.fs.usda.gov/pubs/39409.
124. M. E. Harmon, B. Marks, Effects of silvicultural practices on carbon stores in Douglas-fir
western hemlock forests in the Pacific Northwest, U.S.A.: Results from a simulation
model. Can. J. For. Res. 32, 863–877 (2002).
125. B. Griscom, P. Ellis, F. E. Putz, Carbon emissions performance of commercial logging in
East Kalimantan, Indonesia. Glob. Chang. Biol. 20, 923–937 (2014).
126. D. Lussetti, E. P. Axelsson, U. Ilstedt, J. Falck, A. Karlsson, Supervised logging and climber
cutting improves stand development: 18 years of post-logging data in a tropical rain
forest in Borneo. For. Ecol. Manage. 381, 335–346 (2016).
127. C. D. Oliver, N. T. Nassar, B. R. Lippke, J. B. McCarter, Carbon, fossil fuel, and biodiversity
mitigation with wood and forests. J. Sustain. For. 33, 248–275 (2014).
128. U.S. Department of Agriculture Forest Service, Increasing the Pace of Restoration and Job
Creation on Our National Forests (U.S. Department of Agriculture Forest Service, 2012);
www.fs.fed.us/publications/restoration/restoration.pdf.
129. M. A. Cairns, S. Brown, E. H. Helmer, G. A. Baumgardner, Root biomass allocation in the
world’s upland forests. Oecologia 111, 1–11 (1997).
130. J. C. Jenkins, D. C. Chojnacky, L. S. Heath, R. A. Birdsey, “Comprehensive database of
diameter-based biomass regressions for North American tree species” (General Technical
Report NE-319, U.S. Department of Agriculture Forest Service, 2004).
131. Intergovernmental Panel on Climate Change, IPCC Guidelines for National Greenhouse Gas
Inventories. Chapter 4 Forest Land (Intergovernmental Panel on Climate Change, 2006).
132. J. Q. Chambers, N. Higuchi, J. P. Schimel, L. V. Ferreira, J. M. Melack, Decomposition and
carbon cycling of dead trees in tropical forests of the central Amazon. Oecologia 122,
380–388 (2000).
133. C. A. Williams, H. Gu, R. MacLean, J. G. Masek, G. J. Collatz, Disturbance and the carbon
balance of US forests: A quantitative review of impacts from harvests, fires, insects, and
droughts. Glob. Planet. Change 143, 66–80 (2016).
134. B. Sohngen, S. Brown, Extending timber rotations: Carbon and cost implications.
Clim. Policy 8, 435–451 (2008).
135. U.S. Forest Service, National Fire Plan Operations and Reporting System (U.S. Forest Service,
2016); https://cohesivefire.nemac.org/node/251.
136. D. C. Lee, A. A. Ager, D. E. Calkin, M. A. Finney, M. P. Thompson, T. M. Quigley,
C. W. McHugh, A National Cohesive Wildland Fire Management Strategy (U.S. Department
of Agriculture Forest Service, 2011), pp. 1–44.
137. M. G. Rollins, LANDFIRE: A nationally consistent vegetation, wildland fire, and fuel
assessment. Int. J. Wildland Fire 18, 235–249 (2009).
138. C. Wiedinmyer, M. D. Hurteau, Prescribed fire as a means of reducing forest carbon
emissions in the western United States. Environ. Sci. Technol. 44, 1926–1932 (2010).
139. A. L. Westerling, Increasing western US forest wildfire activity: Sensitivity to changes in
the timing of spring. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150178 (2016).
140. J. Kellndorfer, W. Walker, K. Kirsch, G. Fiske, J. Bishop, L. LaPoint, M. Hoppus, J. Westfall,
NACP Aboveground Biomass and Carbon Baseline Data, V.2. (NBCD 2000), U.S.A., 2000
(Oak Ridge National Laboratory Distributed Active Archive Center, 2013); http://dx.doi.
org/10.3334/ORNLDAAC/1161.
141. Intergovermental Panel on Climate Change, Good Practice Guidance for Land Use,
Land-Use Change and Forestry (Intergovermental Panel on Climate Change, 2003);
www.ipcc-nggip.iges.or.jp.
142. LANDFIRE, Fuel Loading Models, LANDFIRE 1.1.0 (2008); http://landfire.cr.usgs.gov/
viewer/.
143. J. E. Smith, L. S. Heath, A Model of Forest Floor Carbon Mass for United States Forest Types
(U.S. Department of Agriculture Forest Service, 2002); http://purl.access.gpo.gov/GPO/
LPS25756.
144. G. W. Meigs, D. C. Donato, J. L. Campbell, J. G. Martin, B. E. Law, Forest fire impacts on
carbon uptake, storage, and emission: The role of burn severity in the Eastern Cascades,
Oregon. Ecosystems 12, 1246–1267 (2009).
145. G. Collatz, C. Williams, B. Ghimire, S. Goward, J. Masek, CMS: Forest Biomass and
Productivity, 1-degree and 5-km, Conterminous US, 2005 (Oak Ridge National Laboratory
Distributed Active Archive Center, 2014); http://daac.ornl.gov/cgi-bin/
dsviewer.pl?ds_id=1221.
146. S. Dore, M. Montes-Helu, S. C. Hart, B. A. Hungate, G. W. Koch, J. B. Moon, A. J. Finkral,
T. E. Kolb, Recovery of ponderosa pine ecosystem carbon and water fluxes from thinning
and stand-replacing fire. Glob. Chang. Biol. 18, 3171–3185 (2012).
147. J. Eidenshink, B. Schwind, K. Brewer, Z.-L. Zhu, B. Quayle, S. M. Howard, A project for
monitoring trends in burn severity. Fire Ecol. 3, 3–21 (2007).
148. Monitoring Trends in Burn Severity, Annual Burn Severity Mosaics (2016); www.mtbs.
gov/direct-download.
149. O. F. Price, R. A. Bradstock, J. E. Keeley, A. D. Syphard, The impact of antecedent fire area
on burned area in southern California coastal ecosystems. J. Environ. Manage. 113,
301–307 (2012).
150. B. R. Hartsough, S. Abrams, R. James Barbour, E. S. Drews, J. D. McIver, J. J. Moghaddas,
D. W. Schwilk, S. L. Stephense, The economics of alternative fuel reduction treatments in
western United States dry forests: Financial and policy implications from the National
Fire and Fire Surrogate Study. For. Policy Econ. 10, 344–354 (2008).
151. E. D. Reinhardt, R. E. Keane, D. E. Calkin, J. D. Cohen, Objectives and considerations for
wildland fuel treatment in forested ecosystems of the interior western United States.
For. Ecol. Manage. 256, 1997–2006 (2008).
152. LANDFIRE, Biophysical Settings, LANDFIRE 1.3.0 (2012); http://landfire.cr.usgs.gov/
viewer/.
153. U.S. Department of Agriculture Forest Service Automated Lands Program (ALP), Forest
Service Regional Boundaries. S_USA.AdministrativeRegion (2016); http://data.fs.usda.gov/
geodata/edw/datasets.php.
154. J. G. Masek, W. B. Cohen, D. Leckie, M. A. Wulder, R. Vargas, B. de Jong, S. Healey, B. Law,
R. Birdsey, R. A. Houghton, D. Mildrexler, S. Gowardm, W. B. Smith, Recent rates of forest
harvest and conversion in North America. J. Geophys. Res. 116, G00K03 (2011).
155. S. M. Nusser, J. J. Goebel, The National Resources Inventory: A long-term multi-resource
monitoring programme. Environ. Ecol. Stat. 4, 181–204 (1997).
156. U.S. Department of Agriculture, Summary Report: 2007 National Resources Inventory
(Natural Resources Conservation Service, 2009).
157. F. Achard, H. D. Eva, P. Mayaux, H.-J. Stibig, A. Belward, Improved estimates of net carbon
emissions from land cover change in the tropics for the 1990s. Global Biogeochem. Cycles
18, GB2008 (2004).
158. Y. Li, M. Zhao, S. Motesharrei, Q. Mu, E. Kalnay, S. Li, Local cooling and warming effects of
forests based on satellite observations. Nat. Commun. 6, 6603 (2015).
159. C. Milesi, S. W. Running, C. D. Elvidge, J. B. Dietz, B. T. Tuttle, R. R. Nemani, Mapping and
modeling the biogeochemical cycling of turf grasses in the United States.
Environ. Manage. 36, 426–438 (2005).
160. C. D. Campbell, J. R. Seiler, P. Eric Wiseman, B. D. Strahm, J. F. Munsell, Soil carbon
dynamics in residential lawns converted from Appalachian mixed oak stands. Forests 5,
425–438 (2014).
161. U.S. Geological Survey Gap Analysis Program, Protected Areas Database of the United
States (PAD-US), Version 1.4 Combined Feature Class (U.S. Geological Survey, 2016);
https://gapanalysis.usgs.gov/padus/.
162. N. L. Harris, S. C. Hagen, S. S. Saatchi, T. R. H. Pearson, C. W. Woodall, G. M. Domke,
B. H. Braswell, B. F. Walters, S. Brown, W. Salas, A. Fore, Y. Yu, Attribution of net carbon
change by disturbance type across forest lands of the conterminous United States.
Carbon Balance Manage. 11, 24 (2016).
163. D. Zheng, L. S. Heath, M. J. Ducey, J. E. Smith, Carbon changes in conterminous US forests
associated with growth and major disturbances: 1992–2001. Environ. Res. Lett. 6,
019502 (2011).
164. N. E. Thomas, C. Huang, S. N. Goward, S. Powell, K. Rishmawi, K. Schleeweis, A. Hinds,
Validation of North American Forest Disturbance dynamics derived from Landsat time
series stacks. Remote Sens. Environ. 115, 19–32 (2011).
165. P. Olofsson, G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, M. A. Wulder, Good
practices for estimating area and assessing accuracy of land change.
Remote Sens. Environ. 148, 42–57 (2014).
166. S. Hagen, N. Harris, S. S. Saatchi, T. Pearson, C. W. Woodall, S. Ganguly, G. M. Domke,
B. H. Braswell, B. F. Walters, J. C. Jenkins, S. Brown, W. A. Salas, A. Fore, Y. Yu, R. R. Nemani,
C. Ipsan, K. R. Brown, CMS: Forest Carbon Stocks, Emissions, and Net Flux for the
Conterminous US: 2005-2010 (ORNL DAAC, 2016); http://dx.doi.org/10.3334/
ORNLDAAC/1313.
167. J. A. Blackard, M. V. Finco, E. H. Helmer, G. R. Holden, M. L. Hoppus, D. M. Jacobs,
A. J. Lister, G. G. Moisen, M. D. Nelson, R. Riemann, B. Ruefenacht, D. Salajanu,
D. L. Weyermann, K. C. Winterberger, T. J. Brandeis, R. L. Czaplewski, R. E. McRoberts,
P. L. Patterson, R. P. Tymcio, Mapping U.S. forest biomass using nationwide forest
inventory data and moderate resolution information. Remote Sens. Environ. 112,
1658–1677 (2008).
168. U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis National
Program; www.fia.fs.fed.us/tools-data.
169. B. T. Wilson, C. W. Woodall, D. M. Griffith, Imputing forest carbon stock estimates from
inventory plots to a nationally continuous coverage. Carbon Balance Manage.
8, 1 (2013).
170. N. Neeti, R. Kennedy, Comparison of national level biomass maps for conterminous US:
Understanding pattern and causes of differences. Carbon Balance Manage. 11, 19
(2016).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
10 of 14
171. R. N. Lubowski, A. J. Plantinga, R. N. Stavins, Land-use change and carbon sinks:
Econometric estimation of the carbon sequestration supply function. J. Environ. Econ.
Manage. 51, 135–152 (2006).
