Life cycle assessment of greenhouse gas mitigation benefits of biochar

Annette L. Cowie and Alan J. Cowie Rural Climate Solutions

(University of New England/ NSW Department of Primary Industries) Armidale NSW 2351

1. Abstract

This study evaluated the GHG impacts of a range of biochar systems, made from different biomass feedstocks, under different pyrolysis conditions, and applied to different crops. We used life cycle assessment (LCA) to systematically quantify the GHG emissions and removals at each stage of the biochar system life cycle, from procurement of the biomass feedstock, through manufacture of biochar, to application, including transport. The net GHG emissions were calculated for each system, and sensitivity analyses were performed to assess the most critical components of the calculations. To calculate the GHG impacts of biochar, the biochar system was compared with the relevant reference system, representing the conventional use of the biomass, and conventional soil amendments. Besides biochar, the pyrolysis process produces syngas that can be utilised for renewable electricity, replacing fossil fuels. Therefore, avoided fossil fuel emissions are also included in the analysis.

Most biochar scenarios examined led to substantial reduction in greenhouse gas emissions. The greatest reduction, 3.2 kg CO2-e per kg biochar, was estimated for poultry litter biochar applied to maize. The benefits were greater for biochar applied to maize than to wheat, and were greater for higher than lower temperature biochars.

Biochar does not always reduce emissions compared with the reference system: if the biomass would otherwise have gone to a landfill facility with methane capture and electricity generation, the climate change benefits may have been greater than using the biomass for biochar.

The results are highly sensitive to the assumptions employed, including the reference use of the biomass, so it is critical that LCA is undertaken for each situation in which biochar use is proposed. In some situations biochar will not give the greatest mitigation benefits compared with alternative uses for biomass. In each case the optimal use of biomass should be considered, bearing in mind also other environmental and production objectives.

2. Introduction and objective

Biochar systems are commonly considered to be carbon neutral, or even “carbon negative” because they are believed to remove more greenhouse gas (GHG) from the atmosphere than is released in the process of making and using biochar. However, there are few studies quantifying the net GHG impacts of actual biochar systems to support this claim. To calculate the mitigation benefits of biochar a life-

cycle approach must be taken, that considers all aspects of the biochar system - biomass procurement, production of biochar, and its application – and assesses the net GHG impacts across the system.

Life cycle assessment (LCA) is a tool devised to aid in systematically quantifying the total environmental impact of a product or process (International Organization for Standardization, 2006). LCA has been widely employed to assess the climate change impacts of bioenergy systems (Cherubini et al., 2009, 2010). When applied to biochar systems LCA can be used to assess the GHG emissions and sequestration across the biochar life cycle, including fossil fuel use in harvesting, processing, transport and application of biochar; and indirect emissions such as from fertilizer manufacture. To accurately assess the GHG impacts of biochar systems, elements must be included that are often overlooked in conventional LCA (Cherubini et al, 2009) such as the biosphere carbon cycle (including changes in soil and biomass carbon stocks due to procurement of biomass) and the impacts of pyrolysing biomass on the carbon cycle. Emissions of all relevant GHGs must be considered, so N2O and CH4 should be included in addition to CO2. The mitigation value of biochar is determined by comparing the net emissions across the biochar life cycle with emissions from the applicable reference system, representing the conventional practice (i.e. conventional soil amendments and use of the biomass). Where an energy co-product (heat or electricity) is an output of biochar production, the conventional energy source should be included in the reference system.

Several studies have used LCA or partial LCA approaches to estimate the climate change impacts of biochar production and use (Gaunt and Cowie, 2009; Roberts et al., 2010; Woolf et al., 2010; Hammond et al., 2011). These studies have utilised theoretical or average data to provide indicative estimates. The objective of the current study was to undertake LCA of the greenhouse gas impacts of biochar systems that may be deployed in Australian cropping systems, utilising primary data for those systems.

3. Materials and methods

This desktop study used a LCA approach to evaluate greenhouse gas impacts of the application of various biochars to crop production systems. The SimaPro LCA software (version 7.3.2) with the integrated ecoinvent database (ecoinvent, 2009) was used to model the biochar production systems, and the impacts of biochar application to the cropping systems. For each biochar system, a comparable reference system was also modelled, including the base cropping system (conventionally fertilised), and conventional use of biomass (see system boundary, below).

The analysis was confined to the single impact category climate change, calculated in terms of CO2

equivalent emissions, using the Australian indicator set version 2.01. This impact method utilises global warming potential (GWP) of greenhouse gases calculated over 100 years, based on the IPCC’s Fourth Assessment Report), in which methane and nitrous oxide are assigned a GWP of 25 and 298, respectively (Forster et al., 2007).

3.1. LCA scope: System Boundary and functional unit

The analysis is divided into three stages:

Stage one

Stage One compares the production of biochar in two facilities, for a single feedstock, green waste1, for which data were available for both facilities.

Two biochar systems were selected for these assessments: AnthroTerra's mobile unit (the “Charcolator”), and the Pacific Pyrolysis planned commercial scale plant.

1 Garden waste from curbside collection and management of parklands and street trees.

The Charcolator is a road-transportable, field deployed biochar production system. The Charcolator has a design throughput of approximately 440 kg per hour, potentially processing 650 tonnes dry feedstock per year, to produce about 163 kg biochar per hour, or 240 tonnes per year. Some syngas (synthesis gas) is used to fuel the process. The excess syngas is flared on site. Data for this system were obtained from the prototype unit.

The Pacific Pyrolysis production system is a large scale, fixed site, biochar production system with a throughput of 4 tonnes per hour, estimated to have potential capacity to process 32,000 tonnes of dry feedstock per year, to produce about 11,000 tonnes of biochar per year per plant. The excess syngas is scrubbed and then combusted to produce electricity to feed into the grid. Data for this system were obtained from the Pacific Pyrolysis pilot facility.

The system boundary includes collection and processing of green waste, and construction and operation of the biochar plant. The reference system includes disposal of green waste in landfill. The reference system for the Pacific Pyrolysis plant, which produces electricity as a co-product, includes electricity generation from black coal.

The functional unit for Stage One is 1 kg of biochar, produced at the respective plant.

