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Nat Food
Nat Food
Nature Food
2662-1355
Nature Publishing Group UK London

39313684
1039
10.1038/s43016-024-01039-1
Article
Enhanced agricultural carbon sinks provide benefits for farmers and the climate
http://orcid.org/0000-0001-5702-8547
Frank Stefan frank@iiasa.ac.at

1
Lessa Derci Augustynczik Andrey 1
http://orcid.org/0000-0001-5551-5085
Havlík Petr 1
http://orcid.org/0000-0003-3863-8731
Boere Esther 12
http://orcid.org/0000-0001-6515-1174
Ermolieva Tatiana 1
http://orcid.org/0000-0002-6835-9883
Fricko Oliver 1
Di Fulvio Fulvio 1
http://orcid.org/0000-0002-2576-9217
Gusti Mykola 1
http://orcid.org/0000-0002-9241-8628
Krisztin Tamas 1
Lauri Pekka 1
http://orcid.org/0000-0001-8167-9403
Palazzo Amanda 1
http://orcid.org/0000-0002-1990-841X
Wögerer Michael 1
1 https://ror.org/02wfhk785 grid.75276.31 0000 0001 1955 9478 International Institute for Applied Systems Analysis, Laxenburg, Austria
2 https://ror.org/008xxew50 grid.12380.38 0000 0004 1754 9227 Department of Environmental Geography, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
23 9 2024
23 9 2024
2024
5 9 742753
14 11 2023
7 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ 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 Commons 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/.
Carbon sequestration on agricultural land, albeit long-time neglected, offers substantial mitigation potential. Here we project, using an economic land-use model, that these options offer cumulative mitigation potentials comparable to afforestation by 2050 at 160 USD2022 tCO2 equivalent (tCO2e−1), with most of it located in the Global South. Carbon sequestration on agricultural land could provide producers around the world with additional revenues of up to 375 billion USD2022 at 160 USD2022 tCO2e−1 and allow achievement of net-zero emissions in the agriculture, forestry and other land-use sectors by 2050 already at economic costs of around 80–120 USD2022 tCO2e−1. This would, in turn, decrease economy-wide mitigation costs and increase gross domestic product (+0.6%) by the mid-century in 1.5 °C no-overshoot climate stabilization scenarios compared with mitigation scenarios that do not consider these options. Unlocking these potentials requires the deployment of highly efficient institutions and monitoring systems over the next 5 years across the whole world, including sub-Saharan Africa, where the largest mitigation potential exists.

Carbon sequestration on agricultural land holds great promise for combating climate change. This study estimates the mitigation potential of three sequestration practices—soil carbon enhancement, biochar application on cropland and silvo-pastoral systems—while identifying cost-effective mitigation portfolios.

Subject terms

Climate-change mitigation
Environmental impact
Economics
Agriculture
European Union’s H2020 Project ENGAGE (Grant Agreement No. 821471), NAVIGATE (Grant Agreement No. 821124), BRIGHTSPACE (Grant Agreement No 101060075), LAMASUS (Grant Agreement No. 101060423), and ForestNavigator (Grant Agreement No. 101056875).issue-copyright-statement© Springer Nature Limited 2024
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The food system, including its value chains, is one of the key sources of greenhouse gases (GHG) and is estimated to account for one-third (16–18 GtCO2 equivalent (GtCO2e) per year) of global anthropogenic GHG emissions1,2 of which 11.9 ± 4.4 GtCO2e per year are attributed to agriculture, forestry and other land uses (AFOLU) over the period 2010–20193. Given the importance of agriculture as a driver of tropical deforestation4,5 and its substantial share in current and projected emissions6,7, the speed and ambition of climate action in the sector is vital to stabilize the climate. Not only will it determine the level of residual GHG emissions and, hence, the requirement for negative emissions once carbon neutrality has been achieved8, but lack of mitigation action in the food system may preclude reaching the 1.5 °C target in the first place9,10.

Despite large cost-effective abatement potentials in agriculture6,11–13, there persists a reluctance of countries to adopt mandatory price-based mitigation policies in agriculture10,14. Even though many countries refer to agriculture in their nationally determined contributions, only New Zealand was planning to include agricultural emissions in its emission trading scheme as of 202510,15 but recently changed its plans. Aside from challenges related to governance and high transaction, monitoring, reporting and verification costs16–18, concerns related to food security and increasing food prices if stringent agricultural mitigation efforts were adopted in vulnerable regions of the Global South19–21, and also, concerns related to poverty have been raised22.

Climate-smart agricultural practices have the potential to generate a substantial carbon sink23–25. Enhanced CO2 sequestration from soil conservation practices on agricultural land, such as improved fertilizer, tillage and residue management, or cover cropping (0.7–2.5 GtCO2e per year), biochar application (0.3–1.8 GtCO2e per year) and agroforestry (0.4–1.1 GtCO2e per year) are considered promising mitigation options and economically viable at GHG prices up to 100 USD2015 tCO2e−1 (ref. 3). Besides the direct benefit for the climate, these options could help to alleviate other socio-economic and environmental challenges as well26. Increasing the carbon content in soils, for example, via biochar application or soil conservation practices, can increase crop productivity under certain conditions, especially on degraded soils27–32. Agroforestry may increase resilience to climate change impacts and, hence, improve food security33,34 but can at the same time provide additional biomass for energy uses, thereby reducing harvest pressure from managed forests35. Hence, promoting the widespread adoption of climate-smart agricultural practices while considering equity principles can contribute to the wider Sustainable Development Goals36.

Still, CO2 sequestration options on agricultural land and their co-benefits have not been considered in the global mitigation pathways reviewed under the 6th Assessment Report of the Intergovernmental Panel on Climate Change, and their abatement potential was only assessed in isolation from other sectors and from the market dynamics3. While studies explored their isolated technical and economic mitigation potentials in bottom-up assessments23–25,37, economic implications for farmers and market rebound effects across options remain under researched22,38,39. Yet, these economic dynamics are important to avoid over- and/or underestimation of mitigation potentials/costs and are a prerequisite for the inclusion of these mitigation options in Integrated Assessment Models (IAMs).

Here, we apply an economic land-use model (Global Biosphere Management Model, GLOBIOM)13,40 enhanced with a new set of CO2 sequestration options on agricultural land (Methods) to assess economic implications and identify the cost-effective GHG mitigation option portfolios for agriculture under current climatic conditions. We evaluate the importance of CO2 sequestration on agricultural land within the broader AFOLU sector by linking with a forest sector model (Global Forest Model, G4M)41,42. We consider three novel agricultural CO2 sequestration practices that are widely discussed in literature23–25,43: (1) soil carbon (SOC) enhancement in cropland and pastures (for example, different tillage, fertilization or crop residue management practices, and so on), (2) the application of biochar on cropland and (3) the expansion of silvo-pastural systems. We do not consider agroforestry systems on cropland to preclude potential trade-offs with crop production44,45. Our baseline scenario (‘baseline’) is based on the Shared Socio-Economic Pathway 246,47, which represents a middle-of-the road scenario with continuation of current trends (Supplementary Information). We then identify the cost-effective mitigation potentials considering market feedbacks and spillover effects across regions, sectors and mitigation options and quantify synergies and trade-offs of the assessed agricultural CO2 sequestration options with other economic and socio-economic outcomes. Our scenario assessment (Table 1) is built around two elements (Methods): (1) different GHG price trajectories that consider (‘agCO2’) or do not consider (‘default’) agricultural CO2 sequestration options under baseline bioenergy demands (these scenarios are used to estimate the cost-effective mitigation potentials) and (2) an alternative set of scenarios with enhanced biomass demands for bioenergy compatible with the 1.5 °C target (‘agCO2_bio’ and ‘default_bio’). These scenarios are used to investigate the role of agricultural CO2 sequestration options for achieving net zero AFOLU emissions in the context of the 1.5 °C target.Table 1 Quantified scenario matrix in GLOBIOM–G4M and its specifications