172. J. O. Sexton, J. O. Sexton, X.-P. Song, M. Feng, P. Noojipady, A. Anand, C. Huang, D.-H. Kim,
K. M. Collins, S. Channan, C. DiMiceli, J. R. Townshend, Global, 30-m resolution
continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous
fields with lidar-based estimates of error. Int. J. Digit. Earth 6, 427–448 (2013).
173. E. G. Mcpherson, J. R. Simpson, P. J. Peper, S. E. Maco, Q. Xiao, Municipal forest benefits
and costs in five US cities. J. For. 103, 411–416 (2005).
174. U.S. Department of Agriculture, Farm Service Agency, National Agricultural Inventory
Program (2015); www.fsa.usda.gov/programs-and-services/aerial-photography/
imagery-programs/naip-imagery/.
175. D. J. Nowak, E. J. Greenfield, Tree and impervious cover change in U.S. cities. Urban For.
Urban Green. 11, 21–30 (2012).
176. D. M. Olson, E. Dinerstein, E. D. Wikramanayake, N. D. Burgess, G. V. N. Powell,
E. C. Underwood, J. A. D’amico, I. Itoua, H. E. Strand, J. C. Morrison, C. J. Loucks,
T. F. Allnutt, T. H. Ricketts, Y. Kura, J. F. Lamoreux, W. W. Wettengel, P. Hedao, K. R. Kassem,
Terrestrial ecoregions of the world: A new map of life on Earth: A new global map of
terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience
51, 933–938 (2001).
177. N. Bassuk, D. Curtis, B. Marranca, B. Neal, Recommended Urban Trees: Site Assessment and
Tree Selection for Stress Tolerance (Cornell University, 2009); www.hort.cornell.edu/uhi/
outreach/recurbtree/pdfs/~recurbtrees.pdf.
178. L. Bounoua, J. Nigro, P. Zhang, K. Thome, Mapping impact of urbanization in the
continental U.S. from 2001–2020, in 2016 IEEE International Geoscience and Remote
Sensing Symposium (IGARSS) (IEEE, 2016), pp. 6750–6753.
179. E. G. McPherson, J. R. Simpson, P. J. Peper, K. I. Scott, Q. Xiao, Tree Guidelines for Coastal
Southern California Communities (Local Government Commission, 2000); www.lgc.org/
wordpress/docs/freepub/energy/guides/socal_tree_guidelines.pdf.
180. E. G. McPherson, S. E. Maco, J. R. Simpson, P. J. Peper, Q. Xiao, A. Van Der Zanden, N. Bell,
Western Washington and Oregon Community Tree Guide: Benefits, Costs and Strategic
Planting (U.S. Department of Agriculture Forest Service, 2002); www.treesearch.fs.fed.us/
pubs/45962).
181. E. G. McPherson, J. R. Simpson, P. J. Peper, Q. Xiao, S. E. Maco, P. J. Hoefer, Northern
Mountain and Prairie Community Tree Guide: Benefits, Costs and Strategic Planting
(U.S. Department of Agriculture Forest Service, 2003); www.fs.fed.us/psw/topics/urban_
forestry/products/cufr_258.pdf.
182. H. Pretzsch, P. Biber, E. Uhl, J. Dahlhausen, T. Rötzer, J. Caldentey, T. Koike, T. van Con,
A. Chavanne, T. Seifert, B. du Toit, C. Farnden, S. Pauleit, Crown size and growing space
requirement of common tree species in urban centres, parks, and forests. Urban For.
Urban Green. 14, 466–479 (2015).
183. D. J. Nowak, E. J. Greenfield, R. E. Hoehn, E. Lapoint, Carbon storage and sequestration by
trees in urban and community areas of the United States. Environ. Pollut. 178,
229–236 (2013).
184. S. N. Oswalt, W. B. Smith, U.S. Forest Resource Facts and Historical Trends (U.S. Department
of Agriculture, 2014).
185. R. Harper, G. Hernandez, J. Arseneault, M. Bryntesen, S. Enebak, R. Overton, Forest nursery
seedling production in the United States—Fiscal year 2012. Tree Plant. Notes 56,
72–75 (2013).
186. J. Bond, The Inclusion of Large-Scale Tree Planting in a State Implementation Plan:
A Feasibility Study (Davey Research Group, 2006); www.urbanforestanalytics.com/sites/
default/files/pdf/TreesInSIP.pdf.
187. U.S. Census Bureau, Table 1. Annual Estimates of the Resident Population for the United
States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2015 (NST-EST2015-01);
https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.
xhtml?src=bkmk.
188. M. W. Strohbach, E. Arnold, D. Haase, The carbon footprint of urban green space—A life
cycle approach. Landsc. Urban Plan. 104, 220–229 (2012).
189. G. H. Donovan, D. T. Butry, The value of shade: Estimating the effect of urban trees on
summertime electricity use. Energy Build. 41, 662–668 (2009).
190. H. Akbari, Shade trees reduce building energy use and CO2 emissions from power plants.
Environ. Pollut. 116, S119–S126 (2002).
191. M. R. McHale, E. G. McPherson, I. C. Burke, The potential of urban tree plantings to be cost
effective in carbon credit markets. Urban For. Urban Green. 6, 49–60 (2007).
192. L. A. Roman, J. J. Battles, J. R. McBride, Urban Tree Mortality: A Primer on Demographic
Approaches (U.S. Department of Agriculture Forest Service, 2016); www.treesearch.fs.fed.
us/pubs/50688.
193. J. B. Bradford, D. N. Kastendick, Age-related patterns of forest complexity and carbon
storage in pine and aspen–birch ecosystems of northern Minnesota, USA. Can. J. For. Res.
40, 401–409 (2010).
194. WM Financial Strategies, Rates Over Time - Interest Rate Trends; www.munibondadvisor.
com/market.htm.
195. T. Hengl, J. M. de Jesus, G. B. M. Heuvelink, M. R. Gonzalez, M. Kilibarda, A. Blagotić,
W. Shangguan, M. N. Wright, X. Geng, B. Bauer-Marschallinger, M. A. Guevara, R. Vargas,
R. A. MacMillan, N. H. Batjes, J. G. B. Leenaars, E. Ribeiro, I. Wheeler, S. Mantel, B. Kempen,
SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE
12, e0169748 (2017).
196. European Space Agency, Land Cover CCI Product User Guide Version 2.0 (European Space
Agency, 2017), pp. 1–105.
197. P. L. Sims, J. S. Singh, The structure and function of ten Western North American
grasslands: II. Intra-seasonal dynamics in primary producer compartments. J. Ecol. 66,
547–572 (1978).
198. P. L. Sims, J. S. Singh, W. K. Lauenroth, The structure and function of ten Western North
American Grasslands: I. Abiotic and vegetational characteristics. J. Ecol. 66,
251–285 (1978).
199. M. B. Cleary, E. Pendall, B. E. Ewers, Aboveground and belowground carbon pools after
fire in mountain big sagebrush steppe. Rangel. Ecol. Manag. 63, 187–196 (2010).
200. B. A. Bradley, R. A. Houghton, J. F. Mustard, S. P. Hamburg, Invasive grass reduces
aboveground carbon stocks in shrublands of the Western US. Glob. Chang. Biol. 12,
1815–1822 (2006).
201. Conservation Reserve Program Average Payments by County (2017); https://catalog.
data.gov/dataset/conservation-reserve-program-average-payments-by-county.
202. U.S. Department of Agriculture National Agricultural Statistics Service, Crop Production
2016 Summary (U.S. Department of Agriculture National Agricultural Statistics Service,
2017); www.nass.usda.gov/.
203. U.S. Department of Agriculture National Agricultural Statistics Service, 2012 Census of
Agriculture (U.S. Department of Agriculture National Agricultural Statistics Service, 2014);
www.nass.usda.gov/Publications/AgCensus/2012/.
204. K. D. Belfry, L. L. Van Eerd, Establishment and impact of cover crops intersown into corn.
Crop. Sci. 56, 1245–1256 (2016).
205. P. Smith, D. Martino, Z. Cai, D. Gwary, H. Janzen, P. Kumar, B. McCarl, S. Ogle,
F. O’Mara, C. Rice, B. Scholes, O. Sirotenko, M. Howden, T. McAllister, G. Pan,
V. Romanenkov, U. Schneider, S. Towprayoon, M. Wattenbach, J. Smith, Greenhouse
gas mitigation in agriculture. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 789–813
(2008).
206. J. M. Baker, T. E. Ochsner, R. T. Venterea, T. J. Griffis, Tillage and soil carbon
sequestration—What do we really know? Agric. Ecosyst. Environ. 118, 1–5 (2007).
207. Z. Luo, E. Wang, O. J. Sun, Can no-tillage stimulate carbon sequestration in agricultural
soils? A meta-analysis of paired experiments. Agric. Ecosyst. Environ. 139,
224–231 (2010).
208. C. Palm, H. Blanco-Canqui, F. DeClerck, L. Gatere, P. Grace, Conservation agriculture and
ecosystem services: An overview. Agric. Ecosyst. Environ. 187, 87–105 (2014).
209. D. S. Powlson, C. M. Stirling, M. L. Jat, B. G. Gerard, C. A. Palm, P. A. Sanchez, K. G. Cassman,
Limited potential of no-till agriculture for climate change mitigation. Nat. Clim. Change 4,
678–683 (2014).
210. A. J. VandenBygaart, The myth that no-till can mitigate global climate change.
Agric. Ecosyst. Environ. 216, 98–99 (2016).
211. J. Six, S. M. Ogle, F. J. Breidt, R. T. Conant, A. R. Mosier, K. Paustian, The potential to
mitigate global warming with no-tillage management is only realized when practised in
the long term. Glob. Chang. Biol. 10, 155–160 (2004).
212. B. A. Linquist, M. A. Adviento-Borbe, C. M. Pittelkow, C. van Kessel, K. J. van Groenigen,
Fertilizer management practices and greenhouse gas emissions from rice systems:
A quantitative review and analysis. Field Crops Res. 135, 10–21 (2012).
213. P. R. Hill, Use of continuous no-till and rotational tillage systems in the central and
northern Corn Belt. J. Soil Water Conserv. 56, 286–290 (2001).
214. S. S. Snapp, S. M. Swinton, R. Labarta, D. Mutch, J. R. Black, R. Leep, J. Nyiraneza, K. O’Neil,
Evaluating cover crops for benefits, costs and performance within cropping system
niches. Agron. J. 97, 322–332 (2005).
215. M. Liebig, A. J. Franzluebbers, R. F. Follett, Managing Agricultural Greenhouse Gases:
Coordinated Agricultural Research Through GRACEnet to Address Our Changing Climate
(Elsevier, 2012).
216. ICF International, Greenhouse Gas Mitigation Options and Costs for Agricultural Land and
Animal Production Within the United States (U.S. Department of Agriculture, 2013).
217. A. Clark, Managing Cover Crops Profitably (Sustainable Agriculture Network, ed. 3, 2007),
vol. 9.
218. R. L. Cochran, R. K. Roberts, J. A. Larson, D. D. Tyler, Cotton profitability with alternative
lime application rates, cover crops, nitrogen rates, and tillage methods. Agron. J. 99,
1085–1092 (2007).
219. P. Smith, M. Bustamante, H. Ahammad, H. Clark, H. Dong, E. A. Elsiddig, H. Haberl,
R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N. H. Ravindranath, C. W. Rice,
C. Robledo Abad, A. Romanovskaya, F. Sperling, F. Tubiello, Agriculture, Forestry and Other
Land Use (AFOLU) (Cambridge Univ. Press, 2014).
220. C. L. Keene, W. S. Curran, Optimizing high-residue cultivation timing and frequency in
reduced-tillage soybean and corn. Agron. J. 108, 1897–1906 (2016).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
11 of 14
221. K. A. O’Reilly, J. D. Lauzon, R. J. Vyn, L. L. Van Eerd, Nitrogen cycling, profit margins
and sweet corn yield under fall cover crop systems. Can. J. Soil Sci. 92, 353–365
(2012).