Stage Two

Stage Two of the analysis undertook LCA of biochar systems up to the farm gate, utilising biochars assumed to be produced in the Pacific Pyrolysis plant from three alternative feedstocks (poultry litter, wheat straw and green waste), at two pyrolysis temperatures (450°C and 550°C). Pyrolysis temperature is used as an indicator of biochar stability, acknowledging that stability is determined by the combined effects of the highest temperature and residence time in the pyrolysis kiln.

The second stage considers GHG emissions from crop production with biochar as a soil amendment, based on biochars produced from the Pacific Pyrolysis plant. The system boundary includes: procurement of feedstock (harvest/collection, transport to plant); processing of feedstock (drying, comminution); biochar production, including co-products (heat, electricity); biochar transport to field, application to cropping system; decomposition of biochar over 100 years. The reference system includes the reference use of biomass; conventional soil amendment; and conventional supply of co-products (see also Section 3.3). The functional unit for the second stage analysis is the production of one kilogram of product from the cropping system (wheat or maize) at the farm gate.

Figure 1 illustrates the processes included within the system boundary for the scenario where biochar is made from green waste and applied to a maize crop. In the reference case, the green waste goes to landfill and the maize crop is fertilised with chemical fertilisers alone, and electricity is generated from black coal.

Stage Three

Stage Three analyses the entire biochar system to determine the net climate change impact of using biochar as a soil amendment, in terms of the abatement per unit of biochar.

The cropping systems were modelled as one year of a sustainable cropping system, averaged across all years of any rotation or cycle. Long term impacts were estimated over 100 years. For the third stage, the functional unit is the use of 1 kg of biochar to grow either wheat or maize.

3.2. Data

The biochar production systems were modelled using data supplied by the respective manufacturers of the biochar production plants (Table 1). Anthroterra supplied measured data for their prototype charcolator unit. Pacific Pyrolysis supplied theoretical data for a commercial scale plant, derived from their demonstration scale plant.

The crop production systems were modelled using data from published gross margin budgets and additional published information regarding fertilizers, agricultural chemicals and agricultural machinery (sources detailed below).

The biochar characteristics and impacts on the crop systems were modelled using data from the National Biochar Initiative research program. The biochar decay model characteristics were based on other published and unpublished research. Assumptions for impacts of biochar are listed in Appendix A.

Where there was no primary or specific published information available, the most suitable process from the supplied SimaPro libraries was used. Where available, data from the Australian database (Lifecycle Strategies, 2009), were used; otherwise, the most suitable ecoinvent process was selected. Assumptions for key background data variables are listed in Appendix A.

Table 1: Assumptions for pyrolysis

Biomass Poultry Litter Wheat Straw Green waste

Pyrolysis High Heating Temperature (°C) 450 550 450 550 450 550

Code PL450 PL550 WS450 WS550 GW450 GW550

Moisture (% of fresh weight) 40 40 15 15 35 35

Char Yield [%m m-1

] (dry basis) 45.6 43.2 36.1 33.3 36.9 35.0

Natural gas input (GJ t-1

dry feedstock) 0.50 0.50 0.50 0.50 0.57 0.57

Electricity input (KWhe t-1

dry feedstock) 75 75 75 75 75 75

Net syngas output (GJ t-1

dry feedstock) 3.2 3.6 3.6 4.3 3.2 3.6

Electricity output (KWhe t-1

dry feedstock) 300 350 350 440 310 360

C in biochar (%) 44 40 70 63 44 40

Figure 1: System boundary for green waste biochar and reference systems

3.3. Biochar feedstocks

Wheat straw feedstock

It was assumed that the wheat straw feedstock was harvestable residue from no-till dryland wheat cropping. One tonne per hectare of crop residue was assumed to be left in the field for soil protection (after Dunlop et al., 2008). It was assumed that removal of wheat straw in excess of 1 t did not impact on subsequent production. The carbon in the crop residue left in the field was assumed to be released in the year of production, and there was assumed to be no net change in carbon stock in the soil. In the reference case it was assumed that the wheat straw remains in the field, where it decays on the soil surface in the year of production.

Poultry litter feedstock

The poultry litter feedstock, a mixture of manure and bedding material (wood shavings), was assumed to be used untreated as fertiliser in horticulture in the reference case, and to decay within the year of application. In the biochar case, it was assumed that diversion of poultry litter to biochar production would lead to additional use of chemical fertiliser in horticulture, and savings in nitrous oxide emissions from use of raw poultry litter.

Green waste feedstock

The green waste feedstock was considered to comprise metropolitan green waste. We assumed that this green waste would go to landfill if it was not diverted to biochar production. Anaerobic decomposition of biomass in landfill leads to production of the GHGs CO2 and CH4, assumed to be released in equal proportions (Department of Climate Change and Energy Efficiency, 2012). The extent of decomposition is debated – the current IPCC Guidelines (IPCC, 2006) assume that 50% of the C in wood products in landfill is released as a result of decomposition. However, recent research has demonstrated that the extent of decomposition is much lower, at 9% (Ximenes et al., 2008) or less (Wang et al., 2011). The Australian national greenhouse gas inventory assumes that the fraction of degradable organic carbon that dissimilates (DOCf) is 23% for wood (based on an estimate for branches) and 47% for “garden and park” waste. Green waste is likely to comprise a mixture of wood and leaf material. In this study we applied a DOCf of 9%, and assumed that 50% of the carbon dissimilated is released as CH4, of which 10% is oxidised in the landfill cap (IPCC, 2006), and that 24% of the CH4 released was captured and used for electricity generation (national average landfill gas capture, (Global_Renewables, 2012), displacing NSW grid electricity. We examined the influence of these assumptions in a sensitivity analysis (see Section 3.8). Table 2 summarises the biochar feedstocks and their use in the reference system.

Table 2: Summary of biochar and reference system scenarios

Feedstock to biochar

Crop & Biochar System Reference system

Poultry litter Poultry litter used to produce biochar. Biochar applied to wheat or maize crop. Excess syngas from biochar production used for electricity generation.

Poultry litter applied to horticultural crop.

No-till wheat or maize crop with chemical fertilizers.

