Greenhouse gas implications of mobilizing agricultural biomass for energy: a reassessment of global potentials in 2050 under different food-system pathways

Dedicated energy crops, specifically fast-growing trees (short rotation coppice) and energy grasses like Miscanthus (summarized as lignocellulosic or 'second generation' energy crops) appear as a favourable biomass resource for two reasons: (1) They provide relatively high yearly biomass yields per unit area. (2) Establishing such plantations on existing agricultural land typically leads to rising natural C stocks (i.e. C benefits additional to those from fossil fuel displacement) rather than a reduction of (possible long-term) C stocks, as is the case when raising harvests in forests.

However, recent studies have highlighted the C benefits achievable through re- or afforestation (Kreidenweis et al 2016, Griscom et al 2017, Erb et al 2018, Fuss et al 2018, Houghton and Nassikas 2018, Zhang et al 2018, Bastin et al 2019, Braakhekke et al 2019). This option of using spare land for climate-change mitigation, be it through targeted afforestation or simply by allowing agricultural land to regrow vegetation ('regrowth case'), has been acknowledged as natural alternative to producing biomass for bioenergy (Haberl et al 2012, Kalt et al 2019). We here consider vegetation regrowth as default counterfactual scenario to energy crop production, and calculate GHG costs as the difference between C stock changes achieved through regrowth (without harvesting) and C stock changes resulting from establishing energy crop plantations (which are harvested periodically). Since aboveground biomass C stocks in energy plantations are largely depleted with every harvest (to supply biomass for fossil fuel displacement) whereas those in the regrowth case continue to rise until they reach a saturation level (unless disturbed by wildfire or extreme weather events), the GHG costs of energy crops are generally larger than zero⁴ . GHG costs generally refer to emissions or savings per year; C stock changes are calculated as annual averages during 2012 to 2050 by calculating cumulative values and division by the number of years of our timeframe.

We consider regionally specific energy crop yields, dependence on soil types, climatic conditions and the type of 'natural' vegetation in the respective area and determine representative values for each world region and type of agricultural land by calculating weighted averages on the basis of spatially explicit data. For a detailed description of the methodological approach, see Kalt et al (2019) and SI.

The land area available for energy crops in a specific scenario strongly depends on how much grazing land is managed/utilized (see IRENA 2018). We do not allow agricultural land (cropland and grazing land) to encroach into forests because maintaining forest areas is a core environmental objective for various reasons and freeing up land through deforestation is not compatible with our ambition to determine sustainable potentials. However, additional cropland can be made available by land-use change of suitable grazing areas (referred to as highly productive or 'class 1' grazing land' Erb et al 2007). All 'grazing land' is assumed to contribute to feeding livestock, although in some places with very low grazing intensity (stocking density). Grazing intensity is calculated as the fraction of aboveground plant growth fed to livestock (Petz et al 2014, Fetzel et al 2017a). Due to the consistent biomass and land balance approach, all land areas classified as 'degraded', 'unused' or 'wasteland' in other bioenergy studies are considered.

We calculate energy crop potentials under three different premises (figure 2): (a) no grazing intensification; (b) grazing intensification on highly productive sites; and (c) universal grazing intensification. The topmost panel shows a simplified representation of the initial situation, where agricultural land consists of cropland, highly productive and low-productivity grazing land⁵ . This initial situation corresponds to the base year 2012 and is calibrated to actual supply and consumption data. Under (a), cropland only becomes available for energy crops if the cropland area required to satisfy food demand decreases⁶ . In the counterfactual case ('regrowth case'), the same area that is available for energy crops is assumed to regrow vegetation that is not harvested. Under the assumptions (b) and (c), the counterfactual cases are characterized by more complex considerations, because the assumption that the same (highly productive) areas as in the bioenergy case would be set free in the regrowth case under intensification pressure would be against economic rationale: in order to free up land for energy crop production, grazing can be intensified up to a maximum sustainable level. This could happen either on highly productive grazing land only (figure 2(b)), or universally (figure 2(c)). In the latter case, grazing is shifted from highly productive land to less productive, in order to maximize the area for energy crops. The counterfactual (regrowth) cases are shown below the respective 'bioenergy cases': here, increased grazing intensities on highly productive sites set free less productive areas (which are least profitable), facilitating vegetation regrowth. Regrowth areas are larger than energy crop areas, but naturally also less productive, i.e. accumulate less C in biomass than more productive land planted with energy crops in the counterfactual scenario would absorb.