222. R. K. Roberts, J. A. Larson, D. D. Tyler, B. N. Duck, K. D. Dillivan, Economic analysis of the
effects of winter cover crops on no-tillage corn yield response to applied nitrogen.
J. Soil Water Conserv. 53, 280–284 (1998).
223. CTIC, SARE, ASTA, Annual Report 2015-2016: Cover Crop Survey (CTIC, SARE, ASTA, 2016).
224. S. Jeffery, F. G. A. Verheijen, M. van der Velde, A. C. Bastos, A quantitative review of the
effects of biochar application to soils on crop productivity using meta-analysis.
Agric. Ecosyst. Environ. 144, 175–187 (2011).
225. U.S. Department of Energy, 2016 Billion-Ton Report: Advancing Domestic Resources for a
Thriving Bioeconomy, Volume 1: Economic Availability of Feedstocks (Oak Ridge National
Laboratory, 2016).
226. K. A. Spokas, Review of the stability of biochar in soils: Predictability of O:C molar ratios.
Carbon Manage. 1, 289–303 (2010).
227. D. Woolf, J. E. Amonette, F. A. Street-Perrott, J. Lehmann, S. Joseph, Sustainable biochar
to mitigate global climate change. Nat. Commun. 1, 56 (2010).
228. P. Gallagher, M. Dikeman, J. Fritz, E. Wailes, W. Gauther, H. Shapouri, “Biomass from crop
residues: Cost and supply estimates” (Agricultural Economic Report No. 819,
U.S. Department of Agriculture, 2003).
229. A. Milbrandt, “A geographic perspective on the current biomass resource availability in
the United States” (Technical Report NREL/TP-560-39181, National Renewable Energy
Laboratory, 2005).
230. S. Kumarappan, S. Joshi, H. L. MacLean, Biomass supply for biofuel production: Estimates
for the United States and Canada. BioResources 4, 1070–1087 (2009).
231. J. S. Gregg, S. J. Smith, Global and regional potential for bioenergy from agricultural
and forestry residue biomass. Mitigation Adapt. Strategies Glob. Change 15,
241–262 (2010).
232. A. Chatterjee, Annual crop residue production and nutrient replacement costs for
Bioenergy feedstock production in United States. Agron. J. 105, 685–692 (2013).
233. A. Demirbas, Effects of temperature and particle size on bio-char yield from pyrolysis of
agricultural residues. J. Anal. Appl. Pyrolysis 72, 243–248 (2004).
234. H. Sun, W. C. Hockaday, C. A. Masiello, K. Zygourakis, Multiple controls on the chemical
and physical structure of biochars. Ind. Eng. Chem. Res. 51, 3587–3597 (2012).
235. Y. Sun, B. Gao, Y. Yao, J. Fang, M. Zhang, Y. Zhou, H. Chen, L. Yang, Effects of feedstock
type, production method, and pyrolysis temperature on biochar and hydrochar
properties. Chem. Eng. J. 240, 574–578 (2014).
236. Y. Lee, J. Park, C. Ryu, K. S. Gang, W. Yang, Y.-K. Park, J. Jung, S. Hyun, Comparison of
biochar properties from biomass residues produced by slow pyrolysis at 500°C.
Bioresour. Technol. 148, 196–201 (2013).
237. L. Zhao, X. Cao, O. Mašek, A. Zimmerman, Heterogeneity of biochar properties as a
function of feedstock sources and production temperatures. J. Hazard. Mater. 256–257,
1–9 (2013).
238. R. P. Udawatta, S. Jose, Agroforestry strategies to sequester carbon in temperate North
America. Agroforest. Syst. 86, 225–242 (2012).
239. P. K. R. Nair, B. M. Kumar, V. D. Nair, Agroforestry as a strategy for carbon sequestration.
J. Plant Nutr. Soil Sci. 172, 10–23 (2009).
240. A. D. Bambrick, J. K. Whalen, R. L. Bradley, A. Cogliastro, A. M. Gordon, A. Olivier,
N. V. Thevathasan, Spatial heterogeneity of soil organic carbon in tree-based
intercropping systems in Quebec and Ontario, Canada. Agroforest. Syst. 79,
343–353 (2010).
241. R. Cardinael, T. Chevallier, B. G. Barthès, N. P. A. Saby, T. Parent, C. Dupraz, M. Bernoux,
C. Chenu, Impact of alley cropping agroforestry on stocks, forms and spatial distribution
of soil organic carbon—A case study in a Mediterranean context. Geoderma 259–260,
288–299 (2015).
242. P. Tsonkova, C. Böhm, A. Quinkenstein, D. Freese, Ecological benefits provided by alley
cropping systems for production of woody biomass in the temperate region: A review.
Agroforest. Syst. 85, 133–152 (2012).
243. M. Oelbermann, R. P. Voroney, N. V. Thevathasan, A. M. Gordon, D. C. L. Kass,
A. M. Schlönvoigt, Soil carbon dynamics and residue stabilization in a Costa Rican and
southern Canadian alley cropping system. Agroforest. Syst. 68, 27–36 (2006).
244. M. Peichl, N. V. Thevathasan, A. M. Gordon, J. Huss, R. A. Abohassan, Carbon sequestration
potentials in temperate tree-based intercropping systems, Southern Ontario, Canada.
Agroforest. Syst. 66, 243–257 (2006).
245. S. Lu, P. Meng, J. Zhang, C. Yin, S. Sun, Changes in soil organic carbon and total
nitrogen in croplands converted to walnut-based agroforestry systems and orchards
in southeastern Loess Plateau of China. Environ. Monit. Assess. 187, 688 (2015).
246. F. Montagnini, P. K. R. Nair, Carbon sequestration: An underexploited environmental
benefit of agroforestry systems. Agroforest. Syst. 61–62, 281–295 (2004).
247. J. R. Thiessen Martens, M. H. Entz, M. D. Wonneck, Review: Redesigning Canadian prairie
cropping systems for profitability, sustainability, and resilience. Can. J. Plant Sci. 95,
1049–1072 (2015).
248. G. M. M. M. A. Senaviratne, R. P. Udawatta, K. A. Nelson, K. Shannon, S. Jose, Temporal and
spatial influence of perennial upland buffers on corn and soybean yields. Agron. J. 104,
1356–1362 (2012).
249. M. A. Cary, G. E. Frey, D. E. Mercer, The value of versatile alley cropping in the Southeast
US: A Monte Carlo simulation, in Proceedings of the Inaugural Symposium of the
International Society of Forest Resource Economics 2014 (U.S. Department of Agriculture
Forest Service, 2014), pp. 1–6; www.srs.fs.usda.gov/pubs/ja/2014/ja_2014_frey_001.pdf.
250. G. Garrett, W. Walter, L. D. Godsey, Alley cropping: Farming between the trees.
Green Horizons. 19, 1 (2015).
251. L. Harper, W. Kurtz, Economics of Eastern black walnut agroforestry systems, in
Nut Production Handbook for Eastern Black Walnut (Southwest Missouri Resources,
Conservation & Development, 1998), pp. 32–36.
252. W. T. Stamps, R. L. McGraw, L. Godsey, T. L. Woods, The ecology and economics of insect
pest management in nut tree alley cropping systems in the Midwestern United States.
Agric. Ecosyst. Environ. 131, 4–8 (2009).
253. U.S. Department of Agriculture National Agroforestry Center, Alley cropping. A relic from
the past or a bridge to the future? Inside Agroforest. 19, 1–12 (2009).
254. G. E. Frey, D. E. Mercer, F. W. Cubbage, R. C. Abt, Economic potential of agroforestry and
forestry in the lower Mississippi alluvial valley with incentive programs and carbon
payments. South. J. Appl. For. 34, 176–185 (2011).
255. D. E. Mercer, G. E. Cubbage, F. W. Frey, Economics of agroforestry, in Handbook of Forest
Resource Economics, S. Kant, J. R. R. Alavalapati, Eds. (Earthscan from Routledge, 2014),
pp. 188–209.
256. U.S. Department of Agriculture, Summary Report: 2012 National Resources Inventory
(U.S. Department of Agriculture, 2015); www.nrcs.usda.gov/technical/nri/12summary.
257. A. Wotherspoon, N. V. Thevathasan, A. M. Gordon, R. P. Voroney, Carbon sequestration
potential of five tree species in a 25-year-old temperate tree-based intercropping system
in southern Ontario, Canada. Agroforest. Syst. 88, 631–643 (2014).
258. E. A. Davidson, The contribution of manure and fertilizer nitrogen to atmospheric nitrous
oxide since 1860. Nat. Geosci. 2, 659–662 (2009).
259. C. S. Snyder, T. W. Bruulsema, T. L. Jensen, P. E. Fixen, Review of greenhouse gas
emissions from crop production systems and fertilizer management effects.
Agric. Ecosyst. Environ. 133, 247–266 (2009).
260. J. Hill, S. Polasky, E. Nelson, D. Tilman, H. Huo, L. Ludwig, J. Neumann, H. Zheng, D. Bonta,
Climate change and health costs of air emissions from biofuels and gasoline. Proc. Natl.
Acad. Sci. U.S.A. 106, 2077–2082 (2009).
261. J. Lelieveld, J. S. Evans, M. Fnais, D. Giannadaki, A. Pozzer, The contribution of outdoor
air pollution sources to premature mortality on a global scale. Nature 525, 367–371
(2015).
262. G. Myhre, D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch,
J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura,
H. Zhang, 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, T. F. Stocker, D. Qin, G.-K. Plattner,
M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, P. M. Midgley, Eds. (Cambridge
Univ. Press, 2013), vol. 423, pp. 659–740.
263. S. Sela, H. M. van Es, B. N. Moebius-Clune, R. Marjerison, J. Melkonian, D. Moebius-Clune,
R. Schindelbeck, S. Gomes, Adapt-N outperforms grower-selected nitrogen
rates in northeast and midwestern united states strip trials. Agron. J. 108, 1726–1734
(2016).
264. Food and Agriculture Organization of the United Nations, FAOSTAT Online Statistical
Service (Food and Agriculture Organization of the United Nations, 2014); www.fao.org/
faostat/en/#data.
265. E. Stehfest, L. Bouwman, N2O and NO emission from agricultural fields and soils under
natural vegetation: Summarizing available measurement data and modeling of global
annual emissions. Nutr. Cycling Agroecosyst. 74, 207–228 (2006).
266. D. L. Burton, X. Li, C. A. Grant, Influence of fertilizer nitrogen source and management
practice on N2O emissions from two Black Chernozemic soils. Can. J. Soil Sci. 88,
219–227 (2008).
267. R. T. Venterea, M. S. Dolan, T. E. Ochsner, Urea decreases nitrous oxide emissions
compared with anhydrous ammonia in a Minnesota corn cropping system. Soil Sci. Soc.
Am. J. 74, 407–418 (2010).
268. M. J. Bell, J. M. Cloy, C. F. E. Topp, B. C. Ball, A. Bagnall, R. M. Rees, D. R. Chadwick,
Quantifying N2O emissions from intensive grassland production: The role of synthetic
fertilizer type, application rate, timing and nitrification inhibitors. J. Agric. Sci. 154,
812–827 (2016).
269. X. Hao, C. Chang, J. M. Carefoot, H. H. Janzen, B. H. Ellert, Nitrous oxide emissions from an
irrigated soil as affected by fertilizer and straw management. Nutr. Cycling Agroecosyst.
60, 1–8 (2001).
270. C. F. Drury, W. D. Reynolds, X. M. Yang, N. B. McLaughlin, T. W. Welacky, W. Calder,
C. A. Grant, Nitrogen source, application time, and tillage effects on soil nitrous oxide
emissions and corn grain yields. Soil Sci. Soc. Am. J. 76, 1268–1279 (2011).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
12 of 14
271. J. P. Burzaco, D. R. Smith, T. J. Vyn, Nitrous oxide emissions in Midwest US maize
production vary widely with band-injected N fertilizer rates, timing and nitrapyrin
presence. Environ. Res. Lett. 8, 035031 (2013).