Wheat straw Wheat straw residue harvested and used for biochar production. Biochar applied to wheat or maize crop. Excess syngas from biochar production used for electricity generation.

Wheat straw left in field to decay. No-till wheat or maize crop with chemical fertilizers.

Green waste Green waste diverted from landfill to produce biochar, which is then applied to wheat or maize crop, displacing some chemical fertilizer and other agronomic impacts. Excess syngas from biochar production used for electricity generation.

Green waste to landfill with methane capture & electricity generation to offset fossil fuelled grid power. No-till wheat or maize crop with chemical fertilizers.

3.4. Cropping systems

The crop systems selected for modelling were examples of large commercial scale Australian cropping systems for which experimental results for the effect of biochar application were available:

1. Wheat (dryland, no-till), assumed to be grown in the Northam district of WA

2. Maize (dryland, no-till), assumed to be grown in north-eastern NSW

The reference system with which the biochar systems were compared was a conventional crop production system using chemical fertilisers.

The wheat cropping process was based on gross margin budgets for dryland no-till wheat in the Northam district of WA (DAFWA, 2005).

The maize cropping process was based on gross margin budgets for dryland no-till maize in north-east NSW (NSW_DPI, 2011)

3.5. Carbon cycle

Generally in LCA it is assumed that CO2 removal in plant growth and its subsequent release through decay or combustion will balance across the life cycle, so this can be ignored. Especially in the case of production systems based on annual plants, the release generally occurs within 1-2 years from uptake, so exclusion of these fluxes is well-justified and does not impact the results. However, in the case of biochar, the pyrolysis process renders the biomass highly stable, so that decomposition is delayed for hundreds to thousands of years, beyond current GHG accounting time frames. Thus, it is important to model this delay in emissions to demonstrate the climate change impacts of biochar systems.

Therefore, in this study, processes were defined to track the carbon flows in biochar systems. These processes account for the uptake of CO2 from the atmosphere during growth of the feedstocks, and subsequent release of CO2 in the processing of the feedstock, pyrolysis process, and decomposition of the biochar. The flux of carbon into growing biomass, and the release of a portion of this carbon during pyrolysis dominate the carbon dynamics of the biochar system, masking other contributing processes. In illustrating the results, we show the balance of the carbon flux into and out from the biomass utilised for biochar, after 100 years. The fraction of the biomass carbon that remains stabilised in biochar after 100 years of decomposition in soil is considered a “permanent” removal of an equivalent amount of CO2 from the atmosphere.

As for the biochar systems, additional processes were defined to model the stabilisation of biomass carbon in landfill, and biomass carbon assumed to remain after 100 years was considered to be a removal.

3.6. Timing of emissions and removals

In conventional LCA the timing of emissions and removals is not considered. The emissions from a project are summed over the lifetime of that project, including one-off emissions such as due to construction. These emissions are then attributed equally to each unit of output. Where there are long term changes in biomass carbon stock, these are calculated over a finite period such as 100 years. In this study we applied this conventional approach to time. Biochar plant construction emissions were amortised over the expected lifetime of the plant, according to the manufacturer – 5 years for the Charcolator and 15 years for the Pacific Pyrolysis plant – and averaged across the expected biochar production during that period.

3.7. Impacts of biochar as a soil amendment

The impacts of biochar on crop growth, fertiliser requirement and herbicide requirement were estimated for wheat grown in a Tenosol in Northam district of WA wheat belt and maize grown in a Ferrosol in north-eastern NSW, based on data from unpublished trials (Van Zwieten, pers. comm; Murphy, pers. comm; David Hall, pers. comm.). Biochar was assumed to be applied at a rate of 10 t ha-1 every 10 years. The cropping systems were modelled as the average impact across the ten-year biochar application cycle.

The decomposition rates for biochars in the two soils, and their impact on native soil carbon were estimated from incubation studies (Keith et al., 2011; Singh et al., 2012). The quantity of C lost over 100

years through decomposition of biochar, and the change in soil carbon stock due to positive or negative priming, were included in the calculation of the net GHG impacts of biochar systems.

The assumptions for impact of biochar are listed in Table 3.

3.8. Uncertainty and sensitivity analysis

While some estimates of uncertainty for individual elements of the calculation are known with reasonable confidence, this does not apply to the elements with largest contribution to the GHG fluxes. We applied Monte Carlo analysis to quantify uncertainty using “best guess” distributions for each variable. However, the resulting probability distributions were so broad as to imply no significant differences between biochars and between crops. We believe this to be a misleading conclusion: lumping together the various causes for the variation in input variables gives an inaccurate impression of the uncertainty and its derivation. Some uncertainty derives from inherent variation in biological systems (for example, crop yield responses, nitrous oxide emissions from soil), and from factors for which there is limited knowledge (decay of biomass in landfill), and these are legitimately considered additive error. Other sources of variation in probable values for variables derive from intentional choice between options for operation – such as whether landfill gases are captured and flared, or used for electricity generation; while some derive from the situation, such as whether syngas from pyrolysis displaces gas or coal as the marginal conventional fuel in that location. The simple Monte Carlo approach implemented in SimaPro is unable to consider positive or negative correlations between variables, and thus overestimates uncertainty due to elements that vary simultaneously. For these reasons, error bars are not shown on the figures. Instead, sensitivity analysis was conducted to assess the influence of factors that were known to have wide ranges, or considered to be particularly uncertain. These factors included:

Landfill assumptions (the assumed rate of decomposition in landfill, recovery of methane and fate

of methane captured),

Nitrous oxide emissions from raw poultry litter,

Impact of biochar in nitrous oxide emissions from soil, and

Distance over which the feedstock is transported to the biochar plant.

Table 4 shows the values applied in the sensitivity analysis.

A final sensitivity analysis compared alternative options for the use of green waste:

1. Disposal in landfill

2. GW550, as described in Table 1, applied to maize

3. GW450, as described in Table 1, applied to maize

4. “Max syngas” (pyrolysis set to produce syngas as the major product, so limited biochar yield): 5%

biochar yield, biochar applied to maize; 30% C in biochar; syngas output 9.2 GJ t-1 dry feedstock

5. Mulch A: green waste shredded, transported 20 km and spread as mulch, assumed to decay in less

than 100 years (so no long term carbon storage included), releasing CH4 and N2O in accordance

with composting assumptions in (Department of Climate Change and Energy Efficiency, 2012)

6. Mulch B: as for Mulch A, using emissions factors for CH4 and N2O from (Andersen et al., 2010).

7. Burn: green waste burned at site of collection, releasing CH4 and N2O in accordance with savannah

burning assumptions in (Department of Climate Change and Energy Efficiency, 2012).