Scenario acronym	AGRI N2O and CH4	AGRI CO2	FOLU CO2	Bioenergy demand	
baseline	✗	✗	✗	Baseline	
default	✓	✗	✓	Baseline	
agCO2	✓	✓	✓	Baseline	
default_bio	✓	✗	✓	1.5 °C	
agCO2_bio	✓	✓	✓	1.5 °C	
GHG price trajectories are implemented on different GHG sources: AGRI N2O and CH4, nitrous oxide emissions from the application of organic and synthetic fertilizers and manure management, methane emissions from manure management, enteric fermentation and rice cultivation; AGRI CO2, carbon dioxide emissions/removals on agricultural land; FOLU CO2, carbon dioxide emissions/removals from land-use change and forestry.

Finally, to assess the robustness of our results, we conduct a sensitivity analysis where we test alternative parameterizations for the agricultural CO2 sequestration options in the model regarding the costs of adoption, the maximum adoption levels, the time it takes to reach the new carbon stock equilibrium following the adoption of a practice, the number of trees in silvo-pastures and the future demand for livestock products. The aim of this study is to bring new insights on the economic opportunities of agricultural CO2 sequestration options and related socio-economic trade-offs/synergies, and to provide a dataset for the integration of these options into IAMs.

Results

Carbon sequestration potentials on agricultural land

To explore the economic mitigation potential of three agricultural CO2 sequestration options within the overall land-based GHG mitigation potential, we contrast results of the ‘agCO2’ scenario with a GHG price on all AFOLU emissions/removals to a ‘baseline’ scenario without land-based mitigation efforts. Applying a linearly increasing GHG price that reaches 160 (80 and 240) USD2022 tCO2e−1 by 2050, we find that these options can provide a substantial carbon sink on agricultural land of up to 2.8 (1.6 and 2.5) GtCO2e per year by 2050. The smaller GHG mitigation potential at 240 USD2022 tCO2e−1 is explained by the enhanced uptake of CO2 sequestration practices and mitigation early on when moving towards higher GHG prices. At 160 USD2022 tCO2e−1 by 2050, the estimated CO2 sequestration potential on agricultural land represents already 36–41% of the expected total AFOLU GHG mitigation requirements including forests of around 7–8 GtCO2e per year in existing IAM based 1.5 °C climate stabilization scenarios8,48,49.

Globally, 1.1 GtCO2e per year (39%) of the CO2 sequestration potential on agricultural land is sourced from the adoption of practices on cropland and grasslands to enhance SOC, 1.0 GtCO2e per year (35%) from the application of biochar to cropland and 0.7 GtCO2e per year (26%) from transforming pastures to silvo-pastures by planting trees (Fig. 1d). Over time, we first observe the uptake of improved crop- and grassland management practices for enhanced SOC sequestration due to low adoption costs and its benefits for agricultural productivities that amplify the cost-efficiency of these measures, as well as the transformation of pastures to silvo-pastoral systems. Biochar, however, only becomes economically viable at higher GHG prices related to feedstock prices and the competition for biomass as input for the pyrolysis with other energy and non-energy uses.Fig. 1 Adoption of agricultural CO2 sequestration options in the ‘agCO2’ scenario with a GHG price of 160 USD2022 tCO2e−1 by 2050.

a, The GHG mitigation potential of different CO2 sequestration options. b, The area under the different options required to meet these mitigation potentials across world regions. c, Mitigation efficiency across options. d, Mitigation potential across sensitivity scenarios. ‘C-SEQ +’, more sustained (additional 10 years) CO2 sequestration for SOC sequestration options and silvo-pastures; ‘C-SEQ −’, more limited (10 years less) CO2 sequestration for SOC sequestration options and silvo-pastures; ‘COST +’, doubling of adoption costs for all agricultural CO2 sequestration options; ‘DIET’, reduced livestock consumption in Western countries; ‘BIO +’, increased bioenergy demand compatible with 1.5 °C target; ‘TREE 20%’, 20% of silvo-pasture system covered with trees instead of 25%; ‘TREE 15%’, 15% of silvo-pasture system covered with trees instead of 25%; ‘MAX 75%’, limit maximum adoption potential of agricultural CO2 sequestration options to 75% of default; ‘MAX 50%’, limit maximum adoption potential of agricultural CO2 sequestration options to 50% of default; NAM, North America; SAM, South and Central America; CIS, former Soviet Union; EUR, Europe; EAS, East Asia; SAS, South Asia; SEA, Southeast Asia; OCE, Oceania; MAF, Middle East and Northern Africa; SSA, Southern Africa.

Across world regions, sub-Saharan Africa is projected to have the largest cost-effective CO2 sequestration potential on agricultural land at a GHG price of 160 USD2022 tCO2e−1, followed by Latin America (Fig. 1a). While CO2 sequestration options targeting grasslands are primarily located in sub-Saharan Africa (37% of global silvo-pasture area and 32% of improved pasture SOC sequestration practices) and Latin America (20% of global silvo-pasture area and 17% of improved pasture SOC sequestration practices), improved cropland management practices are also largely adopted in South Asia (19% of global area) and North America (14% of global area). Overall, 27% of the cost-effective CO2 sequestration potential at 160 USD2022 tCO2e−1 is located in the Global North, compared with 73% in the Global South.

To realize the estimated mitigation potentials of 2.8 GtCO2e per year by 2050, globally 780 Mha of silvo-pastures are being established (43% of managed grassland), conservation agriculture is adopted on 900 Mha (53% of total cropland) and improved grassland SOC management practices on 1,100 Mha (60% of managed grassland) (Fig. 1b). In terms of sequestration per hectare (see Supplementary Table 3 for regional details), biochar delivers the highest return at global scale (Fig. 1c) generating on average carbon sinks of around 2.1 tCO2e ha−1, followed by silvo-pastures (0.9 tCO2e ha−1) and SOC sequestration practices (0.5–0.6 tCO2e ha−1).

To put the estimated adoption potentials into context with existing literature, Zomer et al.50 calculated that around half of the global agricultural area has fairly low biomass carbon stocks below 10 tC ha−1 that could be elevated substantially by (already incremental) increases in tree coverage51, and Prestele et al.52 estimate a technical adoption potential of conservation agriculture somewhere between 38% and 81% of arable land. Several studies also anticipate large potential of improved pasture management and restoration practices53–55 given that half of the global grassland area has been degraded to some extent56. Biochar is applied on some 460 Mha of cropland (28%) requiring 3,300 Mm3 biomass as input for the pyrolysis, which is mainly sourced from crop residues (54%) and silvo-pastures (34%) and, to a smaller extent, from forestry residues (10%) and short rotation tree plantations (2%). To put this into context, total biomass demand for bioenergy is estimated at 8,300 Mm3 in the baseline scenario and 15,900 Mm3 in a 1.5 °C compatible scenario by 2050.