272. R. T. Venterea, J. A. Coulter, M. S. Dolan, Evaluation of intensive “4R” strategies for
decreasing nitrous oxide emissions and nitrogen surplus in rainfed corn. J. Environ. Qual.
45, 1186–1195 (2016).
273. R. H. Beach, B. J. DeAngelo, S. Rose, C. Li, W. Salas, S. J. DelGrosso, Mitigation potential
and costs for global agricultural greenhouse gas emissions. Agric. Econ. 38,
109–115 (2008).
274. D. S. Reay, E. A. Davidson, K. A. Smith, P. Smith, J. M. Melillo, F. Dentener, P. J. Crutzen,
Global agriculture and nitrous oxide emissions. Nat. Clim. Change 2, 410–416 (2012).
275. O. Oenema, X. Ju, C. Klein, M. Alfaro, A. Prado, J. P. Lesschen, X. Zheng, G. Velthof, L. Ma,
B. Gao, C. Kroeze, M. Sutton, Reducing nitrous oxide emissions from the global food
system. Curr. Opin. Environ. Sustain. 9–10, 55–64 (2014).
276. P. C. West, J. S. Gerber, P. M. Engstrom, N. D. Mueller, K. A. Brauman, K. M. Carlson,
E. S. Cassidy, M. Johnston, Graham K. MacDonald, D. K. Ray, S. Siebert, Leverage points for
improving global food security and the environment. Science 345, 325–328 (2014).
277. M. Ribaudo, J. Delgado, L. Hansen, M. Livingston, R. Mosheim, J. Williamson, “Nitrogen in
agricultural systems: Implications for conservation policy” (Economic Research Report
No. ERR-127, U.S. Department of Agriculture Economic Research Service, 2011).
278. N. Millar, G. P. Robertson, P. R. Grace, R. J. Gehl, J. P. Hoben, Nitrogen fertilizer
management for nitrous oxide (N2O) mitigation in intensive corn (Maize) production:
An emissions reduction protocol for US Midwest agriculture. Mitigation Adapt. Strategies
Glob. Change 15, 185–204 (2010).
279. U. Sehy, R. Ruser, J. C. Munch, Nitrous oxide fluxes from maize fields: Relationship to
yield, site-specific fertilization, and soil conditions. Agric. Ecosyst. Environ. 99,
97–111 (2003).
280. P. C. Scharf, D. K. Shannon, H. L. Palm, K. A. Sudduth, S. T. Drummond, N. R. Kitchen,
L. J. Mueller, V. C. Hubbard, L. F. Oliveira, Sensor-based nitrogen applications
out-performed producer-chosen rates for corn in on-farm demonstrations. Agron. J. 103,
1683–1691 (2011).
281. D. Schimmelpfenning, Farm Profits and Adoption of Precision Agriculture (United States
Department of Agriculture Economic Research Service, 2016); www.ers.usda.gov/
webdocs/publications/80326/err-217.pdf?v=4266.
282. U.S. Department of Agriculture National Agricultural Statistics Service, Agricultural Prices
(U.S. Department of Agriculture National Agricultural Statistics Service, 2017); http://
usda.mannlib.cornell.edu/usda/nass/AgriPric//2010s/2017/AgriPric-12-28-2017.pdf.
283. D. B. Mengel, Types and Uses of Nitrogen Fertilizers for Crop Production (Purdue University,
1986).
284. B. G. Bareja, General information and practices in using urea fertilizer (2013);
www.cropsreview.com.
285. G. A. Helmers, J. Brandle, Optimum windbreak spacing in great plains agriculture.
Great Plains Res. 15, 179–198 (2005).
286. Y. G. Chendev, L. L. Novykh, T. J. Sauer, C. L. Petin, A. N. Zazdravnykh, E. A. Burras, in Soil
Carbon Progress in Soil Science, A. E. Hartemink, K. McSweeney, Eds. (Springer
International Publishing, 2014), pp. 475–482.
287. F. Wang, X. Xu, B. Zou, Z. Guo, Z. Li, W. Zhu Biomass accumulation and carbon
sequestration in four different aged Casuarina equisetifolia coastal shelterbelt plantations
in South China. PLOS ONE 8, e77449 (2013).
288. T. J. Sauer, C. A. Cambardella, J. R. Brandle, Soil carbon and tree litter dynamics in a red
cedar–scotch pine shelterbelt. Agroforest. Syst. 71, 163–174 (2007).
289. J. Kort, R. Turnock, Carbon reservoir and biomass in Canadian prairie shelterbelts.
Agroforest. Syst. 44, 175–186.
290. M. M. Schoeneberger, Agroforestry: Working trees for sequestering carbon on
agricultural lands. Agroforest. Syst. 75, 27–37 (2008).
291. U.S. Department of Agriculture Farm Service Agency, Conservation Reserve Program:
Annual Summary and Enrollment Statistics-FY2010 (U.S. Department of Agriculture Farm
Service Agency, 2010).
292. B. Henderson, A. Falcucci, A. Mottet, L. Early, B. Werner, H. Steinfeld, P. Gerber, Marginal
costs of abating greenhouse gases in the global ruminant livestock sector.
Mitigation Adapt. Strategies Glob. Change 22, 199–224 (2017).
293. L. M. Porensky, E. A. Leger, J. Davison, W. W. Miller, E. M. Goergen, E. K. Espeland,
E. M. Carroll-Moore, Arid old-field restoration: Native perennial grasses suppress weeds
and erosion, but also suppress native shrubs. Agric. Ecosyst. Environ. 184, 135–144
(2014).
294. I. Kämpf, N. Hölzel, M. Störrle, G. Broll, K. Kiehl, Potential of temperate agricultural soils for
carbon sequestration: A meta-analysis of land-use effects. Sci. Total Environ. 566–567,
428–435 (2016).
295. D. L. Gebhart, H. B. Johnson, H. S. Mayeux, H. W. Polley, The CRP increases soil organic
carbon. J. Soil Water Conserv. 49, 488–492 (1994).
296. J. Fargione, J. Hill, D. Tilman, S. Polasky, P. Hawthorne, Land clearing and the biofuel
carbon debt. Science 319, 1235–1238 (2008).
297. B. O. Sander, R. Wassmann, D. L. C. Siopongco, Mitigating Greenhouse Gas Emissions from
Rice Production Through Water-Saving Techniques: Potential, Adoption and Empirical
Evidence (International Rice Research Institute, 2015).
298. X. Yan, H. Akiyama, K. Yagi, H. Akimoto, Global estimations of the inventory and
mitigation potential of methane emissions from rice cultivation conducted using the
2006 Intergovernmental Panel on Climate Change guidelines. Global Biogeochem. Cycles
23, GB2002 (2009).
299. U.S. Department of Agriculture Foreign Agricultural Service, “World agricultural
production” (Circular Series WAP 05-17, U.S. Department of Agriculture Foreign
Agricultural Service, 2017).
300. P. Fazli, H. C. Man, Comparison of methane emission from conventional and modified
paddy cultivation in Malaysia. Agric. Agric. Sci. Proc. 2, 272–279 (2014).
301. M. Peyron, C. Bertora, S. Pelissetti, D. Said-Pullicino, L. Celi, E. Miniotti, M. Romani,
D. Sacco, Greenhouse gas emissions as affected by different water management
practices in temperate rice paddies. Agric. Ecosyst. Environ. 232, 17–28
(2016).
302. C. M. Pittelkow, Y. Assa, M. Burger, R. G. Mutters, C. A. Greer, L. A. Espino, J. E. Hill,
W. R. Horwath, C. van Kessel, B. A. Linquist, Nitrogen management and methane
emissions in direct-seeded rice systems. Agron. J. 106, 968–980 (2014).
303. A. L. Hinson, R. A. Feagin, M. Eriksson, R. G. Najjar, M. Herrmann, T. S. Bianchi, M. Kemp,
J. A. Hutchings, S. Crooks, T. Boutton, The spatial distribution of soil organic carbon in
tidal wetland soils of the continental United States. Glob. Chang. Biol. 23,
5468–5480 (2017).
304. H. J. Poffenbarger, B. A. Needelman, J. P. Megonigal, Salinity influence on methane
emissions from tidal marshes. Wetlands. 31, 831–842 (2011).
305. Intergovernmental Panel on Climate Change, 2013 Supplement to the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories: Wetlands (Intergovernmental Panel on
Climate Change, 2013).
306. S. C. Neubauer, J. P. Megonigal, Moving beyond global warming potentials to quantify
the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).
307. F. E. Anderson, B. Bergamaschi, C. Sturtevant, S. Knox, L. Hastings, L. Windham-Myers,
M. Detto, E. L. Hestir, J. Drexler, R. L. Miller, J. H. Matthes, J. Verfaillie, D. Baldocchi,
R. L. Snyder, R. Fujii, Variation of energy and carbon fluxes from a restored temperate
freshwater wetland and implications for carbon market verification protocols. J. Geophys.
Res. Biogeosci. 121, 777–795 (2016).
308. S. D. Bridgham, J. P. Megonigal, J. K. Keller, N. B. Bliss, C. Trettin, The carbon balance of
North American wetlands. Wetlands 26, 889–916 (2006).
309. U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990–2014 (Environmental Protection Agency, 2016).
310. C. Richardson, R. Evans, D. Carr, in Pocosin Wetlands: An Integrated Analysis of Coastal Plain
Freshwater Bogs in North Carolina (Hutchinson Ross Publishing Company, 1981), pp. 3–19.
311. S. D. Bridgham, C. J. Richardson, Mechanisms controlling soil respiration (CO2 and CH4)
in southern peatlands. Soil Biol. Biochem. 24, 1089–1099 (1992).
312. J. P. Megonigal, W. H. Schlesinger, Enhanced CH4 emission from a wetland soil exposed
to elevated CO2. Biogeochemistry 37, 77–88 (1997).
313. H. Wang, C. J. Richardson, M. Ho, Dual controls on carbon loss during drought in
peatlands. Nat. Clim. Change 5, 584–587 (2015).
314. U.S. Department of Agriculture Natural Resources Conservation Service, Web Soil Survey
(SSURGO) (U.S. Department of Agriculture Natural Resources Conservation Service, 2016);
http://websoilsurvey.nrcs.usda.gov/app/.
315. S. D. Bridgham, C. L. Ping, J. L. Richardson, K. Updegraff, in Wetland Soils: Genesis,
Hydrology, Landscapes, and Classification, J. L. Richardson, M. J. Vepraskas, Eds. (CRC Press,
2001), pp. 343–370.
316. U.S. Fish & Wildlife Service, National Wetlands Inventory (U.S. Fish & Wildlife Service, 2017);
www.fws.gov/wetlands/Data/Data-Download.html.
317. C. Homer, J. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. Herold,
J. Wickham, K. Megown, Completion of the 2011 national land cover database for the
conterminous United States – representing a decade of land cover change information.
Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
318. C. J. Richardson, N. Flanagan, H. Wang, M. Ho, “Impacts of peatland ditching and draining
on water quality and carbon sequestration benefits of peatland restoration” (Duke
University for the Eastern North Carolina/Southeastern Virginia Strategic Habitat
Conservation Team, U.S. Fish and Wildlife Service, Region 4 and The Nature Conservancy
North Carolina Chapter, Final Project, 2014).
319. L. Hansen, D. M. Hellerstein, M. O. Ribaudo, J. Williamson, D. Nulph, C. Loesch,
W. Crumpton, Targeting Investments to Cost Effectively Restore and Protect Wetland
Ecosystems: Some Economic Insights (United States Department of Agriculture
Economic Research Service, 2015); https://ageconsearch.umn.edu/
bitstream/199283/2/ERR183.pdf.
320. J. Howard, A. Sutton-Grier, D. Herr, J. Kleypas, E. Landis, E. Mcleod, E. Pidgeon, S. Simpson,
Clarifying the role of coastal and marine systems in climate mitigation.