Table 3: Assumptions for the impacts of biochar

Biochar feedstock

Effect of biochar application Poultry litter 450

Poultry litter 550

Green waste 450

Green waste 550

Wheat straw 450

Wheat straw 550

Maize in Ferrosol

Δ Crop Yield (fraction of reference)

1.252 1.25 1.13 1.13 1.13 1.13

Δ P Fertiliser (fraction of reference)

0.83 0.83 0.90 0.90 0.90 0.90

Δ N fertiliser (fraction of reference)

0.83 0.83 0.88 0.88 0.88 0.88

Δ N2O emissions (fraction of reference)

0.75 0.75 0.75 0.75 0.75 0.75

Δ SOC (priming) tC ha-1

0.40 0.30 0.46 0.35 0.39 0.30

Δ Atrazine (cf. reference) 1.2 1.2 1.2 1.2 1.2 1.2

Biochar C fraction labile (proportion of total organic carbon - TOC)

0.032 0.007 0.002 0.001 0.005 0.003

Mean residence time recalcitrant biochar C (y)

155 504 402 1607 330 731

Mean residence time labile biochar C (days)

11 19 45 3 30 6

Wheat in Orthic Tenosol

Δ Crop Yield (cf. reference) 1.0 1.0 1.0 1.0 1.0 1.0

Δ P Fertiliser (cf. reference) 1.0 1.0 1.0 1.0 1.0 1.0

Δ N fertiliser (cf. reference) 1.0 1.0 1.0 1.0 1.0 1.0

Δ N2O emissions (cf. reference)

1.0 1.0 1.0 1.0 1.0 1.0

Δ SOC (priming) tC.ha-1

-1.3 -0.60 -0.49 -0.21 -0.78 -0.36

Δ Trifluralin (cf. reference) 1.1 1.1 1.1 1.1 1.1 1.1

Biochar C fraction labile (proportion of TOC)

0.032 0.007 0.002 0.001 0.005 0.003

Mean residence time recalcitrant biochar C (y)

129 420 335 1339 275 260

Mean residence time labile biochar C (d)

11 19 45 3 30 23

2 Relative to reference crop

Table 4: Factors varied in sensitivity analysis

Factor Base Min Intermediate Max

DOCf 0.09, GW550 on maize

0.04 0.23 0.47

Methane capture and fate (fraction CH4 captured; fraction of captured CH4 used for electricity generation)

0.24, 1.0

GW550 on maize

0.0, 0.0 0.75, 0.0 0.75, 1.0

Emissions factor for poultry litter 0.05425

PL550 on maize

0.0071 0.0245 0.085

Impact of biochar on nitrous oxide from soil (relative to N2O emissions from reference crop)

0.75

WS550 on maize

0.55 0.95 1.1

Transport distance Feedstock to plant (km)

20

GW550 on maize

0 10; 100 200

4. Results and discussion 4.1. Comparison of the Biochar production systems

To compare the Charcolator and Pacific Pyrolysis biochar production systems, both were modelled as SimaPro processes for the production of biochar from green waste feedstock. A network diagram showing the GHG emissions from production of biochar in the Charcolator, expressed in kg of CO2e, is shown in Figure 2. Note that the SimaPro network diagram shows the processes that have the greatest contribution to the impact factor, and does not show the many minor processes that contribute to the system. (For Figure 2 the cut-off for inclusion is set to 0.25%.) The production of 1kg of biochar in the Charcolator results in the net emission of 0.67 kg CO2-e. The largest removal is due to the carbon stored in the biochar, while the major emission is due to avoided landfill (i.e. carbon is no longer stored in landfill, and electricity is no longer generated from landfill gas).

The results for production of biochar in the Pacific Pyrolysis facility at 450°C are shown in Figure 3. In this case there is an added component of electricity generation from syngas produced during pyrolysis.

Figure 2: Network diagram illustrating emissions from production of 1 kg biochar from green waste in the Charcolator. Green flows indicate removals and red indicate emissions. The unit “p” for plant assembly represents the manufacture of one unit i.e. one biochar plant. Negative values represent removals or avoided emissions in comparison with the reference system. The top figure in each box is the quantity of that process that contributes to the 1kg biochar produced. The lower figure is the

emissions from that process. The emission values in each box do not sum to the total in the top box because processes that make minor contributions to the product are omitted from the figure (but not from the calculations).

Figure 3: Network diagram illustrating emissions from production of 1 kg biochar from green waste in the Pacific Pyrolysis facility at 450°C. Green flows indicate removals and red indicate emissions. Green flows indicate removals and red indicate emissions. Negative values represent removals or avoided emissions in comparison with the reference system. The top figure in each box is the quantity of that process that contributes to the 1kg biochar produced. The lower figure is the emissions from that process. The emission values in each box do not sum to the total in the top box because processes that make minor contributions to the product are omitted from the figure (but not from the calculations).

Production of biochar in the Charcolator is compared with the Pacific Pyrolysis plant, operating at either 450°C or 550°C, in Figure 4. For each case there is a substantial “avoided landfill” component, which results in a benefit due to methane avoided, but also an emission due to reduction in biomass carbon storage in landfill. The net emission of fossil CO2 in the Charcolator resulted from avoided electricity production in landfill, and fuel use in plant construction. There is a net negative emission for fossil CO2 from the Pacific Pyrolysis plant due to the generation of electricity from excess syngas, which displaces emissions from coal-fired power generation. Overall, GW550 had the lowest emissions per unit biochar, followed by GW450, with the greatest emissions produced by the Charcolator. The apparent advantage of GW550 over GW450 arises because more syngas is produced per unit biochar product at the higher pyrolysis temperature.

Figure 4: Comparison of GHG emissions for production of three biochars, per kg of biochar produced.

4.2. Comparison of target crops

Stage Two compared the six biochars in terms of the net GHG emissions from the production of 1kg of crop. Figure 5 illustrates this comparison for maize, and also shows emissions from the maize reference system. The green waste (GW) biochars led to net emissions similar to that of the reference. PL and WS biochars produced net negative emissions. The net effects of poultry litter (PL) and wheat straw (WS) biochars are quite similar, though the contribution from each factor differed: both sequestered a considerable proportion of the biochar-C, but WS gave greater reduction in fossil fuel, due to greater syngas yield, and PL avoided N2O emissions from application of raw PL (Figure 5b). Higher temperature biochars gave lower emissions due to higher yield of renewable electricity and greater stability of the biochar, leading to less decay. Lower temperature biochars were more effective in stabilising native soil organic carbon (SOC), but this phenomenon contributed little to the net GHG impact.

Figure 7 illustrates the net GHG emissions from use of the six biochars in wheat production, compared with the reference. The reference emissions from wheat are quite low, due to relatively low agrochemical inputs in wheat production and the low N2O emissions factor in this environment. The emissions from use of GW biochar exceed those of the reference system. Emissions are negative where PL and WS biochars are used.

A)

B)

Figure 5: GHG emissions from production of 1 kg maize, comparing six biochars and the reference cropping system (a) total values (b) showing individual greenhouse gases separately.

Figure 6: Emissions per process, in production of 1 kg maize, calculated for six different biochars

Figure 7: GHG emissions from production of 1 kg wheat, comparing six different biochars and the reference cropping system

4.3. Net effect of biochar utilisation in cropping systems

The net GHG emissions per unit of biochar ranged from net removal of 3.2 kg CO2-e to a net emission of 1.2 kg CO2-e (Figure 8) per kg applied biochar. The greatest emissions reduction occurred for maize treated with PL550 and WS550 biochars. The impacts of PL and WS biochars were reduced when applied to wheat, because biochar was assumed to have no impact on wheat yield, fertiliser requirement or nitrous oxide emissions. GW biochars applied to wheat and GW450 applied to maize led to net positive emissions, indicating that under the assumptions applied for landfill, the GHG outcome would be better if GW were deposited in landfill rather than utilised for biochar. The sensitivity to the landfill assumptions is addressed below.

A)

B)

Figure 8: Net GHG emissions from use of 1 kg biochar in a maize or wheat cropping system

4.4. Sensitivity analysis

The result for the net GHG impact of biochar made from green waste is highly sensitive to the assumptions for the avoided emissions from landfill; the fraction of the green waste that decomposes (Figure 9) and the proportion of methane produced that is captured and used to generate electricity (Figure 10). Variation in these factors can lead to prediction of substantial reductions or substantial increases in GHG emissions through use of green waste for biochar. The appropriate value for DOCf will vary with composition of green waste (proportion of leaf versus wood): where green waste consists largely of woody biomass, recent research (Ximenes et al, 2008; Wang et al, 2011) suggests that there is likely to be very little decomposition. The recovery of methane from landfill and the generation of electricity are dependent on landfill management. Captured methane may be flared, to oxidise it to CO2 that is released to the atmosphere, or it may be used as a renewable fuel, for heat of electricity. Operators of large landfill facilities in major centres are likely to invest in the infrastructure and plant to capture methane, and may be required to do so by regulation. Smaller regional landfills are less likely to capture and utilise methane.

Figure 9: Sensitivity to fraction of green waste carbon that decomposes in landfill, modelled for maize amended with GW550 biochar. Y-axis is the net GHG emissions from use of 1 kg biochar. X-axis labels indicate value of DOCf. The first value is the assumption for the base case.

Figure 10: Sensitivity to fraction of methane captured and fate of captured methane. Y-axis is the net GHG emissions from use of 1 kg biochar. X-axis labels indicate fraction of methane captured and fraction of captured methane used to generate electricity. Modelled for maize amended with GW550 biochar.

Figure 11: Sensitivity to emissions factor for raw poultry litter, modelled for PL550 applied to maize. Y-axis is the net GHG emissions from use of 1 kg biochar. X-axis labels indicate emissions factor in kg N2O-N.kgN

-1.

Figure 12: Sensitivity to the impact of biochar on soil nitrous oxide emissions, modelled for WS550 biochar applied to maize. Y-axis is the net GHG emissions from use of 1 kg biochar. X-axis labels indicate the relative emissions of N2O compared with the reference crop, on average across the 10 year biochar application cycle.

Varying the emissions factor for nitrous oxide from raw poultry litter across the range identified through expert consultation was found to vary the result by around 100% (Figure 11). Recent studies (Lukas Van Zwieten, pers. comm.) suggest that the emissions factor in this environment is likely to be close to the maxumum value assessed.

Varying the impactof biochar on nitrous oxide emisssions from soil, within the range anticipated led to a 20% variation in the mitigation value (Figure 12).

Figure 13: Sensitivity to feedstock transport distance, modelled for GW550 applied to maize. Y-axis is the net GHG emissions from use of 1 kg biochar.

The distance for the transport of feedstock to plant was varied between zero, the base model distance (20 km) and ten times the base model distance. Increasing the transport distance from 0 to 200 km reduced the mitigation benefit by 19% (Figure 13). Thus, the result was fairly insensitive to changes in the feedstock transport distance.

4.5. Comparison of alternative uses of biomass

The final question addressed through LCA was whether utilising biomass for biochar will give the best outcome in terms of GHG impact. To examine this question we compared using green waste biomass for biochar (produced at 550°C, applied to maize), with several alternative options for the disposal of green waste: landfill, with 24 % methane recovered and flared; pyrolysis with the yield of biochar minimised and syngas yield maximised; two mulch options, with different assumptions for non-CO2 emissions; and burning the biomass with no energy recovery.

The greatest emissions reduction occurred where green waste was assumed to go to landfill (Figure 14). Utilising green waste for biochar was 30% less effective in reducing emissions. Adjusting the pyrolysis plant so that syngas is the major product, used for electricity generation, gave a 60% lower emissions reduction compared with landfill. The mulch and burning options led to net positive emissions. Utilising biomass for Mulch A, applying emissions factors used in Australia’s national inventory for composting (Department of Climate Change and Energy Efficiency, 2012)), and burning, applying savannah burning emissions factors (Department of Climate Change and Energy Efficiency, 2012)), led to small positive emissions. Using emissions factors that are more typical of published data for compost emissions (Boldrin et al., 2009; Andersen et al., 2010), led to more substantial emissions.

Bearing in mind the strong influence of landfill assumptions on the LCA results (Figure 9; Figure 10), it is clear that the analysis of the impact of alternative disposal options for green waste (Figure 14) will generate quite different results with different landfill assumptions. Factors such as the reference electricity source will also have a strong influence (Cherubini et al., 2009). Note that the GHG emissions impact for GW450 and GW550 shown in Figure 14 differ markedly from Figure 5 because Figure 14 does not include the impact of avoided landfill in the calculated emissions from the biochar systems but instead shows emissions from landfill separately.

Figure 14: GHG impact of alternative options for the disposal of 1t green waste

The major contribution to GHG emissions reduction from GW550 was derived from the stabilisation of carbon, while the impacts on nitrous oxide and fossil fuel emissions each contributed 15% of the reduction (Figure 14). However, we have demonstrated that the factors contributing to GHG outcomes vary between biochars and target crops, so this result should not be generalised. The emissions reduction from use of lower temperature biochar, GW450, was 10% less than GW550, due to faster turnover of biochar in soil, and reduced syngas yield in pyrolysis. 5. General discussion and conclusion

This study demonstrated that biochar can substantially reduce GHG emissions from crop production. However, the magnitude of the impact varies widely between biochars and target crops. The estimated impact ranged from emissions reduction of 3.2 kg CO2-e per kg biochar, for PL550 (Poultry Litter biochar made at 550 degrees °C ) applied to maize, up to an increase in emissions of 1.2 kg CO2-e per kg biochar for GW 450 (Green Waste biochar made at 450 °C) applied to wheat.

Greater benefits were seen from application of biochar to maize than to wheat, due to the assumed lack of response of wheat to biochar, and the greater emissions from conventional maize production compared with wheat, which are a function of the greater fertiliser inputs to maize production, and the higher nitrous oxide emissions due to the wetter climate in which it is grown.

Between feedstocks, poultry litter and wheat straw biochars had similar impacts, though the factors contributing to emissions reduction differed: for poultry litter, there was a substantial reduction in nitrous oxide emissions compared with using raw poultry litter as fertiliser; for wheat straw, the reduction in fossil fuel emissions from use of syngas for electricity generation was a significant contributor. For green waste biochar, stabilisation of biomass carbon was the major contributor to emissions reduction; reduced nitrous oxide and avoided fossil fuel emissions contributed similar, lesser benefit. Green waste biochar showed much lower net benefit compared with other biochars, and some green waste biochar scenarios actually increased emissions. The cause of this result was the assumed alternative fate of green waste: if it would otherwise have been deposited in landfill, where, according to recent research (Ximenes et al., 2008; Wang, 2011) little decomposition occurs, and if the methane generated was captured and used for electricity generation, then diverting green waste to biochar production was calculated to increase emissions. However, if the landfill had no methane capture then use of green waste for biochar was advantageous. If the alternative fate of

green waste was mulch or burning rather than landfill, then utilisation for biochar gave a strong advantage.

Features of the biochar itself had a lower impact on the LCA results than assumptions about the reference system. Biochars made at higher temperature gave a greater benefit than lower temperature biochars, due to their greater stability and higher syngas yield during pyrolysis. Biochar produced from the Charcolator had lower benefit because the syngas was not utilised beneficially. The Pacific Pyrolysis option that produced more electricity and less biochar had lower mitigation value, however if the unit was deployed in a situation where the heat could be used directly, such as in heating a poultry shed, for example, the “max syngas” scenario may become relatively more desirable.

The maximum mitigation, of 3.2 kg CO2-e for PL550 applied to maize, is equivalent to 1.38 kg CO2-e per kg feedstock, or 3.5 kg CO2-e per kg C in feedstock. In comparison, published LCA studies have estimated net emissions reduction for different biochar scenarios at 0.7–3.1 kg CO2-e per kg (dry) feedstock for biochar from residues (Gaunt and Cowie, 2009; Roberts et al., 2010; Hammond et al., 2011). The wide variation in these studies results from differences in the biochar scenarios (feedstock, design and scale of pyrolysis plant, displaced fossil energy source) and differences in assumed impacts of biochar (Cowie et al, 2012). Our result for PL550 applied to maize lies within the range of other studies, though some options considered here give lower mitigation, or even increased emissions.

Many factors in the analysis are highly uncertain, both with respect the impacts of biochar, and the emissions from the reference systems. Further research into the stability of biochar and its effect on nitrous oxide emissions is required to refine components of the model. Research into aspects of the reference systems – nitrous oxide and methane from handling and application of raw poultry litter; decomposition extent of green waste in landfill and fraction released as methane; nitrous oxide and methane emitted from compost and mulch. The establishment of medium and large scale pyrolysis facilities will allow more accurate assessment of the pyrolysis plant variables.

The wide variation in results between biochars and target crops, and the great sensitivity to the assumptions for the reference case, mean that LCA studies should be conducted for each specific situation in which biochar utilisation is being considered.

The LCA results reported here did not consider the significance of timing of emissions and removals of GHG from biochar systems. While conventional LCA does not consider time, it is becoming clear that this is an important aspect governing the climate change impacts of activities that alter fluxes of CO2 and CH4 (Cherubini et al., 2011; Brandão et al., 2012). Therefore future studies should apply approaches such as proposed by Cherubini et al.(2011) or Levasseur et al. (2010) that assess the cumulative radiative forcing impact of emissions of the various GHGs over a specified time period, to evaluate the significance of timing of emissions on the estimated climate change impacts of biochar systems.

6. References

Andersen J.K., Boldrin A., Samuelsson J., Christensen T.H., Scheutz C. (2010) Quantification of Greenhouse Gas Emissions from Windrow Composting of Garden Waste All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. J. Environ. Qual. 39:713-724. DOI: 10.2134/jeq2009.0329.

Boldrin A., Andersen J.K., Møller J., Christensen T.H., Favoino E. (2009) Composting and compost utilization: accounting of greenhouse gases and global warming contributions. Waste Management & Research 27:800-812. DOI: 10.1177/0734242x09345275.

Brandão M., Levasseur A., Kirschbaum M.F., Weidema B., Cowie A., Jørgensen S., Hauschild M., Pennington D., Chomkhamsri K. (2012) Key issues and options in accounting for carbon sequestration and temporary storage in life cycle assessment and carbon footprinting. The International Journal of Life Cycle Assessment:1-11. DOI: 10.1007/s11367-012-0451-6.

Cherubini F. (2010) GHG balances of bioenergy systems - overview of key steps in the production chain and methodological concerns. Renewable Energy 35:1565-1573. DOI: http://dx.doi.org/10.1016/j.renene.2009.11.035.

Cherubini F., Bird N., Cowie A., Jungmeier G., Schlamadinger B., Woess-Gallasch S. (2009) Energy- and greenhouse gas-based LCA of biofuel and bioenergy systems: key issues, ranges and recommendations. Resources, Conservation and Recycling 53:434-447. DOI: http://dx.doi.org/10.1016/j.resconrec.2009.03.013.

Cherubini F., Peters G.P., Berntsen T., StrØmman A.H., Hertwich E. (2011) CO2 emissions from biomass combustion for bioenergy: atmospheric decay and contribution to global warming. GCB Bioenergy 3:413-426. DOI: 10.1111/j.1757-1707.2011.01102.x.

Cowie, AL, Downie AE, George BH, Singh BP, Van Zwieten L, O'Connell D 2012 Is sustainability certification for biochar the answer to environmental risks? Pesquisa Agropecuária Brasileira 47 (5), 637-648

DAFWA. (2005) EXAMPLE WHEAT GROSS MARGIN Northam District, Western Australia Department of Agriculture and Food. http://www.agric.wa.gov.au/PC_91744.html?s=0 (last accessed 19March 2013)

Davis J., Haglund C. (1999) Life Cycle Inventory (LCI) of Fertiliser Production. Fertiliser Products Used in Sweden and Western Europe. Chalmers University of Technology.

Department of Climate Change and Energy Efficiency. (2012) National Inventory Report 2010 Australian national greenhouse gas accounts, Commonwealth of Australia, Canberra, Australia.

Dunlop M., Poulton P., Unkovich M., Baldoc k.J., Herr A., Poole M., O’Connell D. (2008) Assessing the Availability of Crop Stubble as a Potential Biofuel Resource, Global Issues. Paddock Action, Australian Society of Agronomy, Adelaide.

ecoinvent. (2009) Ecoinvent Database. http://www.ecoinvent.ch/ (last accessed 19March 2013) Forster P., Ramaswamy V., Artaxo P., Berntsen T., Betts R., Fahey D.W., Haywood J., Lean J., Lowe

D.C., Myhre G., Nganga J., Prinn R., Raga G., Schulz M., Van Dorland R. (2007) Changes in atmospheric constituents and in radiative forcing, in: S. Solomon, et al. (Eds.), The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. pp. 129-234.

Gaunt J., Cowie A.L. (2009) Biochar, greenhouse gas accounting and emissions trading, in: S. J. J Lehmann (Ed.), Biochar for Environmental Management: Science and Technology Earthscan. pp. 317-340

Global_Renewables. (2012) Diverting waste to an alternative waste treatment facility, Proposed Carbon Farming Initiative methodology. http://www.climatechange.gov.au/government/initiatives/carbon-farming-initiative/methodology-development/determinations/diverting%20waste%20to%20waste%20treatment.aspx (last accessed 19March 2013)

Hammond J., Shackley S., Sohi S., Brownsort P. (2011) Prospective life cycle carbon abatement for pyrolysis biochar systems in the UK. Energy Policy 39:2646-2655. DOI: http://dx.doi.org/10.1016/j.enpol.2011.02.033.

International Organization for Standardization. (2006) Environmental management — Life cycle assessment — Principles and framework, International Organization for Standardization, Switzerland. pp. 28.

IPCC. (2006) Agriculture, forestry and other land use, 2006 IPCC guidelines for national greenhouse gas inventories, Intergovernmental Panel on Climate Change IGES, Hayama, Japan.

Keith A, Singh B and Singh BP (2011). Interactive Priming of Biochar and Labile Organic Matter Mineralization in a Smectite-Rich Soil. Environmental Science & Technology 45, 9611-9618

Levasseur A., Lesage P., Margni M., Deschenes L., Samson R. (2010) Considering time in LCA: dynamic LCA and its application to global warming impact assessments. Environmental Science & Technology 44:3169-3174.

Lifecycle Strategies (2009) Australasian LCI library Version 2009. http://www.lifecycles.com.au/#!Australian%20Data%20&%20Tools/c1s7h (last accessed 19March 2013)

NSW DPI (New South Wales Department of Primary Industries) (2009a) Guide to tractor and implement costs http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0003/175494/177kw-engine.pdf

NSW DPI (New South Wales Department of Primary Industries) (2009b) Guide to header costs http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0006/219993/header.pdf

NSW_DPI. (2011) DRYLAND MAIZE (No-till, feed), Farm Enterprise Budget Series - North-East NSWSummer. http://www.dpi.nsw.gov.au/__data/assets/pdf_file/0003/175908/East-dryland-maize-12-13.pdf (last accessed 19March 2013)

Roberts K.G., Gloy B.A., Joseph S., Scott N.R., Lehmann J. (2010) Life cycle assessment of biochar systems: estimating the energetic, economic, and climate change potential. Environmental Science & Technology 44:827-833. DOI: 10.1021/es902266r.

Singh B.P., Cowie A.L., Smernik R.J. (2012) Biochar Carbon Stability in a Clayey Soil As a Function of Feedstock and Pyrolysis Temperature. Environmental Science & Technology. DOI: 10.1021/es302545b.

Wang X., Padgett, JM, De la Cruz, FB & Barlaz, MA (2011) Wood biodegradation in laboratory-scale landfills. Environmental Science and Technology 45:6864-6871. DOI: 10.1021/es201241g.

Wood S., Cowie A.L. (2004) A review of greenhouse gas emission factors for fertiliser production, IEA Bioenergy Task 38, Sydney, Australia. pp. 20.

Woolf D., Amonette J.E., Street-Perrott F.A., Lehmann J., Joseph S. (2010) Sustainable biochar to mitigate global climate change. Nature Communications 1:56. DOI: http://www.nature.com/ncomms/journal/v1/n5/suppinfo/ncomms1053_S1.html.

Ximenes F.A., Gardner W.D., Cowie A.L. (2008) The decomposition of wood products in landfills in Sydney, Australia. Waste Management 28:2344-2354. DOI: 10.1016/j.wasman.2007.11.006.

7. Acknowledgements

We thank Bhupinderpal Singh, Lukas Van Zwieten, Adriana Downie, David Lau, Jane Lynch, Jason Smith, David Hall and Daniel Murphy for supplying data. Leanne Orr and Jason Smith undertook preliminary modelling. This project received funding from the Australian Government under its Climate Change Research Program, the Grains Research and Development Corporation, and International Energy Agency Bioenergy Task 38 “Greenhouse Gas Balances of Biomass and Bioenergy Systems”.

Appendix A - Assumptions applied in life cycle assessment

A.1 Emissions factors

Input or process Unit Emissions factor (EF) Source

Diesel tCO2e/kL 3.2776 Lifecycle Strategies 2009

Natural gas kgCO2e/GJ 58.315 Lifecycle Strategies 2009

NSW grid electricity kgCO2e/kWh 0.9785 Lifecycle Strategies 2009

N2O – Dryland Wheat from synthetic fertilizer direct & indirect

tN2O-N/tN applied 0.004 DCCEE 2012

N2O – Northern NSW Maize cropping from synthetic fert

tN2O-N/tN applied 0.0243 DCCEE 2012

Poultry manure spread on field - N2O

tN2O-N/tN applied 0.0543 Lukas Van Zwieten 2012 pers com

Fertiliser manufacture - Urea tCO2e/t Urea 0.863 (Wood and Cowie, 2004), Table 6: (Davis and Haglund, 1999) ; Lifecycle Strategies, 2009

Fertiliser manufacture - Ammonium sulphate

tCO2e/t Ammonium Sulphate

0.4157 Lifecycle Strategies 2009

Fertiliser manufacture - MAP tCO2e/t MAP 1.6031 Lifecycle Strategies 2009

A.2 Maize production process assumptions

Product

Maize 3.75 t Maize yield from 1ha. NSW DPI 2012

Materials/fuels

Glyphosate 0.45*(1.5+1.2+1.2)

= 1.755

kg Active ingredient of Glyphosate 450g/l NSW DPI 2012

Herbicides 0.25*(2+1.2)

= 0.8

kg mix of paraquat 1.25g/cm3 diquat 1.25g/cm3 ref MSDS http://www.chemicalbook.com/ChemicalProductProperty_US_CB4384274.aspx

Seed Maize 12 kg NSW DPI 2012

Atrazine 0.6*3.2

= 1.92

kg conc of active agent 600g/l. NSW DPI 2011

Fungicides 0.05*12

= 0.6

kg Based on thiamethoxam treated seed at 50g/kg seed and seeding of 12 kg/ha NSW DPI 2012

Fertiliser - Urea (bulk)

210 kg NSW DPI 2011

Fertiliser-* Granulock SuPreme Z

60 kg NSW DPI 2011

Electricity/heat

Tractor:130-140 KW PTO (173-180 HP)

Tractor use, per litre diesel consumed

36*0.48

= 17.28

l NSWDPI 2009a 149 KW ENGINE , to get 36 l diesel per hr and NSW DPI maize GM for total 0.48hrs/ha operations excluding harvest.

Tractor use, per litre diesel consumed

36/5.82

= 6.1856

l harvesting from NSWDPI 2009b , to get 36 l diesel per hr, 5.82ha/hr and NSW DPI 2012 for harvest cost of $78.70/ha

Farm machinery and equipment

$4.6744

USD USA I/O Table. AUD$8.60 (2012) adjusted for inflation and exchange rate to 1998 USD$ value

A.3 Wheat production process assumptions

Product

Wheat 2.4 t Wheat yield from 1 ha. Gross margins guide 2005 Western Australia DAFWA 2005

Materials/fuels

Wheat 70 kg seed grain from own crop. DAFWA 2005

Urea 100 kg DAFWA 2005

Ammonium Sulphate 42 kg DAFWA 2005

Monoammonium phosphate 28 kg DAFWA 2005

Glyphosate 0.45 kg active agent conc 450g/l, DAFWA 2005

Active pesticide 2 kg DAFWA 2005

Herbicides 0.5 kg sprayseed (paraquat 135g/l, diquat 115g/l) 2l/ha, ref MSDS CSBP 28/1/2011, , MSDS Bayer 27/8/2010.

Dinitroaniline-compounds 1.5 kg Trifluralin 480g/l, 1.5l/ha, ref http://www.agtech.com.au/MSDS/APPARENTag/Trifluralin_480_msds.pdf.

MCPA 0.1 kg tigrex 250 g/l of MCPA 0.4 l/ha, refhttp://www.bayercropscience.com.au/resources/uploads/msds/file7390.pdf.

Fungicides 0.492 kg DAFWA 2005 applied rate of 400ml/ha, density of 1.23g/cm3 from http://www.google.com.au/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDwQFjAB&url=http%3A%2F%2Fwww.epa.gov%2Fopprd001%2Ffactsheets%2Findaziflam.pdf&ei=8bGbT4P3BemRiQfGoKXSDg&usg=AFQjCNHOw1RT0FwXy-ngaXfsW-yQPbsOSw, application rate from http://www.ospray.com.au/msds/images/stories/Fungicides/IMPACT_LABEL.pdf

Tractor use, per litre diesel consumed/AU 35.8 l DAFWA 2005 http://www.agric.wa.gov.au/PC_91744.html, Total machinery cost of $48.70 per Ha, then NSWDPI 2009a 224 KW ENGINE , to get 35.8 l diesel per ha for this number of operations and cost.