The estimated cost-effective GHG mitigation potentials are interlinked with socio-economic and bio-physical scenario drivers and assumptions that affect the cost-efficiency of these options. For example, limiting the adoption potentials across options or varying the sequestration rates across mitigation options is found to have profound impact on the GHG mitigation potentials from agricultural CO2 sequestration options (from −27% to −50%, Fig. 1d). A scenario with dietary changes towards less livestock-based products in Western countries suggests more limited mitigation potentials within the agricultural sector for grassland SOC (−21%), cropland SOC (−18%) and silvo-pastures (−15%) due to the overall decline in agricultural production and abandonment of agricultural areas in response to the diet shift (though delivering additional GHG mitigation of non-CO2 gases and enhanced FOLU CO2 sequestration). If such a diet scenario is combined with 1.5 °C compatible bioenergy demands, total CO2 sequestration on agricultural land is even more reduced (−29%). To achieve climate neutrality, a diverse portfolio of mitigation options will need to be deployed with multiple inter-dependencies. Our results highlight the inter-dependencies across mitigation options and the importance of integrated assessments to avoid overestimation of the cost-effectiveness or GHG mitigation potentials of individual options.

Net zero AFOLU emissions

To assess the potential of CO2 sequestration practices to achieve net zero AFOLU emissions and thereby contributing to 1.5 °C climate stabilization efforts, we contrast the ‘agCO2_bio’ scenario (that reaches a 1.5 °C compatible AFOLU emission trajectory and considers enhanced biomass demands for bioenergy) to the ‘default_bio’ that does not consider agricultural CO2 sequestration options. Overall, CO2 sequestration options allow us to achieve deeper emission reductions over the next decades and consequently net negative AFOLU emissions at lower GHG prices (Fig. 2a). Applying a GHG price of 160 USD2022 tCO2e−1 allows to achieve negative AFOLU emissions (−1.6 GtCO2e per year) by 2050 when considering these options as compared with 0.4 GtCO2e per year without. More than half of the mitigation in 2050 is sourced from the FOLU sector (5.9 GtCO2e per year, 57%), followed by CO2 sequestration on agricultural land (2.3 GtCO2e per year, 23%) and the reduction of agricultural non-CO2 gases (2.1 GtCO2e per year, 20%). Even at lower GHG prices of 80–120 USD2022 tCO2e−1, these options would enable to reduce AFOLU emissions to around from 0.6 to −0.9 GtCO2e per year by 2050, which would be compatible, and even below, existing 1.5 °C climate stabilization scenarios that require on average AFOLU GHG emissions to drop to around 3 GtCO2e per year by 2050 (refs. 8,48).Fig. 2 AFOLU GHG emissions and cumulative GHG mitigation across different GHG price scenarios up to 2050.

a, AFOLU GHG emissions across GHG price scenarios over time with (the solid lines represent ‘agCO2_bio’) and without consideration of CO2 sequestration practices on agricultural land (the dotted lines represent ‘default_bio’). The values below zero indicate net negative AFOLU emission levels. The grey area indicates minimum and maximum AFOLU emission ranges across IAMs for a peak warming 1.5 °C scenario from Hasegawa et al.48, and the arrows indicate additional GHG mitigation when considering agricultural CO2 sequestration options across GHG price scenarios. b, Cumulative AFOLU GHG mitigation from 2020–2050 for different GHG sources at different GHG prices up to 325 USD2022 tCO2e−1 by 2050 (‘agCO2_bio’).

Especially for 1.5 °C climate stabilization scenarios with peak warming by 2050 (ref. 49), agricultural CO2 sequestration options can make an important difference in the economy-wide mitigation option portfolio and reduce mitigation costs in MESSAGEix-GLOBIOM57. Considering these novel CO2 options in the model shows that GHG prices could drop by 48% by 2050 with positive effects on global gross domestic product (GDP) (+0.6% by 2050) as compared with a 1.5 °C scenario without agricultural CO2 sequestration options as counterfactual. These effects are less pronounced when moving towards the end of the century or in less ambitious 2 °C peak warming scenarios. Therefore, particularly in ambitious stabilization scenarios that approach the asymptote of the economy-wide marginal abatement cost curve where any additional mitigation comes with substantial (economic) costs, agricultural CO2 sequestration options can make an important contribution to reduce those costs.

Looking at cumulative AFOLU GHG mitigation from 2020 to 2050 (Fig. 2b), agricultural CO2 sequestration options can provide similar mitigation potentials at the global scale at GHG prices >160 USD2022 tCO2e−1 as compared with other important AFOLU options modelled in this study, such as enhanced FOLU carbon sequestration via afforestation and reforestation or the reduction of agricultural non-CO2 emissions. At a GHG price of 160 USD2022 tCO2e−1, model results indicate that SOC sequestration practices on cropland and grasslands could provide cumulative GHG savings of 24 GtCO2e up to 2050, the establishment of silvo-pastures 18 GtCO2e, while the application of biochar to cropland soils contributes only 5 GtCO2e by 2050. Since the economic mitigation potential from biochar application is conditional on the biomass demands in other sectors that compete for the biomass feedstock, considering 1.5 °C compatible biomass demands for bioenergy production almost halves the economic mitigation potential of biochar (Fig. 1d) due to increased competition for the biomass resource and higher biomass prices. In addition, while other GHG mitigation sources such as FOLU removals tend to saturate at GHG prices >160 USD2022 tCO2e−1, the carbon sink from agricultural CO2 sequestration practices continues to increase providing 65% higher mitigation compared with the FOLU sink at 325 USD2022 tCO2e−1.

Though absolute mitigation potentials of agricultural CO2 sequestration options are mostly located in the Global South, the relative importance within the overall cost-effective AFOLU mitigation option portfolio varies across world regions (Fig. 3). In the countries of the Global South, those options contribute on average some 18% of the AFOLU GHG mitigation potential at 160 USD2022 tCO2e−1 by 2050 (11% in Latin America and 21% in Africa) given the large cost-effective mitigation potentials from reducing land-use change emissions including emissions from deforestation. However, in the Global North, these practices represent with 44% a much larger share of the total AFOLU abatement at 160 USD2022 tCO2e−1.Fig. 3 Global and regional AFOLU mitigation potentials in the ‘agCO2_bio’ scenario applying a GHG price of 160 (270) USD2022 tCO2e−1 by 2050 (2070) on AFOLU GHG emissions/removals compared with the ‘baseline’ scenario.

FOLU removals CO2, removals from afforestation and reforestation, forest management and other land-use changes; FOLU emissions CO2, emissions from deforestation, forest management and other land-use changes; non-CO2 production, CH4 and N2O emission change from changes in agricultural production levels; non-CO2 structural, CH4 and N2O emission change from structural changes in agriculture such as international trade; non-CO2 technical, CH4 and N2O emission change from adoption of technical mitigation options; soil carbon CO2, CO2 sequestration in soils from improved cropland and grassland management; biochar CO2, CO2 sequestration from biochar application; silvo-pasture CO2, CO2 sequestration from silvo-pastures; OECD, OECD countries; REF, former Soviet Union; ASIA, Asia; AFR, Africa; LAM, Latin America.