Front. Ecol. Environ. 15, 42–50 (2017).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
13 of 14
321. CEC, North American Blue Carbon Scoping Study (Commission for Environmental
Cooperation, 2013).
322. B. C. Murray, L. Pendleton, W. A. Jenkins, S. Sifleet, “Green payments for blue carbon:
Economic incentives for protecting threatened coastal habitats” (NI R 11-04, Nicholas
Institute, Duke University, 2011).
323. G. Morrison, H. Greening, in Integrating Science and Resource Management in Tampa Bay,
Florida: U.S. Geological Survey Circular 1348, K. K. Yates, H. Greening, G. Morrison, Eds.
(U.S. Geological Survey Circular 1348, 2011) pp. 105–156.
324. Tampa Bay Regional Planning Council, Integrating Nitrogen Management with Planning
(2013), Final Technical Report #07-13 of the Tampa Bay Estuary Program (available at
https://tbeptech.org/34mkf/6bb2b_ngbz/vxoi13.dol).
325. H. Greening, A. Janicki, Toward reversal of eutrophic conditions in a subtropical estuary:
Water quality and seagrass response to nitrogen loading reductions in Tampa Bay,
Florida, USA. Environ. Manage. 38, 163–178 (2006).
326. Janicki Environmental Inc., Estimates of Total Nitrogen, Total Phosphorus, Total Suspended
Solids, and Biochemical Oxygen Demand Loadings To Tampa Bay, Florida: 2007-2011
(Tampa Bay Estuary Program, 2013); www.tbeptech.org/TBEP_TECH_PUBS/2013/
TBEP_03_13_FINAL_TBEP_Loads_2007-2011 19Mar2013.pdf.
327. S. B. Bricker, Nutrient pollution in US Estuaries: NOAA’s National Estuarine Eutrophication
Assessment informs nutrient management; https://water.usgs.gov/nawqa/headlines/
nut_pest/NOAA-Estuaries-Bricker.pdf.
328. S. Cooper, “Integrating nitrogen management with planning” (Technical Report 07-13,
Tampa Bay Estuary Program, 2012).
329. E. T. Sherwood, 2016 Tampa Bay Water Quality Assessment (Tampa Bay Estuary Program,
2017); www.tbeptech.org/TBEP_TECH_PUBS/2017/TBEP_01_17_2016_Decision_Matrix_
Results_Update.pdf.
330. S. S. Rabotyagov, T. D. Campbell, M. White, J. G. Arnold, J. Atwood, M. L. Norfleet,
C. L. Kling, P. W. Gassman, A. Valcu, J. Richardson, R. E. Turner, N. N. Rabalais, Cost-
effective targeting of conservation investments to reduce the northern Gulf of Mexico
hypoxic zone. Proc. Natl. Acad. Sci. U.S.A. 111, 18530–18535 (2014).
331. S. S. Rabotyagov, C. L. Kling, P. W. Gassman, N. N. Rabalais, R. E. Turner, The economics of
dead zones: Causes, impacts, policy challenges, and a model of the gulf of Mexico
Hypoxic Zone. Rev. Environ. Econ. Policy 8, 58–79 (2014).
332. C. M. Duarte, N. Marbà, D. Krause-Jensen, M. Sánchez-Camacho, Testing the predictive
power of seagrass depth limit models. Estuaries Coast. 30, 652–656 (2007).
333. C. M. Duarte, Seagrass depth limits. Aquat. Bot. 40, 363–377 (1991).
334. J. T. Greiner, K. J. McGlathery, J. Gunnell, B. A. McKee, Seagrass restoration enhances
“blue carbon” sequestration in coastal waters. PLOS ONE 8, e72469 (2013).
335. E. Bayraktarov, M. I. Saunders, S. Abdullah, M. Mills, J. Beher, H. P. Possingham,
P. J. Mumby, C. E. Lovelock, The cost and feasibility of marine coastal restoration.
Ecol. Appl. 26, 1055–1074 (2016).
336. U.S. Environmental Protection Agency, Guidelines for Preparing Economic Analyses
(U.S. Environmental Protection Agency, 2010); www.epa.gov/environmental-economics/
guidelines-preparing-economic-analyses.
337. World Bank, Lending Interest Rate (%) (World Bank, 2017); https://data.worldbank.org/
indicator/FR.INR.LEND?locations=US.
338. U.S. Department of Agriculture Economic Research Service, Fertilizer Use and Price
(U.S. Department of Agriculture Economic Research Service, 2013); www.ers.usda.gov/
data-products/fertilizer-use-and-price.aspx.
339. D. J. Nowak, S. Hirabayashi, A. Bodine, E. Greenfield, Tree and forest effects on air quality
and human health in the United States. Environ. Pollut. 193, 119–129 (2014).
340. R. Harrison, G. Wardell-Johnson, C. McAlpine, Rainforest reforestation and biodiversity
benefits: A case study from the Australian wet tropics. Ann. Trop. Res. 25, 65–76 (2003).
341. K. Niijima, A. Yamane, Effects of reforestation on soil fauna in the Philippines.
Philipp. J. Sci. 120, 1–20 (1991).
342. P. J. Ferraro, K. Lawlor, K. L. Mullan, S. K. Pattanayak, Forest figures: Ecosystem services
valuation and policy evaluation in developing countries. Rev. Environ. Econ. Policy 6,
20–44 (2012).
343. Z. Burivalova, Ç. H. Şekercioğlu, L. P. Koh, Thresholds of logging intensity to maintain
tropical forest biodiversity. Curr. Biol. 24, 1893–1898 (2014).
344. M. F. Jurgensen, A. E. Harvey, R. T. Graham, D. S. Page-Dumroese, J. R. Tonn, M. J. Larsen,
T. B. Jain, Impacts of Timber harvesting on soil organic matter, nitrogen, productivity,
and health of inland northwest forests. For. Sci. 43, 234–251 (1997).
345. T. A. Burton, Effects of basin-scale Timber harvest on water yield and peak streamflow.
J. Am. Water Resour. Assoc. 33, 1187–1196 (1997).
346. S. Vedal, S. J. Dutton, Wildfire air pollution and daily mortality in a large urban area.
Environ. Res. 102, 29–35 (2006).
347. J. Bengtsson, S. G. Nilsson, A. Franc, P. Menozzi, Biodiversity, disturbances, ecosystem
function and management of European forests. For. Ecol. Manage. 132, 39–50
(2000).
348. K. T. Takano, M. Nakagawa, T. Itioka, K. Kishimoto-Yamada, S. Yamashita, H. O. Tanaka,
D. Fukuda, H. Nagamasu, M. Ichikawa, Y. Kato, K. Momose, T. Nakashizuka, S. Sakai,
Social-Ecological Systems in Transition (Global Environmental Studies, 2014); http://link.
springer.com/chapter/10.1007/978-4-431-54910-9_2/fulltext.html.
349. E. Gómez-Baggethun, D. N. Barton, Classifying and valuing ecosystem services for urban
planning. Ecol. Econ. 86, 235–245 (2013).
350. L. Chaparro, J. Terrasdas, Ecological services of urban forest in Barcelona.
Shengtai Xuebao/Acta Ecol. Sin. 29, 103 (2009).
351. U. G. Sandström, P. Angelstam, G. Mikusiński, Ecological diversity of birds in relation to
the structure of urban green space. Landsc. Urban Plan. 77, 39–53 (2006).
352. Y. Depietri, F. G. Renaud, G. Kallis, Heat waves and floods in urban areas: A policy-
oriented review of ecosystem services. Sustain. Sci. 7, 95–107 (2012).
353. M. J. Hartley, Rationale and methods for conserving biodiversity in plantation forests.
For. Ecol. Manage. 155, 81–95 (2002).
354. M. Ausden, W. J. Sutherland, R. James, The effects of flooding lowland wet grassland on
soil macroinvertebrate prey of breeding wading birds. J. Appl. Ecol. 38, 320–338 (2001).
355. G. W. Randall, D. R. Huggins, M. P. Russelle, D. J. Fuchs, W. W. Nelson, J. L. Anderson,
Nitrate losses through subsurface tile drainage in conservation reserve program, alfalfa,
and row crop systems. J. Environ. Qual. 26, 1240–1247 (1997).
356. M. J. Helmers, X. Zhou, H. Asbjornsen, R. Kolka, M. D. Tomer, R. M. Cruse, Sediment
removal by prairie filter strips in row-cropped ephemeral watersheds. J. Environ. Qual. 41,
1531–1539 (2012).
357. H. Jankowska-Huflejt, The function of permanent grasslands in water resources
protection. J. Water Land Dev. 10, 55–65 (2006).
358. P. Smith, M. R. Ashmore, H. I. J. Black, P. J. Burgess, C. D. Evans, T. A. Quine, A. M. Thomson,
K. Hicks, H. G. Orr, The role of ecosystems and their management in regulating climate,
and soil, water and air quality. J. Appl. Ecol. 50, 812–829 (2013).
359. R. Derpsch, T. Friedrich, A. Kassam, H. Li, Current status of adoption of no-till farming in
the world and some of its main benefits. Int. J. Agric. Biol. Eng. 3, 1–25 (2010).
360. M. J. Bell, F. Worrall, Charcoal addition to soils in NE England: A carbon sink with
environmental co-benefits? Sci. Total Environ. 409, 1704–1714 (2011).
361. S. Jose, Agroforestry for ecosystem services and environmental benefits: An overview.
Agrofor. Syst. 76, 1–10 (2009).
362. S. Pattanayak, D. E. Mercer, Valuing soil conservation benefits of agroforestry: Contour
hedgerows in the Eastern Visayas, Philippines. Agric. Econ. 18, 31–46 (1998).
363. D. W. Bussink, Relationships between ammonia volatilization and nitrogen fertilizer
application rate, intake and excretion of herbage nitrogen by cattle on grazed swards.
Fertil. Res. 38, 111–121 (1994).
364. M. D. Einheuser, A. P. Nejadhashemi, S. P. Sowa, L. Wang, Y. A. Hamaamin, S. A. Woznicki,
Modeling the effects of conservation practices on stream health. Sci. Total Environ.
435–436, 380–391 (2012).
365. G. Woodward, M. O. Gessner, P. S. Giller, V. Gulis, S. Hladyz, A. Lecerf, B. Malmqvist,
B. G. McKie, S. D. Tiegs, H. Cariss, M. Dobson, A. Elosegi, V. Ferreira, M. A. S. Graça,
T. Fleituch, J. O. Lacoursière, M. Nistorescu, J. Pozo, G. Risnoveanu, M. Schindler,
A. Vadineanu, L. B.-M. Vought, E. Chauvet, Continental-scale effects of nutrient pollution
on stream ecosystem functioning. Science 336, 1438–1440 (2012).
366. M. Quemada, M. Baranski, M. N. J. Nobel-de Lange, A. Vallejo, J. M. Cooper, Meta-analysis
of strategies to control nitrate leaching in irrigated agricultural systems and their effects
on crop yield. Agric. Ecosyst. Environ. 174, 1–10 (2013).
367. S. R. Carpenter, N. F. Caraco, D. L. Correll, R. W. Howarth, A. N. Sharpley, V. H. Smith,
Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8,
559–568 (1998).
368. M. Bustamante, C. Robledo-Abad, R. Harper, C. Mbow, N. H. Ravindranat, F. Sperling,
H. Haberl, A. de Siqueira Pinto, P. Smith, Co-benefits, trade-offs, barriers and policies for
greenhouse gas mitigation in the agriculture, forestry and other land use (AFOLU) sector.
Glob. Chang. Biol. 20, 3270–3290 (2014).
369. D. Malakoff, Death by suffocation in the Gulf of Mexico. Science 281, 190–192 (1998).
370. P. S. Hooda, A. C. Edwards, H. A. Anderson, A. Miller, A review of water quality concerns in
livestock farming areas. Sci. Total Environ. 250, 143–167 (2000).
371. A. Kruess, T. Tscharntke, grazing intensity and the diversity of grasshoppers, butterflies,
and trap-nesting bees and wasps. Conserv. Biol. 16, 1570–1580 (2002).
372. C. A. Rotz, S. Asem-Hiablie, J. Dillon, H. Bonifacio, Cradle-to-farm gate environmental
footprints of beef cattle production in Kansas, Oklahoma, and Texas. J. Anim. Sci. 93,
2509–2519 (2015).
373. N. M. Haddad, G. M. Crutsinger, K. Gross, J. Haarstad, J. M. H. Knops, D. Tilman, Plant
species loss decreases arthropod diversity and shifts trophic structure. Ecol. Lett. 12,
1029–1039 (2009).
374. E. S. Jensen, H. Hauggaard-Nielsen, How can increased use of biological N2 fixation in
agriculture benefit the environment? Plant Soil 252, 177–186 (2003).
37 5. S. Toze, Reuse of effluent water—Benefits and risks. Agric. Water Manage. 80, 147–159 (2006).
376. B. W. Heumann, Satellite remote sensing of mangrove forests: Recent advances and
future opportunities. Prog. Phys. Geogr. 35, 87–108 (2011).
377. B. A. Polidoro, K. E. Carpenter, L. Collins, N. C. Duke, A. M. Ellison, J. C. Ellison,
E. J. Farnsworth, E. S. Fernando, K. Kathiresan, N. E. Koedam, S. R. Livingstone, T. Miyagi,
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Fargione et al., Sci. Adv. 2018; 4 : eaat1869 14 November 2018
SCIENCE ADVANCES | RESEARCH ARTICLE
14 of 14
G. E. Moore, V. N. Nam, J. E. Ong, J. H. Primavera, S. G. Salmo III, J. C. Sanciangco,
S. Sukardjo, Y. Wang, J. W. H. Yong, The loss of species: Mangrove extinction risk and
geographic areas of global concern. PLOS ONE 5, e10095 (2010).
378. J. B. Zedler, Wetlands at your service: Reducing impacts of agriculture at the watershed
scale. Front. Ecol. Environ. 1, 65–72 (2003).
379. M. D. Correll, W. A. Wiest, T. P. Hodgman, W. G. Shriver, C. S. Elphick, B. J. McGill,
K. M. O’Brien, B. J. Olsen, Predictors of specialist avifaunal decline in coastal marshes.
Conserv. Biol. 31, 172–182 (2017).
380. E. B. Barbier, S. D. Hacker, C. Kennedy, E. W. Koch, A. C. Stier, B. R. Silliman, The value of
estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).
381. A. G. Rappold, S. L. Stone, W. E. Cascio, L. M. Neas, V. J. Kilaru, M. S. Carraway,
J. J. Szykman, A. Ising, W. E. Cleve, J. T. Meredith, H. Vaughan-Batten, L. Deyneka,
R. B. Devli, Peat bog wildfire smoke exposure in rural North Carolina is associated with
cardiopulmonary emergency department visits assessed through syndromic
surveillance. Environ. Health Perspect. 119, 1415–1420 (2011).
382. S. Page, A. Hosciło, H. Wösten, J. Jauhiainen, M. Silvius, J. Rieley, H. Ritzema, K. Tansey,
L. Graham, H. Vasander, S. Limin, Restoration ecology of lowland tropical peatlands in
Southeast Asia: Current knowledge and future research directions. Ecosystems 12,
888–905 (2009).
383. S. Chapman, A. Buttler, A.-J. Francez, F. Laggoun-Défarge, H. Vasander, M. Schloter,
J. Combe, P. Grosvernier, H. Harms, D. Epron, D. Gilbert, E. Mitchell, Exploitation of
northern peatlands and biodiversity maintenance: A conflict between economy and
ecology. Front. Ecol. Environ. 1, 525–532 (2003).
384. J. P. Curry, J. A. Good, Soil Restoration (Springer New York, 1992); http://link.springer.com/
chapter/10.1007/978-1-4612-2820-2_7.
385. D. P. L. Rousseau, E. Lesage, A. Story, P. A. Vanrolleghem, N. De Pauw, Constructed
wetlands for water reclamation. Desalination 218, 181–189 (2008).
386. R. Unsworth, L. C. Cullen-Unsworth, in Coastal Conservation (Cambridge Univ. Press,
2014), pp. 95–130.
387. U.S. Department of Agriculture Forest Service, Fiscal Year 2009 President’s Budget: Budget
Justification (U.S. Department of Agriculture Forest Service, 2008), pp. 1–426.
388. E. Lichtenberg, J. C. Hanson, A. M. Decker, A. J. Clark, Profitability of legume cover crops
in the mid-Atlantic region. J. Soil Water Conserv. 49, 562–565 (1994).
389. M. R. Pratt, W. E. Tyner, D. J. Muth, E. J. Kladivko, Synergies between cover crops and corn
stover removal. Agric. Syst. 130, 67–76 (2014).
390. U.S. Department of Agriculture Natural Resources Conservation Service, Adding Cover
Crops for Seed Production to a Corn/Soybean Rotation (U.S. Department of Agriculture
Natural Resources Conservation Service, 2015); https://efotg.sc.egov.usda.gov/
references/public/MO/Seed_Production_CaseStudy3.pdf).
391. A. R. Smith, R. S. Tubbs, W. D. Shurley, M. D. Toews, G. D. Collins, G. H. Harris, Economics of
Cover Crop and Supplemental Fertilizer in Strip-Tillage Cotton (The University of Georgia,
College of Agricultural and Environmental Sciences, 2014); https://secure.caes.uga.edu/
extension/publications/files/pdf/AP 108-2_1.PDF.
392. D. F. Roberts, N. R. Kitchen, K. A. Sudduth, S. T. Drummond, P. C. Scharf, Economic and
environmental implications of sensor-based nitrogen management. Better Crops 94,
4–6 (2010).
393. P. C. Scharf, N. R. Kitchen, K. A. Sudduth, J. G. Davis, V. C. Hubbard, J. A. Lory, Field-scale
variability in optimal nitrogen fertilizer rate for corn. Agron. J. 97, 452–461 (2005).
394. N. Hong, P. C. Scharf, J. G. Davis, N. R. Kitchen, K. A. Sudduth, Economically optimal
nitrogen rate reduces soil residual nitrate. J. Environ. Qual. 36, 354–362 (2007).
395. C. Snyder, E. Davidson, P. Smith, R. Venterea, Agriculture: Sustainable crop and animal
production to help mitigate nitrous oxide emissions. Curr. Opin. Environ. Sustain. 9–10,
46–54 (2014).
396. Association of American Plant Food Control Officials, The Fertilizer Institute, Commercial
Fertilizers 2013 (Association of American Plant Food Control Officials, The Fertilizer
Institute, Fertilizer/Ag Lime Control Service, University of Missouri, 2014).
397. U.S. Department of Agriculture, USDA Agriculture and Forestry Greenhouse Gas Inventory:
1990–2008 (U.S. Department of Agriculture, 2011).
398. M. J. Brown, G. M. Smith, J. McCollum, Wetland Forest Statistics for the South Atlantic States
(U.S. Department of Agriculture Forest Service, 2001); www.srs.fs.usda.gov/pubs/rb/rb_
srs062.pdf).
Acknowledgments
Funding: This study was made possible by funding from the Doris Duke Charitable
Foundation. C.A.W. and H.G. acknowledge financial support from NASA’s Carbon Monitoring
System program (NNH14ZDA001N-CMS) under award NNX14AR39G. S.D.B. acknowledges
support from the DOE’s Office of Biological and Environmental Research Program under the
award DE-SC0014416. J.W.F. acknowledges financial support from the Florida Coastal
Everglades Long-Term Ecological Research program under National Science Foundation grant
no. DEB-1237517. Any use of trade, firm, or product names is for descriptive purposes only and
does not imply endorsement by the U.S. Government. The findings and conclusions in this
publication have not been formally disseminated by the U.S. Department of Agriculture and
should not be construed to represent any agency determination or policy. We thank L. Hansen
(USDA Economic Research Service) for providing a shapefile of county-level wetland
restoration cost estimates. Author contributions: S.B., T.B., S.D.B., R.T.C., S.C.C.-P., P.W.E., A.F.,
J.E.F., J.W.F., T.G., B.W.G., H.G., B.H., M.D.H., K.D.K., T.K., T.J.L., S.M.L., G.L., R.I.M., J.P.M., D.A.M.,
C.J.R., J.S., D.S., S.A.S, J.W.V., C.A.W., P.B.W., and C.Z. developed individual NCS opportunities.
J.E.F. drafted the manuscript. All authors discussed the results and edited and commented on
the manuscript. Competing interests: G.L. has been a consultant for Virgin Management Ltd.
advising on land-based carbon sequestration strategies. The authors declare no other
competing interests. Data and materials availability: All data needed to evaluate the
conclusions in the paper are present in the paper and/or the Supplementary Materials.
Additional data related to this paper may be requested from the authors.
Submitted 1 February 2018
Accepted 12 October 2018
Published 14 November 2018
10.1126/sciadv.aat1869
Citation: J. E. Fargione, S. Bassett, T. Boucher, S. D. Bridgham, R. T. Conant, S. C. Cook-Patton,
P. W. Ellis, A. Falcucci, J. W. Fourqurean, T. Gopalakrishna, H. Gu, B. Henderson, M. D. Hurteau,
K. D. Kroeger, T. Kroeger, T. J. Lark, S. M. Leavitt, G. Lomax, R. I. McDonald, J. P. Megonigal,
D. A. Miteva, C. J. Richardson, J. Sanderman, D. Shoch, S. A. Spawn, J. W. Veldman, C. A. Williams,
P. B. Woodbury, C. Zganjar, M. Baranski, P. Elias, R. A. Houghton, E. Landis, E. McGlynn, W. H. Schlesinger,
J. V. Siikamaki, A. E. Sutton-Grier, B. W. Griscom, Natural climate solutions for the United States.
Sci. Adv. 4, eaat1869 (2018).
on November 14, 2018http://advances.sciencemag.org/Downloaded from
Natural climate solutions for the United States
Emily McGlynn, William H. Schlesinger, Juha V. Siikamaki, Ariana E. Sutton-Grier and Bronson W. Griscom
Christopher A. Williams, Peter B. Woodbury, Chris Zganjar, Marci Baranski, Patricia Elias, Richard A. Houghton, Emily Landis,
Megonigal, Daniela A. Miteva, Curtis J. Richardson, Jonathan Sanderman, David Shoch, Seth A. Spawn, Joseph W. Veldman,
Hurteau, Kevin D. Kroeger, Timm Kroeger, Tyler J. Lark, Sara M. Leavitt, Guy Lomax, Robert I. McDonald, J. Patrick
W. Ellis, Alessandra Falcucci, James W. Fourqurean, Trisha Gopalakrishna, Huan Gu, Benjamin Henderson, Matthew D.
Joseph E. Fargione, Steven Bassett, Timothy Boucher, Scott D. Bridgham, Richard T. Conant, Susan C. Cook-Patton, Peter
DOI: 10.1126/sciadv.aat1869
(11), eaat1869.4Sci Adv
ARTICLE TOOLS http://advances.sciencemag.org/content/4/11/eaat1869
MATERIALS
SUPPLEMENTARY http://advances.sciencemag.org/content/suppl/2018/11/09/4.11.eaat1869.DC1
REFERENCES http://advances.sciencemag.org/content/4/11/eaat1869#BIBL
This article cites 268 articles, 21 of which you can access for free
PERMISSIONS http://www.sciencemag.org/help/reprints-and-permissions
Terms of ServiceUse of this article is subject to the
registered trademark of AAAS. is aScience Advances Association for the Advancement of Science. No claim to original U.S. Government Works. The title
York Avenue NW, Washington, DC 20005. 2017 © The Authors, some rights reserved; exclusive licensee American
(ISSN 2375-2548) is published by the American Association for the Advancement of Science, 1200 NewScience Advances
on November 14, 2018http://advances.sciencemag.org/Downloaded from
... Particularly missing from current discussions of land-based mitigation scenarios are quantitative assessments of potential solutions that include both nature-based (Fargione et al., 2018) and cellulosic bioenergy (Field et al., 2020) solutions. ...
... Additionally, such estimates typically consider only a subset of available land-based strategies, with an emphasis on BECCS (e.g., Calvin et al., 2019). Bottom-up efforts, on the other hand, effectively identify specific practices with substantial mitigation potentials, whether carbon capture or emissions avoidance, but struggle to capture the spatial resolution needed to avoid double-counting activities with competing land needs (NASEM, 2019), or promote one set of practices (such as reforestation) to the exclusion of others (such as bioenergy) (Fargione et al., 2018). And no efforts to derive land-based estimates capture the combined uncertainties of local practice outcomes, available land base, likely adoption rates, and the durations of different carbon sink strengths. ...
... Recent estimates of U.S. land-based sequestration potentials suggest a maximum sequestration capacity of 1.0-2.4 Gt of CO 2 equivalents (CO 2 e) per year at mid-century (NASEM, 2019), and a recent spatial analysis of potential nature-based solutions (Fargione et al., 2018) suggests an end-of-century capacity for ~74 Gt CO 2 e by 2100. This estimate excludes bioenergy, however, an especially important opportunity in the United States and other countries where an available land base allows capacity to scale appreciably (Hilaire et al., 2019). ...
Article
Full-text available
Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the United States that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest , and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of forest management. Results show a conservative end-of-century mitigation capacity of 110 (57-178) Gt CO 2 e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties, but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now.
... Impact from land-use competitions, especially those between climate change mitigation and securing food provision (e.g., cropland), has been identified as one of the key issues in climate change mitigation studies (IPCC, 2019;Meyfroidt et al., 2022). However, in previous estimation of NCS mitigation potential, impact of land-use competition (e.g., incurred by cropland expansion) has not been taken into consideration as they overlooked the inevitable cropland expansion in the next few decades (Fargione et al., 2018;Griscom et al., 2017). Understanding how NCS potential is limited by future land-use competition is important as it provides us with a more realistic and equitable estimate. ...
... It has also been reported that using a 10-km data would miss out at least 60 % of small-scale land-use change that could be identified in 1-km resolution data . Another consideration is that previous estimation of NCS potential is based on a non-mitigation condition (i.e., baseline scenarios without applying additional mitigation efforts (Fargione et al., 2018;Griscom et al., 2017). Thus, to make consistent analysis on the impact of land-use competition on NCS potential, the land-use projection data should also be under non-mitigation scenarios. ...
... This scenario assumed the total amount of cropland areas maintained the same as baseline level of 2010 from 2010 to 2100 (Fig. 1). This additional scenario was originated from the key assumption embedded in previous NCS mitigation potential estimations: "current extent of cropland land can effectively feed projected future populations as future global food demand can be met via an ideal yield increases" (Fargione et al., 2018;Griscom et al., 2017). When calculating and projecting NCS mitigation potential, previous studies do not take the land-use competition between future cropland expansion and natural areas needed for implementing NCS into consideration. ...
Article
Full-text available
Natural climate solutions (NCS) are an essential complement to climate mitigation and have been increasingly incorporated into international mitigation strategies. Yet, with the ongoing population growth, allocating natural areas for NCS may compete with other socioeconomic priorities, especially urban development and food security. Here, we projected the impacts of land-use competition incurred by cropland and urban expansion on the climate mitigation potential of NCS. We mapped the areas available for implementing 9 key NCS strategies and estimated their climate change mitigation potential. Then, we overlaid these areas with future cropland and urban expansion maps projected under three Shared Socioeconomic Pathway (SSP) scenarios (2020-2100) and calculated the resulting mitigation potential loss of each selected NCS strategy. Our results estimate a substantial reduction, 0.3-2.8 GtCO 2 yr −1 or 4-39 %, in NCS mitigation potential, of which cropland expansion for fulfilling future food demand is the primary cause. This impact is particularly severe in the tropics where NCS hold the most abundant mitigation potential. Our findings highlight immediate actions prioritized to tropical areas are important to best realize NCS and are key to developing realistic and sustainable climate policies.
... As shown by land surface modeling, global warming, drought stress, and deforestation in the past decades have greatly affected the structure and function of forests through change in biomass accumulation, thus altering this crucial natural resource for human well-being (Piao et al., 2009). Therefore, a better understanding of how biomass changes in response to the environment, especially along elevation and climatic gradients in mountainous areas, is vital to inform sustainable forest management (Grassi et al., 2017;Liang et al., 2016) and strengthen natural climate solutions (Fargione et al., 2018). ...
Article
Full-text available
Forest biomass is an important component of terrestrial carbon pools. However, how climate, biodiversity, and structural attributes co-determine spatiotemporal variation in forest biomass remains not well known. We aimed to shed light on these drivers of forest biomass by measuring diversity and structural attributes of tree species in 400-m 2 plots located every 100 m along a 4200-m elevational gradient in the eastern Himalayas. We applied structural equation models to test how climate, species richness, structural attributes, and their interactions influence forest biomass. Importantly, species richness was a stronger driver of biomass than environmental and structural attributes such as annual air temperature or stem density. Integrating the availability of energy and the demand for water, potential evapotranspiration was more strongly correlated with biomass than water availability, likely due to the strong influence of the Indian summer monsoon. Thus, interactions between climate and tree community composition ultimately control how much carbon is stored in woody biomass across bioclimatic gradients. This fundamental understanding will support predictive efforts of the forest carbon sink in this
... To mitigate this risk of slow or absent tree regeneration and kickstart carbon sequestration after fire, actions such as planting trees are increasingly used to mitigate the carbon lost to fire (Povak et al., 2020). These nature-based solutions to climate change are immediate actions to prevent worst case climate forecasts (Fargione et al., 2018;Drever et al., 2021), but also require ongoing estimation of the carbon consequences of these actions (i.e., Graves et al., 2020). Tree planting after fire may coincide with additional treatments that occur prior to planting. ...
Article
Full-text available
Wildfire is a natural disturbance in many forested biomes, with the loss of carbon to the atmosphere and mortality of trees actively sequestering carbon of global concern as a contribution to climate change. Natural regeneration is often successful at reestablishing a forest in ecosystems adapted to fire, but there is increasing concern that the changing size, frequency and severity of wildfire is causing regeneration failures or inadequate densities of trees that sequester and store carbon following these disturbances. It remains unclear whether the action of planting trees accelerates carbon storage following fire compared to forests established through natural regeneration. The central interior of British Columbia recently experienced multiple years of record-breaking fire activity. Rehabilitation planting focused on reestablishing trees in the managed forest but was also prescribed in previously unmanaged forests to initiate carbon sequestration. Planting is often accompanied by other stand treatments such as salvage harvesting or snag removal and debris clearing to ensure planter safety. Here, we determine carbon recovery and stores in 21 wildfires across a chronosequence from the early 1960s to 2015. We measured above and belowground carbon pools to determine the effect of time since fire and planting treatments on carbon. Tree planting did not increase total ecosystem carbon over time, but rather decreased carbon through the loss of dead wood from site preparation. All carbon pools were affected by time since fire except the mineral soil pool, which was best predicted by soil clay content and coarse fragments positive effects. Live tree carbon increased over time, with more stored in planted stands over 60 years compared to stands that were not planted. Projecting growth to 100 years since fire suggests we may see increasing divergence in carbon stores in planted stands over a full fire-return interval, but these differences remain relatively small [mean (sd): 140.8 (19.6) Mg⋅ha–1 in planted compared to 136.9 (27.5) Mg⋅ha–1 in not-planted stands], with 1.4 Mg⋅ha–1 year–1 sequestered in not-planted compared to 1.5 Mg⋅ha–1 year–1 in planted stands. To meet carbon objectives, replanting trees on average sites in burned forests of BC’s central interior would require preserving the carbon legacy of fire, including dead wood.
... On a per-area basis, forests have been reported to rank highest among all ecosystems in the Midwest for potential for carbon sequestration and help mitigate the effects of increasing CO 2 (Fargione et al. 2018). With forest cover currently only at 32% of the baseline at the time of Euro-American settlement, there are opportunities to plan reforestations to consolidate forest and woodland fragments. ...
Article
Full-text available
There have been dramatic changes to forest lands since the end of the last ice age, about 14,000 years before present, when boreal ecosystems were eventually replaced by deciduous forest and grassland. In Illinois at the time of Euro-American Settlement (circa 1820), forest lands, including fire-maintained woodlands and savannas, comprised about 42% of the land area. Habitat destruction, fire absence, livestock grazing, and infestations of non-native species have altered forests since the 1800s. Currently, forest land cover statewide is about 13.5%, mostly (83%) in private ownership and predomi-nately (68%) classified as oak-hickory cover type. Further modifications can be expected due to climate change, predicted for Illinois over the next 100 years to include warmer winter temperatures, warmer and wetter springs, and hotter, drier summers. Models predicting potential futures for 113 tree species as a response to climate change over the next 100 years were generated for ten primary Illinois ecoregions. Results indicate that there are likely to be increases in habitat suitability and capability for some species and decreased habitat suitability and capability for others with variability across ecoregions. Many species demonstrate differential responses to changing climate from north to south in the state. The dominant species in the oak-hickory cover type generally are projected to have fair to good capabilities, with some notable exceptions; however , Acer saccharum, a competitor in many oak-hickory stands, also is projected to have fair to good capability. Dominant species in mesic upland and bottomland forests include a rich variety of species about evenly split between those with fair-to-good capabilities and those expected to have poor capability. Potential 'New Habitat' and 'Migrate' species also are identified. New Habitat species are those that have potential habitat appearing in the state within 100 years; Migrate species have some potential for natural distribution to the state within 100 years and could be considered as candidates for assisted migration northward. Considerations for conservation and management of forest lands are discussed.
Article
Coastal marshes and seagrass beds store millions of tons of carbon in their sediments and sequester carbon at higher per-area rates than most terrestrial ecosystems. There is substantial interest in this “blue carbon” as a carbon mitigation strategy, despite the major threat that sea level rise (SLR) poses to these habitats. Many projections of habitat and carbon change with SLR emphasize the potential for inland marsh migration and increased rates of marsh carbon sequestration, but do not consider carbon fluxes associated with habitat conversion. We integrated existing data and models to develop a spatial model for predicting habitat and carbon changes due to SLR in six mid-Atlantic U.S. states likely to face coastal habitat loss over the next century due to low tidal ranges and sediment supply. Our primary model projection, using an intermediate SLR scenario (1.2 m SLR by 2104), predicts loss of 83% of existing coastal marshes and 26% of existing seagrasses in the study area. In addition, 270,000 hectares of forest and forested wetlands in low-lying coastal areas will convert to coastal marshes. These SLR-driven habitat changes cause the study area to shift from a carbon sink to a source in our primary model projection. Given the many uncertainties about the habitat and carbon changes represented in our model, we also identified the parameters and assumptions that most strongly affected the model results to inform future research needs. These included: land availability for inland marsh migration, the baseline extent and location of coastal marshes, proportion of stored carbon emitted from lost habitats (coastal marsh sediments or terrestrial biomass carbon), and methane emissions from freshwater habitats. The study area switched from a net carbon sink to a net carbon source under SLR for all but three model runs; in those runs, net carbon sequestration declined by 57–99%.
Article
Full-text available
Background The natural removal of carbon dioxide (CO2) from the atmosphere through land conservation, restoration, and management is receiving increasing attention as a scalable approach for climate change mitigation. However, different land-use sectors compete for resources and incentives within and across geopolitical regions, resulting in divergent goals and inefficient prioritization of CO2 removal efforts. Thus, a unifying framework is needed to accelerate basic research and coordinated interventions to accelerate climate change mitigation. Scope We propose a generalizable framework for Enhanced Natural Climate Solutions (NCS +), which we define as activities that can be coordinated to increase carbon drawdown and permanence on land while improving livelihoods and the provision of natural resources in vulnerable communities and ecosystems. The framework builds on interdisciplinary scientific convergence, including critical socioecological interactions, to inform both top-down policy incentives and bottom-up adoption by industries and managers. To achieve this goal, we suggest a multi-tiered approach for the prioritization of projects at local to regional scales that would simultaneously accelerate scientific discovery and broad implementation of CO2 removal projects. Conclusions Our vision leverages input from hundreds of researchers and land managers, including social and environmental scientists as well as representatives from tribal governments, state, and federal agencies in the Pacific Northwest of the USA, as a model system. Five guiding principles orient the framework which would be applicable in any region. As evidence of feasibility, we provide a synthesis of interdisciplinary studies that illustrate how coordinated action, with explicit consideration of system-specific technical and socioecological limitations, can lead to scalable projects with multiple co-benefits. Using theory as a linchpin for innovation, we propose that NCS + could better align climate change mitigation, adaptation, and justice goals at multiple scales.
Article
On the Ground •Natural solutions, such as “avoided conversion of grasslands,” offer agricultural land managers a way to mitigate climate change while monetizing climate benefits. •Managers who avoid converting grasslands to other uses, such as row crops, can quantify the amount of stored carbon and sell credits, but high costs of developing carbon credit projects price many landowners out of the carbon market. •Aggregation can create economies of scale, which lower barriers of entry and allow more landowners to participate in the market. •Given the current low prices in the carbon market, aggregation is not a panacea and aggregated projects are not financially viable for many landowners. •As the demand for carbon credits continues to grow, land managers can position themselves to take advantage of carbon market opportunities should prices increase, and projects become financially viable.
Article
Privately-owned forests in the Pacific Northwest (PNW) are important potential carbon sinks and play a large role in carbon sequestration and storage. Non-industrial private forest (NIPF) owners constitute a substantial portion of overall forest landownership in productive regions of the PNW; however, little is known about their preferences for non-market incentive programs aimed at increased carbon storage and sequestration, specifically by limiting timber harvest, and how those preferences might impact the outcome of forest carbon programs. We simulated landscape-scale outcomes of hypothetical forest carbon incentive programs in western Oregon (USA) by combining empirical models of NIPF owners' participation with spatially explicit forest carbon storage and sequestration data. We surveyed landowners to determine their willingness to enroll in various hypothetical forest management incentive programs that varied in terms of harvest restrictions, contract length, annual payment and incentive payment amounts, and cost-share percentages, as well as the program framing (e.g., carbon versus forest health). We used multinomial logistic regression to model whether landowners might enroll based on program attributes, landowners' attitudes toward climate change and forest management, past and planned future forest harvest activities, and socio-demographics. We found that 36% of respondents stated that they would probably or definitely enroll in at least one of the hypothetical programs they were shown while 21% of respondents refused all programs that they were offered. Our final model of landowner willingness to enroll indicated that higher annual and higher cost-share payments were the strongest positive predictors of whether landowners would enroll vs. not enroll. Landowners' willingness to enroll was not influenced by program framing as either a “forest carbon” or a “forest health”; however, landowner attitudes toward climate change were the next strongest positive predictor of enrollment after annual and cost-share payments. By simulating landowner enrollment in six policy relevant program scenarios, we illustrate that carefully designed forest carbon incentive programs for NIPF owners could have tangible carbon protection benefits (16.25 to 50.31 MMT CO2e cumulative) at relatively low costs per MT CO2e ($3.60 to $7.70). We highlight tradeoffs between maximizing enrollment in forest carbon incentive programs and providing longer term protection of carbon. This research contributes to the literature on the design of potential forest carbon incentive programs and communication about forest carbon management, as well as aims to aid policy makers and program administrators that seek ways to engage private landowners in carbon-oriented forest management.
Article
Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate‐rich seawater. Yet, widespread management of coastal hydrology has restricted tidal exchange in vast areas of coastal wetlands. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls and scaling of carbon exchange in these understudied ecosystems is critical for informing climate consequences of blue carbon restoration and/or management interventions. Here, we (1) examine how carbon fluxes vary across a salinity gradient (4–25 psu) in impounded and natural, tidally unrestricted Phragmites wetlands using static chambers and (2) probe drivers of carbon fluxes within an impounded coastal wetland using eddy covariance at the Herring River in Wellfleet, MA, United States. Freshening across the salinity gradient led to a 50‐fold increase in CH4 emissions, but effects on carbon dioxide (CO2) were less pronounced with uptake generally enhanced in the fresher, impounded sites. The impounded wetland experienced little variation in water‐table depth or salinity during the growing season and was a strong CO2 sink of −352 g CO2‐C m−2 year−1 offset by CH4 emission of 11.4 g CH4‐C m−2 year−1. Growing season CH4 flux was driven primarily by temperature. Methane flux exhibited a diurnal cycle with a night‐time minimum that was not reflected in opaque chamber measurements. Therefore, we suggest accounting for the diurnal cycle of CH4 in Phragmites, for example by applying a scaling factor developed here of ~0.6 to mid‐day chamber measurements. Taken together, these results suggest that although freshened, impounded wetlands can be strong carbon sinks, enhanced CH4 emission with freshening reduces net radiative balance. Restoration of tidal flow to impounded ecosystems could limit CH4 production and enhance their climate regulating benefits. Widespread management of coastal hydrology has restricted tidal exchange in vast areas of coastal wetlands, which often leads to impoundment, freshening, and vegetation shifts like invasion by Phragmites. Using both static chambers and eddy covariance, we demonstrate that freshening causes enhanced methane emission and increases radiative forcing from greenhouse gases. Restoration of tidal flow to impounded ecosystems could suppress methane production and combined with conversion to native salt marsh vegetation, serve as an effective blue carbon climate change mitigation opportunity.
Article
Full-text available
Urban trees reduce respirable particulate matter (PM 10) concentrations and maximum daytime summer temperatures. While most cities are losing tree cover, some are considering ambitious planting efforts. Maximizing PM 10 and heat mitigation for people from such efforts requires cost-effective targeting. We adapt published methods to estimate the impact of a decade (2004-2014) of tree cover change on city-level PM 10 and heat mitigation in 27 U.S. cities and present a new methodology for estimating local-level PM 10 and heat mitigation by street trees and tree patches. We map potential tree planting sites in the 27 cities and use our local-level PM 10 and heat mitigation methods to assess the population-weighted return on investment (ROI) of each site for PM 10 and heat abatement for nearby populations. Twenty-three of the 27 cities lost canopy cover during 2004-2014, reducing estimated city-level PM 10 removal by 6% (168 tons) and increasing city-level average maximum daily summer temperature by 0.1 °C on average across cities. We find large potential for urban reforestation to increase PM 10 and heat abatement. Intra-city variation in planting site ROI-driven primarily by differences in population density around planting sites-exceeds four orders of magnitude, indicating large scope for targeting to increase PM 10 and heat abatement from reforestation. Reforesting each city's top 20% ROI sites could lower average annual PM 10 concentrations by > 2 μg/m 3 for 3.4-11.4 million people and average maximum daily summer temperatures by > 2 °C for 1.7-12.7 million-effects large enough to provide meaningful health benefits-at a combined annual cost of $102 million.
Article
Full-text available
Many areas of the natural and social sciences involve complex systems that link together multiple sectors. Integrated assessment models (IAMs) are approaches that integrate knowledge from two or more domains into a single framework, and these are particularly important for climate change. One of the earliest IAMs for climate change was the DICE/RICE family of models, first published in Nordhaus (Science 258:1315–1319, 1992a), with the latest version in Nordhaus (2017, 2018). A difficulty in assessing IAMs is the inability to use standard statistical tests because of the lack of a probabilistic structure. In the absence of statistical tests, the present study examines the extent of revisions of the DICE model over its quarter-century history. The study finds that the major revisions have come primarily from the economic aspects of the model, whereas the environmental changes have been much smaller. Particularly, sharp revisions have occurred for global output, damages, and the social cost of carbon. These results indicate that the economic projections are the least precise parts of IAMs and deserve much greater study than has been the case up to now, especially careful studies of long-run economic growth (to 2100 and beyond). Additionally, the approach developed here can serve as a useful template for IAMs to describe their salient characteristics and revisions for the broader community of analysts.
Article
Full-text available
Carbon stocks in vegetation have a key role in the climate system. However, the magnitude, patterns and uncertainties of carbon stocks and the effect of land use on the stocks remain poorly quantified. Here we show, using state-of-the-art datasets, that vegetation currently stores around 450 petagrams of carbon. In the hypothetical absence of land use, potential vegetation would store around 916 petagrams of carbon, under current climate conditions. This difference highlights the massive effect of land use on biomass stocks. Deforestation and other land-cover changes are responsible for 53-58% of the difference between current and potential biomass stocks. Land management effects (the biomass stock changes induced by land use within the same land cover) contribute 42-47%, but have been underestimated in the literature. Therefore, avoiding deforestation is necessary but not sufficient for mitigation of climate change. Our results imply that trade-offs exist between conserving carbon stocks on managed land and raising the contribution of biomass to raw material and energy supply for the mitigation of climate change. Efforts to raise biomass stocks are currently verifiable only in temperate forests, where their potential is limited. By contrast, large uncertainties hinder verification in the tropical forest, where the largest potential is located, pointing to challenges for the upcoming stocktaking exercises under the Paris agreement.
Article
Full-text available
Forest resilience to climate change is a global concern given the potential effects of increased disturbance activity, warming temperatures and increased moisture stress on plants. We used a multi-regional dataset of 1485 sites across 52 wildfires from the US Rocky Mountains to ask if and how changing climate over the last several decades impacted post-fire tree regeneration, a key indicator of forest resilience. Results highlight significant decreases in tree regeneration in the 21st century. Annual moisture deficits were significantly greater from 2000 to 2015 as compared to 1985–1999, suggesting increasingly unfavourable post-fire growing conditions, corresponding to significantly lower seedling densities and increased regeneration failure. Dry forests that already occur at the edge of their climatic tolerance are most prone to conversion to non-forests after wildfires. Major climate-induced reduction in forest density and extent has important consequences for a myriad of ecosystem services now and in the future.
Article
Full-text available
Better stewardship of land is needed to achieve the Paris Climate Agreement goal of holding warming to below 2 °C; however, confusion persists about the specific set of land stewardship options available and their mitigation potential. To address this, we identify and quantify "natural climate solutions" (NCS): 20 conservation, restoration , and improved land management actions that increase carbon storage and/or avoid greenhouse gas emissions across global forests, wetlands, grasslands, and agricultural lands. We find that the maximum potential of NCS-when constrained by food security, fiber security, and biodiversity conservation-is 23.8 petagrams of CO 2 equivalent (PgCO 2 e) y −1 (95% CI 20.3-37.4). This is ≥30% higher than prior estimates, which did not include the full range of options and safeguards considered here. About half of this maximum (11.3 PgCO 2 e y −1) represents cost-effective climate mitigation, assuming the social cost of CO 2 pollution is ≥100 USD MgCO 2 e −1 by 2030. Natural climate solutions can provide 37% of cost-effective CO 2 mit-igation needed through 2030 for a >66% chance of holding warming to below 2 °C. One-third of this cost-effective NCS mitigation can be delivered at or below 10 USD MgCO 2 −1. Most NCS actions-if effectively implemented-also offer water filtration, flood buffer-ing, soil health, biodiversity habitat, and enhanced climate resilience. Work remains to better constrain uncertainty of NCS mitigation estimates. Nevertheless, existing knowledge reported here provides a robust basis for immediate global action to improve ecosystem stewardship as a major solution to climate change. climate mitigation | forests | agriculture | wetlands | ecosystems