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With regard to energy crop yields, we assume the regionally specific values according to Kalt et al (2019). With scenario-specific global weighted averages around 10 tonnes dry mass per hectare and year, these yields are in the medium range of literature estimates (see IEA Bioenergy 2007, Berndes et al 2003, Slade et al 2011, 2014, and the SI to Kalt et al 2019).

Assessments of crop residue potentials are usually based on crop production, crop-specific residue-to-product ratios and maximum sustainable removal rates. The assumed sustainable removal rates vary widely: a literature review in Scarlat et al (2010) shows values ranging from 15% to 82% (most values being in the range of 30 to 60%), depending on crop type and tillage practices. Bentsen et al (2014) cite rates 'between nothing to everything, with a trend towards recovery rates [...] between 25% and 60%'. The literature is, however, inconclusive in terms of crop-specific sustainable removal rates, and IPCC (2006b) methods suggest that the amount of residue input to (or removal from) fields is just one factor affecting SOC stocks but generally has an influence on SOC (Monforti et al 2015).

We here consider sustainable removal rates of 40% ('low estimate') and 60% ('high estimate'). We use IPCC (2006b) Tier 1 default methods and parameters to determine the loss of SOC resulting from residue removal for bioenergy (tillage practices are assumed to remain unchanged). This SOC loss is part of the GHG costs of crop residue potentials and depends on site-specific parameters like soil type and climate (see SI for detailed methods). GHG emissions from additional fertilizer demand required for a balanced nitrogen cycle are also taken into account (i.e. upstream emissions of synthetic fertilizer production, as N₂O emissions from fertilizer application are offset by reduced N₂O emissions from residues left on the field). For wet residues (leaves etc from pulses, roots and vegetables), which are assumedly always converted to biogas in case of energy use, we further consider methane leakage in anaerobic digestion plants as GHG costs. Dry residues (straw from cereals, oilcrops etc) are assumed to be used in combustion plants, causing CO₂ emissions that correspond to the CO₂ sequestration during plant growth, i.e. are not assumed to bear additional GHG costs.

In the literature it is sometimes suggested that since SOC stocks are more sensitive to tillage practices than residue removal (Daioglou et al 2016), C stock impacts can be offset by adopting SOC-enhancing management practices. This, however, bespeaks a flawed counterfactual assumption: changing tillage practices is an option for raising C stocks in soils regardless of whether residues are removed for energy or not. For assessing the GHG impact of removing residues for energy, we must assume the same tillage management in the 'bioenergy case' as in the counterfactual case. Otherwise, we would disregard opportunity GHG costs.

Following common practice (e.g. Scarlat et al 2010, Searle and Malins 2015), non-energy uses of residues (mainly feed and animal bedding) are prioritised over bioenergy. Hence, only the difference between the amounts determined from maximum removal rates and demand for non-energy uses is considered as bioenergy potential. Assuming that current practices in residue use (specific to each world region, based on Krausmann et al 2008) are maintained, this considerably limits the quantities available for bioenergy.

Bioenergy potentials from livestock manure are calculated as biogas potentials using specific biogas yields for poultry, pig and ruminant manure (see table 1). We estimate theoretical potentials (all manure excreted by livestock) as well as technical potentials, assuming two different limiting factors: first, we assume that only manure from indoor housing is available for anaerobic digestion, and second, we assume that this is only the case for manure from intensive livestock systems. The respective world region specific shares are adopted from the data basis of the GAINS model (Amann et al 2011, Winiwarter et al 2018) and Robinson et al (2011) and Lowder et al (2016), respectively, and are provided in the SI.