In addition, CO2 sequestration options retain more agricultural land under production under stringent mitigation efforts as they pay farmers for the carbon sink they provide. Enhanced CO2 sequestration on grassland and the establishment of silvo-pastures improves the GHG efficiency of rather GHG intensive pasture-based livestock production systems in tropical countries58. Consequently, more pasture-based livestock production systems remain competitive, even under a GHG price and the abandonment of pastures and subsequent reforestation slightly declines (−210 Mha, 85% of which is in Latin America and sub-Saharan Africa). Hence, the inclusion of agricultural CO2 sequestration options in AFOLU mitigation efforts may relieve some of the socio-economic challenges in the Global South related to agricultural land abandonment in response to a GHG price.

Economic implications for farmers

Agricultural CO2 sequestration practices could also provide an interesting source of revenues in the future to farmers if they were paid for the carbon sink they generate. GHG prices are currently not considered the policy instrument of choice for agriculture. However, if a GHG tax were applied also in agriculture following the polluter pays principle, agricultural CO2 sequestration options would enable producers to offset some of the economic losses from the GHG tax on non-CO2 emissions. Figure 4 displays economic impacts on producers under a GHG pricing scheme for three alternative mitigation scenarios with and without consideration of CO2 sequestration options on agricultural land compared with the ‘baseline’ scenario without mitigation policy. The three scenarios include the default mitigation scenario based on (1) existing AFOLU abatement options in IAMs (‘default_bio’, with AFOLU emissions of 0.4 GtCO2e per year by 2050 at 160 USD2022 tCO2e−1), (2) a scenario applying the same GHG prices but considering agricultural CO2 sequestration options, thus reaching higher GHG mitigation (‘agCO2_bio 160’, −1.6 GtCO2e per year by 2050 at 160 USD2022 tCO2e−1) and (3) a scenario considering agricultural CO2 sequestration options and delivering similar GHG mitigation by 2050 as the ‘default_bio’ scenario (‘agCO2_bio 80’, 0.6 GtCO2e per year by 2050 at 80 USD2022 tCO2e−1).Fig. 4 Economic impact on farmers across different GHG mitigation scenarios.

a, The change in agricultural turnover and GHG price (subsidy and tax) effects across scenarios in 2050 compared with the baseline scenario. Gross turnover for crop (Turnover crp) and livestock (Turnover lsp) products is calculated by multiplying changes in agricultural prices and production quantities compared with the baseline. GHG tax (Tax crp and Tax lsp) and subsidy (Subsidy crp, Subsidy lsp) effects are calculated multiplying emissions or removals from agriculture with the GHG price of 80 or 160 USD2022 tCO2e−1. Net turnover (Net crp and Net lsp) for farmers is calculated as the sum of turnover, tax and subsidy effects. b, Net turnover effects for farmers (+ turnover − tax + subsidy effects) and government budgets (+ tax − subsidy effects) across scenarios in 2050. default_bio 160 – 160 USD2022 tCO2e−1 GHG price on all AFOLU emissions/removals except CO2 on agricultural land, agCO2_bio 80 and agCO2_bio 160 scenarios: 80 or 160 USD2022 tCO2e−1 GHG price on all AFOLU emissions including CO2 on agricultural land. NAM, North America; SAM, South and Central America; CIS, former Soviet Union; EUR, Europe; EAS, East Asia; SAS, South Asia; SEA, Southeast Asia; OCE, Oceania; MAF, Middle East and Northern Africa; SSA, Southern Africa.

Results show that, on a global scale, producers are less impacted if CO2 sequestration options on agricultural land are subsidized under the mitigation policy (‘agCO2_bio 80’ and ‘agCO2_bio 160’). Agricultural gross turnover is higher in the scenario without CO2 sequestration on agricultural land (350 billion USD2022 in the ‘default_bio’ scenario compared with the baseline) as compared with the scenarios including CO2 sequestration options (210 billion USD2022 ‘agCO2_bio 80’ and 330 billion USD2022 ‘agCO2_bio 160’) as food price increases are less pronounced in the latter scenarios and more land remains used for agricultural purposes due to yield co-benefits of carbon sequestration options. GHG tax payments on agricultural emissions amount to some 675 billion USD2022 at 160 USD2022 tCO2e−1, and hence, producers experience net economic turnover losses of around 325 billion USD2022 in the ‘default_bio’ scenario (Fig. 4a).

In the scenarios with carbon sequestration incentives, producers can largely compensate the non-CO2 related GHG tax payments (390 billion USD2022 ‘agCO2_bio 80’ and 690 billion USD2022 ‘agCO2_bio 160’) with carbon credits from CO2 sequestration on agricultural land, which generates, if compensated with the GHG price additional revenues, around 125/375 billion USD2022 at 80/160 USD2022 tCO2e−1. Hence, in the ‘agCO2_bio 80’ scenario, net economic turnover losses can be reduced to 55 billion USD2022 as compared with the baseline scenario in 2050, while in ‘agCO2_bio 160’, slightly positive effects of around 15 billion USD2022 are noted. Net turnover gains are most pronounced in ‘agCO2_bio 160’ for livestock producers in Oceania (+43% change compared with the baseline scenario in 2050), Europe (+13%) and sub-Saharan Africa (+9%) and for crop producers in North America (+14%). Similarly, positive economic effects for producers, though less pronounced, are observed for the ‘agCO2_bio 80’ scenario.

Overall, we estimate global net revenues for producers of 70/235 billion USD2022 (at 80/160 USD2022 tCO2e−1) from the adoption of agricultural CO2 sequestration options by deducting the economic costs (area under the marginal abatement cost curve) of CO2 sequestration practices (55/140 billion USD2022) from the additional revenues generated from the GHG price (125/375 billion USD2022). Figure 4b presents the economic implications for farmers and governmental budgets. Considering agricultural CO2 sequestration options in the mitigation policy impacts government budgets as part of the revenues generated by the GHG tax are assumed to be transferred back to farmers by paying them for the generated carbon sinks on agricultural land. This, in turn, reduces the overall space for distributional measures of governments to, for example, consumers, as the net GHG price revenues (tax minus subsidy payments) would decline from 675 billion USD2022 (‘default_bio’) to only 265 (‘agCO2_bio 80’) and 315 billion USD2022 (‘agCO2_bio 160’) at global scale. Across regions, for sub-Saharan Africa carbon, subsidies exceed GHG tax revenues and, hence, have negative implications for the government budget (−17 billion USD2022) at a GHG price of 160 USD2022 tCO2e−1. These results warrant further examination at a less aggregate scale and highlight the importance of climate finance mechanism.

