sustainable biochar to mitigate global climate change

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© 2010 Macmillan Publishers Limited. All rights reserved.

Received 29 Oct 2009 | Accepted 14 Jul 2010 | Published 10 Aug 2010 DOI: 10.1038/ncomms1053

Production of biochar (the carbon (C)-rich solid formed by pyrolysis of biomass) and its

storage in soils have been suggested as a means of abating climate change by sequestering

carbon, while simultaneously providing energy and increasing crop yields. Substantial

uncertainties exist, however, regarding the impact, capacity and sustainability of biochar at

the global level. In this paper we estimate the maximum sustainable technical potential of

biochar to mitigate climate change. Annual net emissions of carbon dioxide (CO 2 ), methane

and nitrous oxide could be reduced by a maximum of 1.8 Pg CO 2 -C equivalent (CO 2 -C e ) per

year (12 % of current anthropogenic CO 2 -C e emissions; 1 Pg = 1 Gt), and total net emissions

over the course of a century by 130 Pg CO 2 -C e , without endangering food security, habitat or

soil conservation. Biochar has a larger climate-change mitigation potential than combustion of

the same sustainably procured biomass for bioenergy, except when fertile soils are amended

while coal is the fuel being offset.

1 School of the Environment and Society, Swansea University, Singleton Park , Swansea SA2 8PP , UK . 2 Chemical and Materials Sciences Division, Pacifi c

Northwest National Laboratory , Richland , Washington 99352, USA . 3 Department of Crop and Soil Sciences, College of Agriculture and Life Sciences,

Cornell University , Ithaca , New York 14853 , USA . 4 The School of Materials Science and Engineering, University of New South Wales , Sydney,

New South Wales 2052 , Australia . Correspondence and requests for materials should be addressed to J.E.A. (email: [email protected] ) .

Sustainable biochar to mitigate global climate change Dominic Woolf 1 , James E. Amonette 2 , F. Alayne Street-Perrott 1 , Johannes Lehmann 3 & Stephen Joseph 4

NATURE COMMUNICATIONS | 1:56 | DOI: 10.1038/ncomms1053 | www.nature.com/naturecommunications

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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms1053



Since 2000, anthropogenic carbon dioxide (CO 2 ) emissions have risen by more than 3 % annually 1 , putting Earth ’ s eco-systems on a trajectory towards rapid climate change that is

both dangerous and irreversible 2 . To change this trajectory, a timely and ambitious programme of mitigation measures is needed. Several studies have shown that, to stabilize global mean surface temperature, cumulative anthropogenic greenhouse-gas (GHG) emissions must be kept below a maximum upper limit, thus indicating that future net anthropogenic emissions must approach zero 2 – 6 . If humanity oversteps this threshold of maximum safe cumulative emissions (a limit that may already have been exceeded 7 ), no amount of emissions reduction will return the climate to within safe bounds. Mitigation strategies that draw down excess CO 2 from the atmos-phere would then assume an importance greater than an equivalent reduction in emissions.

Production of biochar, in combination with its storage in soils, has been suggested as one possible means of reducing the atmos-pheric CO 2 concentration (refs 8 – 13 and see also Supplementary Note for a history of the concept and etymology of the term). Bio-char ’ s climate-mitigation potential stems primarily from its highly recalcitrant nature 14 – 16 , which slows the rate at which photosyntheti-cally fi xed carbon (C) is returned to the atmosphere. In addition, biochar yields several potential co-benefi ts. It is a source of renew-

able bioenergy; it can improve agricultural productivity, particularly in low-fertility and degraded soils where it can be especially useful to the world ’ s poorest farmers; it reduces the losses of nutrients and agricultural chemicals in run-off ; it can improve the water-holding capacity of soils; and it is producible from biomass waste 17,18 . Of the possible strategies to remove CO 2 from the atmosphere, biochar is notable, if not unique, in this regard.

Biochar can be produced at scales ranging from large industrial facilities down to the individual farm 19 , and even at the domestic level 20 , making it applicable to a variety of socioeconomic situations. Various pyrolysis technologies are commercially available that yield diff erent proportions of biochar and bioenergy products, such as bio-oil and syngas. Th e gaseous bioenergy products are typically used to generate electricity; the bio-oil may be used directly for low-grade heating applications and, potentially, as a diesel substitute aft er suitable treatment 21 . Pyrolysis processes are classifi ed into two major types, fast and slow, which refer to the speed at which the biomass is altered. Fast pyrolysis, with biomass residence times of a few seconds at most, generates more bio-oil and less biochar than slow pyrolysis, for which biomass residence times can range from hours to days.

Th e sustainable-biochar concept is summarized in Figure 1 . CO 2 is removed from the atmosphere by photosynthesis. Sustainably

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Figure 1 | Overview of the sustainable biochar concept. The fi gure shows inputs, process, outputs, applications and impacts on global climate . Within

each of these categories, the relative proportions of the components are approximated by the height / width of the coloured fi elds. CO 2 is removed from

the atmosphere by photosynthesis to yield biomass. A sustainable fraction of the total biomass produced each year, such as agricultural residues, biomass

crops and agroforestry products, is converted by pyrolysis to yield bio-oil, syngas and process heat, together with a solid product, biochar, which is a

recalcitrant form of carbon and suitable as a soil amendment. The bio-oil and syngas are subsequently combusted to yield energy and CO 2 . This energy

and the process heat are used to offset fossil carbon emissions, whereas the biochar stores carbon for a signifi cantly longer period than would have

occurred if the original biomass had been left to decay. In addition to fossil energy offsets and carbon storage, some emissions of methane and nitrous

oxide are avoided by preventing biomass decay (see Supplementary Table S5 for example) and by amending soils with biochar. Additionally, the removal

of CO 2 by photosynthesis is enhanced by biochar amendments to previously infertile soils, thereby providing a positive feedback. CO 2 is returned to the

atmosphere directly through combustion of bio-oil and syngas, through the slow decay of biochar in soils, and through the use of machinery to transport

biomass to the pyrolysis facility, to transport biochar from the same facility to its disposal site and to incorporate biochar into the soil. In contrast to

bioenergy, in which all CO 2 that is fi xed in the biomass by photosynthesis is returned to the atmosphere quickly as fossil carbon emissions are offset,

biochar has the potential for even greater impact on climate through its enhancement of the productivity of infertile soils and its effects on soil GHG fl uxes.

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procured crop residues, manures, biomass crops, timber and for-estry residues, and green waste are pyrolysed by modern techno-logy to yield bio-oil, syngas, process heat and biochar. As a result of pyrolysis, immediate decay of these biomass inputs is avoided. Th e outputs of the pyrolysis process serve to provide energy, avoid emissions of GHGs such as methane (CH 4 ) and nitrous oxide (N 2 O), and amend agricultural soils and pastures. Th e bioenergy is used to off set fossil-fuel emissions, while returning about half of the C fi xed by photosynthesis to the atmosphere. In addition to the GHG emissions avoided by preventing decay of biomass inputs, soil emissions of GHGs are also decreased by biochar amendment to soils. Th e biochar stores carbon in a recalcitrant form that can increase soil water- and nutrient-holding capacities, which typi-cally result in increased plant growth. Th is enhanced productivity is a positive feedback that further enhances the amount of CO 2 removed from the atmosphere. Slow decay of biochar in soils, together with tillage and transport activities, also returns a small amount of CO 2 to the atmosphere. A schematic of the model used to calculate the magnitudes of these processes is shown as Sup-plementary Figure S1 .

Even under the most zealous investment programme, biochar production will ultimately be limited by the rate at which biomass can be extracted and pyrolysed without causing harm to the bio-sphere or to human welfare. Globally, human activity is responsible for the appropriation of 16 Pg C per year from the biosphere, which corresponds to 24 % of potential terrestrial net primary productiv-ity (NPP) 22 . Higher rates of appropriation will increase pressure on global ecosystems, exacerbating a situation that is already unsus-tainable 23 .

Th e main aim of this study is to provide an estimate of the theo-retical upper limit, under current conditions, to the climate-change mitigation potential of biochar when implemented in a sustain-able manner. Th is limit, which we term the maximum sustainable technical potential (MSTP), represents what can be achieved when the portion of the global biomass resource that can be harvested sustainably (that is, without endangering food security, habitat or soil conservation) is converted to biochar by modern high-yield, low-emission, pyrolysis methods. Th e fraction of the MSTP that is actually realized will depend on a number of socioeconomic fac-tors, including the extent of government incentives and the relative emphasis placed on energy production relative to climate-change mitigation. Aside from assuming a maximum rate of capital invest-ment that is consistent with that estimated to be required for cli-mate-change mitigation 24 , this study does not take into account any economic, social or cultural barriers that might further limit the adoption of biochar technology.

Our analysis shows that sustainable global implementation of biochar can potentially off set a maximum of 12 % of current anthro-pogenic CO 2 -C equivalent (CO 2 -C e ) emissions (that is, 1.8 Pg CO 2 -C e per year of the 15.4 Pg CO 2 -C e emitted annually), and that over the course of a century, the total net off set from biochar would be 130 Pg CO 2 -C e . We also show that conversion of all sustainably obtained biomass to maximize bioenergy, rather than biochar, pro-duction can off set a maximum of 10 % of the current anthropogenic CO 2 -C e emissions. Th e relative climate-mitigation potentials of bio-char and bioenergy depend on the fertility of the soil amended and the C intensity of the fuel being off set, as well as the type of biomass. Locations at which the soil fertility is high and coal is the fuel being off set are best suited for bioenergy production. Th e climate-miti-gation potential of biochar (with combined energy production) is higher for all other situations.

Results Sustainable biomass-feedstock availability . To ensure that our estimates represent a sustainable approach, we use a stringent set of criteria to assess potential feedstock availability for biochar

production. Of primary importance is the conversion of land to generate feedstock. In addition to its negative eff ects on ecosystem conservation, land clearance to provide feedstock may also release carbon stored in soils and biomass, leading to unacceptably high carbon-payback times before any net reduction in atmospheric CO 2 is achieved (ref. 25, Supplementary Methods and Supplementary Fig. S2 ). For example, we fi nd that a land-use change carbon debt greater than 22 Mg C ha − 1 (an amount that would be exceeded by conversion of temperate grassland to annual crops 25 ) will result in a carbon-payback time that is greater than 10 years. Clearance of rainforests to provide land for biomass-crop production leads to carbon payback times in excess of 50 years. Where rainforest on peatland is converted to biomass-crop production, carbon-payback times may be in the order of 325 years. We therefore assume that no land clearance will be used to provide biomass feedstock, nor do we include conversion of agricultural land from food to biomass-crop production as a sustainable source of feedstock, both because of the negative consequences for food security and because it may indirectly induce land clearance elsewhere 26 . Some dedicated biomass-crop production on abandoned, degraded agricultural soil has been included in this study as this will not adversely aff ect food security 27 and can improve biodiversity 28,29 . We further assume that extraction rates of agricultural and forestry residues are suffi ciently low to preclude soil erosion or loss of soil function, and that no industrially treated waste biomass posing a risk of soil contamination will be used.

Other constraints on biochar production methods arise because emissions of CH 4 , N 2 O, soot or volatile organic compounds combined with low biochar yields (for example, from traditional charcoal kilns or smouldering slash piles) may negate some or all of the carbon-sequestration benefi ts, cause excessive carbon-payback times or be detrimental to health. Th erefore, we do not consider any biochar pro-duction systems that rely on such technologies, and restrict our analy-sis to systems in which modern, high-yield, low-emission pyrolysis technology can feasibly be used to produce high-quality biochar.

Within these constraints, we derived a biomass-availability sce-nario for our estimate of MSTP, as well as two additional scenarios, Alpha and Beta, which represent lower demands on global biomass resources ( Table 1 ). Attainment of the MSTP would require sub-stantial alteration to global biomass management, but would not endanger food security, habitat or soil conservation. Th e Alpha sce-nario restricts biomass availability to residues and wastes available using current technology and practices, together with a moderate amount of agroforestry and biomass cropping. All three scenarios represent fairly ambitious projects, and require progressively greater levels of political intervention to promote greater adoption of sus-tainable land-use practices and increase the quantity of uncontami-nated organic wastes available for pyrolysis. We do not consider any scenarios that are not ambitious in this study, as the intention is to investigate whether biochar could make a substantial contribution to climate-change mitigation — an aspiration that certainly will not be accomplished by half-hearted measures. Th e range of mitigation results reported thus refers only to the scenarios considered and does not encompass the full range of less-eff ective outcomes correspond-ing to varying levels of inaction. Th e scenarios are based on current biomass availability ( Supplementary Methods and Supplementary Tables S1 and S2 ), the composition and energy contents of diff erent types of biomass and the biochar derived from each ( Supplementary Tables S3 and S4 ), and the rate of adoption of biochar technology ( Supplementary Fig. S3 ). How this biomass resource base changes over the course of 100 years will depend on the potential eff ects of changing climate, atmospheric CO 2 , sea level, land use, agricultural practices, technology, population, diet and economic development. Some of these factors may increase biomass availability and some may decrease it. A full assessment of the wide range of possible future scenarios within plausible ranges of these factors remains outside the scope of this study.

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Avoided GHG emissions . Results for the three scenarios are expressed below as a range from the Alpha scenario fi rst to the MSTP last. Th e model predicts that maximum avoided emissions of 1.0 – 1.8 Pg CO 2 -C e per year are approached by mid-century and that, aft er a century, the cumulative avoided emissions are 66 – 130 Pg CO 2 -C e ( Fig. 2 ). Half of the avoided emissions are due to the net car-bon sequestered as biochar, 30 % to replacement of fossil-fuel energy by pyrolysis energy and 20 % to avoided emissions of CH 4 and N 2 O. Cumulative and annual avoided emissions for the individual gases CO 2 , CH 4 and N 2 O are given in Supplementary Figures S4 – S6 .

A detailed breakdown of the sources of cumulative avoided GHG emissions over 100 years is given in Figure 3 . The two most important factors contributing to the avoided emissions from biochar are carbon stored as biochar in soil (43 – 94 Pg CO 2 -C e ) and fossil-fuel offsets from coproduction of energy (18 – 39 Pg CO 2 -C e ).

Of the benefi cial feedbacks, the largest is due to avoided CH 4 emissions from biomass decomposition (14 – 17 Pg CO 2 -C e ), pre-dominantly arising from the diversion of rice straw from paddy fi elds (see Supplementary Table S5 for estimate of the mean CH 4 emission factor). Th e next largest positive feedbacks, in order of decreasing magnitude, arise from biochar-enhanced NPP on cropland, which contributes 9 – 16 Pg CO 2 -C e to the net avoided emissions (if these increased crop residues are converted to bio-char), followed by reductions in soil N 2 O emissions (4.0 – 6.2 Pg CO 2 -C e ), avoided N 2 O emissions during biomass decomposition (1.8 – 3.3 Pg CO 2 -C e ) and enhanced CH 4 oxidation by dry soils (0.44 – 0.8 Pg CO 2 -C e ).

Of the adverse feedbacks, biochar decomposition is the largest (8 – 17 Pg CO 2 -C e ), followed by loss of soil organic carbon due to diver-sion of biomass from soil into biochar production (6 – 10 Pg CO 2 -C e ), and transport (1.3 – 1.9 Pg CO 2 -C e , see Supplementary Fig. S7 ). Contri-butions to the overall GHG budget from tillage (0.03 – 0.044 Pg CO 2 -C e ) and reduced N-fertilizer production (0.2 – 0.3 Pg CO 2 -C e ) are neg-ligible (although their fi nancial costs may not be).

Th e relative importance of all these factors to the GHG budget varies considerably among feedstocks. Notably, rice residues, green waste and manure achieve the highest ratios of avoided CO 2 -C e emis-sions per unit of biomass-carbon (1.2 – 1.1, 0.9 and 0.8 CO 2 -C e / C, respectively) because of the benefi ts of avoided CH 4 emissions.

Sensitivity and Monte Carlo analyses . Sensitivity and Monte Carlo analyses with respect to reasonable values of key variables were used to estimate the uncertainty of the model results; they suggest areas in which future research is most needed and provide guidance on how biochar production systems might be optimized ( Fig. 4 ).

Th e strongest sensitivity is to the half-life of the recalcitrant frac-tion of biochar (see also Supplementary Table S6 ). Net avoided GHG emissions vary by − 22 % to + 4 % from that obtained using the base-line assumption of 300 years. However, most of this variation occurs for half-life < 100 years, in which range we fi nd (in agreement with previous work 30 ) that sensitivity to this factor is high. Conversely, for a more realistic half-life of the recalcitrant fraction ( > 100 years), sensitivity to this factor is low because biochar can be produced much more rapidly than it decays. As currently available data sug-gest that the half-life of biochar ’ s recalcitrant fraction in soil is in the

Table 1 | Annual globally sustainable biomass feedstock availability.

Biomass available in scenario (Pg C per year)

Alpha Beta Maximum sustainable technical potential

Rice 0.22 0.25 0.28 Rice husks and 70 % of paddy rice straw

not used for animal feed Rice husks and 80 % of paddy rice straw not used for animal feed

Rice husks and 90 % of paddy rice straw not used for animal feed

Other cereals 0.072 0.13 0.18 8 % of total straw and stover (assumes

25 % extraction rate of crop residues minus quantity used as animal feed)

14 % of total straw and stover (35 % extraction rate minus animal feed)

20 % of total straw and stover (45 % extraction rate minus animal feed)

Sugar cane 0.09 0.11 0.13 Waste bagasse plus 25 % of fi eld trash Waste bagasse plus 50 % of fi eld trash Waste bagasse plus 75 % of fi eld

trash

Manures 0.10 0.14 0.19 12.5 % of cattle manure plus 50 % of pig

and poultry manure 19 % of cattle manure plus 70 % of pig and poultry manure

25 % of cattle manure plus 90 % of pig and poultry manure

Biomass crops 0.30 0.45 0.60 50 % of potential production of abandoned,

degraded cropland that is not in other use 75 % of potential production of abandoned, degraded cropland that is not in other use

100 % of potential production of abandoned, degraded cropland that is not in other use

Forestry residues 0.14 0.14 0.14 44 % of difference between reported fellings and extraction

Agroforestry 0.06 0.34 0.62 17 Mha of tropical silvopasture 85 Mha of tropical grass pasture converted

to silvopasture 170 Mha of tropical grass pasture converted to silvopasture

Green / wood waste 0.029 0.085 0.14 75 % of low-end estimate of yard-

trimmings production and wood-milling residues

Alpha plus mid-range estimate of yard trimmings plus urban food waste, including 40 % of waste sawnwood (legislation required to ensure that this fraction of waste wood is free of harmful contaminants)

Beta plus high-end estimate of global yard trimmings and food waste, including 80 % of waste sawn wood

Total 1.01 1.64 2.27

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millennial range (see Supplementary Methods , Supplementary Table S6 and refs 8, 15, 16, 31), the contribution of its decay to the net GHG balance over centennial timescales is likely to be small.

Th e next largest sensitivity is to the pyrolysis carbon yield ( − 9 % to + 11 % ), indicating the importance of engineering to optimize for high yields of biochar rather than for energy production. Th is will be constrained, however, by the sensitivity to the labile fraction of the biochar ( − 7 % to + 4 % ), which indicates the importance of opti-mizing for production of recalcitrant biochar rather than for higher yields of lower-quality biochar.

Aft er carbon yield, the next largest sensitivity is to the carbon intensity of the fuel off set by pyrolysis energy production, with net avoided emissions varying by − 4 % from the baseline assumption when natural gas is the fuel being off set and by + 15 % when coal is off set.

Varying the impact of biochar amendment on soil N 2 O emissions from zero to the largest reported reduction (80 % ; ref. 32) produces a sensitivity of − 4 % to + 11 % . Further variability in the impact of biochar on N 2 O emissions arises from adjusting the fraction of biomass-N that (if left to decompose) would be converted to N 2 O-N, from the Intergovernmental Panel on Climate Change

default values assumed in this study up to the higher rate of 5 % suggested by more recent work 33,34 . Th is would increase the net avoided GHG emissions by up to 8 % .

Uncertainty in the response of crop yields to biochar amend-ment results in estimated range of − 6 % to + 7 % in the impact of enhanced NPP of cropland on net avoided GHG emissions.

Sensitivities to the pyrolysis energy effi ciency ( ± 5 % ), to the half-life of the biochar ’ s labile fraction ( − 4 % to + 1 % ) and to its impact on soil CH 4 oxidation ( ± 1 % ) are small.

Th e net eff ect of covariance of the above factors was assessed using the Monte Carlo analysis ( n = 1,000, Supplementary Table S7 ). Despite limited data on the decomposition rate of biochar in soils and the eff ects of biochar additions on soil GHG fl uxes, sensitiv-ity within realistic ranges of these parameters is small, resulting in an estimated uncertainty of ± 8 to 10 % ( ± 1 s.d.) in the cumulative avoided GHG emissions for the three scenarios.

Comparison of biochar and bioenergy approaches . Th e mitiga-tion impact of the renewable energy obtained from both biochar production and biomass combustion depends on the carbon inten-sity (that is, the mass of carbon emitted per unit of total energy produced) of the off set energy sources 11 . At our baseline carbon inten-sity (17.5 kg C GJ − 1 ; see Methods section), the model predicts that, on an average, the mitigation impact of biochar is 27 – 22 % (14 – 23 Pg CO 2 -C e ) larger than the 52 – 107 Pg CO 2 -C e predicted if the same sus-tainably procured biomass were combusted to extract the maximum amount of energy ( Fig. 2 ). Th is advantage of biochar over bioenergy is largely attributable to the benefi cial feedbacks from enhanced crop yields and soil GHG fl uxes ( Fig. 3 , Supplementary Fig. S8 ).

Because the principal contribution of biomass combustion to avoided GHG emissions is the replacement of fossil fuels ( Fig. 3 ), the bioenergy approach shows a considerably higher sensitivity to carbon intensity than does biochar ( Fig. 5 ). Th e carbon intensity of off set energy varies from near-zero for renewable and nuclear energy to 26 kg C GJ − 1 for coal combustion 35 . Mean cumulative avoided emis-sions from biochar and biomass combustion are equal in our sce-narios when the carbon intensity of off set energy is 26 – 24 kg C GJ − 1 ( Fig. 5 ). In the MSTP scenario, this corresponds to an energy mix to which coal combustion contributes about 80 % , whereas in the Alpha scenario, the mean mitigation benefi t of biochar remains higher than that of bioenergy, even when 100 % coal is off set. Th e cumulative avoided emissions from both strategies decrease as the carbon inten-sity of the off set energy mix decreases, but the rate of decrease for biomass combustion is 2.5 – 2.7 times greater than that for biochar. As expected, the cumulative avoided emissions for biomass combustion are essentially zero when the carbon intensity of the energy mix is also zero. In contrast, the cumulative avoided emissions for biochar are still substantial at 48 – 91 Pg CO 2 -C e .

Given that much of the increased climate mitigation from bio-char relative to biomass combustion stems from the benefi cial feedbacks of adding biochar to soil, and that these feedbacks will be greatest on the least fertile soils, the relative mitigation poten-tials will vary regionally with soil type (see Supplementary Meth-ods , Supplementary Fig. S9 and Supplementary Tables S8 – S11 for an account of how these feedbacks are calculated). Th e distribution of soils of varying fertility on global cropland is shown in Figure 6 . Globally, 0.31 Gha of soils with no fertility constraints are in use as cropland, as well as 0.29 Gha of cropland with few fertility con-straints, 0.21 Gha with slight constraints, 0.32 Gha with moderate constraints, 0.18 Gha with severe constraints, 0.13 Gha with very severe constraints and 0.09 Gha of cropland on soils categorized as unsuitable for crop production. Th e amount of biomass produced in soils of diff erent fertilities is shown in Supplementary Table S12 . Figure 7 shows how the climate mitigation from biochar varies rela-tive to biomass combustion when both soil fertility and the carbon intensity of energy off sets are considered. Th e relative benefi t of

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Figure 2 | Net avoided GHG emissions. The avoided emissions are

attributable to sustainable biochar production or biomass combustion over

100 years, relative to the current use of biomass. Results are shown for

the three model scenarios, with those for sustainable biochar represented

by solid lines and for biomass combustion by dashed lines. The top panel

shows annual avoided emissions; the bottom panel, cumulative avoided

emissions. Diamonds indicate transition period when biochar capacity of

the top 15 cm of soil fi lls up and alternative disposal options are needed.

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producing biochar compared with biomass combustion is greatest when biochar is added to marginal lands and the energy produced by pyrolysis is used to off set natural gas, renewable or nuclear energy. When biochar is added to the most infertile cropland to off set the current global primary energy mix ( M w ), which has a car-bon intensity of 16.5 kg C GJ − 1 , the relative benefi t from biochar is as much as 79 – 64 % greater than that from bioenergy ( Fig. 7 , Supple-mentary Fig. S10 ). Th is net benefi t diminishes as more coal is off set and as biochar is added to soils with higher fertility. Nevertheless, with the exception of those geographical regions having both natu-rally high soil fertility and good prospects for off setting coal emis-sions (in which bioenergy yields up to 16 – 22 % greater mitigation impact than biochar), biochar shows a greater climate-mitigation potential than bioenergy. Th e relative benefi t of producing biochar compared with bioenergy is greatest when biomass crops are used as

feedstocks ( Fig. 7b ), because avoided CH 4 emissions from the use of manure, green waste and rice residues occur regardless of whether these other feedstocks are used for energy or biochar.

Discussion Our analysis demonstrates that sustainable biochar production (with addition to soils) has the technical potential to make a substan-tial contribution to mitigating climate change. Maximum avoided emissions of the order of 1.8 Pg CO 2 -C e annually, and of 130 Pg CO 2 -C e over the course of a century, are possible at current levels of feedstock availability, while preserving biodiversity, ecosystem stability and food security.

Th e biochar scenarios described here, with their very high levels of biomass utilization, are not compatible with simultaneous imple-mentation of an ambitious biomass energy strategy. Th e opportunity

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Figure 3 | Breakdown of cumulative avoided GHG emissions (Pg CO 2 -C e ) from sustainable biochar production. The data are for the three model

scenarios over 100 years by feedstock and factor. The left side of the fi gure displays results for each of eight feedstock types and the additional biomass

residues that are attributed to NPP increases from biochar amendments; the right side displays total results by scenario for both biochar (left column) and

biomass combustion (right column). For each column, the total emission-avoiding and emission-generating contributions are given, respectively, by the

height of the columns above and below the zero line. The net avoided emissions are calculated as the difference between these two values. Within each

column, the portion of its contribution caused by each of six emission-avoiding mechanisms and three emission-generating mechanisms is shown by a

different colour. These mechanisms (from top to bottom within each column) are (1) avoided CH 4 from biomass decay, (2) increased CH 4 oxidation by

soil biochar, (3) avoided N 2 O from biomass decay, (4) avoided N 2 O caused by soil biochar, (5) fossil fuel offsets from pyrolysis energy production,

(6) avoided CO 2 emissions from carbon stored as biochar, (7) decreased carbon stored as soil organic matter caused by diversion of biomass to biochar,

(8) CO 2 emissions from transportation and tillage activities and (9) CO 2 emissions from decomposition of biochar in soil.

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cost of this forgone energy resource must be taken into account in an economic comparison of the two strategies. However, in terms of their potentials for climate-change mitigation, the mitigation impact of biochar is about one-fourth larger, on an average, than that obtained if the same biomass were combusted for energy. Regional deviations from this average are large because of diff erences in soil fertility and available biomass. Our model predicts that the relative climate-mitigation benefi t of biochar compared with bioenergy is greatest in regions in which poor soils growing biomass crops can benefi t most from biochar additions. In contrast, biomass combustion leads to a greater climate-mitigation impact in regions with fertile soils where coal combustion can be eff ectively off set by biomass energy production. Th e global climate-mitigation potential achiev-able from the use of terrestrial biomass may thus be maximized by a mixed strategy favouring bioenergy in those regions with fertile soils where coal emissions can be off set, and biochar elsewhere. Nevertheless, we have included biochar production in fertile, coal-intensive regions in our scenarios because other potential benefi ts of biochar, such as its potential for more effi cient use of water and crop nutrients 36 – 38 , may favour its use even in such regions.

We emphasize that the results presented here assume that future biochar production follows strict sustainability criteria. Land-use changes that incur high carbon debts and biochar production using technologies with poorly controlled emissions lead to both large reduc-tions in avoided emissions and excessively long carbon-payback times, during which net emissions are increased before any net reduction is observed. Biochar production and use, therefore, must be guided by well-founded and well-enforced sustainability protocols if its poten-tial for mitigating climate change is to be realized.

Methods Overall approach . A model (BGRAM version 1.1) to calculate the net avoided GHG emissions attributable to sustainable biochar production as a function of time was developed and applied to the three scenarios. Th is model includes the eff ects of

feedstock procurement, transport, pyrolysis, energy production, soil incorporation, soil GHG fl ux, soil fertility and fertilizer use (see also Supplementary Table S13 ), and biomass and biochar decomposition (see also Supplementary Fig. S11 ). Th e net avoided GHG emissions due to biochar were calculated as the diff erence between the CO 2 -equivalent emissions from biochar production and those that would have occurred as the biomass decomposed by other means had it not been converted to biochar. All emissions (actual or avoided) were calculated with time dependency. Wherever possible, conservative assumptions were used to provide a high degree of confi dence that our results represent a conservative estimate of the avoided GHG emissions achievable in each scenario. A detailed account of both the model and the three scenarios is given in Supplementary Methods .

Sustainability criteria . Biochar can be produced sustainably or unsustainably. Our criteria for sustainable biochar production require that biomass procured from agricultural and silvicultural residues be extracted at a rate and in a manner that does not cause soil erosion or soil degradation; crop residues currently in use as animal fodder not be used as biochar feedstock; minimal carbon debt be incurred from land-use change or use of feedstocks with a long life expectancy; no new lands be converted into biomass production and no agricultural land be taken out of food production; no biomass wastes that have a high probability of contamina-tion, which would be detrimental to agricultural soils, be used; and biomass crop production be limited to production on abandoned agricultural land that has not subsequently been converted to pasture, forest or other uses. We further require that biochar be manufactured using modern technology that eliminates soot, CH 4 and N 2 O emissions while recovering some of the energy released during the pyrolysis process for subsequent use.

Greenhouse gases . We consider three GHGs in this analysis: CO 2 , CH 4 and N 2 O (see Supplementary Table S14 for a summary of estimated global warming poten-tials for these GHGs). Although the diff erent atmospheric lifetimes of these gases ensure that there is no equivalence among them in any strict sense, we nevertheless adopt the common practice of normalizing each gas to a ‘ CO 2 -C equivalent ’ using the estimated radiative forcing produced by the emission of each gas, integrated over a 100-year period following emission, using Intergovernmental Panel on Cli-mate Change 100-year global warming potentials 39 of 23 for CH 4 and 296 for N 2 O.

Comparison with bioenergy . To compare the net avoided GHG emissions stem-ming from biochar with those from bioenergy production, we apply the same model and sustainability criteria, but assume complete combustion to liberate the maxi-mum possible energy, rather than slow pyrolysis, as the conversion technology.

5 10 15 20 25 300

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Figure 5 | Cumulative mitigation potential (100 years) of biochar and biomass combustion as a function of carbon intensity of the type of energy being offset. The black vertical dashed line labelled M b on the

upper x axis refers to the carbon intensity of the baseline energy mix

assumed in this study. Grey vertical dashed lines at 15, 19 and 26 kg C GJ − 1

denote the carbon intensity of natural gas, oil and coal, respectively. The

carbon intensity of renewable forms of energy is close to 0 kg C GJ − 1 .

-20 -10 0 10 20

Deviation from reportedestimate (%)

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Decrease in soil N2O emissions (%)

Cropland NPP (% yield response)

Labile-C fraction (%)

Global N2O emission factor (%)

Pyrolysis energy efficiency (%)

Half-life labile C (y)

Half-life recalcitrant C (y)

Soil CH4 oxidation (mg CH4 per m2

per year)

100 200

Figure 4 | Sensitivity of the model to key variables. Sensitivity is

expressed as a percentage deviation from the reported value of cumulative

net avoided GHG emissions over 100 years for each scenario. Top (blue),

middle (yellow) and bottom (red) bars for each variable correspond to

Alpha, Beta and MSTP scenarios. Minimum and maximum values for

each variable are at the ends of the bars (with additional sensitivities to

recalcitrant carbon half-life of 100 and 200 years shown); baseline values

of the key variables used in this study correspond to 0 % deviation.

See also Supplementary Table S7 .

ARTICLE

8




Soil application and fertility classifi cation . Maximum biochar application to the top 0.15 m of agricultural soils was assumed to be 50 Mg C ha − 1 . It was assumed that only 20 % of pasture soils will receive these application rates because of constraints from terrain, accessibility, fi re and wind. See Supplementary Methods and Supplementary Figure S12 .

Soil-fertility classifi cations were taken from ref. 40. Th ese were combined with a 5-minute resolution map of global cropland distribution 41 to produce a global map of cropland, categorized by the severity of soil-fertility constraints ( Fig. 6 ).

Carbon intensity of fuel offsets . Th e baseline carbon intensity of the fuel off sets ( M b ) used here is 17.5 kg C GJ − 1 . Th e current world primary-energy mix ( M w ) has a carbon intensity of 16.5 kg C GJ − 1 and the current world electricity-generation mix ( M ew ) has a carbon intensity of 15 kg C GJ − 1 (ref. 42). See Supplementary Methods for the derivation of these carbon intensities.

Technology adoption rate . Th e rate at which installed biochar production capac-ity approaches its maximum is constrained by simple economic considerations. Data for estimated capital costs are shown in Supplementary Table S15 . Th ese are implemented in the model using a Gompertz curve ( Supplementary Methods) . Th e model allows for a lead time of 5 years, during which little plant capacity is commissioned. Slow-to-moderate investment for the remainder of the fi rst decade and rapid adoption over the following three decades at a rate of capital investment consistent with the 2 % of global gross domestic product that Lord Stern estimates to be required for climate-change mitigation 24 culminate in near-maximal biochar production rates aft er a total of four decades. Net avoided GHG emissions over the fi rst decade are negligible, because of a combination of initially slow adoption and carbon-debt payback.

References 1 . Raupach , M . R . et al. Global and regional drivers of accelerating CO 2 emissions .

PNAS 104 , 10288 – 10293 ( 2007 ).

Figure 7 | Cumulative mitigation potential of biochar relative to bioenergy. The mitigation potential is reported as a function of both soil

fertility and carbon intensity of the type of energy being offset (in the

MSTP scenario). Points M ew , M w and M b on the upper x axis refer to the

carbon intensity of the current world electricity mix, the current world

primary energy mix and the baseline energy mix assumed in our scenarios,

respectively. Carbon intensity values for natural gas, oil and coal are also

indicated. The relative mitigation is calculated as cumulative avoided

emissions for biochar minus those for bioenergy, expressed as a fraction of

the avoided emissions for bioenergy (for example, a value of 0.1 indicates

that the cumulative mitigation impact of biochar is 10 % greater than that

of bioenergy, a value of − 0.1 indicates that it is 10 % lower and a value of

zero indicates that they have the same mitigation impact). The soil-fertility

classifi cations marked on the vertical axis correspond to the soil categories

mapped in Figure. 6 . Panel a (Residues) includes agricultural and forestry

residues, together with green waste, as biomass inputs; Panel b (Biomass

crops) includes both dedicated biomass crops and agroforestry products

as biomass inputs. Panel c (Manures), includes bovine, pig and poultry

manure as biomass inputs. Panel d (Total) includes all sources of biomass

inputs in the proportions assumed in our model. An analogous fi gure for

the Alpha scenario is shown as Supplementary Figure S10 .

Figure 6 | Soil-fertility constraints to cropland productivity (5 � resolution). Soil fertility is indicated by hue, whereas the percentage of the gridcell

currently being used as cropland is indicated by colour saturation (with white indicating the absence of cropland in a grid cell).

ARTICLE

9




2 . Solomon , S . , Plattner , G . , Knutti , R . & Friedlingstein , P . Irreversible climate change due to carbon dioxide emissions . PNAS 106 , 1704 – 1709 ( 2009 ).

3 . Broecker , W . S . Climate change: CO 2 arithmetic . Science 315 , 1371 ( 2007 ). 4 . Matthews , H . D . & Caldeira , K . Stabilizing climate requires near-zero

emissions . Geophys. Res. Lett. 35 , L04705 ( 2008 ). 5 . Allen , M . R . et al. Warming caused by cumulative carbon emissions towards

the trillionth tonne . Nature 458 , 1163 – 1166 ( 2009 ). 6 . Meinshausen , M . et al. Greenhouse-gas emission targets for limiting global

warming to 2 ° C . Nature 458 , 1158 – 1162 ( 2009 ). 7 . Hansen , J . et al. Target atmospheric CO 2 : where should humanity aim? Open

Atmos. Sci. J. 2 , 217 – 231 ( 2008 ). 8 . Sombroek , W . G . , Nachtergaele , F . O . & Hebel , A . Amounts, dynamics and

sequestering of carbon in tropical and subtropical soils . Ambio 22 , 417 – 426 ( 1993 ). 9 . Lehmann , J . , Gaunt , J . & Rondon , M . Bio-char sequestration in terrestrial

ecosystems — a review . Mitig. Adapt. Strat. Glob. Change 11 , 403 – 427 ( 2006 ). 10 . Glaser , B . , Haumaier , L . , Guggenberger , G . & Zech , W . Th e ‘ Terra Preta ’

phenomenon: a model for sustainable agriculture in the humid tropics . Naturwissenschaft en 88 , 37 – 41 ( 2001 ).

11 . Fowles , M . Black carbon sequestration as an alternative to bioenergy . Biomass Bioenerg. 31 , 426 – 432 ( 2007 ).

12 . Laird , D . A . Th e charcoal vision: a win win win scenario for simultaneously producing bioenergy, permanently sequestering carbon, while improving soil and water quality . Agron. J. 100 , 178 – 181 ( 2008 ).

13 . Lehmann , J . A handful of carbon . Nature 447 , 143 – 144 ( 2007 ). 14 . Schmidt , M . W . I . & Noack , A . G . Black carbon in soils and sediments:

analysis, distribution, implications, and current challenges . Global Biogeochem. Cy. 14 , 777 – 793 ( 2000 ).

15 . Kuzyakov , Y . , Subbotina , I . , Chen , H . , Bogomolova , I . & Xu , X . Black carbon decomposition and incorporation into soil microbial biomass estimated by 14 C labeling . Soil Biol. Biochem. 41 , 210 – 219 ( 2009 ).

16 . Cheng , C . , Lehmann , J . , Th ies , J . E . & Burton , S . D . Stability of black carbon in soils across a climatic gradient . J. Geophys. Res. 113 , doi:10.1029/2007JG000642 ( 2008 ).

17 . Lehmann , D . J . & Joseph , S . Biochar for Environmental Management: Science and Technology ( Earthscan Books Ltd , 2009 ) .

18 . Lenton , T . M . & Vaughan , N . E . Th e radiative forcing potential of diff erent climate geoengineering options . Atmos. Chem. Phys. Discuss. 9 , 2559 – 2608 ( 2009 ).

19 . Lehmann , J . & Joseph , S . Chapter 9: biochar systems . in Biochar for Environmental Management: Science and Technology (eds Lehmann, J., Joseph, S.) ( Earthscan Books Ltd , 2009 ) .

20 . Whitman , T . & Lehmann , J . Biochar — one way forward for soil carbon in off set mechanisms in Africa? Environ. Sci. Policy 12 , 1024 – 1027 ( 2009 ).

21 . Elliott , D . C . Historical developments in hydroprocessing bio-oils . Energy Fuels 21 , 1792 – 1815 ( 2007 ).

22 . Haberl , H . et al. Quantifying and mapping the human appropriation of net primary production in earth’s terrestrial ecosystems . PNAS 104 , 12942 .

23 . Wackernagel , M . et al. Tracking the ecological overshoot of the human economy . PNAS 99 , 9266 ( 2002 ).

24 . Stern , N . Testimony of Lord Nicholas Stern to the Committee on Energy and Commerce . At < http://archives.energycommerce.house.gov/cmte_mtgs/110-eaq-hrg.062608.Stern-Testimony.pdf > ( 2008 ) .

25 . Fargione , J . , Hill , J . , Tilman , D . , Polasky , S . & Hawthorne , P . Land clearing and the biofuel carbon debt . Science 319 , 1235 – 1238 ( 2008 ).

26 . Searchinger , T . et al. Use of US croplands for biofuels increases greenhouse gases through emissions from land use change . Science 319 , 1238 – 1240 ( 2008 ).

27 . Field , C . B . , Campbell , J . E . & Lobell , D . B . Biomass energy: the scale of the potential resource . Trends Ecol. Evol. 23 , 65 – 72 ( 2008 ).

28 . Tilman , D . et al. Benefi cial biofuels — the food, energy, and environment trilemma . Science 325 , 270 – 271 ( 2009 ).

29 . Tilman , D . , Hill , J . & Lehman , C . Carbon-negative biofuels from low-input high-diversity grassland biomass . Science 314 , 1598 – 1600 ( 2006 ).

30 . Gaunt , J . L . & Lehmann , J . Energy balance and emissions associated with biochar sequestration and pyrolysis bioenergy production . Environ. Sci. Technol. 42 , 4152 – 4158 ( 2008 ).

31 . Lehmann , J . et al. Australian climate-carbon cycle feedback reduced by soil black carbon . Nature Geosci. 1 , 832 – 835 ( 2008 ).

32 . Rondon , M . , Ramirez , J . & Lehmann , J . Charcoal additions reduce net emissions of greenhouse gases to the atmosphere . Proc. 3rd USDA Sympos. on Greenhouse Gases and Carbon Sequestration 208 ( 2005 ).

33 . Crutzen , P . J . , Mosier , A . R . , Smith , K . A . & Winiwarter , W . N 2 O release from agro-biofuel production negates global warming reduction by replacing fossil fuels . Atmos. Chem. Phys. Discuss 7 , 11191 – 11205 ( 2007 ).

34 . Del Grosso , S . J . , Wirth , T . , Ogle , S . M . & Parton , W . J . Estimating agricultural nitrous oxide emissions . Eos 89 , 529 – 530 ( 2008 ).

35 . Garg , A . , Kazunari , K . & Pulles , P . 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 2: Energy (eds Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K.) ( IGES , 2006 ) .

36 . Tryon , E . Eff ect of charcoal on certain physical, chemical, and biological properties of forest soils . Ecol. Monogr. 81 – 115 ( 1948 ).

37 . Steiner , C . et al. Nitrogen retention and plant uptake on a highly weathered central Amazonian Ferralsol amended with compost and charcoal . J. Plant Nutr. Soil Sc. 171 , 893 – 899 ( 2008 ).

38 . Lehmann , J . et al. Nutrient availability and leaching in an archaeological Anthrosol and a Ferralsol of the Central Amazon basin: fertilizer, manure and charcoal amendments . Plant Soil 249 , 343 – 357 ( 2003 ).

39 . Ramaswamy , V . et al. Chapter 6: radiative forcing of climate change . in Climate Change 2001: Th e Scientifi c Basis (eds Houghton, et al. ) ( CUP , 2001 ) .

40 . Fischer , G . , van Velthuizen , H . , Nachtergaele , F . & Medow , S . Global Agro-Ecological Assessment for Agriculture in the 21st Century ( International Institute for Applied Systems Analysis & Food and Agriculture Organization of the United Nations, Laxenburg, Austria , 2002 ) .

41 . Erb , K . A comprehensive global 5 min resolution land-use data set for the year 2000 consistent with national census data . J. Land Use Sci. 2 , 191 – 224 ( 2007 ).

42 . International Energy Agency . Key World Energy Statistics: 2009 ( IEA , 2009 ) .

Acknowledgments D.W. and F.A.S.-P. acknowledge support from the United Kingdom ’ s Natural

Environment Research Council (NERC) and Economic and Social Research Council

(ESRC). J.E.A. acknowledges support from the United States ’ Department of Energy

(USDOE) Offi ce of Science, Offi ce of Biological and Environmental Research, Climate

and Environmental Science Division, Mitigation Science Focus Area and from the

USDOE Offi ce of Fossil Energy, Terrestrial Carbon Sequestration Program. Th e Pacifi c

Northwest National Laboratory is operated for the USDOE by Battelle Memorial Institute

under contract DE-AC05-76RL01830. J.L. acknowledges support from the Cooperative

State Research Service of the U.S. Department of Agriculture and from the New York

State Energy Research and Development Authority. S.J. acknowledges support from

VenEarth Group LLC.

Author contributions D.W. and J.E.A. produced the model and wrote the paper. F.A.S.-P. supervised the

NERC / ESRC-funded PhD project of which D.W ’ s contribution formed a part.

J.L. and S.J. provided specialist advice. All authors commented on the paper.

Additional information Supplementary Information accompanies this paper on http: / / www.nature.com/

naturecommunications

Competing fi nancial interests: D.W., J.E.A., F.A.S.-P. and J.L. declare no competing fi nancial

interests. S.J. is Chairman of Anthroterra, a company conducting research into the development

of a biochar mineral complex to replace conventional fertilizers. Th is company plans to

manufacture and sell portable pyrolysers.

Reprints and permission information is available online at http://npg.nature.com/

reprintsandpermissions/

How to cite this article: Woolf, D. et al. Sustainable biochar to mitigate global climate

change. Nat. Commun. 1:56 doi: 10.1038 / ncomms1053 (2010).

Licence: Th is work is licensed under a Creative Commons Attribution-NonCommercial-

NoDerivative Works 3.0 Unported License. To view a copy of this license, visit

http://creativecommons.org/licenses/by-nc-nd/3.0/

Sustainable biochar to mitigate global climate change: Supplementary information Dominic Woolf1, James E. Amonette2, F. Alayne Street-Perrott1, Johannes Lehmann3, Stephen Joseph4 1School of the Environment and Society, Swansea University, Singleton Park, Swansea SA2 8PP, UK. 2Chemical and Materials Sciences Division, Pacific Northwest National Laboratory, Richland WA, 99352 USA (correspondence: [email protected]) 3Department of Crop and Soil Sciences, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USA 4The School of Materials Science and Engineering, University of New South Wales, Sydney NSW 2052, Australia

Contents

1 Supplementary Note ................................................................................................................................ 3 2 Supplementary Methods.......................................................................................................................... 3

2.1 Biomass Feedstock Availability ....................................................................................................... 3 2.1.1 Agricultural and forestry residues ................................................................................................ 3 2.1.2 Cereals excluding rice ................................................................................................................. 4 2.1.3 Rice............................................................................................................................................. 4 2.1.4 Sugar Cane................................................................................................................................. 4 2.1.5 Manure........................................................................................................................................ 4 2.1.6 Biomass crops............................................................................................................................. 5 2.1.7 Harvested wood .......................................................................................................................... 6 2.1.8 Forestry residues ........................................................................................................................ 6 2.1.9 Agroforestry................................................................................................................................. 6 2.1.10 Green Waste ........................................................................................................................... 7 2.1.11 Other excluded feedstocks ...................................................................................................... 7

2.2 Avoided biomass decomposition ..................................................................................................... 8 2.2.1 Carbon dioxide ............................................................................................................................ 8 2.2.2 Methane ...................................................................................................................................... 9 2.2.3 Nitrous oxide ............................................................................................................................. 10

2.3 Transport........................................................................................................................................11 2.4 Pyrolysis.........................................................................................................................................11

2.4.1 Products of pyrolysis ..................................................................................................................11 2.4.2 Energy production and fossil fuel offsets ................................................................................... 12

2.5 Biochar properties ......................................................................................................................... 13 2.5.1 Decay kinetics ........................................................................................................................... 13 2.5.2 Carbon fraction.......................................................................................................................... 14

2.6 Biochar soil application ................................................................................................................. 14 2.6.1 Application rates........................................................................................................................ 14 2.6.2 Tillage........................................................................................................................................ 15 2.6.3 Fertility and enhanced net primary productivity ......................................................................... 15 2.6.4 Estimation of biomass-yield responses to biochar soil applications........................................... 16 2.6.5 Impact on existing stocks of SOC.............................................................................................. 16 2.6.6 Soil N2O emissions.................................................................................................................... 17 2.6.7 Soil CH4 flux.............................................................................................................................. 17 2.6.8 Fertiliser application .................................................................................................................. 17

2.7 Rate of adoption............................................................................................................................ 18 2.8 Aspects not considered in the model............................................................................................. 19 2.9 Monte-Carlo and sensitivity analyses ............................................................................................ 19

3 Supplementary Figures.......................................................................................................................... 20 4 Supplementary Tables ........................................................................................................................... 31 5 Supplementary References ................................................................................................................... 42

2

List of Figures

Fig. S1 Schematic of biochar greenhouse gas assessment model, BGRAM 1.0

Fig. S2 Payback time for the carbon debt incurred by cropping for biochar in dedicated biomass plantations

Fig. S3 Annual global biomass pyrolysed as a function of time in each of the scenarios

Fig. S4 Net avoided CO2 emissions relative to current use of biomass that are attributable to sustainable biochar production or biomass combustion over 100 yr

Fig. S5 Net avoided CH4 emissions relative to current use of biomass that are attributable to sustainable biochar production or biomass combustion over 100 yr

Fig. S6 Net avoided N2O emissions relative to current use of biomass that are attributable to sustainable biochar production or biomass combustion over 100 yr

Fig. S7 Land area over which biochar production is assumed (for the purpose of calculating transport distances) to be dispersed (5' of arc resolution)

Fig. S8 Breakdown of cumulative avoided GHG emissions from both biochar production and biomass combustion for the three model scenarios over 100 years by feedstock and factor

Fig. S9 Relative biomass yields for biochar-amended soils

Fig. S10 Cumulative mitigation potential of biochar relative to bioenergy as a function of both soil fertility and C-intensity of the type of energy being offset (in the Alpha scenario)

Fig. S11 Effect of soil black carbon concentration on labile soil organic carbon pool

Fig. S12 Land currently under pasture that is suitable for tropical silvopasture (5’ of arc resolution)

List of Tables

Table S1 Global quantities of non-rice straw and stover available as feedstock for pyrolysis

Table S2 Summary of biomass availability in scenarios

Table S3 Biomass properties used for modeling

Table S4 Biochar properties used for modeling

Table S5 Methane emission factors for rice straw

Table S6 Estimates of biochar decay half-life

Table S7 Range, mean and standard distribution of variables used in sensitivity and Monte-Carlo analyses

Table S8 Cultivated land area classified by soil fertility constraints

Table S9

Assumed biomass yield responses to biochar amendments as fraction of the maximum potential response

Table S10 Dataset used to develop function describing biomass yield response to biochar soil amendments

Table S11 Relative biomass yields (RBYs) per Mg C ha-1 biochar application

Table S12 Mass of crop residues (Pg DM) produced on soils of different fertility classes

Table S13 Increase in soil cation exchange capacity (CEC) as a function of soil fertility class

Table S14 Calculation of current anthropogenic GHG emissions

Table S15 Estimated capital costs of biomass pyrolysis with power generation

3

1 Supplementary Note

The term “biochar” first appeared in the peer-reviewed scientific literature in 1999 where it was used to describe an activated carbon prepared from sorghum grain for use in a reverse-burn gasification (Chem Char) process for treating hazardous waste43. The lead author of this paper, Bapat, recalls that they invented the term to differentiate the sorghum-based material from coal-derived activated carbon used in earlier implementations of the process; they had intended to develop similar materials from other grains44. For several years thereafter biochar was used primarily in the bioenergy literature to describe a charcoal prepared from various crop residues for use as fuel. The technological concept of using biochar on a large scale as a climate-change mitigation approach stems from two papers published independently in 1993 (refs 45,46), well before the term itself was invented. Ref. 45 clearly described the large reservoir of carbon stored in soils, the historical use of charcoal by indigenous peoples in the Amazon region as a soil amendment with multiple benefits, and its potential as a climate-change mitigation strategy. Ref. 46, on the other hand, focused on the climate-change mitigation aspects from large-scale industrial production and burial of charcoal in landfills; he did not consider its use as a soil amendment per se. To our knowledge, the matching of the term “biochar” with the climate-change mitigation concept did not occur until March of 2005 in a presentation by Lehmann entitled “Bio-char sequestration in soil: A new frontier”47 which was followed by two publications in 2006 that used the term with its current meaning48,49. Lehmann relates that the term stemmed from a discussion he had with Peter Read while working on the revisions to one of these publications and preparing the 2005 presentation.

2 Supplementary Methods

2.1 Biomass Feedstock Availability

We consider three scenarios for global biomass availability: scenario ‘Alpha’, which involves the use of biomass available with little change to current practices; scenario ‘Beta’, which assumes some legislation or incentives to promote both sustainable land-use practices and reduced contamination of biomass waste streams; and a ‘Maximum Sustainable Technical Potential’ (MSTP) scenario representing the maximum biochar production technically achievable within our sustainability constraints if humanity were to strive to do their utmost to mitigate climate change. In assessing the biomass availability within these scenarios, no account has been taken of the impact of climate change on biomass availability. Rather, this analysis is premised upon the assumption that biochar production will form part of a suite of mitigation measures that succeed in controlling climate change to within reasonable bounds. If other strong mitigation measures are not also put in place, reductions in global crop yields may reduce the availability of feedstock from agricultural residues. Other feedstocks such as timber from tree mortalities may however increase. The net impact on biomass availability under various warming scenarios is beyond the scope of this study.

Nor have we included the effects of population change in this assessment for the reason that its main effect on biomass availability is likely to be the clearance of land to increase food production for a growing population, resulting in an increase in production of agricultural residues and thus greater biomass availability. However, for the reasons given in section 2.1.6, land clearance to provide biomass feedstock is not a viable climate mitigation strategy. We have therefore chosen to investigate what the potential for biochar production is assuming that no land clearance is used to provide feedstock. Since, in practice, it will be very difficult to distinguish land-clearance driven by population growth from other drivers, the best way to maintain clarity that this assessment includes only the potential for biochar without land clearance is to omit any potential increases in feedstock arising from population growth. While this may lead to a low (conservative) estimate of the availability of agricultural residues, it does provide transparency about how the sustainability principles are applied.

The availability of sustainable biomass feedstocks, whose utilisation does not entail an increase in human appropriation of global net primary productivity, is broken down by category of feedstock.

2.1.1 Agricultural and forestry residues

World production of crop residues was 3.8 Pg dry matter in 200150, 75% of which was from cereals and 8% from sugar cane. Of the remaining 17% of crop residues, most are produced in too small quantities per hectare to remove them without unacceptable risk of soil erosion and loss of soil organic matter50, or have too high a water content to be well-suited to pyrolysis. Resources such as nut and

4

peanut shells, sunflower residues and orchard prunings may be of local importance, but together amount to only approximately 1.5% of total residue production and were therefore omitted from this analysis on the basis that they are not of high global significance.

2.1.2 Cereals excluding rice

The fraction of crop residues that may be removed without incurring unacceptable deterioration or loss of soil is highly variable depending on soil, climate, terrain and management regime. In practice, therefore, acceptable levels of extraction need to be assessed on a site-by-site basis51. Estimates of the fraction of above-ground cereal crop residues that may be removed without causing undue harm include 25% (ref. 52), 27% (ref. 53), <= 25% in erosion-prone soils (ref. 54), 30% (ref. 51), 25-50% (ref. 55), and 40% with mulch-till and 70% with no-till (ref. 56). Those studies that support higher rates of extraction (refs 55,56) are based on modeling, whereas studies based on field measurements (refs 51-54) tend to support lower levels of extraction. It is also important to note that all of these studies are based on North America; extrapolation to the global situation must therefore be done with some caution. Accordingly, we have assumed maximum 25%, 35% and 45% cereal residue extraction rates in the Alpha, Beta and MSTP scenarios, respectively. Achieving the higher values of residue extraction will require global adoption of best practices for soil conservation, including use of cover crops which can significantly reduce soil erosion57-61. Where harvested straw is utilised as animal bedding, it is assumed that it will still subsequently become available for pyrolysis once mixed with manure. The most important use of harvested straw that is not compatible with subsequent pyrolysis at end of life is its use as animal fodder – a practice that is widespread in the developing world but little employed in the developed world (other than cereals grown for silage which are not included in crop residue production). Where the fraction of cereal straw already used for fodder exceeds the maximum extraction rates determined by soil conservation considerations, we have assumed that no further extraction to provide pyrolysis feedstock will be undertaken. The total straw and stover from non-rice cereals that will thus be available for pyrolysis under each scenario is given in Table S1. Once maximum extraction rates and use of straw as fodder are accounted for, the total amounts of straw and stover available for pyrolysis amount to 8, 14 and 20% of total residues generated in the three scenarios, respectively.

2.1.3 Rice

Globally, 890 Tg yr-1 of paddy rice straw are produced50, of which 230 Tg yr-1 are used for animal feed62. No rice straw is required to be left on the paddy fields for erosion control63. In addition to this, 86 Tg yr-1 rice hulls are produced and being generally regarded as a waste product48 may be available as feedstock. Thus, the maximum biomass from rice that is potentially available is 746 Tg yr-1. In the MSTP scenario, we assume that straw used for purposes such as animal bedding and thatch will become available at end of life and that extraction losses will be 10%, to yield 671 Tg DM yr-1 available for pyrolysis. In the Alpha and Beta scenarios, we reduce the available amount to 70% and 80%, respectively, of the maximum potentially available.

2.1.4 Sugar Cane

Cane production generates 314 Tg yr-1 residues50. Of this, approximately 50% is bagasse and 50% field trash64. We have assumed that all bagasse may be utilised, since bagasse that is currently utilised to provide heat and power to sugar mills could instead be used for cogeneration of biochar and energy, thus using a greater fraction of bagasse to provide the mill’s energy requirements while also generating less waste bagasse. Until recently, field residues were commonly burnt. However, sugar cane production is rapidly adopting the practice of green harvesting with trash left as a mulch or tilled in, with around 50% of this green-harvested field trash being recoverable in 50% of fields with currently available equipment64. We have accordingly assumed 25% field residue utilisation in the Alpha scenario rising to 50% and 75% in the Beta and MSTP scenarios respectively, on the basis that improved technology and soil conservation practices are likely to permit extraction rates greater than the present 50% up to a maximum agronomically desireable extraction rate of 75% (ref. 64). The amounts of sugar cane biomass potentially available for the three scenarios thus total 196, 239, and 275 Tg DM yr-1 for the Alpha, Beta, and MSTP scenarios, respectively.

2.1.5 Manure

Annual production of cattle and pig manures are in the order of 470 Tg C and 34 Tg C respectively65. Assuming mean carbon contents of 29.9% and 37.6% (fraction of dry matter), this gives 1570 and 90 Tg DM yr-1, respectively. We approximate annual poultry manure production as 134 Tg, based on 121 Tg from chickens62 divided by 0.9 – the fraction of poultry that is chickens (FAOSTAT 200766). Since turkeys and geese produce more manure per capita than chickens, this is probably a slightly conservative estimate.

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Estimates of the fraction of cattle manure that might be available for bioenergy feedstock range from 12.5-25%67. Accordingly, for cattle manure we have used availability factors of 12.5%, 18.75% and 25% to estimate total amounts available of 196, 294, and 392 Tg DM yr-1 for the Alpha, Beta and MSTP scenarios, respectively. For pigs and poultry, as they are kept mainly in housing or confined areas, we have assumed higher availability factors of 50, 70, and 90% for the Alpha, Beta and MSTP scenarios, respectively. Total amounts of pig manure available are thus 45, 63, and 81 Tg DM yr-1 for the Alpha, Beta and MSTP scenarios, respectively. Total amounts of poultry manure available are 67, 94, and 121 Tg DM yr-1 for the Alpha, Beta and MSTP scenarios, respectively.

2.1.6 Biomass crops

The potential for growing biomass crops to provide biochar feedstock is limited by land availability constraints including considerations of conservation of habitat, ecosystem services and ecosystem-stored carbon. Conversion of currently unmanaged land or grassland releases as CO2 carbon that was stored in plants and soil. If the purpose of the land conversion is to grow feedstock for biochar or bioenergy, this CO2 emission represents a ‘carbon debt’ that may be repaid over time by subsequent reduced or avoided emissions. The time taken until subsequent reductions in CO2 emissions exceed the initial carbon debt is referred to as the carbon payback time. Figure S2 shows the variation of carbon payback time against carbon debt, assuming that post-conversion 15 Mg ha-1 yr-1 of woody biomass are pyrolysed to biochar with biochar C yields of 0% (bioenergy production only), 25% and 50%.

To ensure that biochar production provides net environmental benefits, criteria for feedstock provision and conversion technologies that are broadly similar to strict guidelines being developed for sustainable biofuels68 will need to be implemented. Although no internationally recognised framework for assessing and certifying sustainable biofuel production has yet been adopted, guidelines under development do include considerations of carbon storage. In particular it has been recommended for the UK Renewable Transport Fuel Obligation (RTFO) that carbon-payback times must be less than 10 years, based on the IPCC guideline that a land-use change (LUC) should be considered to last for 20 years and the suggestion that any biofuel project should yield net greenhouse gas reductions for at least half of that time69. For equity with existing biofuel standards on carbon storage therefore, we have adopted this 10 year maximum carbon payback criterion.

In order to maintain a carbon payback time of less than ten years, the LUC carbon debt must be less than 80 Mg CO2e ha-1 (22 Mg C ha-1). This rules out clearance of forest or wooded savanna, or conversion of grassland to annual crops70 (in which instances even loss of below-ground biomass will exceed the threshold) as a strategy to mitigate climate change. Carbon storage criteria are, thus, broadly convergent with habitat conservation considerations. We have, therefore, not included biomass whose provision requires the clearance of previously unmanaged lands or conversion of agricultural land to biomass-crop production (as this may induce land clearance by displacing agriculture71) in this analysis.

While this precludes ‘LUC for biochar’ that incurs significant carbon debt as a means of climate change mitigation, it does leave open the question of whether original vegetation removed during LUC that is occurring anyway (i.e., as part of business as usual (BAU) rather than for the explicit purpose of feedstock provision) should be considered as a sustainably available feedstock within our scenarios. To remain within our stated objective of making conservative assumptions where possible, we have not included original vegetation from BAU LUC for two principle reasons. Firstly, the large discrepancy between biomass quantities initially generated during the first year of land-clearance (~250 Mg ha-1 from rainforest) and subsequently from agricultural residues (<5 Mg ha-1 yr-1) mean that pyrolysis facilities sized for ongoing operation will not have the capacity to process the large quantity of initial material, whereas no mobile or temporary pyrolysis facility capable of processing this quantity of material in often remote areas with poor infrastructure without producing unacceptable gaseous or soot emissions (see 2.4.1.2) currently exists. The second reason we have not included BAU LUC residues in our analysis is that if such residues were to be admissible under any protocol that delivered financial incentives for biochar production, then LUC that was promoted by the availability of such incentives would, in practice, be impractical to distinguish from BAU LUC. Under such circumstance, biochar might become a driver of increased land-clearance beyond BAU, whereby it would exacerbate rather than mitigate climate change.

Although clearance of previously unmanaged land leads to unacceptably high carbon payback times, planting perennial biomass crops on degraded, abandoned cropland incurs little carbon debt and can lead to “immediate and sustained GHG benefits”70. A maximum of 0.6 Pg C yr-1 biomass may be produced on abandoned cropland that has not subsequently become urban, pasture or forest72. We assume that half of this might be provided by herbaceous crops with a C content of 47%, and half from woody crops with 49%C. In the Alpha scenario, we assume that 50% of this maximum potential might

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be implemented, rising to 75% and 100% in the Beta and MSTP scenarios. The total amounts available from herbaceous biomass crops thus are 319, 479, and 638 Tg DM yr-1 for the Alpha, Beta and MSTP scenarios, respectively. For woody biomass crops, the comparable amounts are 306, 459, and 612 Tg DM yr-1.

We note that, despite concerns over the sustainability of biomass plantations, establishment of biomass crops on degraded land is not fundamentally contrary to the principles of sustainability as there exist land-management options such as long-rotation coppice and use of native flora such as prairie-grass mixes that can enhance biodiversity and simultaneously provide biomass (see for example ref. 73). Furthermore, careful choice of plants can reverse land degradation and rebuild soil fertility.

2.1.7 Harvested wood

Annually, 1.9 Pg of timber is extracted74, of which 1.04 Pg is firewood (derived from 2007 FAO ForeStat data66 using mean hard and soft wood densities of 0.42 and 0.57 Mg m-3 from ref. 74). Using firewood for biochar production would result in a reduction in the useful energy available from the wood and thus lead to greater fuel poverty and/or increased extraction, either of which are unacceptable consequences. Aside from losses due to fire, fungi and termites, most of the remaining industrial wood eventually ends up in various waste streams. However, the wide range of chemicals used in production, preservation and colouration of both wood and paper products means that only a small fraction of this carbon may currently be suitable for production of biochar as a soil amendment. Hazardous waste wood categories include wood panels, chip and fibre boards, and sawnwood treated with preservatives, paints or varnish. Paper products may contain heavy metals in inks and Cl from bleaching. In addition to Cl from bleaching, pulping waste may also contain high concentrations of Na from the Kraft process which would contribute to sodicity or salinization if added to soils. We recognize, however, that biochar from some paper mill residues may have value in some soils, but adopt a conservative approach and do not include them in our calculation.

In the Alpha scenario therefore, we have limited available feedstock from timber to 50.5 Tg of wood milling residues66. For the Beta and MSTP scenarios, however, we assume that legislation will be enacted to replace Cu, Cr and As based wood preservatives with treatments such as acetylation, potassium silicate, tung oil or linseed oil that will not render the wood unsuitable for adding to soil, thus making 40-80% of the 198 Tg yr-1 sawnwood available for pyrolysis at end of life in the Beta and MSTP scenarios respectively. Total amounts available are 51, 130, and 209 Tg DM yr-1 for the Alpha, Beta and MSTP scenarios, respectively.

2.1.8 Forestry residues

The total amount of forest residue is taken to be the difference between reported forest fellings and forest removals, giving a figure of 0.65 Pg biomass yr-1 (ref. 74). As with agricultural residues, forest residues provide a number of important services including providing nutrients, regulating water flows, reducing soil erosion, creating habitat and provide food for fungi and detritivores. Extraction of felling losses can have adverse impacts on these functions. The fraction of felling losses that may be removed without unacceptably impeding one or more of these services is site dependent, and may vary from 0% to 75%75. Removal of nutrients may be minimised by seasoning residues prior to extraction until foliage has been shed76, and by topping crown-wood prior to extraction leaving the finer brash on site77.

In the EU, it has been assessed that, on average, 59% of potentially recoverable forest residues (which, in turn, are 75% of total residues) may be recovered if environmental constraints are taken into consideration75. In the absence of similarly detailed assessments on a global scale, we have assumed that the European figure is applicable globally, allowing 44% of total felling residues to be recovered sustainably. Thus, we have assumed that, in each of our scenarios, 286 Tg DM yr-1 of forestry residues will be available.