With regard to GHG costs or implications, we consider CH₄ and N₂O emission reductions resulting from default manure management being replaced by anaerobic digestion, and methane leakage in anaerobic digestion plants. With regard to management systems, we differentiate between pasture, liquid and solid systems and apply emission factors according to Winiwarter et al (2018) (N₂O) and the GLEAM model (FAO 2018a) (CH₄). Data on the distribution among systems are adopted from the GAINS model and are specific for each world region.

Two particularities about the GHG costs of manure are noteworthy: First, due to the fact that avoided emissions clearly exceed methane leakage, the GHG costs are negative (i.e. GHG savings). Second, in contrast to the GHG costs of energy crops and residues, these GHG savings are usually taken into account in life-cycle assessments.

The following table summarizes the main influencing parameters on the availability and GHG implications of mobilizing each resource. Moreover, our modelling approaches and data sources for calculating availability and GHG impacts are specified.

For the three scenarios based on FAO projections, the agricultural bioenergy potentials do not differ significantly; especially not with regard to crop residue and manure potentials (figure 3). In the BAU scenario, which is based on the narrative of the 'SSP2 scenario' (O'Neill et al 2017) and assumes failure in addressing challenges for food access and stability (FAO 2018b), the energy crop potentials without grazing intensification are close to 20 EJ/yr and about twice as high as in the 'Towards sustainability' (TSS) and the 'Stratified societies' (SSS) scenario. Grazing intensification potentials are similar in BAU and TSS (up to 85 EJ/yr), but significantly lower in SSS (up to 69 EJ/yr). The reasons are higher food crop yields in BAU as compared to TSS and less meat-rich diets (as compared to SSS) in highly developed world regions (see SI), resulting in more spare cropland in 2050. Under the assumption of global convergence to a healthy diet, energy crop potentials are significantly larger than in the other scenarios.

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Energy production potentials from manure reflect the structure of livestock systems and differences between the scenarios in animal product consumption: the total amount of manure corresponds for close to 20 EJ/yr in BAU and SSS. About half of that is attributable to indoor housing and one fourth to intensive systems. In TSS, which assumes 'virtuous social, environmental and economic dynamics' and 'fairly generalized [food] equity' (FAO 2018b) and even more the healthy diet scenario, animal product consumption declines in highly developed and increases in least developed regions. In total, the manure potential in these scenarios is lower than in BAU and SSS, and shifted regionally and in terms of livestock systems (lower proportion of intensive systems).

Crop residue potentials are about 20 EJ/yr in all FAO-based scenarios if 60% sustainable removal rate are assumed and less than 4 EJ/yr for a removal rate of 40%. Due to generally lower crop production in the 'healthy diet scenario', this scenario shows a lower residue potential despite a reduced residue demand for feed and animal bedding. All resources combined, agricultural biomass potentials are lowest in the 'Stratified societies' scenario, based on a narrative of self-protected elites, failure in conserving natural resources and mitigating climate change, increased poverty, food insecurity and poor nutrition.

Results on the level of world regions are provided in the supplementary information.

By associating the different types of biomass potentials with their respective GHG costs (see table 1), ranking them in ascending order and drawing them as stepped curves, we arrive at the GHG-cost supply curves shown in figure 4. The contribution of each of the 11 world region is shown individually, so the curves are composed of up to 11 'steps' (each step represents one world region; potentials may be zero in individual regions). GHG costs are determined as average values per energy unit, i.e. as total difference in GHG emissions to the counterfactual case divided by the total potential.