Discussion and conclusions

To keep the 1.5 °C target within reach, the land-use sector including agriculture will have to contribute substantially to mitigation efforts9,14,48. We find that enhanced CO2 sequestration practices on agricultural land may generate a global carbon sink of up to 2.8 GtCO2e per year by 2050 at 160 USD2022 tCO2e−1, with the majority located in the Global South. Our estimates are smaller compared with bottom-up studies25,51,59 owing to the consideration of interlinkages across options and economic dynamics. However, the estimated potentials still represent 36–41% of the expected GHG mitigation requirements (7–8 GtCO2e per year by 2050) for the AFOLU sector in existing 1.5 °C climate stabilization scenarios8,48. Consequently, AFOLU emissions could be reduced to −1.6 GtCO2e per year by 2050 at 160 USD2022 tCO2e−1 if agricultural CO2 sequestration options were deployed jointly with other land-based mitigation options. The estimated CO2 sequestration potentials on agricultural land are subject to uncertainty and interlinked with other drivers such as biomass demand for bioenergy. Varying assumptions related to the maximum adoption potential and saturation time had profound effects on mitigation potentials. Considering 1.5 °C compatible bioenergy demands reduced the overall cost-effective mitigation potential of agricultural carbon sequestration by 17% to 2.3 GtCO2e per year at 160 USD2022 tCO2e−1 and almost halves the economic mitigation potential of biochar given increased competition for biomass. Climate stabilization pathways that highlight the importance of bioenergy for fossil fuel substitution and carbon capture and storage60 need to consider these potential trade-offs. Our results highlight the benefits of integrated economic assessments to capture trade-offs systematically and avoid overestimating the effectiveness of individual mitigation options.

CO2 sequestration options on agricultural land allow us to achieve deeper emission savings in the land-use sector over the coming decades and deliver economic benefits by reducing the economy-wide costs of climate mitigation. When moving towards GHG prices >160 USD2022 tCO2e−1, these options offer similar GHG mitigation potentials as other important AFOLU mitigation sources, such as increased afforestation and reforestation or the reduction of agricultural non-CO2 emissions. Consequently, the land-use sector could achieve net zero AFOLU emissions already at GHG prices around 80–120 USD2022 tCO2e−1 by 2050 (without considering transaction costs) if all AFOLU mitigation options were deployed jointly. However, agricultural CO2 sequestration options can accumulate carbon only over a limited period of time59. Across economic sectors, considering these options in the mitigation portfolio reduces GHG prices by 48% and increases global GDP by 0.6% in 2050 in a 1.5 °C scenario without temperature overshoot. CO2 sequestration options on agricultural land may also provide an essential source of income for farmers if remunerated accordingly. At 160 USD2022 tCO2e−1 farmers could receive carbon subsidies of 375 billion USD2022, which exceeds current direct transfers to farmers amounting to USD2022 293 billion per year from 2019 to 202161. Still, economic impacts vary across regions, and some of the regions providing the biggest mitigation potential in the Global South would also have to bear the largest costs. These findings highlight the importance of considering equity aspects and climate justice across world regions62–66.

The presented results should be considered within model and parameter uncertainties. For example, our scenario analysis is preformed given current climatic conditions and does not consider climate impacts and disturbances. Though exact magnitudes remain uncertain67, climate impacts can directly decrease the capacity of soils to store carbon by modifying plant carbon inputs and microbial processes, thereby affecting carbon stocks55,68 or indirectly affecting the carbon cycle via extreme weather events, such as floods or fires69,70. In addition, we assumed optimal fertilization rates for silvo-pastures, while fertilization rates may deviate from these optimal levels in practice. Especially in regions with soil nitrogen deficit, plantations in tree mixtures with nitrogen-fixing species may help to mitigate nutrient imbalances71,72. These aspects deserve further investigation in future studies. Besides, our economic cost estimates are on the optimistic side, as certain costs, such as transaction costs, institutional costs and implementation costs, are not accounted for in our modelling framework, which would decrease cost-effectiveness of these options, especially for regions where sequestration rates are low17. Though implementation cost estimates vary widely in literature, these can be as high as 65–85% of the total carbon credit cost for an agricultural offset scheme in Western Canada73. Several structural, institutional or social and behavioural barriers need to be overcome before realizing the estimated mitigation potentials17,74, such as uncertainty on short-term adoption potentials given farm structure, land tenure rights or inertia of land owners, high monitoring, reporting and verification costs that impede adoption beyond large companies and farms or lack of institutional capacity to enforce policy targets74–77. Together with risks related to performance and additionality of generated carbon sinks17, this makes large-scale uptake of these options and inclusion in a policy scheme at a global scale rather unlikely in the short term.

Agricultural mitigation policies should be designed in an integrated and coordinated manner across gases, sectors and world regions to avoid rebound or leakage effects38,75,78. However, since the time it takes to prepare, formulate and adopt agricultural policies is probably the number of years left to keep the 1.5 °C target feasible with current emission levels79 (for example, a first legislative proposal for the EU Common Agriculture Policy for the period 2023–2027 was published by the European Commission in 201880), agricultural mitigation policy design would need to see unprecedented fast tracking to bring any substantial benefits already by 2030.

Given the large variation in emission intensity across agricultural commodities and countries58,73,81,82, mitigation policies should prioritize commodities with high emission intensities. The ruminant sector is an interesting lever from a policy perspective as production is GHG intensive, but it offers large cost-effective GHG mitigation potentials6,20,83,84, as also assessed in this study. Developing best-practice policies by 2030 targeting these GHG intensive commodities in countries with strong institutional capacity with the possibility to up- and out-scale once operational to other countries should be among the priorities. Still, the structure of the livestock sector with a large number of smallholders58,85 complicates the implementation of monitoring, reporting and verification systems73, which is crucial to ensure effectiveness of the mitigation policy and to identify and correct potential negative policy effects timely. Here, targeting key players within the supply chain could facilitate policy implementation and deliver sizeable emission reductions73. For example, the 60 largest companies listed in the Coller FAIRR Protein Producer Index cover approximately 20% of the global livestock and aquaculture market with high dominance in some regional markets, that is, China, where they represent nearly 30% of the Chinese market for animal proteins and 100% of the domestic dairy market (www.fairr.org/resources/reports/coller-fairr-protein-producer-index-2018), and reaching those alone could, thus, bring substantial benefits with limited implementation costs. In addition, unlike smallholders, these companies are more probable to have the capacity to deal with the monitoring, reporting and verification and bear those costs.

Finally, the creation of carbon sinks should be remunerated and included in a policy scheme, which is a non-trivial challenge16,17,86. Once successful, this could increase acceptance of ambitious market-based mitigation policies, such as a tax on non-CO2 emissions or emission trading scheme17, as it helps farmers, similar as other redistribution measures87, to compensate for part of the additional costs incurred through the adoption of a GHG pricing scheme20,88–90. Such policy incentives need to be designed in a way that ensures that the carbon remains in agricultural soils and biomass in the long run and agricultural practices are maintained once carbon accumulation saturates.

Methods

GLOBIOM–G4M

GLOBIOM91 is a global recursive dynamic partial equilibrium model of the forest and agricultural sectors and has been used extensively in different land-based mitigation assessments and for the representation of the land-use sectors in IAMs. It maximizes global producer and consumer surplus of agriculture and forestry calculating market equilibrium, bilateral trade-flows, spatially explicit land use and land-use changes, prices, GHG emissions and other economic and environmental variables. Commodity markets and international trade are represented at the level of 37 economic regions in this study. The spatial resolution of the supply side relies on the concept of simulation units, which are aggregates of 5–30 arcmin pixels belonging to the same altitude, slope and soil class and also the same country92. For crops, livestock and forest products, spatially explicit Leontief production functions covering alternative production systems are parameterized using bio-physical models such as the Environmental Policy Integrated Model93, G4M41,94 or the RUMINANT model58. The model includes six land cover types: cropland, grassland, short rotation plantations, managed forests, unmanaged forests and other natural vegetation land. Depending on the profitability of primary products, byproducts and final products production activities, the model can switch from one land cover type to another. GLOBIOM is linked with the G4M model41,42 for the detailed representation of forest management and carbon flows and an energy system model MESSAGEix57,95 for the interactions with the energy system and economy-wide impacts.