2.1.9 Agroforestry

The largest single contributory cause of human appropriation of net primary productivity (NPP), accounting for a reduction in global NPP of 6 Pg C yr-1, is the loss of production caused by replacing natural ecosystems with less productive ones78. In particular, cropland is dominated by the cultivation of annual crops that waste resources through having insufficient leaf and root development for much of their life cycle to capture available light, nutrients and water. There may, therefore, be considerable scope to increase global net primary production through choice of crops, and in particular through greater use of perennial crops.

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We consider in our scenarios just one of the many ways in which biomass feedstock availability may be increased by planting alternative crops. We consider here the potential impact of replacing grass pasture in the tropics with a silvopastoralism system in which the main source of fodder for the livestock is foliage from fast-growing leguminous shrubs or trees that simultaneously produce wood for biochar. Since the wood producing species are also the source of food crop, this allows for dense planting as in the ‘fodder bank’ style of agroforestry, in contrast to alley cropping agroforestry systems where wood production occupies only a small fraction of the available area. A number of different management options are suited to such production of multi-purpose trees, including allowing the stock to browse directly (on a rotational basis), or coppicing with cut-and-carry (and drying for storage) of the foliage. In addition to providing biochar feedstock, replacing livestock pasture managed by intentional burning with silvopastoral systems will also have the added benefit of reducing the CO2, N2O and CH4 emissions associated with fire2, although this effect has not been included in our model.

A great many species of trees and shrubs are suited to co-production of fodder and biomass, the preferred choice locally depending on factors such as adaptation to soil and climate, invasiveness, pest/disease resistance, nutritive value, palatability, ease of management, and preference to native over exotic species where possible. A few examples of tree legumes that can provide high yields of high quality forage and human food together with timber79, include Leucaena spp.80, Moringa oleifera81, Acacia spp.82 and Caragana spp.83, the relative yields of leafy and woody material being controllable by varying the frequency of cutting. We have assumed no net increase in N2O emissions resulting from nitrification or denitrification of biologically-fixed N under legume crops, for two reasons. Firstly, there may be no net increase in N2O emissions once avoided emissions due to fire and N additions in displaced production systems have been accounted for. Secondly, biologically-fixed N is efficiently used by legume crops to produce protein making it initially less available for nitrification and subsequent denitrification84. Where necessary, any surplus reactive-N remaining in downstream products (e.g. manure) may then be reduced to N2 by pyrolysis for biochar production, as described in Section 2.4.1.2.

Under ideal conditions, Leucaena hybrids can give annual yields in excess of 50 Mg ha–1 total DM biomass of which half is forage85. However, we assume much more modest average yields of 15 Mg DM ha-1 yr-1 (50% food and fodder and 50% woody biomass) to allow for less than ideal conditions, and for selection of species based on the full range of desirable attributes, not just maximum yield.

To produce a spatially explicit estimate of the amount of land currently under pasture that would be suitable for silvopastoralism, we selected from a global 5’ of arc resolution map of existing pasture86 only those regions that are in agroecological zones with no serious climate, soil or terrain constraints to rainfed crop production (i.e., zones having constraints no greater than ‘moderate’ on plate 28 of the joint IIASA / FAO global agroecological zone map87 2000). This yields a total of 170 Mha of land at latitudes less than 25° (Fig. S12). Thus, we estimate that the maximum potential production of woody biomass feedstock from such agroforestry systems in the tropics to be 1280 Tg DM yr-1 of which 10, 55 and 100% are assumed to be implemented in our three scenarios respectively.

2.1.10 Green Waste

Urban organic waste generated has been estimated67 to amount to 1-3 EJ yr-1. Accordingly, we use values of 1, 2, and 3 EJ yr-1 in the three scenarios respectively. Assuming an energy content of 17.5 GJ Mg-1 DM (see section 5), this corresponds to 57-172 Tg DM yr-1. We base our estimate of the useable fraction on a 2007 EPA study of US urban waste in which 20% of urban organic waste was yard trimmings and 20% was food waste88. In the Alpha scenario, we assume that 75% of yard trimmings are used as feedstock, whereas in the Beta and MSTP scenarios we assume that all yard trimmings and food waste will be used. Other categories of organic waste – paper, wood and miscellaneous organics – are excluded due to the high probability of contamination. This gives us an estimated 8.6, 46 and 69 Tg DM yr-1 in the three scenarios respectively.

2.1.11 Other excluded feedstocks

The feedstocks considered in these scenarios do not provide an exhaustive inventory of all potential resources. Together though, they do constitute the majority of the biomass that may be utilised without increasing human appropriation of NPP.

One potential resource that has been excluded from this study is substitution of slash-and-burn agriculture with slash-and-char both for reasons given in section 2.4.1.2 and because it may be impractical to differentiate legitimate use of this practice to displace shifting agriculture from its use as a means of land clearance.

Another potential resource that has been excluded is forest thinnings. We recognize that managed forests in developed countries are thinned during the normal growth cycle and may also be thinned for

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fire control, and that these thinnings might be considered as suitable feedstock for biochar production. Implementation of thinning programs, however, is often controversial, even in the US, and has been subject to abuse. Extraction of thinnings is also often costly, difficult and incurs risks of soil disturbance and nutrient depletion. Estimation of the amount of feedstock potentially obtained by thinning is also difficult due to a lack of suitable data from most countries. For all these reasons, and to avoid even the appearance of promoting conversion of forest land to other purposes, then, we exclude forest thinnings from this assessment.

Another potential feedstock resource that we have not included is the use of invasive species, harvesting of which may provide a means of control. The Global Invasive Species Database (GISD) lists 377 plant species89, many of which might be practically harvested as part of a management strategy. However, although data on the ranges of these species are known, there are no comprehensive global surveys of the quantity of biomass they represent. An assessment of the potential biomass that might be generated from invasive species will require land-based, local, regional surveys and it is not possible to perform on a global study of this nature. For this reason, and also because successful control of these species through harvesting would deplete this resource, we have not included this class of feedstock in our assessment.

2.2 Avoided biomass decomposition

The primary influence of biochar production on greenhouse gas fluxes is due to the avoidance of emissions that would have occurred had the biomass been left to decompose. Here we consider the avoided emissions of carbon dioxide, methane and nitrous oxide that would have occurred under present biomass management methods.

2.2.1 Carbon dioxide

The rate at which biomass will decay, if it is not pyrolysed, varies considerably depending both on the feedstock and on how it is used, stored or disposed. No avoided emissions can be considered to have occurred until after the time that biomass would have decomposed. In the case of ephemeral forms of biomass such as crop residues, this has little impact on the rate at which avoided emissions may accrue. However, pyrolysis of potentially durable forms of biomass, such as live standing timber or timber products that have not yet reached end-of-life disposal, will lead to an up-front increase in GHG) emissions that will not be recouped for some time. The criterion that the carbon-debt payback period for biochar production must be less than ten years precludes the use of such potentially durable feedstocks. We have therefore restricted ourselves to feedstocks with short life expectancy and have thus made the simplifying assumption that biomass “in the wild” would have decayed exponentially with a half-life of 1 yr for non-timber feedstocks, and 3 yr for timber, if not pyrolysed.

To calculate the rate at which biomass carbon would have contributed to atmospheric CO2, account must also be taken of the contribution that biomass would have made to soil organic carbon (SOC) stocks had it not been pyrolysed. Large-scale diversion of biomass from soil may lead to a depletion of non-BC SOC stocks.

The decomposition rate of organic matter in soil varies depending on biomass type, soil, and climate90. In a study of the potential for carbon sequestration in European soils through straw incorporation91, the RothC model was used to predict the rate of SOC accumulation in a silty clay loam soil in north-west Europe over 100 years resulting from annual additions of cereal straw at a rate of 4.25 Mg DM ha−1. The mean rate of carbon accumulation over the first twenty years was 0.46 Mg C ha−1 yr−1, falling to 0.12 Mg C ha−1 yr−1 in the second fifty years. In the absence of similar long-term studies for the full range of biomass types, soils and climatic regions considered in this study, we have used this study as a basis for calculations. It is to be expected that this will lead to an overestimation of the amount of SOC that would accumulate in soils, since the rate of SOC mineralization tends to increase with decreasing latitude90, and also because substituting biochar for more labile forms of SOC will reduce the susceptibility of soil carbon to climate change induced losses92. This will, therefore, lead to a correspondingly conservative estimate of the avoided emissions achievable through biochar production. Thus, although a more accurate assessment of loss of SOC from reduced biomass inputs to soil may be possible, this methodology suits our requirement that we should endeavour to make a conservative estimate of the potential for biochar that can provide a low-risk basis for decision making.

Loss of SOC as a consequence of reduced biomass inputs to soil was thus modelled by fitting a two-pool exponential decay function of the form

ΔSOC = A exp(-k1 t) + B exp(-k2 t) (1)

to the data in ref 91 using the non-linear minimization function, nlm(), in the R statistical programming

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language93, yielding the coefficients

A=7.800, k1=-0.02669, B=130.0, k2=-0.0005246

with a correlation coefficient of r2 = 0.99999

Loss of detritus inputs to soils by diverting biomass to biochar will be offset somewhat by increased crop yields as a result of biochar additions to soils. We have assumed in this study that, where biochar-induced enhanced yields occur (calculated as described in 2.6.3 and 2.6.4 below), increases in above-ground biomass production will become available as a feedstock for biochar production, and will therefore not contribute to SOC stocks. Previous studies have not shown biochar amendment to increase below-ground net primary production as much as above-ground biomass. In a pot trial of cowpea in biochar amended feralsols94, although above-ground yields increased with biochar additions, no increase in below-ground production was observed. In another study of common beans (Phaseolus vulgaris) in an oxisol95, increases in both above- and below-ground production were observed, but the absolute increase in below-ground production was only approximately 20% as much as for above-ground biomass (at biochar additions of 30 g kg-1). Taking the average of these two studies, we have assumed that the increase in below-ground biomass will be, on average, only 10% of above-ground enhanced yield, and that this enhanced below-ground production will add to SOC stocks the increase in SOC being calculated according to equation (1) above.

2.2.2 Methane

When biomass decomposes under anaerobic conditions, methane is evolved. Diverting biomass from these methanogenic processes into biochar can therefore lead to a reduction in CH4 emissions96,97. We consider here avoided emissions from paddy rice cultivation, manure management, and landfill waste. All other biomass feedstocks in our model produce negligible CH4 emissions under current conditions.

2.2.2.1 Rice

In order to arrive at a global emission factor for rice residues that are pyrolysed, we begin by deriving from published studies a mean emission factor of methane per mass of rice straw that is incorporated into paddy fields (see Table S5). There is a factor of over 40 between the largest and smallest values that may be accounted for by regional variations in climate and cultivation practices.

Not all straw that is pyrolysed would otherwise have been added to fields. Approximately 26% (0.23 Pg) of the 0.89 Pg yr-1 rice straw generated is currently not recovered62. Of the straw that is recovered, we assume that none of it is subsequently returned to paddy fields. Thus, we estimate that the amount of rice straw currently being incorporated into paddy fields is 0.23 Pg DM yr-1. Multiplying this by the mean CH4 emission factor of 0.076 kg CH4-C (kg straw)-1 implies a net global production of methane from straw incorporated into paddy fields of 17.5 Tg CH4-C yr-1 (402 Tg CO2e yr-1), or approximately 40% of the total global methane emissions from paddy rice production98 of 41 +/- 13 Tg CH4-C yr-1 (940 +/- 300 Tg CO2e yr-1).

In order to derive an emission factor for all rice straw that is pyrolysed, we must multiply the mean emission factor of 0.076 by the fraction of pyrolysed straw that would have been left in field. To define this fraction we assume that once maximum production has been reached, all currently unharvested straw (0.23 Pg) will be used, plus a fraction of the remainder. Therefore (at time of maximum production when the annual quantity of rice straw pyrolysed is Rmax), the fraction of pyrolysed straw that would otherwise have been left in the field is equal to 0.23 Pg / Rmax and the emission factor for pyrolysed straw will be 0.076 * 0.23 Pg / Rmax. We assume that this emission factor remains constant over time, i.e., that the fraction of pyrolysed straw derived from residues that are currently unharvested remains constant.

2.2.2.2 Manure

Estimates of global CH4 emissions from livestock manure are 10.4 (ref. 99) and 25.5 Tg CH4-C yr-1 (ref.65), most of this deriving from anaerobic storage systems such as lagoons or manure heaps. We base our calculations on the more conservative of these, 10.4 Tg CH4-C yr-1, as total CH4 emissions from ruminants are 68 +/- 14 Tg CH4-C yr-1 (ref. 100), the bulk of which arises from enteric fermentation. Of this total CH4 flux, 4.5, 4.0, and 1.0 Tg CH4-C yr-1 originate from cattle, pig and poultry livestock manures, respectively99. We derive a global mean emission factor of methane from livestock manure by dividing total CH4 emissions from manure by global manure production. Total manure production is taken to be 470 Tg C yr-1 for cattle65, 34 Tg C yr-1 for pigs65, and 46 Tg C yr-1 for poultry manure (section 2.1.5). Assuming carbon contents of 29.9%, 37.6% and 34.7% for cattle, pig and poultry manures respectively (see Table S3), this yields mean

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emission factors of 0.0029, 0.044, and 0.0025 kg CH4-C kg-1 DM (0.0874, 1.35 and 0.0759 kg CO2e kg-1 DM) for cattle, pig and poultry manures, respectively. Given that in the Alpha and Beta scenarios, manure for pyrolysis will be sourced principally from housed livestock, not from manure deposited directly on land (from which CH4 emissions are negligible65), we assume that the emission factor for cattle manure will be the global mean divided by the fraction of manure that is derived from housed livestock. Of the 100.6 Tg (wet) cattle manure produced annually, 65.5 Tg (65%) is from housed cattle65, giving a CH4 emission factor for housed cattle manure of 0.0045 kg CH4-C kg-1 DM (0.138 kg CO2e kg-1 DM). In the MSTP scenario, we assume that avoided methane emissions accrue only from manure that is currently produced from housed livestock. Any additional manure procurement is not considered to contribute to avoided methane emissions.

2.2.2.3 Landfill

Globally, 23±11 Tg CH4 yr-1 are emitted from landfill refuse100. Diversion of waste from landfill to pyrolysis can avoid some of these emissions. In order to calculate the avoided methane by diverting green waste from landfill to pyrolysis, we use a CH4 emission factor for landfill green waste of 0.038 ± 0.015 kg CH4-C kg-1 DM, derived from the mean of seven experimental incubation and field studies101,102. As most of the methane is evolved during the first year101, we assign all the avoided methane emissions to the same year in which the biomass was pyrolysed. We assume that 50% of green waste pyrolysed has been diverted from landfill, the rest being diverted from disposal methods that generate negligible methane. Although anaerobic or partially anaerobic composting of green waste also leads to methane emissions, the increasing use of fully aerobic in-vessel composting entails negligible methane production103.

2.2.3 Nitrous oxide

Global N2O emissions from manure management and other biomass are 205 and 187 Tg CO2e yr-1, respectively104. While some of these emissions may be avoided by diverting biomass into pyrolysis, this must be balanced against emissions due to nitrogen compounds formed during pyrolysis.

During pyrolysis, feedstock nitrogen is partitioned between the biochar and gaseous products. Of the nitrogen that remains in the biochar, its biological availability – and thus the rate at which it may contribute to soil N2O emissions – depends upon the compounds in which it occurs. Despite the expectation that nitrogen forms relatively stable heterocyclic aromatic compounds105 in a study of 15N labelled chicken manure biochar, it was found that the same amount of the nitrogen in the biochar was plant-available as in uncharred poultry litter106. Thus, if this result is generally true of biochars produced under a range of conditions, N retained in the biochar will still contribute to soil reactive nitrogen concentrations and may contribute to N2O emissions with a similar emission factor to N in the original biomass. For the purpose of this study we take the conservative assumption that this is generally the case, and do not attribute any avoided N2O emissions to the N fraction retained in the biochar.

The remainder of biomass N is evolved in the volatile fraction where it may be present as a range of compounds including N2, NH3, HCN, NCO, N2O, NO and NO2. Of these, N2 is the most abundant, accounting for around 80% of N in the gaseous fraction107-109. We therefore calculate the amount of biomass N converted to N2 as 0.8 times the difference between mass of N in feedstock and in biochar (using N concentrations as given in section 5).

Of the other gaseous N compounds, NH3 is the most abundant. Given increasingly stringent regulation, pyrolysis of high-N feedstocks can only be considered a viable option if measures are taken to minimise emission of environmentally deleterious N compounds including NOx and NOx precursors such as NH3 and HCN107. NH3 may be converted to ammonium and adsorbed onto the biochar, where it will act as a fertiliser. While this usefully recaptures some of the biomass N, it means that nitrogen evolved as NH3 will be returned to the soil in a biologically available form where a portion of it will be converted to N2O with an emission factor similar to that for N in the original feedstock. Thus, N evolved as NH3 (or any gas other than N2) can not justifiably be said to contribute to a net reduction in atmospheric N2O emissions. N2O and NOx yields are generally small, surface reactions on biochar aiding in their reduction to N2

110, with further reductions to acceptable levels being achievable, if necessary, through flue-gas scrubbing111. Production of HCN is negligible below around 500°C107, and may be suppressed by the presence of CO2

112.

The emission factors we use for calculating avoided N2O emissions are the IPCC-recommended values113,114. For direct emissions, these are 1% (kg N2O–N kg-1 N) for N applied to cropland, and 2% for cattle and pig (but not poultry) manure deposited at pasture, range or paddock. It has been estimated that, globally, 50% of manure N is applied to cropland115; thus the mean emission factor we have used for direct emissions from cattle and pig manure is 1.5%. An indirect emission factor (to account for N2O emissions from leached and volatized N) of 0.325%, derived from IPCC default

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values116, is also added to the direct emissions to calculate total avoided N2O.

It has been suggested that the IPCC emission factors may seriously underestimate N2O emissions from N applied to soils113, with the actual rate being 3-5%, due to indirect processes that occur in groundwaters and surface waters after reactive N has left the soil column. Although we have used IPCC recommended values for the main results, we have therefore also included a sensitivity analysis for N2O emission factors in the range 1-5%.

Avoided N2O emissions are thus calculated as the fraction of biomass N converted to N2 multiplied by the N2O emission factor, according to the equation:

0.8 * (Mf * Nf – Mbc * Nbc) * EN2O, (2)

where, Mf = mass of feedstock, Nf = N content of feedstock, Mbc = mass of biochar, Nbc = N content of biochar, EN2O = emission factor of biomass-N to N2O.

2.3 Transport

The worst-case scenario for transport costs will occur when pyrolysis plant size is large, requiring biomass to be transported to a central facility from a large area. To provide a conservative estimate, we base our transport costs on this worst case, whilst recognising that, in practice, a more distributed network of smaller plants will be more amenable to both lower transport costs and possibly also greater utilisation of process heat close to areas of heat demand. We do not, therefore, propose that a network of large, highly centralised plants as assumed for this calculation will be the most likely or economic form of infrastructure, but make this assumption only to remain true to our aim of making conservative assumptions where possible. To calculate the mean transportation distance, we calculate how many 1-GW (feedstock higher heating value (HHV)) capacity pyrolysis plants would be required to convert the biomass. We estimate that these are distributed evenly across an area of 6.6 Gha, calculated by summing the area of all grid cells on a 5’ of arc raster map of the globe that contain some cropland (Fig. S7). We then calculate the mean separation (S) between plants as the square root of the area served by each plant. The mean transport distance (D) for both biomass and biochar is then calculated as D=S/2, a value that accounts for indirect routing of roads by assuming that the distance travelled between two points (x,y) and (0,0) is given by x+y as it would be if roads were set out on an orthogonal grid.

Transport energy per kilometre is then calculated according to the UK average road-freight consumption of 2.2 MJ Mg-1 km-1 (ref. 117). Although this is a somewhat crude estimate, given that according to these calculations transport energy contributes less than 2% of the overall GHG budget, a more sophisticated analysis is unlikely to affect the overall results greatly. This is supported by analysis of the greenhouse gas balance from charcoal production, in which transport emissions were found to be a negligible component3.

2.4 Pyrolysis

2.4.1 Products of pyrolysis

2.4.1.1 Carbon yield

We define C yield as the mass of C in the solid biochar residue divided by the mass of C in the initial dry biomass feedstock. We have assumed a C yield of 49% (the mean of values obtained by slow pyrolysis at atmospheric pressure in Ref. 118) to generate Figures 2 and 3 in the main text. For the sensitivity and Monte-Carlo analyses, we have investigated C yields up to 62% as demonstrated by a pressurized, flash-pyrolysis process118. C yields close to 100% have been demonstrated using a high pressure, catalysed hydrothermal pyrolysis process, but we have not included this process in our analysis because the low stability of the resultant char (mean residence time of 4-29 yr) means that it does not provide a stable form of carbon sequestration119.

2.4.1.2 Gas emissions

Gaseous products of pyrolysis include CO2, CO, CH4, H2, N2O, NOx and smaller quantities of volatile organic compounds (VOCs). In order to calculate the GHG budget of pyrolysis, we consider emissions of CO2, CH4, and N2O. CO has low global warming potential and a short lifetime in the atmosphere before oxidising to CO2. For simplicity, therefore, CO emissions are simply treated as CO2. Yields of VOCs contribute a negligible amount to the overall GHG budget. We thus assume that all feedstock-C is converted to either biochar-C, CH4, or CO2, the yield of CO2–C being calculated as (1 – biochar-C – CH4-C). N2O emissions will also be negligible for low N feedstocks, and may be controlled or eliminated in high-N feedstocks (see 2.2.3).

Depending on the conversion technology utilised, the pyrolysis gas may or may not be fully combusted before venting to atmosphere. Failure to combust the pyrolysis gases (as may occur,

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for example, in traditional charcoal kilns or smouldering slash piles) will lead to the squandering of a potential energy source combined with increased greenhouse-gas emissions.

In a study of emissions from 8 different charcoal kilns in Brazil and Kenya120, methane emission factors varied from 24-47 g CH4-C kg-1 charcoal, or approximately 6-11 g CH4-C kg-1 dry feedstock, the lower figures being from earth-mound kilns and the higher value from a brick and metal kiln. Average emissions from all charcoal making are approximately 8 g CH4-C kg-1 dry feedstock121. This model calculates that yields of just 1 g CH4-C kg-1 dry feedstock are enough to cause the carbon-payback time to exceed 10 years. Thus pyrolysis in traditional kilns will not satisfy our carbon-payback criteria. The suggested practice of slash-and-char agriculture122 through such low-tech conversion methods would not, therefore, be a viable greenhouse-gas reduction strategy unless it replaces slash-and-burn with an increase in CH4 emissions of no more than 1 g CH4-C kg-

1 dry feedstock compared to those from burning. CH4 emissions from biomass burning are typically in the order of 5 +/- 2 g CH4-C kg-1 dry feedstock121. Air-quality considerations also require that pyrolysis plants be implemented in such a way that emissions of soot, oxides of sulphur or nitrogen, carbon monoxide and other pollutants are negligible. Therefore, we will not include biochar production through low-tech conversion.

2.4.2 Energy production and fossil fuel offsets

If we assume that the gaseous and volatile products of pyrolysis (referred to hereafter simply as ‘pyrolysis gas’ for brevity) are completely combusted at close to stoichiometric aeration, then the net energy extracted from the process (i.e., the difference between the feedstock enthalpy and the combined enthalpies of the biochar produced and the exhaust gases) is dictated by the yield of biochar and the temperature at which the exhaust is vented. If we also assume that sufficient energy is recovered from the exhaust gas stream to lower its temperature to just above the water condensation temperature, then the maximum energy (per unit mass of feedstock) available from the process without condensing the exhaust stream is given by

Emax = mdm.LHVdm – mc.HHVc – mw.(ΔHvap + (Tvap - Ta).cw), (3)

where, mdm =mass of dry matter feedstock LHVdm = lower heating value (LHV) of feedstock dry matter. mc = mass of biochar HHVc = biochar higher heating value (the higher heating value being used here since none of the enthalpy remaining in the biochar is available for energy) mw = mass of water ΔHvap = specific heat of vaporization of water, taken as 2.26 kJ g-1 Tvap = 100°C Ta = ambient temp, taken as 20°C cw = specific heat capacity of water, taken as 4.18 J g-1 K-1 In practice, however, inefficiencies arise from heat losses and parasitic loads. The energy recovered from pyrolysis will therefore be given by E=ηpEmax, (4) where ηp, the pyrolysis energy efficiency, is defined as the fraction of theoretically recoverable energy that is recovered in practice. We base our estimate of ηp on the operating efficiency of a currently commercially available slow pyrolysis plant from Best Energies which, it is reported, typically obtains an energy yield of 38% of the feedstock energy when the conditions are optimised for maximum biochar yield96. If we assume that this energy yield of 38% is achieved under favourable conditions using a low water-content feedstock such as wheat straw, then (using values of HHVc, LHVdm, C% and mw, given for wheat straw in sections 5 and 6 and a pyrolysis C-yield of 48%), ηp is given by ηp = E/Emax = [0.38 LHVdm] / [LHVdm – mc.HHVc – mw.(ΔHvap + (Tvap - Ta).cw)], (5) in which mc is determined by the pyrolysis C-yield and the biochar C% and mw is determined by the biomass water content. This yields an estimated value of ηp of 75%. While we recognise that higher efficiencies are achievable, and might become the norm over the course of a century, this provides a conservative estimate of the energy production that can be obtained. It is evident from equation (3) that increasing water content of biomass carries a penalty in terms of reduced energy efficiency. For example, high-ash feedstocks such as manure, will require more

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energy to pyrolyse than may be recovered if the water content is greater than approximately 58% (although the net avoided greenhouse emissions may still be positive when sequestered carbon and other feedbacks are considered).

The energy recovered from pyrolysis may be used to offset fossil fuel consumption. The fossil fuel emissions offset by the co-production of energy with biochar then depend on two factors – what fuel is being offset, and ratio of the efficiencies with which the fossil fuel and pyrolysis gas are utilised123 – according to the equation

Affo = E Cff ηb /ηf (6)

where Affo = avoided CO2-C emissions, Cff = fossil fuel C penalty (i.e., CO2-C emissions per unit energy of fossil-fuel heating value), ηb = thermal efficiency by which pyrolysis gas is converted to useful energy, ηf = thermal efficiency by which fossil fuel is converted to useful energy

We use IPCC default values for Cf of 0.026 Mg C GJ-1 for coal, 0.019 Mg C GJ-1 for oil and 0.015 Mg C GJ-1 for natural gas123.

To estimate the C-intensity of the energy mix that may be offset by biomass energy (with or without simultaneous biochar production) we begin by considering the current mix of world energy generation using IEA figures124. Current total primary energy production is 27% coal, 33% oil, 21% gas and 19% non-fossil, yielding a mean C-intensity of 0.0165 Mg C GJ-1. In our analysis, however, we consider how climate mitigation from biochar might be maximised. The sensitivity results given in the main paper show that mitigation is maximised by optimising for maximum yield of recalcitrant char. We therefore base our analysis on the assumption that slow rather than fast pyrolysis with its higher char yield will be the preferred biochar production process. This will tend to favour the production of syngas for static applications over liquid fuels for transport. Therefore, of more relevance than the overall world energy mix is the energy mix for static applications. Current world electricity production is 41% coal, 6% oil, 21% gas and 31% non-fossil, yielding a combined C-intensity of 0.015 Mg C GJ-1. If we also consider heat demand, the current average C-intensity becomes 0.0154 Mg C GJ-1. We adjust our baseline from these current statistics somewhat, though, to take account of two considerations. First, that rising prices and the prospect of depletion are driving a trend away from use of oil in electricity production. Second, it is likely that, to some extent, policy mechanisms will be used to promote the offsetting of higher C-intensity fuels more than lower C-intensity ones. We therefore use a baseline C-intensity for offsets of 0.0175 Mg C GJ-1 calculated from a mix of 50% coal, 30% gas and 20% of pyrolysis plants that will not generate any fossil fuel offsets, either because no energy is recovered, because the energy source being offset is renewable or nuclear, or because the energy recovered represents an increase in overall energy consumption (the ‘rebound effect’).

For (pyrolysis gas → electricity) offsetting (coal → electricity), the ratio ηb/ηf is assumed to be 32%:40% = 0.8. For applications (including CHP) where heat is recovered, the expected efficiency will be in the order of 85% for both pyrolysis gas and fossil fuels, yielding a ratio of 1.

In order to calculate the maximum energy available when optimising for energy rather than char production, we use a ηb/ηf ratio of 0.92, C-yield = zero (i.e., negligible char is produced when optimising for energy production), and all other assumptions identical to the biochar scenarios.

2.5 Biochar properties

2.5.1 Decay kinetics

Biochar is not a homogeneous substance, but comprises both condensed or residual aliphatic compounds and black carbon (BC) which is itself a continuum ranging from slightly charred biomass that is readily decomposed to highly recalcitrant condensed aromatics125. That biochar consists of a mixture of compounds with different decay kinetics in soil is evident from incubation studies in which decay rates are initially rapid but slow down over time126,127, and from the long mean residence times of BC in some soils92. For simplicity, we have accordingly modelled biochar as a two-phase material with a labile and a recalcitrant component, each following an exponential decay curve as expressed by the equation

M(t) = M0 [L exp(-ln(2)/t½L t) + R exp(ln(2)/t½R t)],

where

M(t) = mass of biochar carbon at time t

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M0 = initial mass of biochar carbon

L = labile fraction of biochar,

R = recalcitrant fraction of biochar,

t½L = labile half-life

t½R = recalcitrant half-life

The labile fraction in this model subsumes both highly labile material with t½ in the order of months-years, and moderately labile material with t½ in the decadal range. While this approximation may be inaccurate for describing biochar decay kinetics over short timescales, for our purposes in modeling the net response over the course of a century, the distinction between highly labile and somewhat labile becomes substantially irrelevant, as both largely decay over the course of 100 years. We have used a ranges of t½L=1-25 years and L=5-30% for the sensitivity and Monte-Carlo analyses, and values of t½L=20 years and L=15% to generate Figures 2 and 3 in the main paper. While data on decay kinetics over the decadal timescale are scarce, these values are in accord with incubation studies that show 4.5% C loss from perennial ryegrass (Lolium perenne) biochar after 3 years126, and 7-10% C loss in high-ash biochars and 3-5% loss for wood biochar after 2 years127.

The half-life of complete (both labile and recalcitrant) BC in soil represents a lower bound on the half-life of the recalcitrant fraction alone. Achieving reliable estimates of biochar half-life in soils is subject to considerable methodological difficulties - estimates derived from studies of historic BC in soils are confounded by the difficulty of distinguishing between mineralization and other loss processes such as leaching and erosion128. Incubation studies on the other hand are poor indicators of behaviour in soils in situ129,130. Published estimated values for the half-life of BC in soils are highly variable (see Table S6). In part, this variability may be attributed to differences in environment such as mean annual temperature131. Importantly though, this variation may reflect differences in the degree of carbonisation of the initial BC stock as BC includes materials on a spectrum from slightly to highly charred with a corresponding variation in recalcitrance125,132. We note that all of the studies in Table S6 that suggest BC half-lives of less than 400 years (refs 129,133,134) consider BC formed under uncontrolled conditions during wildfires, which is therefore likely to have contained considerable quantities of lightly charred (and thus labile) material. This is supported by the fact that in ref 134, initial mass loss over the first 30 years was rapid after which BC concentration stabilised, implying both a high labile content of the biochar and a long half-life of its recalcitrant fraction. Thus, the recalcitrant half-life of 300 years that we assume for Figures 2 and 3 in the main paper seems highly conservative and the range of 50-1000 years that we use for sensitivity and Monte-Carlo analyses covers only the lower end of plausible values.

While BC losses from the top soil horizons due to horizontal or vertical transport will not affect the carbon-sequestration benefits of biochar, they will affect its impact on soil fertility.

2.5.2 Carbon fraction

The C fraction of biochar is highly dependent on the ash (and, to a lesser extent, H and O) contents of the feedstock135. Assumed values for C and ash content of biochars are given in Table S4. We have based application rates to land (see below) on mass of carbon rather than mass of biochar. Thus high ash biochars (for example from manure) will have a higher application rate to land to achieve the equivalent additions of C.

2.6 Biochar soil application

2.6.1 Application rates

The available evidence suggests that application rates of at least 50 Mg C ha-1 can be made to the top 15 cm of soils with positive or neutral effects on biomass yield (see Section 2.6.4 below). In some instances, initial N-immobilization may occur, caused by metabolism of highly labile C present in some biochars, resulting in an associated yield decrease. Where such decreases in yield have been observed, a year or two of weathering may be needed before this is overcome [e.g., see yield data of Deenik et al.136]. We assume here, then, that single application rates of 50 Mg C ha-1 can be safely made on all soil types. Given that 1.53 Gha of cultivated land are available, the minimum global soil storage capacity for biochar can thus be calculated at 76.5 Pg. This capacity is sufficient to account for at least a century of biochar production at sustainable production rates. Further soil capacity may be achieved by application to extensive pasture (range) land. However, much range land may be remote from sources of biochar feedstock and access for machinery is often also constrained by terrain. While intensively managed grasslands could readily have biochar added during cultivation

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prior to sowing a new ley, such seeded grasslands are already included in the 1.53 Gha of cropland. Surface application of up to 10 Mg C ha-1 biochar to range grasslands may be practical in some instances, but will be limited in regions prone to wind erosion and would be wholly inappropriate on grasslands managed by, or at high risk of, fire. We therefore estimate that, at most, 20% of the theoretical capacity from applying 50 Mg C ha-1 biochar to 2.5 Gha of global extensive pasture land might be practically achievable, giving a total additional storage capacity of 25 Pg C in pasture soils.

If soil capacity is not to be the limiting factor to biochar’s potential for climate-change mitigation, alternative sites for biochar storage will need to be found. These might include deeper incorporation into soils (a depth of 30 cm effectively doubling the soil storage capacity), disused mines and quarries, aggregate in construction materials, or biochar-filled ditches along field boundaries and riparian zones to reduce agricultural leaching. However, we do not explicitly model other potential storage options for biochar than in soils other than to calculate when the maximum capacity of soils for biochar has been reached as outlined above. The model assumes that no further increases in GHG feedbacks due to soil N2O or CH4 fluxes or enhanced yields accrue beyond this point. We do not assume, however, that production ceases at this time, as other storage options may be available. The decomposition rate of biochar produced after this time is assumed to be the same as for biochar added to soil. Future research will need to establish whether and where more biochar may be sustainably stored in soils, and the suitability and availability of alternative sinks.

2.6.2 Tillage

Incorporation of biochar into soil requires tillage. We calculate CO2 emissions from tillage using a value of 1.5 hr ha-1 tractor time at a diesel fuel consumption rate of 15.4 dm3 hr-1, giving 19.6 kg diesel ha-1 (assuming the density of diesel is 0.85 kg dm-3) based on mean figures for rotary tillage from the EcoInvent life-cycle analysis database137. We then calculate CO2 emissions using an energy density of 45 GJ kg-1 diesel, and a C-penalty of 0.019 Mg CO2-C GJ-1. We assume incorporation of biochar into soil is a one-off operation with all the biochar being added to the soil in a single batch.

2.6.3 Fertility and enhanced net primary productivity

The prime focus of this paper is an assessment of the potential of biochar for climate change mitigation rather than of its potential for agronomy. Nonetheless, an assessment of the potential for biochar to increase agricultural yields is a necessary part of our calculation. Increases in crop yield as a result of biochar additions will create a feedback by altering the NPP on croplands and increasing the mass of crop residues available as biochar feedstock. In order to model this response, we have used published data from field trials to estimate the expected response to biochar additions of different crops on soils of varying fertility, and combined this with an analysis of the global geographical distribution of these crop-soil combinations.

Soil fertility classifications were taken from a joint FAO / IIASA assessment of global agro-ecological zones138. Extracting from this just the layer of data regarding soil fertility constraints to crop production provides a 5-minute of arc resolution raster of soils categorised into seven classes of soil fertility constraints, namely; none, few, slight, moderate, severe, very severe, or unsuitable. This was combined with a 5-minute resolution map of global cropland distribution86 to produce a global map of cropland, categorised by severity of soil-fertility constraints (Fig. 6 in the main paper). The global area of land under cultivation in each of the soil-fertility classes is given in Table S8.

Next, these map data were combined with a 5-minute resolution data on the geographical distribution of the harvested area and yield of 175 distinct crops of the world circa AD 2000139. Combining this with harvest indices or residue-to-crop ratios, we calculated the production of crop residues on soils of each fertility class. It should be noted that the assessment in ref 139 of the distribution of crop yields is based on a combination of a 5-minute resolution map of cropland distribution with lower resolution data on aggregated yields from geographic regions, the lower resolution aggregated yields being distributed equally over all cropland within the region. As the geographic regions from which the aggregated crop yield data are taken will in general contain soils of various qualities, the assumption that yields were homogeneously distributed over the cropland in the region will likely lead to an overestimation of yields on poor soils and a corresponding underestimation of yields on more fertile soils. Despite this shortcoming, no better high-resolution data on global crop distribution are currently available. Our analysis based on it will, therefore, likely lead to an overestimation of the potential to increase crop yields on poor soils, and thus runs counter to our general philosophy of making conservative assumptions where possible. The calculated values of the potential for increased crop residue yields to provide feedstock for further biochar production should therefore be interpreted as an upper bound on the expected size of this feedback.

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2.6.4 Estimation of biomass-yield responses to biochar soil applications.

We approached this in two stages. First, we assumed that the actual yield response would progressively decrease from a maximum level seen only in soils having maximum soil fertility constraints (i.e., ranked as unsuitable). Thus, a soil having no soil-fertility constraints would show no yield response to amendment with biochar, whereas one having a moderate soil-fertility constraint would show 50% of the maximum response and one having a very severe soil fertility constraint would show 90% of the maximum response. These assumed yield responses, which are basically fractions of the potential yield response seen in a soil rated as unsuitable for cultivation, are summarized for different levels of soil-fertility constraints in Table S9.

We then calibrated these fractional responses by estimating the relative yield response to biochar application rates based on the very limited literature on this topic, which dominantly involves field and greenhouse studies with soils classified as having very severe to unsuitable soil-fertility constraints. This response was assumed to be equivalent to the maximum biomass yield response.

We converted all biochar application rate data to units of biochar-C ha-1, estimating this value for one study where a biochar C analysis was not provided140. Relative biomass yields (RBY) were calculated as the ratio of biomass yield for a plot receiving biochar to the yield from an otherwise identical control plot that did not receive biochar, and then normalised per Mg C ha-1 of biochar applied. Most of these comparisons involved light fertiliser applications to both types of plots. To avoid fertility responses to non-carbonaceous constituents of biochar such as phosphorus and lime from poultry-litter feedstocks, only studies using biochar prepared from wood were included in the analysis. Poultry-litter biochars generally show excellent crop-yield responses (e.g. refs141,142), but only account for about 2% of the total biochar applications considered in this work.

In addition to these selection criteria, we did not use data from some studies where the results seemed extreme, and therefore unlikely to be generally replicated [Iswaran et al.143; Topoliantz et al.144; Yamoto et al. (peanut, Site A)145; Steiner et al. (sorghum)146; Baronti et al. (irrigated maize)140] or where insufficient information was presented to estimate a response (Oguntunde et al.147). In general, these excluded data showed very strong positive responses to biochar amendments. However, a single datapoint for irrigated maize (Baronti et al.140) was also excluded because it showed a 20% decrease in yield and was thus inconsistent with the remaining yield data for cereals collected under dryland or greenhouse conditions. We note that yield decreases at low biochar levels are observed sometimes during the first year after incorporation due to nitrogen immobilisation while the most labile C in the biochar is metabolised148,136 (unless sufficient N is added to compensate for this effect), and hypothesize that this may be the cause of the decreased yield observed by Baronti et al.140. The dataset used to develop the relationship is shown in Table S10.

In general, we found that field and greenhouse data agreed well, but that leguminous species and cereals responded differently to biochar application, with cereals showing roughly a three-fold higher response. We also found instances where application rates greater than 50 Mg C ha-1 resulted in yield decreases. We thus developed relationships for leguminous species and cereals separately that are valid only for biochar application rates below 50 Mg C ha-1. These are simply the slope of a line fit to the relative biochar yield data plotted in terms of the biochar application rate. The values obtained for each crop type and study type, together with the values used in our model, are given in Table S11, while plots of the data and fitted lines for each crop type are given in Fig.S9. As implemented in the model, one RBY value (0.022 ha Mg-1 C) is used for cereal crops including maize, wheat, and rice, and the legume RBY value (0.0066 ha Mg-1 C) is used for all other crops.

2.6.5 Impact on existing stocks of SOC

We identify three distinct ways in which biochar can affect the quantity of SOC. Firstly, as discussed in section 2.2.1, there will be reduced inputs of detritus to soil as a result of diverting biomass into biochar production rather than adding it directly to soil. Secondly, where biochar additions to soil result in greater plant biomass yield, the unharvested fraction of this enhanced yield (i.e., increased below-ground biomass) will increase the rate of input of detritus to soil and thus increase SOC stocks. We calculate this by assuming that increased above-ground yields as calculated above are matched by an equal increase in below-ground biomass, and that this biomass will add to SOC pools according to the equation 1 in section 2.2.1. Thirdly, as we discuss here, the presence of biochar in soils may alter the rates of humification, stabilisation and respiration of soil organic matter (SOM).

Greatly enhanced rates of mass loss from boreal forest (Pinus sylvestris) litter mixed with charcoal (50:50 mixture) have been observed, giving rise to concern that biochar additions to soils might lead to significant losses of native (non-biochar) soil organic carbon and a corresponding reduction in the net carbon sequestered149. Enhanced soil-respiration rates in the presence of BC have also been observed in incubation experiments126,150,119, all with the caveat that C loss could not be attributed to

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biochar or SOC. Where attribution was possible126, both increased and decreased mineralization of SOC was observed in a 2-year incubation study. We should be wary, however, of extrapolating from short term laboratory incubations or litter-bag studies to long-term kinetics in soils in their native environment where other processes such as humification and mineral stabilization of SOC and development of complex soil microbial communities (including mycorrhiza that obtain their carbon from roots rather than soil) will also affect SOC stocks and may be influenced by BC. Indeed, in terra preta soils, where high BC concentrations have existed in soil for long periods, higher concentrations of BC are found to occur with higher concentrations of non-BC SOC relative to adjacent soils151. Too little is known, though, about the formation processes of terra preta to assume that the same increases in humified and mineral-stabilized SOC will be generated by additions of biochar to other soils globally.

In a study of BC-rich soils from historical charcoal blast-furnace sites131, total SOC in both the BC-rich soils and adjacent soils was measured. The labile fraction of the soils was also determined by incubation. No statistically significant effect of BC concentration on labile-SOC concentration was found, with a 5% mean increase of labile-SOC stocks in BC-rich soils compared to adjacent soils (Fig. S11).

As these soils cover a climatic sequence with mean annual temperature ranging from 4 to 17 °C, and charcoal was deposited in all of these soils approximately 130 years ago, this study provides the best currently available data to estimate the effect of biochar additions to global soils over the course of the century timescale considered in this paper. It suggests that no significant effect may be expected. However, we note that the world’s soils contain an estimated 1500 Pg organic C to 1 m depth152. Since only a small percentage change in this carbon pool would create a significant feedback, further research to determine the size of this effect with greater confidence is a high priority.

2.6.6 Soil N2O emissions

Biochar additions have been observed to reduce soil nitrous oxide emissions by 50 – 80% at an application rate of 20 Mg ha-1 in an acid savanna oxisol153, and by 70-80% at an application rate of 5 g kg-1 (equivalent to approximately 20 Mg ha-1 if incorporated to a depth of 0.3 m) in a slightly acidic to neutral upland mollisol developed on glacial till150. Although further research is required to determine the range of soil conditions under which reduced N2O emissions will occur48, the striking reduction noted in those studies that have measured this effect and the absence of studies that did not find a reduction in N2O, mean that it is important at least to estimate how important this feedback might be. We begin by calculating a global mean emission rate of N2O from cropland. Globally, 120 Tg N yr-1 is used in agriculture154, of which 79% is applied to cropland115, yielding a mean application rate of 62 kg N ha-1 yr-1 to the 1.53 Gha of global cropland. Direct soil emissions of N2O-N, while highly variable, are on average 1% of applied N116,155 plus a further 1 kg N2O-N ha-1 yr-1 that is not related to quantity of applied fertilizer155. This gives us an estimated global mean of 1.6 kg N2O-N ha-1 yr-1 from cropland (equivalent to 2.5 kg N2O ha-1 yr-1). We have then calculated annual avoided soil N2O emissions (EN) to be this mean emission rate multiplied by the area of land that has been amended by biochar (Ab) and a reduction factor RN.

EN = RN (2.5 kg N2O ha-1 yr-1) Ab

For the sensitivity and Monte-Carlo analyses we investigate values for RN in the range 0-80%, and for the main results, given that these emission reductions have not been demonstrated over a wide range of conditions, we assume a modest value of RN=25%.

2.6.7 Soil CH4 flux

There is some reported evidence of reduced CH4 emissions from soil after biochar additions153; however the evidence for this effect is weak, particularly given that dry soils are generally net sinks rather than sources of CH4. A study of biochar amendments to paddy rice systems found no reduction in CH4 emissions as a result of biochar additions156. Accordingly, we have assumed no effect of biochar on CH4 emissions from soils. Enhanced CH4 sinks of 150 mg CH4-C m-2 yr-1 in dry soils have also been reported157, and we have accordingly used the range 0-150 mg CH4-C m-2 yr-1 in both the sensitivity and Monte-Carlo analyses, with a median value of 75 mg CH4-C m-2 yr-1 used to generate Figures 2 and 3 in the main paper.

2.6.8 Fertiliser application

Biochar can improve fertiliser-use efficiency, thus lowering fertiliser input requirements and avoiding CO2 emissions associated with fertiliser manufacture. Several studies have shown biochar-induced reductions > 50% in ammonium- and nitrate-N leaching158. Leaching from terra preta soils is also significantly lower than from adjacent soils suggesting that the reduced leaching effect persists over time94. Given that gaseous losses of N have not been found to increase with biochar application (and may, rather, decrease), reduced leaching losses of N imply an increase in N use efficiency159 once N

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immobilized by initial adsorption to biochar has been accounted for.

Current global N-fertiliser production is 120 Tg N yr-1 (ref. 154). We assume that CO2 emissions due to N-fertiliser production, transport and application are 0.858 Mg C Mg-1 N (ref. 160), although values as large as 1.23 Mg C Mg-1 N have been estimated84. Energy requirements for phosphorus (P) and potassium (K) fertilisers are an order of magnitude lower160, and are therefore omitted from this calculation. Thus, global fertiliser production is currently responsible for emission of approximately 103 Tg CO2-C yr-1. To estimate how much this might be reduced by biochar production, we begin by assuming that a 50% increase in N use efficiency will reduce global fertiliser production by 50% times the fraction of cropland that has been amended with biochar. After also taking into account reactive N converted to N2 during pyrolysis (calculated as described in section 2.2.3 above), we estimate that 360 Tg CO2-C equivalent cumulative emissions from fertiliser production over 100 years would be avoided in the Alpha scenario – approximately 0.5% of the total avoided emissions from biochar.

Some N will initially be immobilized following biochar application due to both microbial decomposition of the biochar labile fraction and to adsorption of N onto biochar. Although there may be numerous opportunities to supply some of this N by using biochar to remove N from effluent sources such as sewage, slurry and aquaculture, we make the conservative assumption that this immobilised N will have to be replaced by mineral fertiliser. Despite its agronomic importance, we do not include N immobilized by microbial decomposition of biochar in the overall greenhouse gas balance as it is a temporary effect and will over time become plant-available again as the microbes die. Furthermore, microbial N immobilization is a problem only when using biochar with a significant volatile or labile fraction136,161, and can thus be minimised or eliminated by ensuring that only biochars that have a low content of volatiles are used. The quantity of N immobilized by adsorption is linearly related to the soil cation exchange capacity (CEC). Thus, a doubling of CEC will require a doubling of soil N in order to maintain the same concentration of N in solution. This allows us to make a 1st order estimate of adsorption immobilized N. In highly infertile soils, BC concentrations of 15 g kg-1 (equivalent to approximately 58 Mg ha-1 if biochar is incorporated to a depth of 0.3 m) increase soil CEC by a factor of two162. In fertile soils with a high native CEC, little or no increase in CEC as a result of biochar amendment is likely. Interpolating linearly between soil fertility classes, we thus estimate the biochar induced increase in CEC in soils of different fertility constraints to be as in Table S13.

The mean relative increase in CEC, weighted by land area under cultivation in each fertility classification (as given in Table S8), is thus 1.5. Thus, approximately 50% increase in N applications will be required during the first year of biochar application to maintain dissolved N concentrations. Over the course of a century then, this will require an additional half a year’s equivalent of fertiliser production, leading to a total GHG emission of 52 Tg CO2-C. This is approximately 14% of the avoided emissions due to increased N-use efficiency, and represents just 0.07% of total avoided GHG emissions due to biochar production in the Alpha scenario.

2.7 Rate of adoption

Although this model is designed to calculate the effect on GHG emissions from biochar, not its economics, we nevertheless must include an estimate of costs in order to make realistic assumptions about how rapidly production capacity could potentially be implemented. Published estimates of capital cost for biomass pyrolysis with power generation are given in Table S15.

We use a sigmoidal Gompertz function (Fig. S3) of the form Ba = Bmax exp(-k1 exp(-k2.(t-t0)) to model the rate at which annual biomass utilisation (Ba) approaches its maximum value (Bmax). The point of inflection at which maximum slope is reached occurs when the 2nd derivative is zero at t= (ln(k1)/k2) – t0. The annual increase in biomass carbonisation rate is given by the 1st derivative dBa/dt = B.k1.k2 exp(-k1

2 – ln(k1)). The parameters k1 = 11, k2 = 0.25 and t0 = 4 were set such that the maximum rate of capital expenditure is reached after 15 years and, in the Alpha scenario, is 0.1% of 2007 global GDP, assuming a capital intensity of US$395 Mg-1 yr (dry feedstock), based on the highest estimate for an intermediate sized plant in Table S15 (German Energy Agency figures), and on a mean life expectancy of the plant of 20 years.

The expenditure rate on biochar capital of 0.1% GDP was chosen to be compatible with the estimate that total expenditure on climate mitigation needs to be in the order of 2% of global GDP163 together with the assessment, based on the results of this model that, in the Alpha scenario, biochar might provide around 5% of the 15 Pg CO2-Ce yr-1 abatement required (see Table S14) to stabilise atmospheric GHG concentrations. We did not include operating costs or feedstock costs in this analysis, since it has been estimated that together they are approximately equal to the value of the energy produced164. The estimate that maximum capital expenditure is reached after 15 years allows for a lead-time of approximately 5 years during which little plant capacity is commissioned to provide time for further research that is a prerequisite to large-scale deployment. Longer development times

19

than this are not expected to be necessary, since plant is already commercially available and early deployment is already under way particularly in regions with poor soils. At these rates, maximum production could be approached by mid-century.

2.8 Aspects not considered in the model

We have attempted to include in this model the most important effects of biochar on the net GHG balance. Greenhouse gases are not the only manner, though, in which biochar can impact on the climate system. Biochar production also has potential to affect both atmospheric aerosol loading and albedo.

Combustion of biomass is a major contributor to atmospheric soot emissions165. Pyrolysis of biomass in well-designed, modern plant does not entail significant atmospheric emissions of soot. Thus, replacing biomass combustion in developing regions with pyrolysis is likely to both reduce tropospheric BC concentrations and improve indoor air quality where biomass is used as a cooking fuel. Tropospheric BC is the second most important contributor to anthropogenic global warming after carbon dioxide166 and, in the case of the Asian Brown Cloud, responsible for reduced monsoon rainfall and agricultural yields167,168. Poor indoor air quality as a result of biomass burning for cooking is the primary cause of acute respiratory disease in less developed countries where it is the chief cause of child mortality169. Thus, replacement of traditional cooking fires with pyrolysis stoves may also have important health benefits.

Although pyrolysis itself should not produce significant particulates, we should be aware of the possibility that mismanagement of biochar during storage, transport and field application could lead to significant amounts of black carbon dust becoming airborne. If the material is finely divided, then some of these particles might be small enough to remain airborne for significant periods and to travel large distances. It may therefore be necessary to pelletize biochar in order to minimise dust.

Soil darkening as a result of biochar addition may also reduce the albedo of cropland during bare fallow periods and while crop cover is sparse.

An evaluation of the size of these effects is, however, beyond the scope of this study and will require further research.

2.9 Monte-Carlo and sensitivity analyses

The ranges of the variables explored by sensitivity analyses are given in Table S7. For the Monte-Carlo anaylsis, random values were generated for all variables (with the exception of N2O emission factor) with probabilities following a Gaussian distribution with mean equal to the estimated value and standard deviation set such that the range is a 95% confidence interval. That is, the standard deviation, σ (determined numerically), is such that Φμ,σ

2(XH) - Φμ,σ2(XL) = 0.95, where, Φμ,σ

2(X) is the Gaussian cumulative distribution function Φ[(X – μ)/σ], μ is the mean, XL is the lower limit of the range and XH is the upper limit of the range. Random values for the N2O emission factor were generated on a flat probability distribution within the given range as there was no rationale to suggest that values suggested by Ref. 78 are more or less likely than the IPCC default values.

20

3 Supplementary Figures

Figure S1: Schematic of biochar greenhouse gas assessment model, BGRAM 1.0

22

Figure S2: Payback time for the carbon debt incurred by cropping for biochar in dedicated biomass plantations. Estimates assume annual pyrolysis of 15 Mg ha-1 of woody biomass after conversion of the land to dedicated biomass production. No increase in this biomass production rate due to biochar amendment is assumed. These estimates do not include any contribution from changes to soil N2O emissions that may arise from increased N-fertiliser use on the biomass crops relative to the prior land use, nor do they include any mitigation of this potential increase in soil N2O emission due to biochar additions to the soil. Example values of carbon debt are from ref 70.

23

Figure S3: Annual global biomass pyrolysed as a function of time in each of the scenarios

24

Figure S4: Net avoided CO2 emissions relative to current use of biomass that are attributable to sustainable biochar production (solid lines) or biomass combustion (dashed lines) over 100 yr for the three model scenariosa. (top) annual avoided emissions; (bottom) cumulative avoided emissions. Diamonds indicate transition period when biochar capacity of top 15 cm of soil fills up and alternative disposal options will be needed.

aaAll data used to produce plots in Figs S4-S6, Fig. S8 and Figs 2 & 3 in the main paper are available as an Excel spreadsheet in the Supplementary Dataset online.

Net

Av

oid

ed C

O2 E

mis

sio

ns

(Pg

CO

2-C

)

25

Figure S5: Net avoided CH4 emissions relative to current use of biomass that are attributable to sustainable biochar production (solid lines) or biomass combustion (dashed lines) over 100 yr for the three model scenarios. (top) annual avoided emissions; (bottom) cumulative avoided emissions. Diamonds indicate transition period when biochar capacity of top 15 cm of soil fills up and alternative disposal options will be needed.

26

Figure S6: Net avoided N2O emissions relative to current use of biomass that are attributable to sustainable biochar production (solid lines) or biomass combustion (dashed lines) over 100 yr for the three model scenarios. (top) annual avoided emissions; (bottom) cumulative avoided emissions. Diamonds indicate transition period when biochar capacity of top 15 cm of soil fills up and alternative disposal options will be needed.

27

Figure S7: Land area over which biochar production is assumed (for the purpose of calculating transport distances) to be dispersed (5' of arc resolution). Every grid cell in which some feedstock from existing agricultural cropland is available has been coloured black. Note that this does not imply that all of this land area will be used for biochar production or soil incorporation as, in general, only a fraction of any individual black gridcell will contain cropland.

Figure S8: Breakdown of cumulative avoided GHG emissions (Pg CO2-Ce) from sustainable biochar production and biomass combustion for the three model scenarios over 100 years by feedstock and factorb. Left side of figure displays results for each of eight feedstock types and the additional biomass residues attributed to NPP increases from biochar amendments; right side displays total results for each scenario. For each pair of columns, results for biochar are given in the left column and for biomass combustion in the right column. For each column, the total emission-avoiding and emission-generating contributions are given respectively by the height of the columns above and below the zero line. The net avoided emissions are calculated as the difference between these two values. Within each column, the portion of its contribution due to each of six emission-avoiding mechanisms and three emission-generating mechanisms is shown by a different color. These mechanisms (from top to bottom within each column) are 1) avoided CH4 from biomass decay (CH4 Biomass), 2) increased CH4 oxidation by soil biochar (CH4 Soil), 3) avoided N2O from biomass decay (N2O Biomass), 4) avoided N2O due to soil biochar (N2O Soil), 5) fossil fuel offsets due to pyrolysis energy production (Fossil Fuel Offset), 6) avoided CO2 emissions due to C bAll data used to produce plots in Figs S4-S6, Fig. S8 and Figs 2 & 3 in the main paper are available as an Excel spreadsheet in the Supplementary Dataset online.

28

stored as biochar (C in Biochar), 7) decreased C stored as soil organic matter due to diversion of biomass to biochar (Soil Organic C), 8) CO2 emissions from transportation and tillage activities (Transportation and Tillage), and 9) CO2 emissions from decomposition of biochar in soil (Biochar Decomposition).

Figure S9: Relative biomass yields for biochar-amended soils. Results are calculated from data presented by field and greenhouse studies of (a) leguminous and (b) cereal crops and summarized in Table S10. Relative yield of 1 (shown by horizontal grey line) implies no yield response to biochar application. Soils are assumed to have maximum fertility constraints (i.e., to be “unsuitable” for cultivation) and yield responses thus are maximal values. Slopes of regression lines correspond to Relative Biochar Yield (RBY) factor used in model and listed in Table S11.

29

Figure S10: Cumulative mitigation potential of biochar relative to bioenergy as a function of both soil fertility and C-intensity of the type of energy being offset (in the Alpha scenario). Points Mew, Mw and Mb on the x-axis refer to the current world electricity mix, the current world primary energy mix, and the baseline energy mix assumed in our scenarios, respectively. The relative mitigation is calculated as cumulative avoided emissions for biochar minus those for bioenergy, expressed as a fraction of the avoided emissions for bioenergy (e.g., a value of 0.1 indicates that the cumulative mitigation impact of biochar is 10% greater than that of bioenergy, a value of -0.1 indicates that it is 10% lower, and a value of zero indicates that they have the same mitigation impact). The soil-fertility classifications marked on the vertical axis correspond to the soil categories mapped in Fig. 6. Panel a (Residues) includes agricultural and forestry residues, together with green waste. Panel b (Biomass crops) includes both dedicated biomass crops and agroforestry. Panel c (Manures) includes cattle, pig and poultry manures. Panel d (Total) includes all feedstocks in the scenario.

30

50 100 150 200 250 300 350 400

-10

-8

-6

-4

-2

0

2

4

6

8

10 ME

NY

PA-1

OH

TN

GA

AL

recalcitrant OC in BC-soil (g/kg)

de

ficit

in la

bile

-OC

co

mp

are

d to

ad

jace

nt s

oil

(g/k

g)

Figure S11: Effect of soil black carbon concentration on labile soil organic carbon pool. X-axis gives the concentration of recalcitrant organic carbon (OC) in black-carbon amended soil. Y-axis shows the difference in labile OC concentration between these soils and similar adjacent soils that have not been amended with black carbon (derived from Cheng et al.132)

Figure S12: Land currently under pasture that is suitable for tropical silvopasture (5’ of arc resolution). Although currently used as pasture, this land occurs in agroecological zones that have no serious constraints to rain-fed crop production.

31

4 Supplementary Tables

Table S1: Global quantities of non-rice straw and stover available as feedstock for pyrolysis

Non-rice straw & stover (Tg DM yr-1)

Available for pyrolysis

Region

Total generateda

Foddera Alpha Beta MSTP

East Asia 539.8 116.9 18.0 72.0 126.0

East Europe 146.2 14.8 21.8 36.4 51.0

Latin America & Caribbean

279.0 120.4 0.0 0.0 5.1

North & West Africa

122.4 52.8 0.0 0.0 2.2

North America & Oceania

388.4 11.4 85.7 124.5 163.3

South & Central Asia

269.0 195.8 0.0 0.0 0.0

Sub-Saharan Africa 218.0 141.1 0.0 0.0 0.0

Western Europe 183.6 5.8 40.1 58.5 76.8

Total 2146.4 659.1 165.6 291.4 424.5 a Derived from Ref 62

Table S2: Summary of biomass availability in scenarios (C content derived from values given in section 5)

Biomass availability in scenario (Pg yr-1)

Alpha Beta MSTP Feedstock

DM C DM C DM C

Cereals excluding rice 0.17 0.07 0.29 0.13 0.42 0.18

Rice 0.52 0.22 0.60 0.25 0.67 0.28

Sugar cane 0.20 0.09 0.24 0.11 0.27 0.13

Manure 0.31 0.10 0.45 0.14 0.59 0.19

Biomass crops 0.63 0.30 0.94 0.60 1.25 0.60

Harvested wood 0.05 0.03 0.13 0.07 0.21 0.11

Forestry residues 0.29 0.14 0.29 0.14 0.29 0.14

Agroforestry 0.13 0.06 0.70 0.34 1.28 0.62

Green waste 0.009 0.004 0.05 0.02 0.07 0.03

TOTAL 2.3 1.0 3.7 1.6 5.1 2.3

32

Table S3: Biomass properties used for modeling. Composition and energy content of biomass feedstocks used in BGRAM (and, where multiple sources are available, their standard deviations, “s.d.”), expressed on a dry ash-free (DAF) and dry-mass (DM) basis. Except where indicated, data are derived from phyllis database at http://www.ecn.nl/phyllis/. HHV = highest heating value. LHV = lowest heating value.

Biomass Water Ash HHV LHV C N HHV LHV C N Fraction of category

% % GJ Mg-1 DAF

GJ Mg-1 DAF

% DAF % DAF GJ Mg-1 DM

GJ Mg-1 DM

% DM

% DM

%

[% s.d.] [% s.d.] [% s.d.] [% s.d.] [% s.d.] [% s.d.] [s.d.] [s.d.] [s.d.] [s.d.]

Rice Hulls 9.6 17.8 18.67 17.34 48.7 0.83 15.3 14.3 40.0 0.7 11.53

[16] [23.4] [6] [7] [3] [78] [1.2] [1.2] [2.4] [0.5]

Rice Straw 28.4 15.3 18.83 17.52 48.8 1.39 15.9 14.8 41.3 1.2 88.47

[123] [39] [3] [3] [7] [90] [1.2] [1.1] [4.1] [1.1]

Rice hulls + straw

26.23 15.59 18.81 17.49 48.79 1.33 15.88 14.77 41.18 1.12

Wheat Straw 12.1 7.4 19.56 18.18 48.9 0.88 18.1 16.8 45.3 0.8 45.10

[97] [59] [5] [5] [3] [86] [1.2] [1.2] [2.5] [0.7]

Maize Stover 19.1 7.9 19.08 17.84 44.4 1.4 17.6 16.4 40.9 1.3 31.39

[158] [67] [3] [4] [29] [98] [1.1] [1.2] [12.1] [1.3]

Maize cobs 24.7 6.7 17.46 16.15 46.8 1.55 16.3 15.1 43.7 1.4 6.12

[142] [88] [7] [9] [5] [119] [1.5] [1.7] [3.5] [1.7]

Barley Straw 17.9 6.3 18.68 17.48 47.5 1.16 17.5 16.4 44.5 1.1 10.93

[146] [78] [7] [8] [5] [116] [1.5] [1.6] [3.2] [1.3]

Rye straw 24.7 5.7 17.55 16.33 47.9 1.1 16.6 15.4 45.2 1.0 1.80

[142] [89] [7] [9] [5] [129] [1.5] [1.6] [3.3] [1.3]

Sorghum straw

6 6.1 17.88 16.6 45.2 0.61 16.8 15.6 42.4 0.6 4.48

[0] [19] [12] [12] [11] [117] [2.0] [1.9] [4.7] [0.7]

cereals ex rice

15.64 7.29 19.04 17.74 46.94 1.11 17.64 16.44 43.52 1.02

Sugar cane Bagasse

50192 5.8 19.34 18.04 49.3 0.56 18.22 16.99 46.4 0.5 50

[10]192 [77] [4] [4] [3] [102] [1.13] [1.05] [2.6] [0.5]

Sugar cane trash

6.5 5.5 49.7 0.74 47.0 0.7 50

Sugar cane total

15.15 5.65 19.34 18.04 49.5 0.65 18.25 17.02 46.70 0.61

Hardwood 16.5 2.4 19.71 18.38 49.8 0.41 19.2 17.9 48.6 0.4 29

[90] [121] [5] [5] [4] [107] [1.1] [1.0] [2.4] [0.4]

Softwood 19.9 0.9 20.3 18.95 51.4 0.16 20.1 18.8 50.9 0.2 71

[93] [131] [6] [6] [4] [75] [1.2] [1.1] [2.1] [0.1]

Wood 18.91 1.34 20.13 18.78 50.94 0.23 19.86 18.53 50.26 0.23

33

Miscanthus 28.9 3.6 19.77 18.52 49.6 0.52 19.1 17.9 47.8 0.5 50

[56] [38] [3] [3] [2] [52] [0.6] [0.6] [1.2] [0.3]

Switchgrass 12.1 6 19.17 17.83 49.4 0.64 18.0 16.8 46.4 0.6 50

[19] [24] [3] [4] [5] [29] [0.6] [0.7] [2.4] [0.2]

Herbaceous biomass crops

20.5 4.8 19.77 18.52 49.5 0.58 18.82 17.63 47.13 0.55

Willow 10.9 1.9 19.83 18.48 49.8 0.61 19.4 18.1 48.9 0.6

[76] [48] [4] [4] [2] [51] [0.8] [0.7] [1.1] [0.3]

Cattle manure

38.1 40.6 21.36 19.85 50.4 3.36 12.7 11.8 29.9 2.0

[83] [51] [16] [16] [10] [64] [4.9] [4.5] [10.9] [1.5]

Pig manure 59.3 25.3 21.19 19.91 50.4 3.56 15.8 14.9 37.6 2.7

[70] [56] [1] [0] [9] [20] [3.0] [2.8] [7.9] [0.7]

Chicken manure

44.4 22.8 19.68 18.5 45 5.74 15.2 14.3 34.7 4.4

[58] [30] [8] [10] [18] [22] [1.8] [1.9] [7.0] [1.1]

forest residues

29.6 1.2 18.32 17 48.8 0.71 18.1 16.8 48.2 0.7

Green waste 25.4 16.3 22.37 20.9 52.1 0.77 18.7 17.5 43.6 0.6

[61] [73] [20] [20] [13] [55] [4.6] [4.3] [8.4] [0.4]

34

Table S4: Biochar properties used for modeling. Carbon, nitrogen, and energy content of biochars prepared from different feedstock classes as used in BGRAM. (HHV = higher heating value)

cereals ex. rice

rice sugar cane

cattle manure

pig manure

poultry manure

biomass crops (herb)

biomass crops

(wood)

wood waste

felling losses

Agro-forestry

green waste

Biochar HHV

GJ Mg-1 21.4a 16.47b 26.5c 11.0d 11.0d 11.0d 19.1e 31.2f 31.2f 31.2f 33.2g 26.4h

Biochar C %

Mg C Mg-1 biochar

59.9a 56.0i 69.9j 0.25k 0.25 k 0.25 k 56.8l 75.0f 75.0f 75.0f 63.0g 67.5h

Biochar N %

Mg N Mg-1 biochar

0.67a 0.60m 0.51n 1.0o 1.4 o 2.3 k 1.0l 0.31f 0.31f 0.31f 0.8g 0.18p a Mean of wheat straw char in Refs 193-196 b Mean of values derived from Refs 197,198 (calculated using Boie equation199). c Ref 200 gives 18.9-23 MJ kg-1 (gasifier char); Ref 198 (calculated using Boie equation), 27.36 MJ kg-1 (fast pyrolysis char); Ref 201, 32.4 and 30.7 MJ kg-1 (fast pyrolysis char); Ref 202, 27.7 MJ kg-1 (slow pyrolysis); Ref 203 gives 26.2, 24.8, 27.3 for slow pyrolysis char; we use mean of slow pyrolysis chars = 26.5 MJ kg-1

d No data found for cattle or pig manure chars, therefore we have assumed value given for chicken manure in Ref 204 for all manure chars. e Mean of 20.1 MJ kg-1 from Ref 205 and 18.04 MJ kg-1 derived from Ref 206 using Milne equation (both sources for switchgrass char). f Mean of 18 sources (all sources in wood char category excluding demolition wood) in Ref 207. g Based on Leucaena leucocephala char in Ref 208. h Value for yard trimmings estimated as mean of values for straw and wood. i Ref 197 j Ref 198 gives 71.41% (fast pyrolysis); Ref 201 gives 85.6, 81.5% (vacuum pyrolysis) at 500-530˚C; Ref 202 gives 71.6% (slow pyrolysis 380˚C); Ref 203 gives 68.1%, 68.2%, 71.6% (slow pyrolylsis at 400 C); we use mean of slow pyrolysis chars = 69.88% k Mean of Ref 106 and non-activated char from Ref 142 l Mean of Refs 205,206 for switchgrass m Mean of Refs 197,198,209 n Ref 198 gives 1.77%, (fast pyrolysis); Ref 201 gives 1.3%, 0.8% (gasifier char); Ref 202 gives 0.53% (slow pyrolylsis); Ref 203 gives 0.5%, 0.5% and 0.5% (slow pyrolysis); we use mean of slow pyrolysis = 0.508%). o Calculated by assuming the same ratio between biochar N% to biomass N% as for chicken manure p Ref 210

Table S5: Methane emission factors for rice straw

Rice methane emissions kg CH4-C kg-1 straw

Source

0.069 Jermsawatdipong et al., 1994170 0.010 Singh et al., 1996171 0.043 Lee et al., 1997172 0.019 Sass & Fisher, 1997173 0.042 Kumagai and Konno, 1998174 0.179 Corton et al., 2000175 0.126 Goto et al., 2000176 0.19 Naser et al., 2007177 0.004 Liou et al., 2003178 0.076 Mean

35

Table S6: Estimates of biochar decay half-life

Source T½ /yr Range /yr Notes

Hammes et al. (2008)129

210 130-380 Measurements of in situ concentrations of historic BC from biomass burning in the Russian Steppe

Kuzyakov et al. (2009)126

1400 Incubation of laboratory biochar formed from perennial ryegrass at an unspecified temperature in Haplic Luvisol and Loess soils

Bird et al. (1999)133

< 100 BC concentration in fire-protected soils relative to regularly burned soils in sandy savanna soils in Zimbabwe.

Cheng et al. (2008)131

930 BC soils from historic blast furnace sites. SOC decomposition measured during incubation, and extrapolated to in situ conditions with a mean annual temperature of 10°C.

Nguyen et al. (2008)134

6 Western Kenyan agricultural fields cleared from forest by fire at 2-100 years BP. Short residence time was attributed to a combination of labile component of BC and to initially rapid vertical transport. Following early losses, BC concentrations stabilised after approximately 30 years.

Liang et al. (2008)179

Several centuries to several millennia

Measurements of turnover rate of aged char in terra preta soil.

Preston and Schmidt (2006)180

6600 Coastal temperate rain forest on western Vancouver Island, British Columbia

Lehmann et al. (2008)92

900-1800 1877 soil samples from Australian National Soil Archive analysed for BC and SOC content, and fitted to models of BC formation and loss rates.

36

Table S7: Range, mean and standard distribution of variables used in sensitivity and Monte-Carlo analyses

Variable Range Estimated value σ

Pyrolysis C yield (%) 40 – 60 49 5.2

Pyrolysis energy efficiency (%) 65 – 85 75 5.1

C intensity of fuel offsets

(kg CO2-C GJ-1)

15 – 26 17.5 1.52

Biochar labile fraction (%) 5 – 30 15 5.9

½ life of biochar labile fraction (yr) 1 – 25 20 3.0

½ life of biochar recalcitrant fraction (yr) 50 – 1000 300 152

Increase in CH4 reoxidation rate as a result of biochar (mg CH4 –C m-2 yr-1)

0 – 150 75 38.2

Relative biomass yield (RBY) modifier (i.e. the fraction of the RBY predicted in section 1.7.4 that is realised in practice)

0.5 – 1.5 1 0.255

% reduction in soil N2O emissions due to biochar application

0 – 80 25 15

Biomass N2O emission factor (kg N2O-N kg-1 applied-N)

1% – 5% 1.05%a

Values populated from a flat probability distribution constrained within the stated range

a weighted mean of IPCC default values for different feedstocks as outlined in 1.3.3

Table S8: Cultivated land area classified by soil fertility constraints

Severity of soil fertility constraints

Global area of cropland

(Gha)

None 0.31

Few 0.29

Slight 0.21

Moderate 0.32

Severe 0.18

Very severe 0.13

Unsuitable 0.09

37

Table S9: Assumed biomass yield responses to biochar amendments as fraction of the maximum potential response

Severity of soil fertility constraint

Biomass yield response as fraction of potential maximum

None 0

Few 0.1

Slight 0.3

Moderate 0.5

Severe 0.7

Very severe 0.9

Unsuitable 1.0

38

Table S10: Dataset used to develop function describing biomass yield response to biochar soil amendments

Reference Greenhouse

/ Field Crop

Biochar C level (Mg C ha-1)

NPK Level (kg ha-1)

Yield (Mg ha-1)

Relative Biomass

Yield

Yamoto et al. 2006 (Ref. 145)

Field Cowpea,

site A 0 75-75-75 12 1.00

Ref. 145 Field Cowpea,

site A 14.7 75-75-75 13.5 1.13


site B 0 75-75-75 11.5 1.00


site B 14.7 75-75-75 12.3 1.07

Ref. 145 Field Peanut, site B

0 75-75-75 6 1.00

Ref. 145 Field Peanut, site B

14.7 75-75-75 6.5 1.08

Major 2009 (Ref 181)

Field Soybean 0 16-10-104 --ab 1.00

Ref 181 Field Soybean 5.8 16-10-104 --ab 1.00

Ref 181 Field Soybean 14.6 16-10-104 --ab 1.00

Ref. 145 Field Maize, site

C 0 0 9 1.00


C 14.7 0 11.3 1.26


C 0 75-75-75 10.5 1.00


C 14.7 75-75-75 15 1.43

Steiner et al. 2007 (Ref 146)

Field Rice 0 30-35-50-2100(lime)

--a 1.00

Ref 146 Field Rice 7.8 30-35-50-2100(lime)

--a 1.52

Baronti et al. 2008 (Ref 140)

Field Durum Wheat

0 ? 8.11 1.00

Ref 140 Field Durum Wheat

8 ? 9.54 1.18

Ref 181 Field Maize 0 163-34-105 --ab 1.00

Ref 181 Field Maize 5.8 163-34-105 --ab 1.26

Ref 181 Field Maize 14.6 163-34-105 --ab 1.50

Rondon et al. 2007 (Ref. 95)

Greenhouse Common

Bean 0

20-20-0-300(lime)

4.4 1.00

Ref. 95 Greenhouse Common

Bean 37.1

20-20-0-300(lime)

5.5 1.25


Bean 74.2

20-20-0-300(lime)

6.1 1.39

39


Bean 111.2

20-20-0-300(lime)

4.7 1.07

Ref. 95 Greenhouse Common Bean, no N fixation

0 20-20-0-300(lime)

3.4 1.00


37.1 20-20-0-300(lime)

3.8 1.12


74.2 20-20-0-300(lime)

3.75 1.10


111.2 20-20-0-300(lime)

2.7 0.79

Lehmann et al. 2003 (Ref 94)

Greenhouse Cowpea 0 0 0.73 1.00

Ref 94 Greenhouse Cowpea 47.9 0 1.05 1.44

Ref 94 Greenhouse Cowpea 95.7 0 1.34 1.84

Ref 94 Greenhouse Cowpea 0 0-59-0 0.7 1.00

Ref 94 Greenhouse Cowpea 47.9 0-59-0 0.89 1.27

Ref 94 Greenhouse Cowpea 95.7 0-59-0 1.15 1.64

Ref 140 Greenhouse Perennial Ryegrass

0 ? 0.31 1.00


7 ? 0.33 1.06


21 ? 0.38 1.23


42 ? 0.62 2.00


70 ? 0.28 0.90


84 ? 0.22 0.71

aBased on relative yields given by authors bGrain yields

Table S11: Relative biomass yields (RBYs) per Mg C ha-1 biochar application (derived from dataset given in Table S10)

Crop Field Greenhouse All

----------- (ha Mg-1 C) -----------

Cereals 0.028 0.024 0.022 Legumes 0.0048 0.0066 0.0066 Cowpea 0.0066 0.0077 --

40

Table S12: Mass of crop residues (Pg DM) produced on soils of different fertility classes

Soil fertility constraints Cereals

Sugar Cane Oil Crops Pulses

None 0.69 0.03 0.14 0.008

Few 0.68 0.05 0.08 0.009

Slight 0.38 0.04 0.06 0.006

Moderate 0.45 0.1 0.14 0.007

Severe 0.22 0.03 0.07 0.004

Very Severe 0.14 0.02 0.06 0.002

Unsuitable 0.10 0.03 0.04 0.001

Table S13: Increase in soil cation exchange capacity (CEC) as a function of soil fertility class

Table S14: Calculation of current anthropogenic GHG emissions. Global warming potentials (100-yr), anthropogenic emissions, and calculated emissions in Pg CO2-Ce yr-1 for CO2, CH4, and N2O.

100 yr GWPa Pg yr-1

Pg CO2-Ce yr-1 Year Source

CO2 from Land-Use Changes 1 1.467 2005 Ref. 188

Fossil-Fuel CO2 Emissions 1 8.23 2006 Ref. 189 Boden et al. (2009)

Anthropogenic CH4 Emissions (as CH4) 25 0.582 3.968 2000-2004 Ref. 190,Table 7.6 p. 542

Anthropogenic N2O Emissions (as N) 298 0.00670 1.711 1990s Ref. 190,Table 7.7 p. 546

Total for CO2, CH4, and N2O Only 15.38

a GWP= Global Warming Potential from Table 2.14 in Ref. 191

Level of soil fertility constraint

Relative biochar induced increase in soil CEC

None 1

Few 1.2

Slight 1.4

Moderate 1.6

Severe 1.8

Very Severe / Unsuitable 2

41

Table S15: Estimated capital costs of biomass pyrolysis with power generation

Source Capital Intensity

(US$ Mg-1 yr)

Notes

Bridgwater182 217-617 Capital intensity related to plant size. $217 relates to 200 kg hr-1 (dry feedstock), $617 to 4 Mg hr-1. Intermediate plant of 1 Mg hr-1 has capital intensity of $370. All costs have been converted from yr 2000 € to 2007 US$ using a factor of 1.08.

McCarl et. al.164 270

Islam & Ani183 159 – 233

Roberts184 222 Refers to cost of Coaltec plant reported in article plus an additional $110 Mg-1 yr for power generation from Ref 164.

Crucible Carbon185 173 – 422

German Energy Agency186 395 Based on 106 Mg yr-1 fast pyrolysis plant, including all ancillary plant, site, planning and contingency. (This figure excludes the Fischer-Tropsch gas to liquid conversion stage also costed in the report).

Joseph187 425 Biochar cooking stoves for use in the developing world.

42

5 Supplementary References

43. Bapat H., Manahan S.E. & Larsen D.W. An Activated Carbon Product Prepared from Milo (Sorghum Vulgare) Grain for Use in Hazardous Waste Gasification by ChemChar Cocurrent Flow Gasification. Chemosphere 39, 23-32 (1999).

44. Bapat, H. Pers. comm. (2010). 45. Sombroek, W.G., Nachtergaele, F.O. & Hebel, A. Amounts, dynamics and sequestering of carbon in

tropical and subtropical soils. Ambio. 22, 417-426 (1993). 46. Seifritz, W. Should we store carbon in charcoal? International Journal of Hydrogen Energy 18, 405-407

(1993). 47. Lehmann, J., Gaunt, J. & Rondon, M. Bio-char sequestration in soil: a new frontier. (2005).at

<http://soilcarboncenter.k-state.edu/conference/PowerPoint_files/Lehmann_Baltimore_05_files/frame.htm>

48. Lehmann, J., Gaunt, J. & Rondon, M. Bio-char sequestration in terrestrial ecosystems – a review. Mitig. Adapt. Strat. Glob. Change 11, 403-427 (2006).

49. Lehmann, J. & Rondon, M. Bio-char soil management on highly weathered soils in the humid tropics. Biological Approaches to Sustainable Soil Systems 517 (2006).

50. Lal, R. World crop residues production and implications of its use as a biofuel. Environ. Int. 31, 575-584 (2005).

51. Andrews, S. Crop residue removal for biomass energy production: effects on soils and recommendations. (2006).at <http://soils.usda.gov/sqi/management/files/AgForum_Residue_White_Paper.pdf>

52. Blanco-Canqui, H. & Lal, R. Corn stover removal for expanded uses reduces soil fertility and structural stability. Soil Sci Soc Am J 73, 418-426 (2009).

53. Lafond, G.P. et al. Quantifying straw removal through baling and measuring the long-term impact on soil quality and wheat production. Agron. J. 101, 529-537 (2009).

54. Blanco-Canqui, H. & Lal, R. Soil and crop response to harvesting corn residues for biofuel production. Geoderma 141, 355-362 (2007).

55. Kim, S., Dale, B. & Jenkins, R. Life cycle assessment of corn grain and corn stover in the United States. Int. J. Life Cy. Assess. 14, 160-174 (2009).

56. Sheehan, J. et al. Energy and environmental aspects of using corn stover for fuel ethanol. J. Ind. Ecol. 7, 117–146 (2003).

57. Decker, A.M., Clark, A.J., Meisinger, J.J., Mulford, F.R. & McIntosh, M.S. Legume cover crop contributions to no-tillage corn production. Agron. J. 86, 126-135 (1994).

58. Langdale, G.W. et al. Cover crop effects on soil erosion by wind and water. Cover Crops for Clean Water. Soil and Water Conservation Society, Ankeny, IA 15–22 (1991).

59. Lee, J.J., Phillips, D.L. & Liu, R. The effect of trends in tillage practices on erosion and carbon content of soils in the US corn belt. Water Air Soil Poll. 70, 389-401 (1993).

60. Hartwig, N. & Ammon, H. Cover crops and living mulches. (2009).at <http://www.bioone.org/doi/full/10.1614/0043-1745%282002%29050%5B0688%3AAIACCA%5D2.0.CO%3B2>

61. Raimbault, B.A., Vyn, T.J. & Tollenaar, M. Corn response to rye cover crop management and spring tillage systems. Agron. J. 82, 1088-1093 (1990).

62. Wirsenius, S. Human use of land and organic materials: modeling the turnover of biomass in the global food system. Thesis for the degree of Doctor of Philosophy (Chalmers University of Technology and Gothenburg University, Sweden, 2000).

63. Kim, S. & Dale, B.E. Global potential bioethanol production from wasted crops and crop residues. Biomass Bioenerg. 26, 361-375 (2004).

64. Braunbeck, O., Bauen, A., Rosillo-Calle, F. & Cortez, L. Prospects for green cane harvesting and cane residue use in Brazil. Biomass Bioenerg. 17, 495-506 (1999).

65. Berges, M. & Crutzen, P. Estimates of global N2O emissions from cattle, pig and chicken manure, including a discussion of CH4 emissions. J. Atmos. Chem. 24, 241-269 (1996).

66. FAOSTAT. at <http://faostat.fao.org/> 67. Hoogwijk, M. et al. Exploration of the ranges of the global potential of biomass for energy. Biomass

Bioenerg. 25, 119-133 (2003). 68. van Dam, J. et al. Overview of recent developments in sustainable biomass certification. Biomass

Bioenerg. 32, 749-780 (2008). 69. Dehue, B. et al. Sustainability reporting within the RTFO: framework report. (Ecofys: Utrecht (NL),

43

2008).at <http://www.renewablefuelsagency.org/_db/_documents/080215_RTFO_Sustainability_Framework_report_final.pdf>

70. Fargione, J., Hill, J., Tilman, D., Polasky, S. & Hawthorne, P. Land clearing and the biofuel carbon debt. Science 319, 1235-1238 (2008).

71. Searchinger, T. et al. Use of US croplands for biofuels increases greenhouse gases through emissions from land use change. Science 319, 1238-1240 (2008).

72. Field, C.B., Campbell, J.E. & Lobell, D.B. Biomass energy: the scale of the potential resource. Trends Ecol. Evol. 23, 65-72 (2008).

73. Tilman, D., Hill, J. & Lehman, C. Carbon-negative biofuels from low-input high-diversity grassland biomass. Science 314, 1598-1600 (2006).

74. Krausmann, F., Erb, K.H., Gingrich, S., Lauk, C. & Haberl, H. Global patterns of socioeconomic biomass flows in the year 2000: A comprehensive assessment of supply, consumption and constraints. Ecol. Econ. 65, 471-487 (2008).

75. European Environment Agency (EEA). How much bioenergy can Europe produce without harming the environment? (EEA: 2006).at <http://reports.eea.europa.eu/eea_report_2006_7/en/eea_report_7_2006.pdf>

76. Wall, A. Effect of removal of logging residue on nutrient leaching and nutrient pools in the soil after clearcutting in a Norway spruce stand. Forest Ecol. Manage. 256, 1372-1383 (2008).

77. Raison, R. Chapter 5: Environmental sustainability of forest energy production. Bioenergy from sustainable forestry, Richardson (Ed.) 344 (2002).

78. Haberl, H. et al. Quantifying and mapping the human appropriation of net primary production in earth's terrestrial ecosystems. PNAS 104, 12942 (2007).

79. Gutteridge, R. & Shelton, H. The role of forage tree legumes in cropping and grazing systems. Forage tree legumes in tropical agriculture (1998).at <http://www.fao.org/ag/AGP/AGPC/doc/Publicat/Gutt-shel/x5556e05.htm#1.1%20the%20role%20of%20forage%20tree%20legumes%20in%20cropping%20and%20grazing%20systems>

80. Stewart, J. & Dunsdon, A. Preliminary evaluation of potential fodder quality in a range of Leucaena species. Agroforest. Syst. 40, 177-198 (1998).

81. Sánchez, N., Ledin, S. & Ledin, I. Biomass production and chemical composition of Moringa oleifera under different management regimes in Nicaragua. Agroforest. Syst. 66, 231-242 (2006).

82. Shelton, H. Advances in forage legumes: shrub legumes. 19th International Grassland Congress (2001).at <http://www.fao.org/ag/AGP/AGPC/doc/Present/Shelton/Foragelegumes/docforage.htm>

83. Niu, X. & Gao, H. Caragana in China. International Conference on Grassland Science and Industry (2001).at <http://www.fao.org/ag/AGP/AGPC/doc/pasture/peashrub/caraganachina.htm>

84. Snyder, C., Bruulsema, T., Jensen, T. & Fixen, P. Review of greenhouse gas emissions from crop production systems and fertilizer management effects. Agr. Ecosyst. Environ. 133, 247-266 (2009).

85. Austin, M.T., Early, R.J., Brewbaker, J.L. & Sun, W. Yield, psyllid resistance, and phenolic concentration of Leucaena in two environments in Hawaii. Agron. J. 89, 507 (1997).

86. Erb, K. A comprehensive global 5 min resolution land-use data set for the year 2000 consistent with national census data. J. Land Use Sci. 2, 191-224 (2007).

87. IIASA & FAO GAEZ Global Agro-Ecological Zones. (2000).at <http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm>

88. United States Environmental Protection Agency. Municipal solid waste in the United States: 2007 facts and figures. (2007).at <http://www.epa.gov/osw/nonhaz/municipal/pubs/msw07-rpt.pdf>

89. Global Invasive Species Database. at <http://www.issg.org/database/welcome/> 90. Trumbore, S. Potential responses of soil organic carbon to global environmental change. PNAS 94,

8284-8291 (1997). 91. Powlson, D., Riche, A., Coleman, K., Glendining, M. & Whitmore, A. Carbon sequestration in European

soils through straw incorporation: Limitations and alternatives. Waste Manage. 28, 741-746 (2008). 92. Lehmann, J. et al. Australian climate-carbon cycle feedback reduced by soil black carbon. Nature

Geosci 1, 832-835 (2008). 93. The R Project for Statistical Computing. at <http://www.r-project.org/> 94. Lehmann, J. et al. Nutrient availability and leaching in an archaeological Anthrosol and a Ferralsol of

the Central Amazon basin: fertilizer, manure and charcoal amendments. Plant Soil 249, 343-357 (2003).

95. Rondon, M., Lehmann, J., Ramírez, J. & Hurtado, M. Biological nitrogen fixation by common beans ( Phaseolus vulgaris L.) increases with bio-char additions. Biol. Fert. Soil. 43, 699-708 (2007).

96. Gaunt, J.L. & Lehmann, J. Energy balance and emissions associated with biochar sequestration and pyrolysis bioenergy production. Environ. Sci. Technol. 42, 4152-4158 (2008).

97. Gaunt, J. & Cowie, A. Biochar, greenhouse gas accounting and emissions trading. Biochar for Environmental Management, Lehmann and Joseph (Eds) 416 (2009).

98. Mikaloff Fletcher, S.E., Tans, P.P., Bruhwiler, L.M., Miller, J.B. & Heimann, M. CH4 sources estimated

44

from atmospheric observations of CH4 and its 13C/12C isotopic ratios: 2. Inverse modeling of CH4 fluxes from geographical regions. Global Biogeochem. Cy. 18, (2004).

99. Johnson, D.E. & Ward, G.M. Estimates of animal methane emissions. Environ. Monit. Assess. 42, 133-141 (1996).

100. Mikaloff Fletcher, S.E., Tans, P.P., Bruhwiler, L.M., Miller, J.B. & Heimann, M. CH4 sources estimated from atmospheric observations of CH4 and its 13C/12C isotopic ratios: 1. Inverse modeling of source processes. Global Biogeochem. Cy. 18, (2004).

101. Bogner, J. & Matthews, E. Global methane emissions from landfills: New methodology and annual estimates 1980–1996. Global Biogeochem. Cy. (2003).

102. El-Fadel, M., Findikakis, A.N. & Leckie, J.O. Environmental impacts of solid waste landfilling. J. Environ. Manage. 50, 1-25 (1997).

103. Walker, L., Charles, W. & Cord-Ruwisch, R. Comparison of static, in-vessel composting of MSW with thermophilic anaerobic digestion and combinations of the two processes. Bioresource Technol. 100, 3799-3807 (2009).

104. Fisher, B.S. et al. Issues related to mitigation in the long term context. Climate change 169–250 (2007). 105. Amonette, J. & Joseph, S. Chapter 3: Characteristics of biochar - microchemical properties. Biochar for

environmental management: science and technology, Lehmann, J. & Joseph, S. (eds) (2009). 106. Tagoe, S., Horiuchi, T. & Matsui, T. Effects of carbonized and dried chicken manures on the growth,

yield, and N content of soybean. Plant Soil 306, 211-220 (2008). 107. Tian, F., Li, B., Chen, Y. & Li, C. Formation of NOx precursors during the pyrolysis of coal and biomass.

Part V. Pyrolysis of a sewage sludge. Fuel 81, 2203-2208 (2002). 108. Becidan, M., Skreiberg & Hustad, J.E. Products distribution and gas release in pyrolysis of thermally

thick biomass residues samples. J. Anal. Appl. Pyrol. 78, 207-213 (2007). 109. Becidan, M., Skreiberg, O. & Hustad, J.E. NOx and N2O precursors (NH3 and HCN) in pyrolysis of

biomass residues. Energ. Fuel 21, 1173-1180 (2007). 110. Shen, B.X. et al. N2O emission under fluidized bed combustion condition. Fuel Process. Technol. 84,

13-21 (2003). 111. Golesworthy, T. A review of industrial flue gas cleaning (3). Filtr. Separat. 36, 16-19 (1999). 112. Chang, L. et al. Formation of NOx precursors during the pyrolysis of coal and biomass. Part VI. Effects

of gas atmosphere on the formation of NH3 and HCN. Fuel 82, 1159-1166 (2003). 113. Crutzen, P.J., Mosier, A.R., Smith, K.A. & Winiwarter, W. N2O release from agro-biofuel production

negates global warming reduction by replacing fossil fuels. Atmos. Chem. Phys. Discuss 7, 11191-11205 (2007).

114. Del Grosso, S.J. et al. DAYCENT national-scale simulations of nitrous oxide emissions from cropped soils in the United States. J. Environ. Qual. 35, 1451-1460 (2006).

115. FAO & IFA Global Estimates of Gaseous Emissions of NH3, NO and N2O from Agricultural Land. (2001).at <http://www.fertilizer.org/ifa/Home-Page/LIBRARY/Publications.html/Global-Estimates-of-Gaseous-Emissions-of-NH3-NO-and-N2O-from-Agricultural-Land.html>

116. De Klein, C. et al. IPCC guidelines for national geenhouse gas inventories: Chapter 11 N2O emissions from managed soils, and CO2 emissions from lime and urea application. (2006).at <http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf>

117. Vanek, F.M. & Campbell, J.B. UK road freight energy use by product: trends and analysis from 1985 to 1995. Transp. Policy 6, 237-246 (1999).

118. Antal et al. Attainment of the Theoretical Yield of Carbon from Biomass. Industrial & Engineering Chemistry Research 39, 4024-4031 (2000).

119. Steinbeiss, S., Gleixner, G. & Antonietti, M. Effect of biochar amendment on soil carbon balance and soil microbial activity. Soil Biol. Biochem. 41, 1301-1310 (2009).

120. Pennise, D.M. et al. Emissions of greenhouse gases and other airborne pollutants from charcoal making in Kenya and Brazil. J. Geophys. Res-Atmos 106, 24 (2001).

121. Andreae, M.O. & Merlet, P. Emission of trace gases and aerosols from biomass burning. Global Biogeochem. Cy. 15,

122. Steiner, C., Teixeira, W.G. & Zech, W. Slash and char: An alternative to slash and burn practiced in the Amazon Basin. Amazonian Dark Earths: Explorations in Space and Time 1, 183 (2004).

123. Fowles, M. Black carbon sequestration as an alternative to bioenergy. Biomass Bioenerg. 31, 426-432 (2007).

124. International Energy Agency Key world energy statistics. (2009).at <http://www.iea.org/textbase/nppdf/free/2009/key_stats_2009.pdf>

125. Masiello, C. New directions in black carbon organic geochemistry. Mar. Chem. 92, 201-213 (2004). 126. Kuzyakov, Y., Subbotina, I., Chen, H., Bogomolova, I. & Xu, X. Black carbon decomposition and

incorporation into soil microbial biomass estimated by 14C labeling. Soil Biol. Biochem. 41, 210-219 (2009).

127. Cowie, A. & Singh, B. Decomposition rate of biochar in soil - an important factor affecting the greenhouse gas balance. IBI conference 2008 (2008).at <http://www.biochar-

45

international.org/images/Cowie_poster_IBI_Newcastle.pdf> 128. Bird, M.I., Moyo, C., Veenendaal, E.M., Lloyd, J. & Frost, P. Stability of elemental carbon in a savanna

soil. Glob. Biogeochem. Cy. 13, (1999). 129. Hammes, K., Torn, M.S., Lapenas, A.G. & Schmidt, M.W.I. Centennial black carbon turnover observed

in a Russian steppe soil. Biogeosciences 5, 1339-1350 (2008). 130. Lehmann, J., Czimczik, C., Laird, D. & Sohi, S. Chapter 11: Stability of biochar in soil. Biochar for

environmental management: science and technology, Lehmann, J. & Joseph, S. (eds) (2009). 131. Cheng, C., Lehmann, J., Thies, J.E. & Burton, S.D. Stability of black carbon in soils across a climatic

gradient. J. Geophys. Res. 113, G02027, doi:10.1029/2007JG000642 (2008). 132. Baldock, J.A. & Smernik, R.J. Chemical composition and bioavailability of thermally altered Pinus

resinosa (Red pine) wood. Org. Geochem. 33, 1093-1109 (2002). 133. Bird, M.I., Moyo, C., Veenendaal, E.M., Lloyd, J. & Frost, P. Stability of elemental carbon in a savanna

soil. Global Biogeochem. Cy. 13, PAGES 923–932 (1999). 134. Nguyen, B. et al. Long-term black carbon dynamics in cultivated soil. Biogeochemistry 89, 295-308

(2008). 135. Krull, E., Baldock, J.A., Skjemstad, J.O. & Smernik, R. Chapter 4: Characteristics of biochar: organo-

chemical properties. Biochar for environmental management: science and technology, Lehmann, J. & Joseph, S. (eds) (2009).

136. Deenik, J. et al. Charcoal volatile matter content affects plant growth and nitrogen availability in an infertile tropical soil. (2008).at <http://www.biochar-international.org/sssa2008presentations.html>

137. Ecoinvent: Publications. at <http://www.ecoinvent.org/publications/> 138. Fischer, G., van Velthuizen, H., Shah, M. & Nachtergaele, F. Global agro-ecological assessment for

agriculture in the 21st century. (International Institute for Applied Systems Analysis & Food and Agriculture Organization of the United Nations: 2002).at <http://www.iiasa.ac.at/Research/LUC/SAEZ/pdf/gaez2002.pdf>

139. Monfreda, C., Ramankutty, N. & Foley, J.A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles (2008).at <http://www.agu.org/pubs/crossref/2008/2007GB002947.shtml>

140. Baronti, S. et al. The Italian Biochar Initiative (ITABI): Effects on soil fertility and on crops production. (2008).at <http://www.biochar-international.org/ibi2008conference/ibiposterpresentations.html>

141. Van Zwieten, L. et al. Agro-economic valuation of biochar using field-derived data. (2008).at <http://www.biochar-international.org/images/agroeco_biochar_poster.pdf>

142. Chan, K., Van Zwieten, L., Meszaros, I., Downie, A. & Joseph, S. Using poultry litter biochars as soil amendments. Aust. J. Soil Res. 46, 437-444 (2008).

143. Iswaran, V., Jauhri, K.S. & Sen, A. Effect of charcoal, coal and peat on the yield of moong, soybean and pea. Soil Biol. Biochem. (1980).

144. Topoliantz, S., Ponge, J. & Ballof, S. Manioc peel and charcoal: a potential organic amendment for sustainable soil fertility in the tropics. Biol. Fert. Soil. 41, 15-21 (2005).

145. Yamato, M., Okimori, Y., Wibowo, I.F., Anshori, S. & Ogawa, M. Effects of the application of charred bark of Acacia mangium on the yield of maize, cowpea and peanut, and soil chemical properties in South Sumatra, Indonesia. Soil Sci. Plant Nutr. 52, 489-495 (2006).

146. Steiner et al. Long term effects of manure, charcoal and mineral fertilization on crop production and fertility on a highly weathered Central Amazonian upland soil. Plant Soil 291, 275-290 (2007).

147. Oguntunde, Fosu, Ajayi & Giesen Effects of charcoal production on maize yield, chemical properties and texture of soil. Biol. Fert. Soil. 39, 295-299 (2004).

148. Major, J. Biochar application to a Colombian Savanna Oxisol: fate and effect on soil fertility, crop production, nutrient leaching and soil hydrology. Thesis for the degree of Doctor of Philosophy (2009).

149. Wardle, D.A., Nilsson, M. & Zackrisson, O. Fire-derived charcoal causes loss of forest humus. Science 320, 629 (2008).

150. Rogovska, N., Fleming, P., Cruse, R. & Laird, D. Greenhouse gas emissions from soils as affected by addition of biochar. ASA, CSSA, and SSSA Joint Annual Meeting (2008).

151. Glaser, B., Balashov, E., Haumaier, L., Guggenberger, G. & Zech, W. Black carbon in density fractions of anthropogenic soils of the Brazilian Amazon region. Org. Geochem. 31, 669-678 (2000).

152. Batjes, N.H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sc. 47, 151–163 (1996). 153. Rondon, M., Ramirez, J. & Lehmann, J. Charcoal additions reduce net emissions

of greenhouse gases to the atmosphere. Proceedings of the 3rd USDA Symposium on Greenhouse Gases and Carbon Sequestration 208 (2005).

154. Galloway, J.N. et al. Nitrogen cycles: past, present, and future. Biogeochemistry 70, 153-226 (2004). 155. Stehfest, E. & Bouwman, L. N2O and NO emission from agricultural fields and soils under natural

vegetation: summarizing available measurement data and modeling of global annual emissions. Nutr. Cycl. Agroecosys. 74, 207-228 (2006).

156. Knoblauch, A., Marifaat, A. & Haefele, S. Biochar in rice-based systems: Impact on carbon mineralization and trace gas emissions. (2008).at <http://www.biochar-

46

international.org/images/Knoblauch_et_al._IBI_2008.pdf> 157. Rondon, M. et al. Enhancing the productivity of crops and grasses while reducing greenhouse gas

emissions through bio-char amendments to unfertile tropical soils. (2006).at <http://www.ldd.go.th/18wcss/techprogram/P16849.HTM>

158. Major, J., Steiner, C., Downie, A. & Lehmann, J. Chapter 15: Biochar effects on nutrient leaching. Biochar for environmental management: science and technology, Lehmann, J. & Joseph, S. (eds) (2009).

159. Steiner, C. et al. Nitrogen retention and plant uptake on a highly weathered central Amazonian Ferralsol amended with compost and charcoal. J. Plant Nutr. Soil Sc. 171, 893-899 (2008).

160. West, T.O. & Marland, G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: comparing tillage practices in the United States. Agr. Ecosyst. Environ. 91, 217-232 (2002).

161. Gundale, M. & DeLuca, T. Charcoal effects on soil solution chemistry and growth of Koeleria macrantha in the ponderosa pine/Douglas-fir ecosystem. Biol. Fert. Soil. 43, 303-311 (2007).

162. Liang, B. et al. Black carbon increases cation exchange capacity in soils. Soil Sci Soc Am J 70, 1719-1730 (2006).

163. Stern, N. Testimony of Lord Nicholas Stern to the Committee on Energy and Commerce. (2008).at <http://energycommerce.house.gov/cmte_mtgs/110-eaq-hrg.062608.Stern-Testimony.pdf>

164. McCarl, B., Peacocke, C., Chrisman, R., Chih-Chun, K. & Sands, R. Chapter 19: Economics of biochar production, utilisation and emissions. Biochar for environmental management: science and technology, Lehmann, J. & Joseph, S. (eds) (2009).

165. Gustafsson, O. et al. Brown clouds over South Asia: Biomass or fossil fuel combustion? Science 323, 495-498 (2009).

166. Ramanathan, V. & Carmichael, G. Global and regional climate changes due to black carbon. Nature Geosci. 1, 221 (2008).

167. Cramer, W. Air pollution and climate change both reduce Indian rice harvests. PNAS 103, 19609-19610 (2006).

168. Auffhammer, M., Ramanathan, V. & Vincent, J.R. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. PNAS 103, 19668-19672 (2006).

169. Smith, K.R., Samet, J.M., Romieu, I. & Bruce, N. Indoor air pollution in developing countries and acute lower respiratory infections in children. Thorax 55, 518-532 (2000).

170. Jermsawatdipong, P. et al. Methane emission from plots with differences in fertilizer application in Thai paddy fields. Soil Sci. Plant Nutr. 40, 63-71 (1994).

171. Singh, J.S., Singh, S., Raghubanshi, A.S., Singh, S. & Kashyap, A.K. Methane flux from rice/wheat agroecosystem as affected by crop phenology, fertilization and water level. Plant and Soil 183, 323-327 (1996).

172. Lee, K.B., Lee, D.B., Kim, J.G. & Kim, Y.W. Effect of rice cultural patterns on methane emission from a Korean paddy soil. Journal of Korean Society of Soil Science and Fertilizer (1997).

173. Sass, R.L. & Fisher, F.M. Methane emissions from rice paddies: a process study summary. Nutr. Cycl. Agroecosys. 49, 119-127 (1997).

174. Kumagai, K. & Konno, Y. Methane emission from rice paddy fields after upland farming. Japanese Journal of Soil Science and Plant Nutrition (1998).

175. Corton, T.M. et al. Methane emission from irrigated and intensively managed rice fields in Central Luzon (Philippines). Nutr. Cycl. Agroecosys. 58, 37-53 (2000).

176. Goto, E., Miyamori, Y., Hasegawa, S. & Inatsu, O. Reduction effects of accelerating rice straw decomposition and water management on methane emission from paddy fields in a cold district. Japanese Journal of Soil Science and Plant Nutrition 75, 191-201 (2004).

177. Naser, H.M., nagata, O., Tamura, S. & Hatano, R. Methane emissions from five paddy fields with different amounts of rice straw application in central Hokkaido, Japan. Soil Sci. Plant Nutr. 53, 95-101 (2007).

178. Liou, R.M., Huang, S.N., Lin, C.W. & Chen, S.H. Methane emission from fields with three various rice straw treatments in Taiwan paddy soils. J. Environ. Sci. Heal. B 38, 511-527 (2003).

179. Liang, B. et al. Stability of biomass-derived black carbon in soils. Geochimica et Cosmochimica Acta 72, 6078-6096 (2008).

180. Preston, C.M. & Schmidt, M.W.I. Black (pyrogenic) carbon: a synthesis of current knowledge and uncertainties with special consideration of boreal regions. Biogeosciences 3, 397-420 (2006).

181. Major, J., Lehmann, J. & Rondon, M. Fate of biochar applied to a Colombian savanna Oxisol during the first and second years. at <http://www.biochar-international.org/images/J_Major_biogeochem.pdf>

182. Bridgwater, A.V., Toft, A.J. & Brammer, J.G. A techno-economic comparison of power production by biomass fast pyrolysis with gasification and combustion. Renewable and Sustainable Energy Reviews 6, 181-246 (2002).

183. Islam, M.N. & Ani, F.N. Techno-economics of rice husk pyrolysis, conversion with catalytic treatment to produce liquid fuel. Bioresource Technology 73, 67-75 (2000).

184. Roberts, K.G., Gloy, B.A., Joseph, S., Scott, N.R. & Lehmann, J. Life Cycle Assessment of Biochar

47

Systems: Estimating the Energetic, Economic, and Climate Change Potential. Environ. Sci. Tech. 0, 185. Crucible Carbon Biomass technology review: Processing for energy and materials. (2008).at

<http://www.sustainability.vic.gov.au/resources/documents/Biomass_Technology_Review.pdf> 186. The German Energy Agency (DENA) Biomass to liquid – BtL implementation report. (Berlin, Germany,

2006).at <http://www.dena.de/fileadmin/user_upload/Download/Dokumente/Publikationen/mobilitaet/btl_implementation_report.pdf>

187. Joseph, S. Chapter 20: Socio-economic assessment and implementation of small-scale biochar projects. Biochar for environmental management: science and technology, Lehmann, J. & Joseph, S. (eds) (2009).

188. Houghton, R.A. Carbon flux to the atmosphere from land-use changes: 1850-2005. TRENDS: A Compendium of Data on Global Change (2008).at <http://cdiac.ornl.gov/trends/landuse/houghton/houghton.html>

189. Boden, T., Marland, G. & Andres, R. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn. doi:doi 10.3334/CDIAC/00001

190. Denman, K.L. et al. Couplings between changes in the climate system and biogeochemistry. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment, Solomon et al. (eds.) 500–587 (2007).

191. Forster, P. et al. Changes in atmospheric constituents and in radiative forcing. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment, Solomon et al. (eds.) (2007).

192. EPA Bagasse Combustion In Sugar Mills. (1996).at <http://www.epa.gov/ttn/chief/ap42/ch01/final/c01s08.pdf>

193. Di Blasi, C., Signorelli, G., Di Russo, C. & Rea, G. Product distribution from pyrolysis of wood and agricultural residues. Ind. Eng. Chem. Res. 38, 2216–2224 (1999).

194. Ghaly, A.E., Erg\üdenler, A. & Laufer, E. Agglomeration characteristics of alumina sand-straw ash mixtures at elevated temperatures. Biomass Bioenerg. 5, 467–467 (1993).

195. Lucchesi, A., Maschio, G. & Communities, C.O.T.E. Study on the pyrolysis of agricultural wastes. Energy from biomass, Palz, W. & Pirrwitz, D. (Eds) 289-296 (1983).

196. Di Blasi, C., Buonanno, F. & Branca, C. Reactivities of some biomass chars in air. Carbon 37, 1227-1238 (1999).

197. Yun, C.H., Park, Y.H. & Park, C.R. Effects of pre-carbonization on porosity development of activated carbons from rice straw. Carbon 39, 559-567 (2001).

198. Tsai, W.T., Lee, M.K. & Chang, Y.M. Fast pyrolysis of rice straw, sugarcane bagasse and coconut shell in an induction-heating reactor. J. Anal. Appl. Pyrol. 76, 230–237 (2006).

199. Annamalai, K., Sweeten, J.M. & Ramalingam, S.C. Estimation of gross heating values of biomass fuels. Trans ASAE 30, 1205–8 (1987).

200. Jorapur, R. & Rajvanshi, A.K. Sugarcane leaf-bagasse gasifiers for industrial heating applications. Biomass Bioenerg. 13, 141–146 (1997).

201. Garcı̀a-Pèrez, M., Chaala, A. & Roy, C. Vacuum pyrolysis of sugarcane bagasse. J. Anal. Appl. Pyrol. 65, 111–136 (2002).

202. Lutz, H., Esuoso, K., Kutubuddin, M. & Bayer, E. Low temperature conversion of sugar-cane by-products. Biomass Bioenerg. 15, 155–162 (1998).

203. Bilba, K. & Ouensanga, A. Fourier transform infrared spectroscopic study of thermal degradation of sugar cane bagasse. J. Anal. Appl. Pyrol. 38, 61–73 (1996).

204. Florin, N., Maddocks, A., Wood, S. & Harris, A. High-temperature thermal destruction of poultry derived wastes for energy recovery in Australia. Waste Manage. 29, 1399-1408 (2009).

205. Boateng, A.A. Characterization and thermal conversion of charcoal derived from fluidized-bed fast pyrolysis oil production of switchgrass. Ind. Eng. Chem. Res. 46, 8857-8862 (2007).

206. Agblevor, F., Besler-Guran, S., Montane, D. & Wiselogel, A. Biomass feedstock variability and its effect on biocrude oil properties. Developments in thermochemical biomass conversion, Bridgewater, A.and Boocock, D. (Eds) 741-755 (1997).

207. Phyllis, database for biomass and waste. at <http://www.ecn.nl/phyllis/> 208. Fuwape, J.A. & Akindele, S.O. Biomass yield and energy value of some fast-growing multipurpose

trees in Nigeria. Biomass Bioenerg. 12, 101–106 (1997). 209. Jung, S.H., Kang, B.S. & Kim, J.S. Production of bio-oil from rice straw and bamboo sawdust under

various reaction conditions in a fast pyrolysis plant equipped with a fluidized bed and a char separation system. J. Anal. Appl. Pyrol. 82, 240–247 (2008).

210. Chan, K., Van Zwieten, L., Meszaros, I., Downie, A. & Joseph, S. Agronomic values of greenwaste biochar as a soil amendment. Aust. J. Soil Res. 45, 629-634 (2007).

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