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The GHG costs of energy crops are typically low if no grazing intensification is assumed (figure 4(a)). Through intensification, larger potentials are made available, but usually also at higher GHG costs: apart from intensification itself (which leads to C stock loss on grazing land), claiming highly productive areas for energy crops shifts land-use patterns in a way that is often (albeit not necessarily) detrimental to C balances in the 'bioenergy case', as compared to the 'regrowth case'. The numerical results depend on numerous parameters, e.g. initial and maximum grazing intensities on the region's grazing areas, the area distribution among grazing classes (see SI) and influencing factors on C stock changes resulting from land-use change at a specific site (e.g. soil type, climate zone, speed of C sequestration in vegetation). This explains the wide range of GHG costs among scenarios and the fact that changes in GHG costs from one scenario to another are not uniform. Nevertheless, it can be concluded that intensification, while making area available for energy crops, also leads to increasing GHG costs. Full exploitation of energy crop potentials therefore appears as inefficient in terms of climate effects, as GHG costs are partly within the range of combustion emissions of the most relevant fossil fuels (natural gas: 56 Mt CO₂e/EJ; lignite: 101 Mt CO₂e/EJ (IPCC 2006a)). The curves illustrate that if the missed opportunity of vegetation regrowth (i.e. opportunity GHG costs) is factored in, energy crop potentials start at GHG-costs of 20 Mt CO2e/EJ and may easily rise to much higher values.

Crop residues are usually more favourable in terms of GHG costs (figure 4(b); note the different scaling of axes). Differences in GHG costs between world regions and scenarios are mainly due to regionally diverse shares of crops⁷ and different soil and climate types affecting the extent of soil C stock loss when residues are removed for bioenergy. Typical GHG costs of crop residues are estimated between 10 and 25 Mt CO₂e/EJ. Relatively high GHG costs of about 50 Mt CO₂e/EJ in the 'low estimate' (i.e. 40% removal rate) occur for low-yielding crops with wet residues in developing countries and are likely worst-case estimates.

Livestock manure is clearly most beneficial in terms of GHG implications (figure 4(c)), because considerable quantities of CH₄ and N₂O emissions are avoided when anaerobic treatment is substituted for conventional manure management systems. The size of the potential is, however, relatively small when limitations to actual feasibility are considered; the total amount of livestock manure certainly has only illustrative character. Even the 'indoor potential' is not considered to be fully exploitable, given the large contribution of small-scale farms with indoor housing in developing countries. The limitation to manure from intensive farming is considered a reasonable approximation of a techno-economically feasible potential of biogas from manure if appropriate support schemes are in place; economics are a significant barrier to utilizing the energy and climate mitigation potential of livestock manure (see Einarsson and Persson 2017, Scarlat et al 2018 for Europe).

In the case of manure, differences in GHG costs between world regions and scenarios originate from different distributions among animal species, intensities, housing and manure management systems. The highest average GHG savings are possible in industrialized world regions where liquid manure storage systems are common (e.g. Western Europe, North America).

Figure 5 aggregates GHG-cost supply curves from each of the three biomass sources. We differentiate between a 'high' (using the maximum potential estimates in each case) and a 'low mobilization' case (see figure caption). This representation emphasizes the differences in sizes and GHG costs of biomass potentials, especially the dominance of energy crops in the high mobilization case. Moreover, the figure illustrates that without intensification (associated with GHG costs), the global agricultural biomass potentials are quite moderate and cannot cover the additional biomass consumption projected in ambitious climate mitigation scenarios (see figure 1).

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Figure 6 illustrates the global structure of agricultural land use in 2012 and 2050. The figure includes the 'bioenergy case' as well as the counterfactual 'regrowth case', and illustrates how the mechanisms described in figure 2 manifest in each scenario. Without grazing intensification, cropland areas of 580 000–3130 000 km² are available for bioenergy.⁸ Under universal grazing intensification, the available area extends to 4010 000–7770 000 km².

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By comparison, up to 15 240 000 km² of agricultural land (mainly grazing areas) could revert to wilderness until 2050 under the land sparing rationale of the 'regrowth case'. This could contribute to an unprecedented trend reversal in human appropriation of natural resources and regeneration of biodiversity and ecosystem services. This highly relevant trade-off inherent to large-scale bioenergy deployment is often overlooked.

Whether opportunity GHG costs need to be factored in or not depends on the scope and system boundaries of the respective analysis or modelling approach. We observe that agricultural intensification freeing up agricultural land for energy crops is often an ex-ante assumption, but there are no considerations of alternative uses of freed-up land. In this case, it is correct and important to factor in opportunity GHG costs of vegetation regrowth, as energy crop production eliminates GHG savings that would otherwise occur. This is why opportunity GHG costs are by default included here. In other instances, it is correct to disregard opportunity GHG costs; for example within an integrated assessment model that adequately reflects GHG effects of intensification, implications of changes in grazing pressures etc, and further includes vegetation regrowth as a land-based mitigation option. In this case, the (direct) GHG costs of energy crops are usually negative because establishing perennial energy plantations on grassland or cropland with annual crops leads to C stock gains in soil, biomass and litter (see SI).

If vegetation regrowth is considered as counterfactual scenario to energy crop production, it could be assumed that the same area should be assumed for energy crop production and vegetation regrowth. However, counterfactual scenarios should represent the most likely situation that would occur in the absence of the considered events (the factual case; see Parish et al 2017); it is highly unlikely that vegetation regrowth would occur on the highly productive land where profitable energy crops cultivation is possible. It is much more likely that low-productivity areas would be abandoned, while highly productive areas are used for food or feed production. This is the reason for different land-use patterns in the factual ('bioenergy') and counterfactual ('regrowth') cases illustrated in figure 1. It implies that regrowth areas are usually larger than energy crop areas (see figure 6), but at the same time less efficient in accumulating C. This is adequately reflected in our calculations, because each productivity class has their specific C accumulation dynamics, based on their distributions across climate zones, soil types and ecological zones (see SI).

However, for illustrative purposes we also calculated the GHG costs of energy crops under the assumption that regrowth occurs on the same areas where energy crops are cultivated. Under this assumption, the global weighted average GHG costs are 40%–65% lower than under default assumptions (see SI). Ranges on world regional level are much larger, reflecting the different patterns in land distribution, C accumulation dynamics and livestock densities.

Table 2 shows that our results regarding biomass primary energy potentials are largely in agreement with the low to medium estimates in literature. Due to the large number of original assessments of global biomass potentials as well as review articles, this comparison is limited to reviews and recent studies that provide valuable comparable values on global scale or for Europe/EU. For more comprehensive reviews of global biomass potential assessments, we recommend IPCC (2014) and Slade et al (2014).

With regard to cropland availability and energy crop potentials, our approach of assuming different levels of grazing intensification helps explaining the exceedingly wide ranges in the literature. Our energy crop potentials without grazing intensification are partly lower than the ranges according to review studies, indicating that intensification is a basic assumption in most potential assessments. However, some previous studies have even reported zero potential for dedicated energy crops (see Edenhofer et al 2011, Roth et al 2018). With regard to the highest estimates in literature, we conclude that they are not compatible with current FAO projections regarding food and feed crop yields and diets. Expectations on future energy crop yields are intrinsically uncertain and appear as a main contributing factor to vast differences in biomass potential assessments (see Searle and Malins 2014, 2015, Slade et al 2014). With weighted global averages of about 10 tonnes dry matter per hectare, our assumptions are in the medium range of literature assumptions (cf IEA Bioenergy 2007).

Our results for crop residues are also well comparable to previous assessments on global and European/EU scale: For the EU, they are in good agreement with Scarlat et al (2010). Compared to Scarlat et al (2019) on European scale and Daioglou et al (2016) on global scale, our low estimates appear quite conservative. Our conclusion is that BioBaM assumes higher shares of residues being used in the livestock sector, because our sustainable removal rate in the low estimate (40%) is not exceptionally conservative.

The result of a previous assessment for 2050 with a precursor version of BioBaM (Haberl et al 2011) was 64–161 EJ of agricultural biomass per year (excluding manure). The upper value refers to a universal 'fair and frugal' diet, i.e. a global convergence scenario similar to 'BAU with healthy reference diet'. Despite entirely revised input data and slightly different assumptions regarding grazing intensification, the results for energy crops are surprisingly consistent (about 130 EJ/yr, assuming universal grazing intensification).

Potential assessments must be explicit on underlying assumptions and narratives (regarding intensification, socio-economic developments etc), in order to facilitate realistic visions about the future role of bioenergy. Requirements for biomass mobilization, especially the need for grazing intensification, are often insufficiently addressed in the literature, contributing to confusion regarding large ranges of biomass potentials and their causes. Furthermore, notions about currently unused land are questionable. Many studies found that these lands may in fact be under more or less intensive use, in particular in developing countries with widespread pastoral livestock systems (Young 1999, p 199, Lambin and Meyfroidt 2011, Nalepa and Bauer 2012, Baka and Bailis 2014, Exner et al 2015, Erb et al 2016a, Bartels et al 2017). The assumptions that energy crops would be cultivated in low-productivity areas (marginal land) are also dubious. Economic rationale and empirical evidence (see Novo et al 2010, Rathmann et al 2010, Harvey and Pilgrim 2011, Piroli et al 2012, Lapola et al 2014, Rajcaniova et al 2014) suggest that energy crop production will (i) enhance competition for existing cropland and (ii) foster intensification and cropland expansion into highly productive grassland.

GHG-cost curves convey additional information that, in the context of climate mitigation, is just as crucial as primary bioenergy potentials. Information on the GHG implications of mobilizing biomass potentials is of paramount importance for developing prudent climate and energy strategies and avoiding GHG leakage effects. Therefore, we suggest implementing GHG-cost curves in energy models and integrated assessment models that do not (or insufficiently) cover GHG emissions from agriculture and land-use.

To quantify net GHG savings achievable with different biomass resources, we can compare GHG-cost curves with combustion emissions from fossil fuels. Combustion emissions from biomass may be disregarded in this context because C cycles of agricultural biomass are brief; and lasting effects (C stock changes) are considered in the GHG costs. Figure 7(a) illustrates that the GHG costs of agricultural biomass in the BAU scenario are mostly lower than combustion emissions of fossil fuels. However, differences among biomass fractions are considerable, and systemic effects can lead to GHG costs that even exceed fossil fuel combustion emissions⁹ . Marginal net GHG costs from agricultural bioenergy may be notably lower or higher than the avoided fossil fuel emissions depending on the amount of biomass to be used.

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To determine actual net GHG savings from bioenergy (as compared to different fossil fuels), two more aspects must be considered: first, upstream emissions from fossil fuels as well as biomass production and supply that are not considered in our GHG costs (e.g. transport, storage). Depending on the respective supply chains, fossil fuel deposits etc, upstream emissions can be considerable, as figure 7(b) illustrates. And second, this comparison referring to primary energy disregards differences in subsequent conversion processes to final or useful energy. Bioenergy technologies usually have lower conversion efficiencies than fossil fuel-based counterparts. Thus, a GHG comparison on the level of primary energy, as shown in figure 7, is of limited significance. A thorough analysis of the net GHG balance of agricultural bioenergy with consideration of the various biomass conversion paths and their fossil-based counterparts is, however, beyond the scope of this work (see Kalt et al 2019).

Since average GHG emissions/sinks from C stock changes are sensitive to the considered timeframe, the time reference of GHG-cost supply curves is of particular importance. The counterfactual scenario of energy crop cultivation, vegetation regrowth, is characterized by declining annual C benefits, as biomass accumulation eventually becomes saturated and net C uptake converges towards zero (see Kalt et al 2019). Hence, the GHG costs of energy crops depend on the considered timeframe and typically decrease for longer timeframes.