Scenarios

Next to our baseline scenario that is based on the Shared Socioeconomic Pathway 2 (SSP2)46 under historical climate, we implement different land-based mitigation scenarios to assess the cost-effective mitigation potential of agricultural CO2 sequestration options. Main elements of the quantified mitigation scenarios are different GHG price trajectories on AFOLU emissions/removals and alternative bioenergy demand trajectories. We simulate a linearly increasing AFOLU GHG price from 2030 onwards that reaches 25, 50, 75, 100, 125, 150, 175 and 200 USD2000 tCO2e−1 by 2050. GHG prices were converted ex post from USD2000 to USD2022, applying a global uniform conversion rate of 1.63 using the US GDP deflator from the World Bank. This simplified approach does not capture differences in regional macro-economic developments. However, the proportional scaling ensures consistency of the presented results with the underlying partial equilibrium modelling performed in constant USD2000. Non-CO2 gases were converted to CO2 equivalents using global warming potentials from the 4th IPCC Assessment Report (298 for N2O and 25 for CH4). In the mitigation scenarios that do not consider agricultural CO2 sequestration options (‘default’ and ‘default_bio’), this GHG price is only included on agricultural non-CO2 emissions and FOLU CO2 emissions/removals, while in ‘agCO2’ and ‘agCO2_bio’ the GHG price is also applied to agricultural CO2 removals. Bioenergy demand is either kept at baseline levels or at levels compatible with the 1.5 °C target (Table 1).

Agricultural carbon sequestration options and crop residues

In this study, three carbon sequestration options on agricultural land were included:Silvo-pasture systems for biomass and biochar production or carbon sequestration

Carbon sequestration through improved cropland and pasture management

Biochar application on cropland

To represent the new mitigation technologies, we applied a similar approach as described in Frank, Havlík20 and introduced explicit CO2 mitigation technologies on agricultural land using information on carbon sequestration coefficients per technology, economic costs, as well as information on the potential impact on crop and pasture productivities. Consequently, a marginal abatement cost curve can be emulated from the model by applying a GHG price, which triggers the adoption of mitigation technologies if the expected revenues, for example, through the avoided GHG price payments or improved productivities, exceed the costs of adoption of a given technology. The different CO2 options are assumed to be additive and can be adopted jointly on a piece of cropland (SOC and biochar) or pastures (SOC and silvo-pastures) in the model.

Silvo-pasture systems

3-PGmix model

3-PGmix is a simplified process-based forest growth model that uses a big-leaf approach to simulate stands dynamics. Moreover, 3-PGmix expands the original 3PG model96 by including modified processes for light interception, canopy transpiration and additional model features, enabling to simulate mixed forest stands and more complex canopy configurations97. The model operates in a monthly time step and simulates GPP using a light use efficiency approach, which considers multiple environmental drivers, including temperature, vapour pressure deficit, available soil water, soil fertility, number of frost days, atmospheric CO2 concentration and stand age, as well as absorbed photosynthetically active radiation and the canopy quantum efficiency. Net primary production is then calculated as a constant fraction of gross primary production. Subsequently, carbon is allocated to different tree compartments (roots, foliage and stem). The allocation to roots is prioritized, where harsher growing conditions induce a higher allocation of carbon to roots, and the allocation to foliage and roots follows from the remaining net primary production fraction, maintaining a balance between the growth rates of foliage and stem. Besides the dynamics related to the different biomass compartments, the model allows to derive several attributes relevant to management, including stand diameter at breast height, volume, basal area and mean annual increment, among others, based on allometric relationships98.

Tree plantations in silvo-pasture systems were assumed to be fertilized, hence, with no nutrient limitations. The fertilization demand was computed based on the available soil nitrogen and nitrogen demand from the plantations. To account from the available soil nitrogen, we have coupled the 3-PGmix model with the Yasso20 soil model99 and derived the nitrogen dynamics in the soil with the help of stochiometric relationships on the decomposition of various SOC compartments100. The nitrogen fertilization amounts were defined based on the increment of the biomass compartments in the plantations and the respective nitrogen concentration in plant tissues. Phosphorus demand was established as a constant fraction of the nitrogen demand.

Simulation setup

The productive potentials were computed in the model, using the GLOBIOM 5 to 30 arcmin simulation units92, where typical growing conditions were defined. Soil inputs to the model (maximum available soil water, soil texture, carbon and nitrogen stocks) were retrieved from the ISRIC soil database101. Climate inputs (minimum temperature, maximum temperature, mean temperature, precipitation, solar radiation and number of forest days) were computed for each simulation unit for historic climate based on the WorldClim version 2.1 data102.

For each simulation unit, we selected the appropriate species based on the climate attributes. Plantations were primarily composed by different Eucalypt species, including Eucalyptus saligna, Eucalyptus pellitta, Eucalyptus grandis, Eucalyptus urophylla and Eucalyptus globulus, as well as poplar (Populus spp.), depending on the climate attributes, specifically temperature and precipitation regimes, based on Booth103. For grid cells in temperate and boreal ecosystems not suitable for E. globulus (mean annual temperature below 11 °C), poplar plantations were established. The parameters for each species were retrieved from the 3-PGmix parameter database, contained in the R package r3PG104.

Silvo-pasture representation in GLOBIOM

Two explicit silvo-pasture systems105–110 were implemented in GLOBIOM based on the bio-physical 3-PGmix simulation.

Silvo-pastures for bioenergy and biochar production

The 3-PGmix model was used to simulate productivities, carbon sequestration in above- and belowground biomass (Supplementary Table 3) and nitrogen inputs of short rotation tree plantations for a 10 year rotation period, consistent with GLOBIOM internal logic. These data were combined with pasture productivities in GLOBIOM13 assuming that 25% of the pasture area would be planted in alleys with short rotation tree plantations110,111 and harvested in a 10 year rotation, which corresponds to approximately 1,250–2,500 trees per hectare, depending on the species, with higher density for poplar plantations. Harvested biomass from short rotation tree plantations can be used for either bioenergy or biochar production in the model. In this system, the new equilibrium in biomass carbon stocks is assumed to be reached after 10 years following the establishment. Costs for the establishment, maintenance and harvest of short rotation tree plantations are based on Havlík et al.112.

Silvo-pasture for carbon sequestration

The 3-PGmix model was used to simulate productivities, carbon sequestration in above- and belowground biomass (Supplementary Table 3) and nitrogen inputs of fast-growing tree species for a 30 year rotation period. As for silvo-pasture system for biomass production, 25% of the pasture area was assumed to be planted with trees. Given the longer rotation period of 30 years, this results in a lower tree density of around 400–600 trees per hectare, depending on the species, but higher biomass accumulation over a longer rotation period. Accumulation of carbon in biomass is assumed to continue over a 30 year period. Short rotation tree plantations costs based on ref. 112 were decomposed to account only for establishment and maintenance costs that were calculated using a bottom-up costing approach113,114. Owing to the longer rotation time, the reduced planting density, limited maintenance and no harvesting costs, this system is much cheaper with only 8% (on global average) of the total costs of the silvo-pasture system for bioenergy production.

In both systems, we applied the conservative assumption of no pasture productivity increases due to efficiency gains in response to the conversion to silvo-pasture system. Hence, grazing biomass supply declines by 25% to account for the planting of trees on 25% of the area. Adoption of silvo-pasture systems was limited to 50% of the total pasture area in a region.

Enhanced SOC sequestration on cropland and pastures

Annualized carbon sequestration coefficients at the country level over the 2020–2050 period are based on Roe et al.25 for cropland and pastures. Sequestration rates (Supplementary Table 3) are assumed not to change dynamically over time, and a saturation of the carbon sequestration potential is assumed after 20 years in line with IPCC guidelines115. Associated yield increases for the improvement of cropland SOC on degraded land following Smith et al.116 have been implemented for Africa, Latin America and Asia based data from Lal117. Annual yield increases of crop aggregates reached 1.5%, 1.2% and 0.7% in Africa, Latin America and Asia, respectively, and 0.9% at world average, per tCO2 ha−1 sequestered annually.

A quadratic cost function was implemented to calibrate the adoption rates of these mitigation technologies in GLOBIOM. The slope of the cropland and pasture cost curve was fitted to approximate adoption rates (90% for cropland and 60% for grassland) at a carbon price of 100 USD2000 tCO2−1 as presented in Roe et al.25. The maximum adoption potential of 90% of cropland area and 60% of pastures was assumed following Roe et al.25. Improved cropland management can be combined with other mitigation options on the same spot of land, such as improved fertilization or biochar application. Improved pasture management can be combined with silvo-pasture systems.

Biochar application

Emission factors (Supplementary Table 3) for biochar application on cropland are based on the annualized (2020–2050 period) data from Roe et al.25 and assuming saturation of the carbon sequestration potential following 30 years of application. Crop yield improvement from biochar application were calculated using the carbon sequestration coefficients and applying the method, as done for improved crop- and grassland management options following Lal117. Costs for pyrolysis, storage and processing and application to land of 35 USD2000 tCO2e−1 were based on Homagain et al.118. Conversion factors for biochar production were based on Griscom et al.24 assuming 0.45% carbon content per ton biomass input, 50% of which is retained and 79.6% stored for more than 100 years in biochar once applied to the soil, which yields a conversion efficiency of 0.18 tCe biochar per tdm biomass input. In the model, biochar competes for biomass with other energy and material uses119 and can be produced from crop residues, logging residues, bark, wood chips, recycled wood or short rotation coppices/tree plantations. A total of 50% of the biomass feedstock was assumed to be available for bioenergy production during the pyrolysis process as byproduct following Wang et al.32.

On the supply side (Supplementary Table 4), the technical crop residue potential was parameterized in GLOBIOM using endogenous crop productivity estimates and applying crop specific residue-product ratios (except for oil palm) from Holmatov et al.120. It was assumed that 50% of the technical potential could be sustainable removed121 without impacts on crop yields and SOC stocks. Costs for crop residue baling, recovery and transportation were based on the BMLFUW122 and rescaled across world regions using GDP per capita differences. Secondary crop residues from processing were not considered for bioenergy or biochar production. On the demand side, crop residue demand for livestock production (occasional feeding and bedding) is based on the coefficients from Herrero et al.58. In our baseline, we assume that 50% of the other (non-forest) solid biomass demand is sourced from crop residues (~16 EJ per year in 2020). Overall, crop residue demand competes across the different uses in the model (livestock, bioenergy and biochar production).

Economic impact on farmers

To assess the economic impact of a GHG price on farmers, an ex post calculation was performed. The results presented in Fig. 4 show changes of three mitigation scenarios compared with the baseline scenario without mitigation efforts. Changes in gross turnover for crop and livestock products were calculated by multiplying differences in production quantities and prices of agricultural products when comparing the mitigation scenarios with the baseline in 2050. Positive values indicate an increase in gross turnover for producers. GHG tax payments were calculated by multiplying agricultural GHG emissions with the GHG price. Tax payments were shown as negative values indicating a cost for producers. Carbon subsidy payments were calculated by multiplying CO2 removals (sequestration) on agricultural land with the GHG price. Carbon subsidy payments were shown as positive value indicating a payment to producers. The total net turnover effect for producers was calculated by summing up gross turnover changes (typically positive), carbon subsidy revenues (positive) and GHG tax payments (negative).

To assess impacts on producers and government budgets, the data were rearranged in Fig. 4b. Effects on producers are equivalent to net turnover effect (gross turnover, GHG tax payments and carbon subsidy revenues). The impact on government budget is calculated by summing up GHG tax and carbon subsidy payments. Unlike in Fig. 4a, the sign is different as the GHG tax represents a payment for producers (negative) but an income for the government (positive). Hence, a negative value in Fig. 4b for the government indicates that carbon subsidy payments exceed the GHG tax revenues the government receives.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Supplementary information

Supplementary Information Supplementary information with additional information on the applied modelling framework.

Reporting Summary

Supplementary Data 1 Dataset containing GLOBIOM model results.

Supplementary information

The online version contains supplementary material available at 10.1038/s43016-024-01039-1.

Acknowledgements

This study has been funded by the European Union’s H2020 Project ENGAGE (grant agreement no. 821471), BRIGHTSPACE (grant agreement no. 101060075), LAMASUS (grant agreement no. 101060423) and ForestNavigator (grant agreement no. 101056875).

Author contributions

S.F. designed and coordinated the study. Model development and scenario implementation were carried out by S.F., E.B., P.L. and A.P. (GLOBIOM), A.L.D.A. (3-PGmix) and M.G. (G4M). T.E., T.K. and M.W. downscaled GLOBIOM results for linking with G4M. S.F. performed a first analysis of the results, produced the figures and led the writing of the paper. All authors provided feedback and contributed to the discussion and interpretation of the results.

Peer review

Peer review information

Nature Food thanks Laurent Drouet, Jean-Francois Soussana and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Data availability

The results that support the findings of the study are provided in the paper and in Supplementary Information. The sources of all data used in this study are referenced in Methods.

Code availability

A GitHub repository at https://iiasa.github.io/GLOBIOM/introduction.html provides GLOBIOM documentation, links to GLOBIOM resources, GAMS script descriptions and dependency links that match the Trunk version of the GLOBIOM. Additional code can be made available upon request from the corresponding author.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Tubiello FN Greenhouse gas emissions from food systems: building the evidence base Environ. Res. Lett. 2021 16 065007 10.1088/1748-9326/ac018e
Tubiello, F. N. et al. Greenhouse gas emissions from food systems: building the evidence base. Environ. Res. Lett. 16, 065007 (2021).
2. Crippa M Food systems are responsible for a third of global anthropogenic GHG emissions Nat. Food 2021 2 198 209 10.1038/s43016-021-00225-9 37117443
Crippa, M. et al. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food 2, 198–209 (2021).37117443
3. IPCC Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) (Cambridge Univ. Press, 2022).
4. Pendrill F Disentangling the numbers behind agriculture-driven tropical deforestation Science 2022 377 eabm9267 10.1126/science.abm9267 36074840
Pendrill, F. et al. Disentangling the numbers behind agriculture-driven tropical deforestation. Science 377, eabm9267 (2022).36074840
5. Curtis PG Classifying drivers of global forest loss Science 2018 361 1108 1111 10.1126/science.aau3445 30213911
Curtis, P. G. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).30213911
6. Frank S Agricultural non-CO2 emission reduction potential in the context of the 1.5 °C target Nat. Clim. Change 2019 9 66 72 10.1038/s41558-018-0358-8
Frank, S. et al. Agricultural non-CO2 emission reduction potential in the context of the 1.5 °C target. Nat. Clim. Change 9, 66–72 (2019).
7. Tubiello FN The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012 Glob. Change Biol. 2015 21 2655 2660 10.1111/gcb.12865
Tubiello, F. N. et al. The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Change Biol. 21, 2655–2660 (2015).
8. Rogelj J Scenarios towards limiting global mean temperature increase below 1.5 °C Nat. Clim. Change 2018 8 325 332 10.1038/s41558-018-0091-3
Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).
9. Clark MA Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets Science 2020 370 705 10.1126/science.aba7357 33154139
Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705 (2020).33154139
10. Reisinger A How necessary and feasible are reductions of methane emissions from livestock to support stringent temperature goals? Philos. Trans. A 2021 379 20200452
Reisinger, A. et al. How necessary and feasible are reductions of methane emissions from livestock to support stringent temperature goals? Philos. Trans. A 379, 20200452 (2021).
11. Ripple WJ Ruminants, climate change and climate policy Nat. Clim. Change 2014 4 2 5 10.1038/nclimate2081
Ripple, W. J. et al. Ruminants, climate change and climate policy. Nat. Clim. Change 4, 2–5 (2014).
12. Herrero M Greenhouse gas mitigation potentials in the livestock sector Nat. Clim. Change 2016 6 452 461 10.1038/nclimate2925
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).
13. Havlík P Climate change mitigation through livestock system transitions Proc. Natl Acad. Sci. USA 2014 111 3709 3714 10.1073/pnas.1308044111 24567375
Havlík, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).24567375
14. Leahy, S., Clark, H. & Reisinger, A. Challenges and prospects for agricultural greenhouse gas mitigation pathways consistent with the Paris Agreement. Front. Sustain. Food Syst. 10.3389/fsufs.2020.00069 (2020).
15. Henderson, B., Frezal, C. & Flynn, E. A Survey of GHG Mitigation Policies for the Agriculture, Forestry and Other Land Use Sector OECD Food, Agriculture and Fisheries Papers No. 145 (OECD, 2020).
16. Amundson R Biardeau L Soil carbon sequestration is an elusive climate mitigation tool Proc. Natl Acad. Sci. USA 2018 115 11652 11656 10.1073/pnas.1815901115 30425181
Amundson, R. & Biardeau, L. Soil carbon sequestration is an elusive climate mitigation tool. Proc. Natl Acad. Sci. USA 115, 11652–11656 (2018).30425181
17. Henderson, B. et al. Soil Carbon Sequestration by Agriculture OECD Food, Agriculture and Fisheries Papers No. 174 (OECD, 2022).
18. DeFries R Land management can contribute to net zero Science 2022 376 1163 1165 10.1126/science.abo0613 35679411
DeFries, R. et al. Land management can contribute to net zero. Science 376, 1163–1165 (2022).35679411
19. Hasegawa T Risk of increased food insecurity under stringent global climate change mitigation policy Nat. Clim. Change 2018 8 699 703 10.1038/s41558-018-0230-x
Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).
20. Frank S Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett. 2017 12 105004 10.1088/1748-9326/aa8c83
Frank, S. et al. Reducing greenhouse gas emissions in agriculture without compromising food security? Environ. Res. Lett. 12, 105004 (2017).
21. Fujimori S A multi-model assessment of food security implications of climate change mitigation Nat. Sustain. 2019 2 386 396 10.1038/s41893-019-0286-2
Fujimori, S. et al. A multi-model assessment of food security implications of climate change mitigation. Nat. Sustain. 2, 386–396 (2019).
22. Hussein Z Hertel T Golub A Climate change mitigation policies and poverty in developing countries Environ. Res. Lett. 2013 8 035009 10.1088/1748-9326/8/3/035009
Hussein, Z., Hertel, T. & Golub, A. Climate change mitigation policies and poverty in developing countries. Environ. Res. Lett. 8, 035009 (2013).
23. Bossio DA The role of soil carbon in natural climate solutions Nat. Sustain. 2020 3 391 398 10.1038/s41893-020-0491-z
Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).
24. Griscom BW Natural climate solutions Proc. Natl Acad. Sci. USA 2017 114 11645 10.1073/pnas.1710465114 29078344
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645 (2017).29078344
25. Roe S Land-based measures to mitigate climate change: potential and feasibility by country Glob. Change Biol. 2021 27 6025 6058 10.1111/gcb.15873
Roe, S. et al. Land-based measures to mitigate climate change: potential and feasibility by country. Glob. Change Biol. 27, 6025–6058 (2021).
26. IPCC Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (eds Sukla, P. R. et al.) (Cambridge Univ. Press, 2019).
27. Jeffery S Biochar boosts tropical but not temperate crop yields Environ. Res. Lett. 2017 12 053001 10.1088/1748-9326/aa67bd
Jeffery, S. et al. Biochar boosts tropical but not temperate crop yields. Environ. Res. Lett. 12, 053001 (2017).
28. Diatta AA Effects of biochar on soil fertility and crop productivity in arid regions: a review Arabian J. Geosci. 2020 13 595 10.1007/s12517-020-05586-2
Diatta, A. A. et al. Effects of biochar on soil fertility and crop productivity in arid regions: a review. Arabian J. Geosci. 13, 595 (2020).
29. Dokoohaki H Where should we apply biochar? Environ. Res. Lett. 2019 14 044005 10.1088/1748-9326/aafcf0
Dokoohaki, H. et al. Where should we apply biochar? Environ. Res. Lett. 14, 044005 (2019).
30. Amelung W Towards a global-scale soil climate mitigation strategy Nat. Commun. 2020 11 5427 10.1038/s41467-020-18887-7 33110065
Amelung, W. et al. Towards a global-scale soil climate mitigation strategy. Nat. Commun. 11, 5427 (2020).33110065
31. Lal R Soil organic matter content and crop yield J. Soil Water Conserv. 2020 75 27A 10.2489/jswc.75.2.27A
Lal, R. Soil organic matter content and crop yield. J. Soil Water Conserv. 75, 27A (2020).
32. Wang L Role of biochar toward carbon neutrality Carbon Res. 2023 2 2 10.1007/s44246-023-00035-7
Wang, L. et al. Role of biochar toward carbon neutrality. Carbon Res. 2, 2 (2023).
33. Duffy C Agroforestry contributions to smallholder farmer food security in Indonesia Agrofor. Syst. 2021 95 1109 1124 10.1007/s10457-021-00632-8
Duffy, C. et al. Agroforestry contributions to smallholder farmer food security in Indonesia. Agrofor. Syst. 95, 1109–1124 (2021).
34. Waldron A Agroforestry can enhance food security while meeting other Sustainable Development Goals Trop. Conserv. Sci. 2017 10 1940082917720667 10.1177/1940082917720667
Waldron, A. et al. Agroforestry can enhance food security while meeting other Sustainable Development Goals. Trop. Conserv. Sci. 10, 1940082917720667 (2017).
35. Iiyama M The potential of agroforestry in the provision of sustainable woodfuel in sub-Saharan Africa Curr. