Land-neutral negative emissions through biochar-based fertilization—assessing global potentials under varied management and pyrolysis conditions

Abstract

Climate stabilization is crucial for restabilizing the Earth system but should not undermine biosphere integrity, a second pillar of Earth system functioning. This is of particular concern if it is to be achieved through biomass-based negative emission (NE) technologies that compete for land with food production and ecosystem protection. We assess the NE contribution of land- and calorie-neutral pyrogenic carbon capture and storage (LCN-PyCCS) facilitated by biochar-based fertilization, which sequesters carbon and reduces land demand by increasing crop yields. Applying the global biosphere model LPJmL with an enhanced representation of fast-growing species for PyCCS feedstock production, we calculated a land-neutral global NE potential of 0.20–1.10 GtCO2 year⁻¹ assuming 74% of the biochar carbon remaining in the soil after 100 years (for + 10% yield increase; no potential for + 5%; 0.61–1.88 GtCO2 year⁻¹ for + 15%). The potential is primarily driven by the achievable yield increase and the management intensity of the biomass producing systems. NE production is estimated to be enhanced by + 200–270% if management intensity increases from a marginal to a moderate level. Furthermore, our results show sensitivity to process-specific biochar yields and carbon contents, producing a difference of + 40–75% between conservative assumptions and an optimized setting. Despite these challenges for making world-wide assumptions on LCN-PyCCS systems in modeling, our findings point to discrepancies between the large NE volumes calculated in demand-driven and economically optimized mitigation scenarios and the potentials from analyses focusing on supply-driven approaches that meet environmental and socioeconomic preconditions as delivered by LCN-PyCCS.

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Mitig Adapt Strateg Glob Change (2024) 29:34
https://doi.org/10.1007/s11027-024-10130-8
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ORIGINAL ARTICLE
Land‑neutral negative emissions throughbiochar‑based
fertilization—assessing global potentials undervaried
management andpyrolysis conditions
ConstanzeWerner1,2,3 · WolfgangLucht1,2,3· ClaudiaKammann4· JohannaBraun1
Received: 30 September 2022 / Accepted: 19 March 2024 / Published online: 5 April 2024
© The Author(s) 2024
Abstract
Climate stabilization is crucial for restabilizing the Earth system but should not undermine
biosphere integrity, a second pillar of Earth system functioning. This is of particular con-
cern if it is to be achieved through biomass-based negative emission (NE) technologies that
compete for land with food production and ecosystem protection. We assess the NE con-
tribution of land- and calorie-neutral pyrogenic carbon capture and storage (LCN-PyCCS)
facilitated by biochar-based fertilization, which sequesters carbon and reduces land demand
by increasing crop yields. Applying the global biosphere model LPJmL with an enhanced
representation of fast-growing species for PyCCS feedstock production, we calculated a
land-neutral global NE potential of 0.20–1.10 GtCO2 year−1 assuming 74% of the biochar
carbon remaining in the soil after 100 years (for + 10% yield increase; no potential for +
5%; 0.61–1.88 GtCO2 year−1 for + 15%). The potential is primarily driven by the achiev-
able yield increase and the management intensity of the biomass producing systems. NE
production is estimated to be enhanced by + 200–270% if management intensity increases
from a marginal to a moderate level. Furthermore, our results show sensitivity to process-
specific biochar yields and carbon contents, producing a difference of + 40–75% between
conservative assumptions and an optimized setting. Despite these challenges for making
world-wide assumptions on LCN-PyCCS systems in modeling, our findings point to dis-
crepancies between the large NE volumes calculated in demand-driven and economically
optimized mitigation scenarios and the potentials from analyses focusing on supply-driven
approaches that meet environmental and socioeconomic preconditions as delivered by
LCN-PyCCS.
* Constanze Werner
constanze.werner@pik-potsdam.de
1 Potsdam Institute forClimate Impact Research, Member oftheLeibniz Association,
Telegraphenberg, D-14473Potsdam, Germany
2 Department ofGeography, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099Berlin,
Germany
3 Integrative Research Institute onTransformations ofHuman-Environment Systems, Unter den
Linden 6, D-10099Berlin, Germany
4 Department ofApplied Ecology, Hochschule Geisenheim University, Von-Lade Str. 1,
D-65366Geisenheim, Germany
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Keywords Carbon dioxide removal· Negative emissions· Biochar· Pyrolysis· PyCCS
1 Introduction
Negative emissions (NE; see Table1 for a list of abbreviations) pose a significant and com-
plex challenge to science and policy searching for feasible pathways to achieve the climate
targets of the Paris Agreement. In addition to deep emission reductions, NEs are being
considered to offset residual hard-to-abate emissions, but also to compensate for delays
in stringent decarbonization. Yet, while especially biomass-based negative emissions tech-
nologies (NETs) like bioenergy with carbon capture and storage (BECCS) are considered
feasible in economic optimization, land-based options require vast areas, thereby compet-
ing with food production and ecosystem protection (Boysen etal. 2017; Heck etal. 2018;
Humpenöder etal. 2018). As an alternative to land-demanding BECCS, we here assess fea-
sible NE contributions of more sustainable pyrogenic carbon capture and storage (PyCCS)
based on land- and calorie-neutral biomass production, capitalizing on yield increases
induced by biochar-based fertilization (BBF) to maintain calorie production while realizing
net CO2 removal from the atmosphere.
In climate economic models with cost optimization (Integrated assessment models,
IAMs), scenarios compatible with a maximum warming of below 1.5 °C or 2 °C frequently
rely on extensive BECCS deployment. They project required rates of up to more than 9
GtCO2 year−1 around the year 2050 (median: 2.75 GtCO2 year−1), reaching maximum lev-
els of more than 16 GtCO2 year−1 by 2100 (median: 8.96 GtCO2 year−1, 15th–85th percen-
tile: 2.63–16.15 GtCO2 year−1) (IPCC 2022). However, there is large skepticism whether
these simulated high deployment volumes of BECCS can realistically be achieved given
economic, political, and technological constraints on the assumed rapid scale-up of NETs
(Bednar et al. 2019; Lenzi et al. 2018; Nemet etal. 2018). Also, serious concerns have
been raised regarding substantial environmental and social side effects: Large-scale deploy-
ment of BECCS from dedicated bioenergy crops would lead to additional land degrada-
tion, competition for land with both food production and biodiversity protection, and could
cause strong increases in human water and fertilization use, among others (Boysen etal.
2017; Stenzel etal. 2019). All of these contribute to planetary destabilization by further
increasing the pressure on planetary boundaries characterizing humanity’s safe operating
space (Heck etal. 2018).
PyCCS is proposed as an alternative biomass-based NET and scalable approach with a
high level of technological readiness and applicability across a broad spectrum of usages
Table 1 List of abbreviations
BBF Biochar-based fertilization LCN-PyCCS Land- and calorie-neutral PyCCS
BC100 Biochar carbon remaining after 100 years NE Negative emissions
BECCS Bioenergy with carbon capture and
storage
NET Negative emission technology
BFT Biomass functional type PBIAS Percent bias after Moriasi etal. (2007)
DACCS Direct air carbon capture and storage PyCCS Pyrogenic carbon capture and storage
IAM Integrated assessment model YI Biochar-mediated yield increases
la/sa Ratio of leaf area to sapwood area
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including diverse agricultural systems, waste management, and material production
(Osman etal. 2022). This NET is based on pyrolysis, the thermochemical decomposition
of biomass at high temperatures (350–900 °C) in an oxygen-deficient atmosphere. The
three main carbonaceous pyrolysis products can subsequently be stored in different ways to
produce NE: as solid biochar in soils or building material, as bio-oil in depleted fossil oil
repositories, and as CO2 after combustion of permanent-pyrogas in geological storages in
very advanced technological settings (Schmidt etal. 2019).
The term PyCCS has been introduced to cover the whole range of sequestration options
arising from the pyrolysis process, which however differ in their level of technological
readiness and storage permanence (Schmidt etal. 2019; Werner etal. 2018). This is essen-
tially different to the terminology of BECCS and DACCS (direct air carbon capture and
storage), where CCS exclusively refers to processing and storing CO2 (IPCC 2018). While
biochar applications to soil have been practiced for centuries and researched for more than
one decade, the combination of chemical looping combustion and pyrolysis, which would
result in the most efficient way for the geological storage of combustion products of per-
manent-pyrogases, has not been tested widely yet (Schmidt etal. 2019). Once deployed,
the geological storage of processed pyrogases can be considered permanent (unless leaked
through permeable faults or fractures in the seal) according to the assumptions for the same
processes for BECCS and DACCS.
In case of carbon sequestration through biochar, however, the fate of carbon differs
between applications. High durability of biochar carbon storage in soils can be attributed to
the development of fused aromatic structures during biomass pyrolysis (Wang etal. 2016).
These structures render biochar considerably less susceptible to microbial decomposition
in comparison to fresh biomass. To ensure biochars exhibit high durability, production
must occur at elevated temperatures with extended residence times, promoting complete
carbonization and the formation of fused aromatic structures, indicated by low H/C ratios
(Ippolito etal. 2020; Spokas 2010). Established methodologies quantifying 100-year bio-
char persistence (e.g., IPCC (2019)) mainly extrapolate short-term decomposition of bio-
char components with a lower degree of aromaticity observed under laboratory conditions.
Yet, uncertainties remain as this falls short of capturing processes explaining millennial
persistence and dynamics in the open environment (Leng etal. 2019). Following these
quantification methods, the fraction of biochar carbon remaining in the soil after 100 years
is estimated to be around 70–80% for H/C ratios below 0.5 and pyrolysis temperatures
above 450 °C (Camps-Arbestain etal. 2015; IPCC 2019; Lehmann etal. 2021). Yet, the
permanence of pyrogenic carbon sequestration would be significantly increased when the
biochar is used in building materials.
In this study, we solely account for biochar sequestration in soils and its particular co-
benefit in agriculture, as applying biochar to arable soils potentially leads to significant
increases in agricultural yields as well as reduced water and nutrient demand (Schmidt
et al. (2021) and metastudies therein; Bai et al. (2022)), reducing the pressure on land,
water, and fertilizer resources. Furthermore, large-scale ubiquitous biochar sequestration
in soils might be favored over industrial-scale top-down approaches to NETs because it can
be deployed from small-scale to the large-scale (subsistence to industrial) and therefore
might support the UN Sustainable Development Goals (SDGs). This might be achieved
by reducing dependencies on external resources, realizing higher agroecosystem resilience
and water purification, as well as delivering clean cooking technology with pyrolyzers that
can reduce biomass demand, as reported for biochar in Smith etal. (2019).
Yet, as holds true for all biomass-based NETs, the source of the feedstock is the most
critical factor for the environmental impact of PyCCS. The land and water footprints
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of PyCCS feedstock production are thus minimal if based on residues from cropland or
forestry (Woolf etal. 2010) but can be more substantial if based on dedicated planta-
tions (Werner etal. 2018). Unfortunately, the global availability of crop residues not
already used for other purposes is highly uncertain (Hanssen etal. 2020). An intrigu-
ing additional option for sustainable feedstock production is unique to PyCCS: biomass
input from dedicated fast-growing stocks produced in land-neutrality. If significant lev-
els of biochar-mediated yield increases (YI) were achieved, the same amount of food
could be produced on less land. Thus, a fraction of the cropland could be dedicated
to fast-growing biomass supplying PyCCS feedstocks without requiring additional land
(Fig.1).
Werner et al. (2022) estimated the NE potential of LCN-PyCCS (land- and calorie-
neutral PyCCS) as 0.44–2.62 Gt CO2 year−1 depending on the achievable degree of YI
above present levels on (sub-)tropical cropland (15–30%) assuming an application rate of
2 t ha−1. Note that the higher end of the range requires very optimistic assumptions such as
the development of optimized biochar applications adapted to specific soils and crops (see
below) and/or the increase of soil-crop system resilience against extreme weather/climatic
events that strongly reduce agricultural production.
However, recent studies and meta-analyses indicate that significant YI can still be
reached with lower application rates (such as < 1 t ha−1) if operated as BBF instead of
as a general soil amendment. Bulk soil amendment with biochar is the incorporation of
pure, untreated biochar to agricultural land where it is ploughed or drilled into the soil.
Fig. 1 Schematic representation of land- and calorie-neutral PyCCS (LCN-PyCCS) indicating the ranges
of the operation space assessed in this study (white boxes; green frame: ranges for feedstock management,
blue frame: ranges for pyrolysis process, brown frame: ranges for crop yield response to biochar-based ferti-
lization). Details on the assessment ranges are given in Table S1
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In contrast, BBF refers to either biochar-fertilizer mixtures (mineral or organic) placed
concentrated in the root zone (Schmidt et al. 2017; Sutradhar et al. 2021), or granular/
pelletized biochar fertilizers that often consist of clay/silicate minerals, (mineral) fertiliz-
ers containing nitrogen, phosphorus and potassium, and other nutrients, plus an untreated,
pre- or post-treated (functionalized) biochar component (Joseph etal. 2021). With BBFs,
comparably low biochar additions of < 1 t ha−1 can have considerable effects (Grafmüller
etal. 2022; Qian etal. 2014). The meta-analysis of Melo etal. (2022) reported a grand
mean effect of 10% YI in comparison to the fertilized control at an average application rate
of 0.8 t ha−1 and even 17% for chars with a carbon content of > 30%.
In comparison to bulk amendment with biochar, tailored BBF could extend the geo-
graphic applicability of LCN-PyCCS for two reasons: (i) the positive yield effects of BBF
could also be observed in temperate regions whereas the amendment approach increases
yields mostly only in the (sub-)tropics; (ii) the lower application rates of BBF decrease the
biochar demand and thereby the yield requirements for LCN-feedstock production.
To investigate the potentially extended applicability of LCN-PyCCS based on BBF, we
quantify its global NE potential by applying the biogeochemical biosphere process model
LPJmL to simulate the biomass that can potentially be produced as pyrolysis feedstock
under this land- and calorie-neutral approach. We extend the analysis further by addressing
the sensitivity of LCN-PyCCS potentials to assumptions about (i) pyrolysis process param-
eters, (ii) the management intensity of the feedstock producing system, and (iii) biochar
durability in soils. In the case of (i), we consider a range between two sets of parameters
representing a conservative assumption and an optimized setting to account for the calcula-
tion’s sensitivity towards assumed process-specific biochar yields and carbon contents in
the char. Regarding (ii), we account for two levels of management of feedstock-produc-
ing systems to reflect on the potential of management intensification. For (iii), we assess a
range of biochar residence times in soils centered around a base assumption to reflect the
uncertainty in regard to durability. Furthermore, as the extent and overall NE potential of
LCN-PyCCS strongly depends on the biomass yields, the analysis is preceded by adapt-
ing the most important parameters for the representation of fast-growing plants potentially
used as pyrolysis feedstock based on comparisons of simulated yields and observations.
2 Methods
LCN-PyCCS is a system of land-neutral biomass production on croplands using biochar-
mediated YI to maintain calorie production while realizing net CO2 extraction from the
atmosphere. Through the YI, a fraction of the cropland can be dedicated to PyCCS feed-
stock production to provide self-sufficient biochar supplies and NE while preserving levels
of food production (Fig.1). Assuming + 10% YI, for example, would allow 110% calo-
rie production on the same area or 100% production on 91% of the area, leaving 9% for
PyCCS feedstock production. Whether cropland is suitable for the LCN-PyCCS approach
therefore depends on the potential biomass production on the rededicated land. Only if the
biomass yield provided enough feedstock to supply the remaining cropland with sufficient
biochar (i.e., 0.8 t ha−1 year−1 mean in Melo et al. (2022)) for maintaining the calories
produced, a fraction of the land would be considered for biochar feedstock production. Yet,
this is a conservative assumption, because it does not include (a fraction of) the crop resi-
dues, which are in practice often added to biochar production, e.g., by smallholder farmers
(Schmidt etal. 2017).
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2.1 The global biosphere model LPJmL
A spatially explicit estimate of potential biomass production is required for an assessment
of global theoretical potentials of the LCN-PyCCS approach. In this study, we apply the
process-based global biogeochemical vegetation model LPJmL (version 4.0) to simulate
the growth of dedicated PyCCS feedstocks (lignocellulosic grasses and fast-growing tree
species) with a daily time step and a spatial resolution of 0.5° × 0.5°. Simulating key
ecosystem processes in direct coupling of the carbon and hydrological cycle, the model
estimates the vegetation dynamics of 11 natural plant functional types (Sitch etal. 2003)
and 12 crop functional types and managed grassland (Bondeau etal. 2007); for detailed
descriptions and validations of the biogeochemical dynamics, see Schaphoff etal. (2018a)
and Schaphoff etal. (2018b). Additionally, three types of second generation energy crops
are included (biomass functional types; BFTs) to estimate potential feedstock production
for biomass-based NETs: two fast-growing tree species for woody biomass parameterized
as eucalypt in tropical climates and poplar and willow in temperate climates and lignocel-
lulosic C4 grass for herbaceous energy crops (Beringer etal. 2011; Heck etal. 2016).
2.2 Sensitivity analysis ofparameters forsimulating biomass production
We primarily calculate the LCN-PyCCS potential based on herbaceous feedstock to ensure
annual biomass supply. This focus has been established in prior studies on global esti-
mates, i.e., Werner etal. (2018) and Werner etal. (2022), because the grassy BFT shows
higher yields in LPJmL, and biochars from herbaceous feedstock often have better yield-
increasing properties than woody biochars; see meta-studies compiled in Schmidt etal.
(2021). Systems of wood harvest and short rotation coppice are typically harvested in a
multi-annual cycle (Li etal. 2018), which could cause a biochar deficit in the LCN-PyCCS
approach (if not supplemented by residues or other biomass sources). However, the imple-
mentation of LCN-PyCCS can be diverse depending on the farm’s conditions and needs,
where the rededication of cropland to woody species (e.g., hedgerows) might be preferred
for ecological reasons and the biomass deficit might be balanced through annual pruning or
selective logging, which is not represented in the model. To provide a first estimate of the
LCN-PyCCS potential of these woody feedstocks, we additionally applied our calculations
to the biomass harvest simulated for the two woody BFTs in LPJmL, averaged over the
plantation lifetime.
To ensure a robust representation of BFTs, we investigate the sensitivity of the simu-
lated yields to variations of selected parameters characterizing plant physiology and man-
agement, which are most relevant for the simulation of biomass production in the model.
The grassy BFT follows growth dynamics of tropical C4 grass in LPJmL, represent-
ing fast-growing species like Miscanthus and switchgrass. While the lignin-rich support-
ive tissue enabling annual harvests through continuous growth that is characteristic for,
i.e., Miscanthus, is not represented in the model, it can still represent a highly productive
grass functional type optimized towards biomass production through multiple harvests per
year. In LPJmL, the grassy BFT is harvested whenever aboveground biomass reaches a
certain threshold and when senescence is reached. The harvest threshold controls the inter-
vals between harvests and regrowth dynamics and thereby significantly impacts simulated
yield levels. Low values result in longer growing periods with stagnating productivity and
thereby lower yields. Furthermore, the yields depend on the harvest index, i.e., the frac-
tion of biomass removed. To best match reported annual harvest sums, we revisit these
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parameters (harvest threshold of 400 gC m2 and harvest index of 0.75 selected in Heck
et al. (2016)) based on a comparison of simulated biomass yields with a new extensive
observational dataset on bioenergy crop yields (Li etal. 2018). For this, we vary the har-
vest threshold and harvest index in a literature-based range (150–450 gC m−2 and 0.7–0.9,
respectively; S1) and compare simulated yields to observations from 90 sites in Li etal.
(2018). We exclude switchgrass observations because the biomass production of Mis-
canthus is significantly higher (Li etal. 2018; Li etal. 2020), which makes a combined
representation of both species less relevant (Ai etal. 2020) and decisions to grow the more
productive crop more likely (Zhuang etal. 2013).
In case of the woody BFTs, we assess the response to varying the ratio of the char-
acteristic areas of leaves and sapwood (la:sa). The parameter la:sa has been identified by
Zaehle etal. (2005) as one of the parameters of plant growth that influence the productivity
of trees most significantly. However, it has not been adapted for the woody biomass plan-
tations yet (while the other important parameters were adjusted, see S2). As lower la:sa
values increase the amount of carbon required for leaves and associated transport tissue,
thereby reducing the leaf area but enhancing the carbon storage in wood, lower la:sa values
can be expected for species chosen for their enhanced biomass production. Here, we assess
the sensitivity of simulated biomass production in LPJmL to a literature-based range of
la:sa values (tropical: 2500–5000, temperate: 2000–5500; see S1) and evaluate the respec-
tive model performance according to observations.
While the management of biomass plantations can vary widely in practice (i.e., ferti-
lization, pest control, soil preparation, irrigation, etc.), variation in plantation manage-
ment for BFTs in LPJmL is represented by cell-specific irrigation (representing manage-
ment intensity) and for woody BFTs by a BFT-specific rotation length, i.e., the years
of growth before coppice. While irrigation can be used to spatially vary management
intensity levels for different scenarios, the rotation length is predefined for each woody
BFT and has been set to 8 years for both types in the original parameterization (Ber-
inger etal. 2011). However, the rotation length can be quite variable in practice with a
median of 3 years for short rotation coppice systems of willow or poplar and 6 years for
eucalypt plantations reported in the Li etal. (2018) database. In combination with the
range of la:sa, we assess the model’s response to varying this parameter for a range of
1–12 and 2–10 for the rotation length of tropical and temperate trees, respectively, cov-
ering the 10th to 90th percentile of plantation age (including all experiments, temperate n
= 1068, tropical n = 439) and rotation length (reported as common practice, temperate
n = 678, tropical n = 96).
The global yield dataset for major lignocellulosic bioenergy crops reported for field
measurements compiled by Li etal. (2018) provides an extensive database for evaluation
of simulated biomass yields and thereby provides a suitable reference for parameter selec-
tion based on the performed sensitivity analyses. We simulate the growth and harvest of
irrigated and rainfed plants under climate conditions of 1985–2014 and calculate the mean
yields over five rotations for woody types and over 30 years for the herbaceous type. These
LPJmL-computed mid-range yields between rainfed (no irrigation) and intensified (full
irrigation) are then compared to the mean of the minimum and maximum reported yields
of experimental test sites located in the respective grid cell (varying in observations peri-
ods (1968–2016), mean sampling year: 1999). The model performance is assessed by the
metric of percent bias (PBIAS), the sum of biases divided by the sum of observed values
(Moriasi et al. 2007) excluding outliers of the relative difference between observed and
simulated yields, defined as values below the 25th percentile minus 1.5*interquartile range
or above the 75th percentile plus 1.5*interquartile range.
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2.3 Simulation set‑up forLCN‑PyCCS scenarios
For the assessment of the theoretical NE potential of LCN-PyCCS, we apply LPJmL to
simulate the growth of the BFTs under a parameter selection that is based on the sensi-
tivity analysis and evaluation of model performance described above.
The model is driven by climate input from the general circulation climate model
HadGEM2-ES as contributed to the ISIMIP2b ensemble for the RCP2.6 SSP2 pathways
(Frieler etal. 2017) and corresponding CO2 concentrations as well as data on soil texture
based on the Harmonized World Soil Database (FAO etal. 2012). Preceding the simula-
tions from 2025 to 2099, an initial spin-up of 5000 years is performed to achieve an equi-
librium of soil carbon and distribution of natural vegetation followed by 390 years of a
transient spin-up introducing the influence of agriculture on the carbon balance with his-
toric land use change until 2015 based on HYDE 3.2 (Klein Goldewijk etal. 2017).
The reallocation of cropland to biomass production for PyCCS is based on the land
use projections of a RCP2.6 SSP2 scenario realization of the land allocation model
MAgPIE (Dietrich etal. 2019), provided in the ISIMIP2b ensemble that is consistent
with the HadGEM2-ES climate input (Frieler et al. 2017). The fraction of cropland
dedicated to biochar feedstock production (9%) is based on the assumption of 10% YI
achievable through BBF, corresponding to the grand mean of yield responses reported
in Melo etal. (2022). In the assessment, we draw a range of 5% and 15% YI around this
base assumption (according to the respective confidence interval in Melo etal. (2022))
to account for uncertainties and dependencies in the yield response. In addition, we test
for a scenario of biochar application optimized towards carbon sequestration and yield
responses with 20% YI (Fig.1, S1), which is within the range of the confidence interval
for biochar with a carbon content > 30% (CI 11–24%) in Melo etal. (2022).
2.4 Management intensities
To analyze the effect of management of feedstock production and resulting yields on NE
potentials, we assess two management intensities on the rededicated cropland (Fig.1, S1).
First, we assume minimal management, reflecting a case where the farmer’s management
efforts focus on the remaining cropland. The feedstock is then simulated as rainfed biomass
yields in LPJmL. In the sensitivity analysis, irrigation meeting the total water demand of the
plantation represents the upper end of the range of agricultural management. In line with this,
we assess a second scenario assuming moderate management as the mid-range yield between
rainfed (no irrigation) and intensified (full irrigation).
2.5 Pyrolysis parameters andsequestration efficiencies
For the pyrolysis process transforming the harvested biomass into biochar, we assume param-
eters for slow pyrolysis with a highest heating temperature of 500 °C to ensure relatively high
biochar yields at the same time as high fractions of recalcitrant biochar. As NE potentials of
simulated biochar applications strongly depend on the assumed process- and feedstock-spe-
cific biochar yields and carbon contents in the char, we study two sets of parameters represent-
ing a conservative and an optimized setting, setting a range (Fig.1, S1). The first set shown in
Table2 is based on Woolf etal. (2021) by averaging over a large number of different pyrolysis
technologies, while the second set represents settings that are optimized towards biochar pro-
duction for carbon sequestration following the biochar yield equations of Schmidt etal. (2019)
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and an enhanced carbon conversion efficiency through ash amendment based on Grafmüller
etal. (2022).
Regarding the permanence of biochar, our base assumption applies a conservative esti-
mate of 74% carbon remaining in the soil after 100 years (BC100 = 74%) based on an annual
decay rate of 0.3% per year for biochar with H/C ratios < 0.4 based on the findings of Camps-
Arbestain et al. (2015). Acknowledging the uncertainties associated with biochar durability
in soils, we have additionally computed the sequestration potential under both a lower and a
higher estimate for biochar durability to establish a range that envelopes this base assumption.
The lower range presumes a 70% retention of biochar carbon in the soil after a century, which
aligns with the estimate derived from the linear regression in Lehmann et al. (2021) based
on observational data for pyrolysis temperatures of 500 °C. On the contrary, the upper range
operates with an assumption of 80% biochar carbon remaining in the soil after 100 years, as
suggested in the Refinement to the IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC 2019).
To finally account for net carbon capture efficiency (Table2), we consider a carbon expend-
iture of about 18% of the sequestered biomass carbon including upstream CO2 emissions from
biomass cultivation, harvest, processing, and transport and downstream CO2 emissions associ-
ated with the conversion of biomass into biochar at the pyrolysis plant, as well as the transport
and application of biochar on soil (S3, Chiquier etal. (2022)). Yet, it has to be noted that with
biochar production and use by subsistence farmers, such expenditures may be close to zero.
3 Results
3.1 Sensitivity analysis andselection ofparameters forsimulating biomass
production
Preceding the evaluation of global NE potentials for LCN-PyCCS, we assessed the sen-
sitivity of the model representation of biomass growth for dedicated stocks to the most
critical parameters (see “Methods”) and used this as a basis for a final parameter selection.
For the herbaceous BFT, higher fractions of harvested biomass lead to lower yields
because of smaller leaf area and therefore decreased photosynthetic activity after harvest.
However, the decrease in biomass yields is much stronger for larger harvest thresholds.
Higher thresholds show lower average productivity because of productivity stagnation over
time, while lower values benefit from shorter intervals of regrowth, except for the very low
value of 150 gC m−2 where the productivity is limited due to relatively low leaf area after
harvest (Figure S1). Considering these two parameter responses, the PBIAS (percent bias,
see “Methods”) could be reduced from 18.73 to 2.74% with a harvest threshold of 450 gC
m−2 and a harvest index of 0.7. Thus, the overall overestimation of herbaceous BFT yields
in the model could be reduced significantly (Figure S3).
For the woody types, we find that biomass production is significantly enhanced for lower
la:sa values, as these lead to more allocation of carbon to the wood. In case of the tropical
BFT, the biomass yields (averaged over growing years) further increase with longer rota-
tion cycles. Yet, this dynamic is divergent for the temperate tree, where the yields decline
again for rotation lengths > 5 years, because here, the biomass increment is lower than the
production averaged over the preceding years, as more carbon is lost via respiration of the
living tissue.
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Table 2 Pyrolysis parameters with numbers in brackets indicating the values for the lower (70%) and upper (80%) range for the biochar durability in soils after 100 years
Feedstock type Biochar yield [% ash-free dry
matter]
Carbon conversion efficiency [% biomass
carbon in biochar]
Carbon capture over 100 years [%
biomass carbon]
Net carbon capture
efficiency [% biomass
carbon]
Conservative
Herbaceous 23.13 38.66 28.61 (27.06–30.93) 23.51 (22.24–25.42)
Woody 26.67 42.97 31.80 (30.08–34.38) 26.14 (24.72–28.26)
Optimized
Herbaceous 30.89 52.99 39.21 (37.09–42.39) 32.22 (30.49–34.85)
Woody 35.05 61.21 45.30 (42.85–48.97) 37.24 (35.22–40.25)
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For the tropical BFT, the parameter combination leading to the best fit with observa-
tion data considering PBIAS is the extremes of the assessed ranges, i.e., la:sa = 2500 and
rotation length = 12 years with a PBIAS of − 2.45% (Figure S2). This indicates that other
processes and parameters beyond the selected parameters for biomass production are likely
relevant and might also have to be calibrated for the representation of eucalypt wood yields.
However, as we are dealing here with a global biosphere model for generalized global-scale
simulations rather than a dedicated crop model for plantation dynamics of a specific type,
we consider performance with a PBIAS of − 2.45% sufficient for representing biomass
production on a global scale. Compared to the PBIAS of −27.06% of the original param-
eterization (la:sa = 4000, rotation length = 8 years), the new parameter selection could
significantly reduce the underestimation of yields for this BFT in the model (Figure S4).
For the temperate BFT, we find a number of combinations that result in an acceptable fit
of simulated yields with observed yields: the best performing 10% of parameter combina-
tions (considering PBIAS) lie between ± 2% PBIAS with la:sa between 2000 and 3500 and
rotations ranging between 3 and 10 years (Figure S2). Given the similar fits for different
parameter combinations, we additionally base the parameter selection on the median of
reported rotation lengths in Li etal. (2018) while prioritizing a parameterization of la:sa
that results in a better fit across all rotation lengths, strengthening the role of plant physi-
ology compared to primarily management-driven dynamics. Based on these arguments,
we chose the parameters of la:sa = 3500 and rotation length = 4 years with a PBIAS of
− 1.97% for our simulations. The representation could thus be enhanced by reducing the
overall underestimation in comparison to the old parameterization with a PBIAS of −
10.83% (Figure S4).
While the parameter selection based on model performance in comparison to observed
yields could improve the overall representation in the regions covered by test sites (Fig-
ure S3, Figure S4), there is limited evidence for the compatibility of the global biosphere
model in the rest of the world. As shown for Miscanthus in Fig.1b, there is a clear focus
of test sites in the northern temperate zone. Here, LPJmL-computed rainfed yields range
from approximately 10 to 20 t dry matter ha−1, whereas some tropical regions exceed 30
t dry matter ha−1 even without irrigation (Fig. 2a). Yet, as no suitable reference data is
available, the higher productivity simulated in the tropics is based on process-based mod-
eling, matching other findings on enhanced plant productivity in the tropics (Cramer etal.
1999; Turner etal. 2006), rather than parameters fitted to observations. However, the upper
end of reported yields in the temperate zone is comparable to the simulated yields in the
tropics, indicating that the plant physiology of Miscanthus has the potential for yields of
such magnitude. For the LCN-PyCCS analysis, robustness of simulated yields is particu-
larly relevant, as a certain level of biomass production has to be reached on the rededicated
land in order to produce sufficient biochar for the remaining cropland (yield thresholds in
Fig.2b). Yet, our assessment assumes marginal management (represented as rainfed plants
in LPJmL) and moderate management (represented by the mean of irrigated and rainfed
yields), while the simulated maximum of irrigated yields would represent highly intensi-
fied agriculture that is typically not considered for the management of pyrolysis feedstock
production systems.
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Fig. 2 Yields of LPJmL-simulated herbaceous biomass stock shown as global distribution of rainfed yields (a) and as a range between the 10th percentile of rainfed yields
and 90th percentile of irrigated yields grouped by latitude and compared to observations (orange circles) of corresponding location (b). The light green boxes in b indicate the
mean values of rainfed and irrigated yields (mid-range) averaged over the latitude group; the vertical purple and pink lines show the thresholds of biomass yields on rededi-
cated land required to supply the remaining cropland with sufficient biochar under the assumption of optimized and conservative pyrolysis parameters, respectively
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3.2 Negative emission potential
Based on the enhanced simulation of feedstock yields, we quantified the global NE poten-
tial of the LCN-PyCCS approach for parameters and geographical extents representing the
findings for BBF in the literature. We found that the results strongly depend on the accom-
plishable YI, as previously indicated in Werner etal. (2022), but additionally also on the
pyrolysis parameters, biochar durability in soils, and management intensity of the feed-
stock production systems assumed. For the main analysis, we follow the base assumption
of herbaceous biomass input (see above). The calculated NE potentials under the assump-
tion of BC100 = 74% and marginal management of the feedstock production range from
0.20 GtCO2 year−1 based on conservative assumptions for pyrolysis parameters (for 10%
YI; 0–0.61 GtCO2 year−1 for 5% and 15%, respectively) to 0.37 GtCO2 year−1 for the opti-
mized pyrolysis case (0–0.82 GtCO2 year−1, Table3; Fig.3b). As the biochar yield is lower
for conservative pyrolysis parameters (Table 2), more biomass and thereby particularly
productive regions are required to supply the corresponding cropland with the sufficient
rate of biochar. Thus, the suitable area is restricted to the most productive regions in the
tropics resulting in a relatively low global NE potential (Fig.3). Furthermore, in addition
to the limited extent, the lower biochar yield and carbon content in the conservative param-
eter set lead to lower overall carbon sequestration per biomass carbon input.
If only + 5% YI was accomplished through BBF, the feedstock yield requirements
would rise significantly, because a smaller area dedicated to PyCCS feedstock production
would need to supply biochar for a larger area of remaining cropland. As our simulations
barely reach this yield level, no NE potential can be quantified here. Accordingly, land- and
calorie-neutral biochar sequestration can only be realized under such low yield responses if
the feedstock production of the LCN-PyCCS approach (i.e., land rededication) was largely
supplemented by other land- and calorie-neutral sources.
For the base assumption of 10% YI, the productivity and thus the NE supply could
potentially be increased by intensifying the management of the biomass production sys-
tems. With higher yields due to enhanced management, more areas produce sufficient
biochar to supply the corresponding cropland and become suitable for the LCN-PyCCS
approach (Fig.3). While the LCN-PyCCS applicability shows a strong focus on tropical
regions under marginal management, the moderate intensification expands the suitability
into the subtropics and even into the temperate zone in wide areas of Eastern USA. Based
on the conservative pyrolysis parameters, moderate intensification may sequester up to
0.74 GtCO2 year−1 (for +10% YI and BC100 = 74%; 1,64 GtCO2 year−1 for + 15% YI),
while the optimized pyrolysis case might even reach NE rates of about 1.10 GtCO2 year−1
(for + 10% YI and BC100 = 74%; 1.88 GtCO2 year−1 for + 15% YI).
Accordingly, the cumulative NE sums from 2025 to 2100 could be increased by feed-
stock production management from 15.09 and 28.07 GtCO2 year−1 to 55.49 and 82.59
GtCO2 year−1 for conservative and optimized pyrolysis parameters under BC100 = 74%,
respectively. Comparing these results to IAM scenarios, the cumulative sums for margin-
ally managed feedstock systems correspond to 3–6% of the total NE demand until the end
of the century in scenarios compatible with 1.5 °C or 2 °C in the 6th Assessment Report
of the IPCC (IPCC 2022), while the numbers for moderately managed systems compare to
10–16%, respectively.
Yet, while the LCN-PyCCS potentials that we quantified lie at the lower end of pro-
jected NE demands for Paris-compatible IAM scenarios, their potential for moderate man-
agement is comparable to the separate mid-century NE supply by BECCS required in the
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Table 3 Negative emission potentials of land- and calorie-neutral PyCCS calculated for 10% yield increase achieved by biochar-based fertilization given as a mean annual
potential and sums over 2025–2100 with results for 5% and 15% yield increases in brackets. The base assumption of a biochar durability of 74% of the biochar carbon remain-
ing in the soil after 100 years is given in bold, whereas the lower range of 70% is shown in plain and 80% in italic font
Management Annual NE potential [GtCO2] Cumulative NE potential 2025–2100 [GtCO2]
Conservative pyrolysis parameters
Marginal 0.19 (0–0.57) 0.20 (0–0.61) 0.22 (0–0.65) 14.27 (0–43.01) 15.09 (0–45.47) 16.31 (0–49.16)
Moderate 0.70 (0–1.55) 0.74 (0–1.64) 0.80 (0–1.78) 52.49 (0–116.59) 55.49 (0–123.26) 59.98 (0–133.25)
Optimized pyrolysis parameters
Marginal 0.35 (0.01–0.78) 0.37 (0.01–0.82) 0.40 (0.01–0.89) 26.55 (0.87–57.89) 28.07 (0.92–61.20) 30.35 (0.99–66.16)
Moderate 1.04 (0.01–1.78) 1.10 (0.01–1.88) 1.19 (0.01–2.03) 78.13 (0.94–134.08) 82.59 (0.99–141.74) 89.29 (1.07–153.23)
70% 74% biochar C
after 100 years 80% 70% 74% biochar C after 100 years 80%
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Fig. 3 Cell fractions dedicated to LCN-PyCCS in 2099 (a) and annual global sums of negative emissions averaged over 2025–2099 (b) based on different assumptions of
pyrolysis parameters and management of the feedstock-producing systems, assuming 10% biochar-mediated yield increase. Combinations of higher potential (highest: moder-
ate stock management plus optimized pyrolysis parameters, green) include the area of combinations with lower potential (lowest: marginal stock management plus conserva-
tive pyrolysis parameters, purple). The segments of the bar plots in b represent the potential under the assumption of 74% biochar carbon remaining in the soil after 100 years,
while the error bars show the range for the lower (70%) and higher (80%) durability tested
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illustrative mitigation pathway of Shifting Pathways which assumes far-reaching trans-
formations in society and economy (IPCC 2022; Soergel etal. 2021). The annual PyCCS
rates based on moderate management correspond to 82–115% (conservative to optimized
assumptions for pyrolysis under BC100 = 74%) of the BECCS demand in this illustrative
mitigation pathway with a focus on shifting transition towards Sustainable Development
Goals, including poverty reduction and broader environmental protection in addition to
deep GHG emissions cuts. Consequently, there is evidence from IAM assessments that NE
rates of the magnitude quantified in this study could support the climate targets of the Paris
Agreement. This is the case, however, only if the stringent decarbonization measures in
combination with the comprehensive socioeconomic transformations (e.g., diet changes,
minimized food waste, and improved distribution of goods) assumed in these scenarios are
successful, and not for the mainstream collection of IAM pathways beyond the illustrative
pathway mentioned.
In addition to enhancing the pyrolysis processes and the productivity of the feedstock
supply, we tested a case of improved BBF application leading to 20% yield increases, i.e.,
as observed for chars with particularly high carbon content in Melo etal. (2022) (17% for
> 30% carbon content) and expected for tailored biochars (Joseph etal. 2021). This optimi-
zation on the application side may increase the NE production to up to 2.45 GtCO2 year−1
under the assumption of optimized pyrolysis parameters, moderate feedstock management,
and BC100 = 74%.
While pyrolysis parameters and feedstock production impact the suitable area for the
LCN-PyCCS approach (Fig.3a), the presumed biochar carbon durability in soils drives
the depiction of carbon losses over 100 years, ultimately affecting the final sequestration
potential (Fig.3b). A biochar carbon retention rate as low as 70% of the initial biochar
carbon input would result in sequestration potentials of 0.19 GtCO2 year−1 for marginal
and 0.70 GtCO2 year−1 for moderate feedstock management intensity, considering 10% YI
and conservative pyrolysis parameters (Table 3). Under optimized pyrolysis conditions,
with this lower BC100 of 70%, potential sequestration could reach 0.35 GtCO2 year−1 for
marginal and 1.04 GtCO2 year−1 for moderate feedstock management intensity. Assum-
ing a higher fraction of biochar carbon remaining in soil with BC100 = 80% would sig-
nificantly increase the overall sequestration potential to 0.22 GtCO2 year−1 for marginal
and 0.80 GtCO2 year−1 for moderate feedstock management intensity, given 10% YI and
conservative pyrolysis parameters. Optimizing the pyrolysis process in this case could raise
the potentials up to 0.40 GtCO2 year−1 for marginal and 1.19 GtCO2 year−1 for moderate
feedstock management intensity.
As feedstock types for pyrolysis can be very diverse and woody inputs are also widely
considered for biochar production (Ye etal. 2020), we additionally tested woody feedstock
for the LCN-PyCCS approach considering the fast-growing tree functional types repre-
sented in LPJmL and parameterized in this study. We find that the NE potentials are signifi-
cantly lower than quantified for the grassy feedstock because the required biomass yields
for sufficient biochar supply are barely reached with the woody functional types in the
model. Only if these biomass production systems are moderately managed, woody feed-
stock as represented in this study may become suitable for LCN-PyCCS in a few highly
productive regions, resulting in relatively low NE potentials of 0.10 GtCO2 year−1 and 0.15
GtCO2 year−1 for conservative and optimized pyrolysis parameters, respectively.
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4 Discussion
Climate stabilization is crucial for Earth system stability, but when biomass-based NETs
that compete for land with food production and ecosystem protection are implemented
without consideration of the environmental (e.g. land required) and societal (e.g. calo-
ries produced) repercussions, stabilization measures may at the same time threaten earth
system stability by undermining biosphere integrity. Instead of expanding NET deploy-
ment in order to reach a certain NE target as in optimization models of climate economics,
this assessment quantified the NE supply achievable “bottom-up” under the constraints of
land- and calorie-neutrality. While staying within the bounds of cropland and maintain-
ing calorie production, LCN-PyCCS based on BBF may produce 0 to 2.03 GtCO2 year−1
(0.19–1.19 GtCO2 year−1 for the base assumption of + 10% YI; 0–0.01 GtCO2 year−1 for
+ 5%; 0.57–2.03 GtCO2 year−1 for +15%) depending on (i) the YI achieved through BBF,
(ii) the pyrolysis parameters assumed, (iii) the management intensity of the biomass pro-
ducing system, and (iv) the biochar durability in soils. We argue that in order to estimate
realistic potentials of NE, the discrepancy found between the demand-driven NE volumes
calculated in economic optimization models (IAMs) for paths reaching ambitious climate
targets and the results of supply-driven approaches as assessed here needs to be transpar-
ently discussed.
Our results support the findings in Werner etal. (2022) that global LCN-PyCCS poten-
tials are driven by the biochar-mediated YI that can be accomplished. At the lower range,
we find that a level of + 5% YI is not sufficient for the LCN-PyCCS approach based on
BBF. Besides the high feedstock yield requirements or large biomass substitution demand,
such low yield responses are not likely to encourage rededication of land to feedstock
production. In cases where biochar application is still preferred despite low YI (i.e., for
enhanced soil resilience), feedstock would thus need to be supplied by other (sustainable)
sources.
In our assessment, we employed the insights from Melo etal. (2022) to substantiate
universal YI levels in the adoption of a systematic methodology. However, the actual
yield improvement achievable across diverse locations might surpass or fall short of this
value, because the response to BBF exhibits considerable variability, as evidenced by the
data compiled by Melo etal. (2022). A more precise, spatially explicit computation of YI
under current conditions would necessitate the integration of diverse factors, among others
encompassing soil category, fertilizer type, and crop variety. Yet, the currently available
data is not sufficient for a statistical model of that kind. In the Melo etal. (2022) data-
set, the observations associated with one category of an explanatory variable can show a
substantial range in BBF response; for instance, the category of “weakly developed soils”
exhibits a confidence interval spanning from 1 to 25% YI. Additionally, responses across
categories within a variable, like “weakly developed soils” and “highly weathered soils,”
might not exhibit significant differences. While a comprehensive statistical model for
deriving BBF-induced yield increase through multiple explanatory variables is presently
absent, our study follows a systematic approach with theoretically universal levels of YI
to reflect on magnitudes of the sequestration potential of LCN-PyCCS and its sensitivity
to achievable YI, management intensity, pyrolysis parameters, and storage durability. Yet,
as areas identified as suitable for LCN-PyCCS are not distributed equally, we have verified
that the designated regions for LCN-PyCCS predominantly coincide with soil orders that
have shown significant response to BBF (S4). Beyond the evaluated distribution of LCN-
PyCCS, other regions of agricultural production are largely marked by more pronounced
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responses to fertilization and/or soils different from highly weathered and weakly devel-
oped soils. It needs to be noted that these distinct soil characteristics exhibited less signifi-
cant to no yield responses to biochar addition in the meta-analysis by Melo etal. (2022).
Thus, if BBF applications were assumed to concentrate on these less responsive soils, the
overall net impact could be significantly reduced.
In addition to a focus on responsive soils, we assume feedstock treatment during pyroly-
sis that maximizes carbon storage via biochar. Consequently, the envisioned BBF involves
biochar characterized by elevated carbon content (conservative estimate: 63% and opti-
mized estimate: 67%). This aligns with the subset of chars in the > 30% carbon content
category, which demonstrated notably elevated yield responses in the Melo etal. dataset
(mean YI: 17%).
Enhancing the biochar application in terms of stronger responses to BBF in plant pro-
ductivity (also by combining it with another land-based NET such as enhanced weather-
ing) may significantly increase the NE potential. Research and development on BBFs are
picking up pace, alongside with a growing understanding of biochar-surface interactions
with major nutrients such as nitrogen and pre- and post-production treatment options to
increase desired effects for crop yields, increased nitrogen use efficiency, and reduced
environmental nitrogen pollution (state of knowledge reviewed in Rasse etal. (2022)). An
example is the infiltration of the porous biochar structure with molten urea, providing a
slow-release compound biochar fertilizer (Wang etal. 2021; Xiang etal. 2020), the coat-
ing of conventional fertilizers with biochar to increase the nitrogen use efficiency (Jia etal.
2021), biochar surface oxygenation to increase ammonia (NH3) sorption or acid treatments,
and organic coating to increase nitrate capture (Rasse etal. 2022) and other strategies. It
can hence be expected that the upper ceiling of YI achieved with tailored BBFs has not yet
been reached.
Beyond the impact of accomplishable YI, our results indicate a strong sensitivity of the
NE potentials to the assumed pyrolysis parameters. Basing the calculations on the opti-
mized parameter set instead of the conservative assumption increased the NE potential
by 40–75%. Thus, practitioners aiming for carbon sequestration should follow settings for
their pyrolysis plant/kiln that enhance the biochar yield and consider ash or rock powder
supplements (Buss et al. 2022; Mašek et al. 2019); rock powder (enhanced weathering)
represents another NET that can also increase yields (Beerling etal. 2020; Kantzas et al.
2022).
Furthermore, we identified the management of the biomass supplying systems as
another factor driving the NE potentials in this assessment. The NE production could be
increased by + 200–270% if the management of biomass production was assumed to be
intensified from marginal to moderate levels. Elevated yields do not only increase the NE
production per hectare, but also expand the area that is suitable for the approach because
more regions meet the biomass production on rededicated land required to supply suffi-
cient biochar for the cropland. This is particularly relevant for the expansion in the sub-
tropics or even temperate regions that only become suitable for the LCN-PyCCS approach
investigated in this assessment when moderate management is assumed. Yet, in most of
the subtropical and temperate regions, such a degree of intensification is also more likely
because the agronomic development in these countries already provides the infrastructure
and resources required.
At the same time, the temperate regions are also best represented in the observational
dataset that was used for the comparison with simulated yields, driving the parame-
ter selection in this analysis. While the PBIAS could be increased significantly with the
new parameterization for all BFTs, we also identified shortcomings in representing plant
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physiology. Furthermore, the model performance could only be assessed where observa-
tion data was provided. Yet, for the tropics, there is a significant lack of data on lignocel-
lulosic energy crops in the literature (Fig.2, Li etal. 2018). We therefore conclude that
in order to extent the robustness of the representation of such crops in global modeling,
both, the simulated processes of plant physiology and the geographic coverage of valida-
tion data, would need to be enhanced.
As we tested the quantification of LCN-PyCCS for woody biomass by applying fast-
growing tree functional types as represented in LPJmL for the feedstock supply, we cal-
culated NE potentials that were significantly lower than for the assessment of grassy feed-
stock. However, these functional types fail to represent all possible wood-like feedstocks
that could be used for LCN-PyCCS (e.g., coffee, tea, cocoa, or fruit/nut tree pruning
wood). Moreover, depending on the climatic, soil, and management conditions of the crop-
land, fast-growing tree species (e.g. agroforestry systems) can be more beneficial for plant
growth, soil resilience, and intermediate-term landscape carbon sequestration (Dollinger
and Jose 2018; Lorenz and Lal 2018) and might thus be preferred over grassy species, or
be combined with them. Resulting potential benefits and site-specific effects like nutrient
cycling, shade cover, increasing relative air humidity, and root growth preventing soil ero-
sion (Fahad etal. 2022; Torralba etal. 2016) are not represented in our model and thus did
not contribute to this assessment, but would reinforce rather than compromise our findings.
In addition to feedstock production and the pivotal role of pyrolysis conditions in deter-
mining the input of biochar carbon into the soil, we also assessed a range of alternative
assumptions for the biochar durability in soils to account for the uncertainty associated
with this aspect. It is known that biochar durability is enhanced by a greater proportion
of fused aromatic structures, which form at higher temperatures and with extended resi-
dence times (Ippolito etal. 2020; Wang etal. 2016). However, estimating the portion that
remains after, for instance, 100 years is reliant on estimations, given the absence of long-
term experiments (Leng etal. 2019). Current methodologies for quantifying 100-year bio-
char persistence, such as those used in IPCC inventory guidelines, primarily extrapolate
short-term soil decomposition processes. These methods fail to entirely encompass the
mechanisms contributing to millennial persistence. Consequently, the actual biochar car-
bon residence time might exceed these estimations. Concurrently, future research should
focus on biochar incubation in field conditions, aiming to highlight distinctions from labo-
ratory settings (Leng etal. 2019). This field research may uncover factors that could inten-
sify decomposition compared to laboratory experiments.
Furthermore, the study is limited to pyrogenic carbon sequestration and does not con-
sider other biochar-mediated processes in the soils that could potentially contribute to
shifting the land use sector from a greenhouse gas source into a sink. Biochar-enriched
soils have been shown to enhance the build-up of soil organic carbon (Bai et al. 2019;
Blanco-Canqui etal. 2020; Weng etal. 2017) and reduce soil acidity (Singh et al. 2017),
nitrate leaching (Borchard etal. 2019; Hagemann etal. 2017), and N2O and CH4 emissions
(Borchard etal. 2019; He etal. 2017; Jeffery etal. 2016). In total, biochar treatments can
thus enhance soil quality, cut down management costs, and lower agricultural greenhouse
gas emissions (Kammann etal. 2017; Lehmann etal. 2021).
While we assessed the potential for purpose-grown pyrolysis feedstock in the LCN-
PyCCS approach, it needs to be emphasized that harvest residues and waste can provide addi-
tional sustainable biomass inputs, particularly because of the avoided land competition with
food and nature. These additional sources for biochar production may even expand the area
of LCN-PyCCS applicability as they could supplement the biochar supply in regions where
the production by purpose-grown feedstock on rededicated land falls below the threshold of
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sufficient biochar application. Yet, the availability of residues and waste is uncertain and lim-
ited (Hanssen etal. 2020; IPCC 2022), and they cannot be considered freely available in the
light of competing uses or benefits, such as soil carbon built-up against land degradation or
the replacement of animal feed (Kalt etal. 2020; van Zanten etal. 2018). Another source
that should receive more attention as the biomass market comes under increasing pressure
is feedstocks that are often overlooked, for example, annual pruning wood or trunk and root
weeding wood (at replanting cycles) of subtropical or tropical annual and perennial crops
such as coffee, tea, cocoa, palm oil trees, banana, or fruit/nut trees. For example, waste stream
and heat demand of the work flow at coffee production sites would favor pyrolysis (i) for dry-
ing coffee beans in a controlled manner to increase product quality and reduce the danger of
fungal contamination and (ii) to compost coffee cherry pulp with biochar. Using such a prod-
uct as a soil amendment at replanting has been shown to accelerate young tree growth and
shorten the time until the first harvest is gained (Neumann Coffee Group, H. Faessler, practi-
cal trials, pers. comm.); biochar-composts can improve soil fertility for demanding crops such
as coffee in particular in tropical soils (Zhao etal. 2020).
This global assessment aims to illustrate potential magnitudes of NE achievable through
LCN-PyCCS, along with its sensitivity to specific influencing factors. We acknowledge
however that broad-scale generalizations cannot fully encapsulate the unique local condi-
tions that ultimately drive the sequestration process. Established methodologies employed to
evaluate the sequestration potential within specific systems are lifecycle assessments (LCAs)
integrating all information about inputs and outputs along the particular production and stor-
age chain (Tisserant and Cherubini 2019). The NE outcome is contingent upon a multitude
of variables across the lifecycle stages, including feedstock selection, pyrolysis conditions,
transportation distances, and energy/fuel mix (Azzi etal. 2021). While LCAs serve as valu-
able tools for assessing the CDR potential of particular systems and their economic viabil-
ity, often, the specific assumptions they entail are not extrapolatable to large-scale global
scenarios. Consequently, they may focus on refining carbon sequestration within biochar
production under specific circumstances. For instance, Fawzy etal. (2022) demonstrated
this by optimizing the pyrolysis of olive tree pruning residue, yielding a total sequestration
of 2.69 tCO2-equivalent per ton of biochar. Compared to our study, the NE potential per unit
biochar is thus approximately 1.7- to 1.9-fold higher and can be attributed to their optimized
pyrolysis process, tailored for maximum carbon sequestration from a lignin-rich feedstock.
Notably, the composition of biochar in the Fawzy etal. study, characterized by a carbon
content of 84.9%, BC100 of 92%, and CO2-equivalent expenditures along the lifecycle of 7%,
stands in contrast to our assessment, where these figures are 62.7%, 74%, and 18%, respec-
tively. Yet, to estimate the magnitude of global potentials, more generalized assumptions—
albeit not aligned with the tenets of LCA—become necessary. Consequently, we assessed
pyrolysis parameter ranges following functions based on observations for the broader cat-
egory of grassy (and woody) feedstock (Grafmüller etal. 2022; Schmidt etal. 2019; Woolf
etal. 2021). More generic assumptions were also made for the carbon expenditure through-
out the lifecycle which corresponds to a moderately decarbonized energy and fuel blend,
considering an average global transport distance of 55 km (see S3).
As described above, our global assessment reveals several limitations across various
dimensions: In terms of feedstock supply, the availability of observational data for ligno-
cellulosic grass growth in tropical regions remains sparse. Additionally, LPJmL currently
lacks representation of most suitable wood-like feedstocks, especially those derived from
integrated systems like agroforestry. Furthermore, this assessment does not incorporate
other land-neutral resources, such as residues and waste, as biomass inputs. Moreover,
the global assumptions concerning yield increase and carbon expenditure rely on global
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Mitig Adapt Strateg Glob Change (2024) 29:34
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Page 21 of 28 34
averages rather than accounting for the specific efficiencies of individual systems. Regard-
ing the NE potential, the assessment does not incorporate other benefits linked to carbon
sequestration that may arise from the application of biochar to soils. Yet, despite these con-
straints, systematic approaches like this study can contribute to advancing the discourse on
negative emissions strategies, as they offer a means to assess the magnitude and respon-
siveness of global LCN-PyCCS potentials concerning diverse parameters.
Building on the first global quantification of LCN-PyCCS potentials based on biochar
soil amendments of 2 t ha−1, this study does not only add results for the promising practice
of BBF at lower application rates (around 0.8 t ha−1), but also insights on the sensitivity
of the results towards assumptions on pyrolysis parameters and the management intensity
of feedstock-producing systems. While the annual rate of 0.44 GtCO2 year−1 reported for
+ 15% YI at 2 t ha−1 biochar application in Werner etal. (2022) was solely based on opti-
mized pyrolysis parameters and marginal management, this analysis illustrates an operation
space for different pyrolysis processes and management intensities. Our calculation of the
BBF-based NE potential following optimized pyrolysis parameters and marginal manage-
ment results in 0.37 GtCO2 year−1 as we assume a YI of + 10% according to the grand
mean reported in the BBF meta-analysis by Melo etal. (2022). Assuming + 15% YI in
the BBF setting however shows higher NE potential (0.82 GtCO2 year−1, Table3) than in
Werner etal. (2022). This can be explained by the lower application rate that leads to a
lower yield threshold of biomass production on the rededicated land required for sufficient
biochar supply on the remaining cropland. Consequently, the application can be expanded
into less productive regions that only then become suitable for LCN-PyCCS. Furthermore,
the assumptions on the pyrolysis process were revised, now referring to 500 °C instead of
450 °C for the highest heating temperature. This is closer to practice and leads to a lower
biochar yield but longer residence times in the soil, which we consider a more reasonable
balance for long-term carbon sequestration. To increase the robustness of the assumptions
about pyrogenic carbon residence time in the soil, we additionally adjusted the fraction of
carbon that remains after 100 years from 90% (Werner etal. 2022) to 74% based on find-
ings by Camps-Arbestain etal. (2015). In light of the latest analyses and new evidence on
the recalcitrant nature of biochar produced at this temperature, the assumed fraction can be
considered conservative (Azzi etal. 2024; Sanei etal. 2024).
Yet, addressing the differences between biochar used as soil amendment or BBF and
the sensitivity towards pyrolysis parameters und management intensities of the biomass
supply, we conclude that biogeochemical evaluations of the global LCN-PyCCS potential
can only provide a range of estimates that mark the outer bounds of the maximum potential
under the given assumptions. It is highly unlikely that a universally uniform level of YI
will be achieved at a global scale. Such a concept is employed herein solely as a theoreti-
cal assumption, facilitating an exploration of sequestration potentials and parameter sensi-
tivities. The usage and design of the pyrolysis plant/kiln as well as the biochar application
methods and yield responses will always be specific for the explicit needs and conditions at
the respective producer. Our findings of significantly higher NE potentials with optimized
pyrolysis parameters and biochar application show that applying the constantly expand-
ing knowledge on optimal carbon sequestration and yield response in practice can increase
the global potential to a large degree. These scenarios are largely theoretical by assum-
ing global applications; in that sense, they are purely analytical. Achievable real-world
potentials depend on the feasibility of implementation (e.g., cultural, social, or political
uptake or resistance), sufficient incentives for integrating PyCCS into agricultural practices
worldwide, and established proof of successful operation for NE certification. Addressing
potential resistance can be facilitated through adherence to robust accounting regulations,
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Mitig Adapt Strateg Glob Change (2024) 29:34
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34 Page 22 of 28
integrated within comprehensive measurement, reporting, and verification frameworks,
which PyCCS already exhibits promising entry points for (Lehmann etal. 2021). A com-
pelling illustration of stringent quality control of the chemical composition of biochar is,
for example, already practiced with the European Biochar Certificate (EBC 2023; Fawzy
etal. 2021). Furthermore, to circumvent competition for land and resources, the implemen-
tation of PyCCS should concentrate on accounting for the wide spectrum of co-benefits,
particularly within the agricultural sector (see above), while concurrently integrating it
within cross-sectoral planning rather than considering it an isolated climate change miti-
gation measure. Furthermore, one should not consider findings as found here to be yet a
sufficient basis for planning on mitigation paths that assume these potentials will be avail-
able. However, it is a particular strength of LCN-PyCCS that this NET can (and already
is) adopted by diverse agricultural systems and contributes to NE in a bottom-up dynamic,
rather than top-down approaches like BECCS that likely rely on centralized markets and
massive investments for costly infrastructure.
While it is therefore challenging to make global assumptions on the NE production
potential of LCN-PyCCS systems, we still consider it illuminating to place the global esti-
mates quantified in our analyses within the ongoing discussion of the large-scale imple-
mentation of NETs for climate stabilization—even with the large ranges we find. Only
through such comparisons, at the same scale, can the discrepancies be identified between
the large NE volumes calculated in demand-driven optimization models and the potentials
from analyses focusing on supply-driven approaches, characterized by preconditions like
minimizing the pressure on land resources and calorie production.
The argument of comparability holds true for the time scale of technological devel-
opment: while PyCCS and particularly biochar assessments are usually based on param-
eters and process understanding that is directly derived from operating plants and real-
world applications, the evaluations of BECCS and DACCS typically follow assumptions
of future development based on only scarce evidence at commercial scale (Chatterjee
and Huang 2020; Haikola etal. 2019). The Orca facility in Iceland is the first DACCS
plant to operate at a commercial scale with a capacity of 4000 t CO2 year−1 (Carbfix
2021). In contrast to this, the European Biochar Industry Consortium (EBI 2022) reports
about 53,000 t biochar production capacity built in 2022 (i.e., PyCCS) in Central Europe
alone (EBI 2023), potentially sequestering about 90,000–110,000 t CO2 based on the
sequestration efficiencies used for soil applications in this study. Plant construction
was projected to increase the European biochar production to above 90,000 t in 2023
(EBI 2023). For BECCS, the development is focused on North America that counts four
BECCS plants capturing ≥ 100,000 t CO2 plus the Decatur plant with potentially one
million t CO2 capture per year. In Europe, the DRAX facility in the UK, with a capac-
ity of 330 t CO2 capture per year, is currently the only BECCS plant in operation, yet
several projects are planned (Fajardy 2022; Shahbaz etal. 2021). Thus, the real-world
carbon sequestration of PyCCS in Europe exceeds that of DACCS and BECCS at the
moment. Also, on a global scale, PyCCS (particularly biochar sequestration) is showing
the highest number of operating plants and catches up in regard to capacity, when com-
pared to BECCS and DACCS (note that a global quantification of biochar production is
still lacking and would result in much higher numbers).
Our analysis integrating biochar-mediated yield increases in the scenario development
for global NET deployment identifies a potential for land- and calorie-neutral NE produc-
tion. The LCN-PyCCS approach could thus contribute to a broader NET assessment con-
sidering critical additional limitations in environmental, social, and policy dimensions.
Furthermore, as biochar application to soils potentially enhances soil properties in diverse
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Mitig Adapt Strateg Glob Change (2024) 29:34
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Page 23 of 28 34
ways (see above), the role of PyCCS could be enhanced in such impact-sensitive analyses
if more biochar-mediated processes (i.e., liming, water-holding capacity, enhanced micro-
bial activity, increased nutrient retention) were represented in the models. Yet, in parallel
to those efforts in process representation within vegetation models, more elaborate models
and databases for residue and waste availability (or beneficial waste-stream management
strategies involving biochar) should be developed, as estimates and projections for sustain-
able biomass sources are crucial. While the LCN-PyCCS approach assessed here may con-
tribute to some degree to global (more sustainable) NE production, a much higher potential
could be unlocked if the outstanding feature of PyCCS that a variety of materials can be
processed were to be exploited at a larger scale, particularly using residues and wastes that
are of no other use (or where biochar production and use in organic waste management
offer further SDG-supporting benefits).
In exploring various alternatives, it is crucial to prevent biomass-based NETs—
exclusively intended for climate stabilization—from becoming a major driver of det-
rimental future land use change. This concern is particularly pertinent considering the
already substantial degradation of the Earth’s biosphere. In this study, we specifically
highlight BBF-based LCN-PyCCS as one potential avenue but underscore the sensitiv-
ity of potential global NE production to factors such as achievable YI, pyrolysis settings,
and the management intensity of feedstock production. While our assessment addresses
the need of strict preconditions for large-scale NET deployment (i.e., land and calorie
neutrality), the findings reveal that the resulting potentials are subject to large remaining
uncertainties. Given these uncertainties, the study reinforces the imperative for prior-
itizing deep emission reductions as the foremost strategy in climate stabilization efforts.
Supplementary Information The online version contains supplementary material available at https:// doi.
org/ 10. 1007/ s11027- 024- 10130-8.
Funding Open Access funding enabled and organized by Projekt DEAL. This project has received fund-
ing from the European Union’s Horizon 2020 research and innovation program under grant agreement
No 869192 (C.W, J.B.). BMBF BioCAP-CCS project (Grant No. #01LS1620A and B). C. K. received
FACCE-JPI funding for developing biochar-based fertilization approaches (project ABC4Soil, Grant No.
031B0588B).
Data availability Data supporting the main findings of this study are available via 10.5281/zenodo.7116841.
Land use and climate input data can be downloaded from the ISIMIP repository, https:// data. isimip. org/
(Frieler etal. 2017) and 10.48364/ISIMIP.208515 (Lange and Büchner 2017).
Declarations
Competing Interest The authors declare that they have no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Mitig Adapt Strateg Glob Change (2024) 29:34
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34 Page 24 of 28
References
Ai Z, Hanasaki N, Heck V, Hasegawa T, Fujimori S (2020) Simulating second-generation herbaceous bio-
energy crop yield using the global hydrological model H08 (v.bio1). Geosci Model Dev 13(12):6077–
6092. https:// doi. org/ 10. 5194/ gmd- 13- 6077- 2020
Azzi ES, Karltun E, Sundberg C (2021) Assessing the diverse environmental effects of biochar systems: an
evaluation framework. J Environ Manag 286:112154. https:// doi. org/ 10. 1016/j. jenvm an. 2021. 112154
Azzi ES, Li H, Cederlund H, Karltun E, Sundberg C (2024) Modelling biochar long-term carbon storage
in soil with harmonized analysis of decomposition data. Geoderma 441:116761. https:// doi. org/ 10.
1016/j. geode rma. 2023. 116761
Bai SH, Omidvar N, Gallart M, Kämper W, Tahmasbian I, Farrar MB, Singh K, Zhou G, Muqadass B, Xu
C-Y, Koech R, Li Y, Nguyen TTN, van Zwieten L (2022) Combined effects of biochar and fertilizer
applications on yield: a review and meta-analysis. Sci Total Environ 808:152073. https:// doi. org/ 10.
1016/j. scito tenv. 2021. 152073
Bai X, Huang Y, Ren W, Coyne M, Jacinthe P-A, Tao B, Hui D, Yang J, Matocha C (2019) Responses
of soil carbon sequestration to climate-smart agriculture practices: a meta-analysis. Glob Chang Biol
25(8):2591–2606. https:// doi. org/ 10. 1111/ gcb. 14658
Bednar J, Obersteiner M, Wagner F (2019) On the financial viability of negative emissions. Nat Commun
10(1):1783. https:// doi. org/ 10. 1038/ s41467- 019- 09782-x
Beerling DJ, Kantzas EP, Lomas MR, Wade P, Eufrasio RM, Renforth P, Sarkar B, Andrews MG, James
RH, Pearce CR, Mercure J-F, Pollitt H, Holden PB, Edwards NR, Khanna M, Koh L, Quegan S, Pidg-
eon NF, Janssens IA etal (2020) Potential for large-scale CO2 removal via enhanced rock weathering
with croplands. Nature 583(7815):242–248. https:// doi. org/ 10. 1038/ s41586- 020- 2448-9
Beringer T, Lucht W, Schaphoff S (2011) Bioenergy production potential of global biomass plantations
under environmental and agricultural constraints. GCB Bioenergy 3(4):299–312. https:// doi. org/ 10.
1111/j. 1757- 1707. 2010. 01088.x
Blanco-Canqui H, Laird DA, Heaton EA, Rathke S, Acharya BS (2020) Soil carbon increased by twice the
amount of biochar carbon applied after 6 years: field evidence of negative priming. GCB Bioenergy
12(4):240–251. https:// doi. org/ 10. 1111/ gcbb. 12665
Bondeau A, Smith PC, Zaehle S, Schaphoff S, Lucht W, Cramer W, Gerten D, Lotze-Campen H, Muller C,
Reichstein M, Smith B (2007) Modelling the role of agriculture for the 20th century global terrestrial
carbon balance. Glob Chang Biol 13(3):679–706. https:// doi. org/ 10. 1111/j. 1365- 2486. 2006. 01305.x
Borchard N, Schirrmann M, Cayuela ML, Kammann C, Wrage-Mönnig N, Estavillo JM, Fuertes-
Mendizábal T, Sigua G, Spokas K, Ippolito JA, Novak J (2019) Biochar, soil and land-use interactions
that reduce nitrate leaching and N2O emissions: a meta-analysis. Sci Total Environ 651:2354–2364.
https:// doi. org/ 10. 1016/j. scito tenv. 2018. 10. 060
Boysen LR, Lucht W, Gerten D (2017) Trade-offs for food production, nature conservation and climate limit
the terrestrial carbon dioxide removal potential. Glob Chang Biol 23(10):4303–4317. https:// doi. org/
10. 1111/ gcb. 13745
Buss W, Wurzer C, Manning DAC, Rohling EJ, Borevitz J, Mašek O (2022) Mineral-enriched biochar deliv-
ers enhanced nutrient recovery and carbon dioxide removal. Commun Earth Environ 3(1):67. https://
doi. org/ 10. 1038/ s43247- 022- 00394-w
Camps-Arbestain M, Amonette JE, Singh B, Wang T, Schmidt HP (2015) A biochar classification system
and associated test methods. In: Lehmann J, Joseph S (eds) Biochar for Environmental Management:
Science, Technology and Implementation. Routledge, pp 165–193
Carbfix. (2021). The world’s largest direct air capture and CO2 storage plant is ON https:// www. carbfi x.
com/ the- worlds- large st- direct- air- captu re- and- co2- stora ge- plant- is- on
Chatterjee S, Huang K-W (2020) Unrealistic energy and materials requirement for direct air capture in deep
mitigation pathways. Nat Commun 11(1):3287. https:// doi. org/ 10. 1038/ s41467- 020- 17203-7
Chiquier S, Patrizio P, Bui M, Sunny N, Mac Dowell N (2022) A comparative analysis of the efficiency,
timing and permanence of CO2 removal options. Energy Environ Sci 15(10):4389–4403
Cramer W, Kicklighter DW, Bondeau A, Moore B, Churkina G, Nemry B, Ruimy A, Schloss AL, Inter-
compariso PPNM (1999) Comparing global models of terrestrial net primary productivity (NPP):
overview and key results. Glob Chang Biol 5:1–15. https:// doi. org/ 10. 1046/j. 1365- 2486. 1999.
00009.x
Dietrich JP, Bodirsky BL, Humpenöder F, Weindl I, Stevanović M, Karstens K, Kreidenweis U, Wang
X, Mishra A, Klein D, Ambrósio G, Araujo E, Yalew AW, Baumstark L, Wirth S, Giannousakis A,
Beier F, Chen DMC, Lotze-Campen H, Popp A (2019) MAgPIE 4 – a modular open-source frame-
work for modeling global land systems. Geosci Model Dev 12(4):1299–1317. https:// doi. org/ 10.
5194/ gmd- 12- 1299- 2019
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Mitig Adapt Strateg Glob Change (2024) 29:34
1 3
Page 25 of 28 34
Dollinger J, Jose S (2018) Agroforestry for soil health. Agrofor Syst 92(2):213–219. https:// doi. org/ 10.
1007/ s10457- 018- 0223-9
EBC. (2023). European Biochar Certificate - guidelines for a sustainable production of biochar (Version
10.3 from 5th Apr 2023 ed.). Carbon Standards International (CSI). http:// europ ean- bioch ar. org
EBI. (2022). European Biochar Market Report 2021/2022. https:// www. bioch ar- indus try. com/ market-
overv iew- 21- 22/
EBI. (2023). European Biochar Market Report 2022/2023. https:// www. bioch ar- indus try. com/ wp- conte
nt/ uploa ds/ 2023/ 03/ Europ ean- Bioch ar- Market- Report_ 20222 023. pdf
Fahad S, Chavan SB, Chichaghare AR, Uthappa AR, Kumar M, Kakade V, Pradhan A, Jinger D, Rawale
G, Yadav DK, Kumar V, Farooq TH, Ali B, Sawant AV, Saud S, Chen S, Poczai P (2022) Agrofor-
estry systems for soil health improvement and maintenance. Sustainability 14(22):14877. https://
www. mdpi. com/ 2071- 1050/ 14/ 22/ 14877
Fajardy M (2022) Chapter5 Bioenergy with carbon capture and storage (BECCS). In: Greenhouse Gas
Removal Technologies. The Royal Society of Chemistry, pp 80–114. https:// doi. org/ 10. 1039/ 97818
39165 245- 00080
FAO, IIASA, ISRIC, ISSCAS, & JRC (2012) Harmonized World Soil Database (version 1.2). https:// www.
fao. org/ soils- portal/ data- hub/ soil- maps- and- datab ases/ harmo nized- world- soil- datab ase- v12/ en/
Fawzy S, Osman AI, Mehta N, Moran D, Ala’a H, Rooney DW (2022) Atmospheric carbon removal
via industrial biochar systems: a techno-economic-environmental study. J Clean Prod 371:133660.
https:// doi. org/ 10. 1016/j. jclep ro. 2022. 133660
Fawzy S, Osman AI, Yang H, Doran J, Rooney DW (2021) Industrial biochar systems for atmos-
pheric carbon removal: a review. Environ Chem Lett 19(4):3023–3055. https:// doi. org/ 10. 1007/
s10311- 021- 01210-1
Frieler K, Lange S, Piontek F, Reyer CPO, Schewe J, Warszawski L, Zhao F, Chini L, Denvil S, Emanuel
K, Geiger T, Halladay K, Hurtt G, Mengel M, Murakami D, Ostberg S, Popp A, Riva R, Stevanovic
M etal (2017) Assessing the impacts of 1.5°C global warming – simulation protocol of the Inter-
Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geosci Model Dev 10(12):4321–4345.
https:// doi. org/ 10. 5194/ gmd- 10- 4321- 2017
Grafmüller J, Böhm A, Zhuang Y, Spahr S, Müller P, Otto TN, Bucheli TD, Leifeld J, Giger R, Tobler
M, Schmidt H-P, Dahmen N, Hagemann N (2022) Wood ash as an additive in biomass pyroly-
sis: effects on biochar yield, properties, and agricultural performance. ACS Sustain Chem Eng
10(8):2720–2729. https:// doi. org/ 10. 1021/ acssu schem eng. 1c076 94
Hagemann N, Joseph S, Schmidt H-P, Kammann CI, Harter J, Borch T, Young RB, Varga K, Taherymoosavi
S, Elliott KW, McKenna A, Albu M, Mayrhofer C, Obst M, Conte P, Dieguez-Alonso A, Orsetti S,
Subdiaga E, Behrens S, Kappler A (2017) Organic coating on biochar explains its nutrient retention
and stimulation of soil fertility. Nat Commun 8(1):1089. https:// doi. org/ 10. 1038/ s41467- 017- 01123-0
Haikola S, Hansson A, Fridahl M (2019) Map-makers and navigators of politicised terrain: expert under-
standings of epistemological uncertainty in integrated assessment modelling of bioenergy with car-
bon capture and storage. Futures 114:102472. https:// doi. org/ 10. 1016/j. futur es. 2019. 102472
Hanssen SV, Daioglou V, Steinmann ZJN, Frank S, Popp A, Brunelle T, Lauri P, Hasegawa T, Huijbregts
MAJ, Van Vuuren DP (2020) Biomass residues as twenty-first century bioenergy feedstock—a
comparison of eight integrated assessment models. Clim Chang 163(3):1569–1586. https:// doi. org/
10. 1007/ s10584- 019- 02539-x
He Y, Zhou X, Jiang L, Li M, Du Z, Zhou G, Shao J, Wang X, Xu Z, Hosseini Bai S, Wallace H, Xu C
(2017) Effects of biochar application on soil greenhouse gas fluxes: a meta-analysis. GCB Bioen-
ergy 9(4):743–755. https:// doi. org/ 10. 1111/ gcbb. 12376
Heck V, Gerten D, Lucht W, Boysen LR (2016) Is extensive terrestrial carbon dioxide removal a ‘green’
form of geoengineering? A global modelling study. Glob Planet Chang 137:123–130. https:// doi.
org/ 10. 1016/j. glopl acha. 2015. 12. 008
Heck V, Gerten D, Lucht W, Popp A (2018) Biomass-based negative emissions difficult to reconcile with
planetary boundaries. Nat Clim Chang 8(2):151–155. https:// doi. org/ 10. 1038/ s41558- 017- 0064-y
Humpenöder F, Popp A, Bodirsky BL, Weindl I, Biewald A, Lotze-Campen H, Dietrich JP, Klein D, Krei-
denweis U, Müller C, Rolinski S, Stevanovic M (2018) Large-scale bioenergy production: how to
resolve sustainability trade-offs? Environ Res Lett 13(2):024011. https:// doi. org/ 10. 1088/ 1748- 9326/
aa9e3b
IPCC. (2018). Annex I: glossary. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of
global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission path-
ways, in the context of strengthening the global response to the threat of climate change, sustainable
development, and efforts to eradicate poverty. https:// www. ipcc. ch/ site/ assets/ uploa ds/ sites/2/ 2022/ 06/
SR15_ AnnexI. pdf
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Mitig Adapt Strateg Glob Change (2024) 29:34
1 3
34 Page 26 of 28
IPCC. (2019). Refinement to the 2006 IPCC guidelines for national greenhouse gas inventories. Volume
4: Agriculture, Forestry and Other Land Use. Appendix 4: Method for Estimating the Change in
Mineral Soil Organic Carbon Stocks from Biochar Amendments: Basis for Future Methodological
Development.
IPCC (2022) Climate change 2022. Mitigation of Climate Change. Working Group III contribution to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University
Press. https:// doi. org/ 10. 1017/ 97810 09157 926
Ippolito JA, Cui L, Kammann C, Wrage-Mönnig N, Estavillo JM, Fuertes-Mendizabal T, Cayuela ML,
Sigua G, Novak J, Spokas K, Borchard N (2020) Feedstock choice, pyrolysis temperature and type
influence biochar characteristics: a comprehensive meta-data analysis review. Biochar 2(4):421–438.
https:// doi. org/ 10. 1007/ s42773- 020- 00067-x
Jeffery S, Verheijen FGA, Kammann C, Abalos D (2016) Biochar effects on methane emissions from soils:
a meta-analysis. Soil Biol Biochem 101(Supplement C):251–258. https:// doi. org/ 10. 1016/j. soilb io.
2016. 07. 021
Jia Y, Hu Z, Ba Y, Qi W (2021) Application of biochar-coated urea controlled loss of fertilizer nitrogen
and increased nitrogen use efficiency. Chem Biol Technol Agric 8(1):3. https:// doi. org/ 10. 1186/
s40538- 020- 00205-4
Joseph S, Cowie AL, Van Zwieten L, Bolan N, Budai A, Buss W, Cayuela ML, Graber ER, Ippolito JA,
Kuzyakov Y, Luo Y, Ok YS, Palansooriya KN, Shepherd J, Stephens S, Weng Z, Lehmann J (2021)
How biochar works, and when it doesn’t: a review of mechanisms controlling soil and plant responses
to biochar. GCB Bioenergy 13(11):1731–1764. https:// doi. org/ 10. 1111/ gcbb. 12885
Kalt G, Lauk C, Mayer A, Theurl MC, Kaltenegger K, Winiwarter W, Erb K-H, Matej S, Haberl H (2020)
Greenhouse gas implications of mobilizing agricultural biomass for energy: a reassessment of global
potentials in 2050 under different food-system pathways. Environ Res Lett 15(3):034066. https:// doi.
org/ 10. 1088/ 1748- 9326/ ab6c2e
Kammann C, Ippolito J, Hagemann N, Borchard N, Cayuela ML, Estavillo JM, Fuertes-Mendizabal T, Jef-
fery S, Kern J, Novak J, Rasse D, Saarnio S, Schmidt H-P, Spokas K, Wrage-Mönnig N (2017) Biochar
as a tool to reduce the agricultural greenhouse-gas burden – knowns, unknowns and future research
needs. J Environ Eng Landsc Manag 25(2):114–139. https:// doi. org/ 10. 3846/ 16486 897. 2017. 13193 75
Kantzas EP, Val Martin M, Lomas MR, Eufrasio RM, Renforth P, Lewis AL, Taylor LL, Mecure J-F, Pollitt
H, Vercoulen PV, Vakilifard N, Holden PB, Edwards NR, Koh L, Pidgeon NF, Banwart SA, Beerling
DJ (2022) Substantial carbon drawdown potential from enhanced rock weathering in the United King-
dom. Nat Geosci 15(5):382–389. https:// doi. org/ 10. 1038/ s41561- 022- 00925-2
Klein Goldewijk K, Beusen A, Doelman J, Stehfest E (2017) Anthropogenic land use estimates for the Holo-
cene – HYDE 3.2. Earth System Science Data 9(2):927–953. https:// doi. org/ 10. 5194/ essd-9- 927- 2017
Lange S, Büchner M (2017) ISIMIP2b bias-adjusted atmospheric climate input data (v1.0). ISIMIP Reposi-
tory. Version 1.0. https:// doi. org/ 10. 48364/ ISIMIP. 208515
Lehmann J, Cowie A, Masiello CA, Kammann C, Woolf D, Amonette JA, Cayuela ML, Camps-Arbestain
M, Whitman T (2021) Biochar in climate change mitigation. Nat Geosci. https:// doi. org/ 10. 1038/
s41561- 021- 00852-8
Leng L, Xu X, Wei L, Fan L, Huang H, Li J, Lu Q, Li J, Zhou W (2019) Biochar stability assessment by
incubation and modelling: methods, drawbacks and recommendations. Sci Total Environ 664:11–23.
https:// doi. org/ 10. 1016/j. scito tenv. 2019. 01. 298
Lenzi D, Lamb WF, Hilaire J, Kowarsch M, Minx JC (2018) Don’t deploy negative emissions technologies
without ethical analysis. Nature 561:303–305. https:// doi. org/ 10. 1038/ d41586- 018- 06695-5
Li W, Ciais P, Makowski D, Peng S (2018) A global yield dataset for major lignocellulosic bioenergy crops
based on field measurements. Sci Data 5(1):180169. https:// doi. org/ 10. 1038/ sdata. 2018. 169
Li W, Ciais P, Stehfest E, van Vuuren D, Popp A, Arneth A, Di Fulvio F, Doelman J, Humpenöder F, Harper
AB, Park T, Makowski D, Havlik P, Obersteiner M, Wang J, Krause A, Liu W (2020) Mapping the yields
of lignocellulosic bioenergy crops from observations at the global scale. Earth Syst Sci Data 12(2):789–
804. https:// doi. org/ 10. 5194/ essd- 12- 789- 2020
Lorenz K, Lal R (2018) Agroforestry systems. In: Carbon Sequestration in Agricultural Ecosystems. Springer
International Publishing, pp 235–260. https:// doi. org/ 10. 1007/ 978-3- 319- 92318-5_6
Mašek O, Buss W, Brownsort P, Rovere M, Tagliaferro A, Zhao L, Cao X, Xu G (2019) Potassium doping
increases biochar carbon sequestration potential by 45%, facilitating decoupling of carbon sequestration
from soil improvement. Sci Rep 9(1):5514. https:// doi. org/ 10. 1038/ s41598- 019- 41953-0
Melo LC, Lehmann J, Carneiro JS, Camps-Arbestain M (2022) Biochar-based fertilizer effects on crop produc-
tivity: a meta-analysis. Plant Soil. https:// doi. org/ 10. 1007/ s11104- 021- 05276-2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Mitig Adapt Strateg Glob Change (2024) 29:34
1 3
Page 27 of 28 34
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD and Veith TL (2007) Model evaluation guide-
lines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885-900.
https:// doi. org/ 10. 13031/ 2013. 23153
Nemet GF, Callaghan MW, Creutzig F, Fuss S, Hartmann J, Hilaire J, Lamb WF, Minx JC, Rogers S, Smith P
(2018) Negative emissions—part 3: innovation and upscaling. Environ Res Lett 13(6):063003. https:// doi.
org/ 10. 1088/ 1748- 9326/ aabff4
Osman AI, Fawzy S, Farghali M, El-Azazy M, Elgarahy AM, Fahim RA, Maksoud MIAA, Ajlan AA, Yousry
M, Saleem Y, Rooney DW (2022) Biochar for agronomy, animal farming, anaerobic digestion, compost-
ing, water treatment, soil remediation, construction, energy storage, and carbon sequestration: a review.
Environ Chem Lett 20(4):2385–2485. https:// doi. org/ 10. 1007/ s10311- 022- 01424-x
Qian L, Chen L, Joseph S, Pan G, Li L, Zheng J, Zhang X, Zheng J, Yu X, Wang J (2014) Biochar compound
fertilizer as an option to reach high productivity but low carbon intensity in rice agriculture of China. Car-
bon Management 5(2):145–154. https:// doi. org/ 10. 1080/ 17583 004. 2014. 912866
Rasse DP, Weldon S, Joner EJ, Joseph S, Kammann CI, Liu X, O’Toole A, Pan G, Kocatürk-Schumacher NP
(2022) Enhancing plant N uptake with biochar-based fertilizers: limitation of sorption and prospects. Plant
Soil 475(1):213–236. https:// doi. org/ 10. 1007/ s11104- 022- 05365-w
Sanei H, Rudra A, Przyswitt ZMM, Kousted S, Sindlev MB, Zheng X, Nielsen SB, Petersen HI (2024) Assess-
ing biochar’s permanence: an inertinite benchmark. Int J Coal Geol 281:104409. https:// doi. org/ 10. 1016/j.
coal. 2023. 104409
Schaphoff S, Forkel M, Müller C, Knauer J, von Bloh W, Gerten D, Jägermeyr J, Lucht W, Rammig A,
Thonicke K, Waha K (2018a) LPJmL4 – a dynamic global vegetation model with managed land – part 2:
model evaluation. Geosci Model Dev 11(4):1377–1403. https:// doi. org/ 10. 5194/ gmd- 11- 1377- 2018
Schaphoff S, von Bloh W, Rammig A, Thonicke K, Biemans H, Forkel M, Gerten D, Heinke J, Jägermeyr J,
Knauer J, Langerwisch F, Lucht W, Müller C, Rolinski S, Waha K (2018b) LPJmL4 – a dynamic global
vegetation model with managed land – part 1: model description. Geosci Model Dev 11(4):1343–1375.
https:// doi. org/ 10. 5194/ gmd- 11- 1343- 2018
Schmidt H-P, Anca-Couce A, Hagemann N, Werner C, Gerten D, Lucht W, Kammann C (2019) Pyrogenic car-
bon capture and storage. GCB Bioenergy 11(4):573–591. https:// doi. org/ 10. 1111/ gcbb. 12553
Schmidt H-P, Kammann C, Hagemann N, Leifeld J, Bucheli TD, Sánchez Monedero MA, Cayuela ML (2021)
Biochar in agriculture – a systematic review of 26 global meta-analyses. GCB Bioenergy 13(11):1708–
1730. https:// doi. org/ 10. 1111/ gcbb. 12889
Schmidt H-P, Pandit BH, Cornelissen G, Kammann CI (2017) Biochar-based fertilization with liquid nutrient
enrichment: 21 field trials covering 13 crop species in Nepal. Land Degrad Dev 28:2324–2342. https:// doi.
org/ 10. 1002/ ldr. 2761
Shahbaz M, AlNouss A, Ghiat I, McKay G, Mackey H, Elkhalifa S, Al-Ansari T (2021) A comprehensive
review of biomass based thermochemical conversion technologies integrated with CO2 capture and utili-
sation within BECCS networks. Resour Conserv Recycl 173:105734. https:// doi. org/ 10. 1016/j. resco nrec.
2021. 105734
Singh B, Dolk MM, Shen Q, Camps-Arbestain M (2017) Biochar pH, electrical conductivity and liming poten-
tial. In: Singh BC-A, Lehmann MJ (eds) Biochar: A guide to analytical methods (Vol. 23). CSIRO. https://
doi. org/ 10. 1111/ sum. 12389
Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT,
Thonicke K, Venevsky S (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial car-
bon cycling in the LPJ dynamic global vegetation model. Glob Chang Biol 9(2):161–185. https:// doi. org/
10. 1046/j. 1365- 2486. 2003. 00569.x
Smith P, Adams J, Beerling DJ, Beringer T, Calvin KV, Fuss S, Griscom B, Hagemann N, Kammann C, Kraxner
F, Minx JC, Popp A, Renforth P, Vicente Vicente JL, Keesstra S (2019) Land-management options for
greenhouse gas removal and their impacts on ecosystem services and the sustainable development goals.
Annu Rev Environ Resour 44(1):255–286. https:// doi. org/ 10. 1146/ annur ev- envir on- 101718- 033129
Soergel B, Kriegler E, Weindl I, Rauner S, Dirnaichner A, Ruhe C, Hofmann M, Bauer N, Bertram C, Bod-
irsky BL, Leimbach M, Leininger J, Levesque A, Luderer G, Pehl M, Wingens C, Baumstark L, Beier
F, Dietrich JP etal (2021) A sustainable development pathway for climate action within the UN 2030
Agenda. Nat Clim Chang 11(8):656–664. https:// doi. org/ 10. 1038/ s41558- 021- 01098-3
Spokas KA (2010) Review of the stability of biochar in soils: predictability of O:C molar ratios. Carbon Man-
agement 1(2):289–303. https:// doi. org/ 10. 4155/ cmt. 10. 32
Stenzel F, Gerten D, Werner C, Jägermeyr J (2019) Freshwater requirements of large-scale bioenergy planta-
tions for limiting global warming to 1.5 °C. Environ Res Lett 14(8):084001. https:// doi. org/ 10. 1088/ 1748-
9326/ ab2b4b
Sutradhar I, Jackson-deGraffenried M, Akter S, McMahon SA, Waid JL, Schmidt H-P, Wendt AS, Gabry-
sch S (2021) Introducing urine-enriched biochar-based fertilizer for vegetable production: acceptability
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Mitig Adapt Strateg Glob Change (2024) 29:34
1 3
34 Page 28 of 28
and results from rural Bangladesh. Environ Dev Sustain 23(9):12954–12975. https:// doi. org/ 10. 1007/
s10668- 020- 01194-y
Tisserant A, Cherubini F (2019) Potentials, limitations, co-benefits, and trade-offs of biochar applications to
soils for climate change mitigation. Land 8(12):179 https:// www. mdpi. com/ 2073- 445X/8/ 12/ 179
Torralba M, Fagerholm N, Burgess PJ, Moreno G, Plieninger T (2016) Do European agroforestry systems
enhance biodiversity and ecosystem services? A meta-analysis. Agric Ecosyst Environ 230:150–161.
https:// doi. org/ 10. 1016/j. agee. 2016. 06. 002
Turner DP, Ritts WD, Cohen WB, Gower ST, Running SW, Zhao M, Costa MH, Kirschbaum AA, Ham JM,
Saleska SR, Ahl DE (2006) Evaluation of MODIS NPP and GPP products across multiple biomes.
Remote Sens Environ 102(3):282–292. https:// doi. org/ 10. 1016/j. rse. 2006. 02. 017
van Zanten HHE, Herrero M, Van Hal O, Röös E, Muller A, Garnett T, Gerber PJ, Schader C, De Boer IJM
(2018) Defining a land boundary for sustainable livestock consumption. Glob Chang Biol 24(9):4185–
4194. https:// doi. org/ 10. 1111/ gcb. 14321
Wang JY, Xiong ZQ, Kuzyakov Y (2016) Biochar stability in soil: meta-analysis of decomposition and priming
effects. Glob Change Biol Bioenergy 8(3):512–523. https:// doi. org/ 10. 1111/ gcbb. 12266
Wang M, Xiang A, Gao Z, Zhang K, Ren Y, Hu Z (2021) Study on the nitrogen-releasing characteristics and
mechanism of biochar-based urea infiltration fertilizer. Biomass Convers Biorefinery. https:// doi. org/ 10.
1007/ s13399- 021- 01848-5
Weng Z, Van Zwieten L, Singh BP, Tavakkoli E, Joseph S, Macdonald LM, Rose TJ, Rose MT, Kimber SWL,
Morris S, Cozzolino D, Araujo JR, Archanjo BS, Cowie A (2017) Biochar built soil carbon over a decade
by stabilizing rhizodeposits. Nat Clim Chang 7:371–376. https:// doi. org/ 10. 1038/ nclim ate32 76
Werner C, Lucht W, Gerten D, Kammann C (2022) Potential of land-neutral negative emissions through biochar
sequestration. Earth’s Future 10(7):e2021EF002583. https:// doi. org/ 10. 1029/ 2021E F0025 83
Werner C, Schmidt HP, Gerten D, Lucht W, Kammann C (2018) Biogeochemical potential of biomass pyrolysis
systems for limiting global warming to 1.5 °C. Environ Res Lett 13(4):044036. https:// doi. org/ 10. 1088/
1748- 9326/ aabb0e
Woolf D, Amonette JE, Street-Perrott FA, Lehmann J, Joseph S (2010) Sustainable biochar to mitigate global
climate change. Nat Commun 1:56. https:// doi. org/ 10. 1038/ Ncomm s1053
Woolf D, Lehmann J, Ogle S, Kishimoto-Mo AW, McConkey B, Baldock J (2021) Greenhouse gas inventory
model for biochar additions to soil. Environ Sci Technol 55(21):14795–14805. https:// doi. org/ 10. 1021/ acs.
est. 1c024 25
Xiang A, Qi R, Wang M, Zhang K, Jiang E, Ren Y, Hu Z (2020) Study on the infiltration mechanism of molten
urea and biochar for a novel fertilizer preparation. Ind Crop Prod 153:112558. https:// doi. org/ 10. 1016/j.
indcr op. 2020. 112558
Ye L, Camps-Arbestain M, Shen Q, Lehmann J, Singh B, Sabir M (2020) Biochar effects on crop yields with
and without fertilizer: a meta-analysis of field studies using separate controls. Soil Use Manag 36(1):2–18.
https:// doi. org/ 10. 1111/ sum. 12546
Zaehle S, Sitch S, Smith B, Hatterman F (2005) Effects of parameter uncertainties on the modeling of terrestrial
biosphere dynamics. Glob Biogeochem Cycles 19(3). https:// doi. org/ 10. 1029/ 2004G B0023 95
Zhao S, Schmidt S, Qin W, Li J, Li G, Zhang W (2020) Towards the circular nitrogen economy – a global
meta-analysis of composting technologies reveals much potential for mitigating nitrogen losses. Sci Total
Environ 704:135401. https:// doi. org/ 10. 1016/j. scito tenv. 2019. 135401
Zhuang Q, Qin Z, Chen M (2013) Biofuel, land and water: maize, switchgrass or Miscanthus? Environ Res Lett
8(1):015020. https:// doi. org/ 10. 1088/ 1748- 9326/8/ 1/ 015020
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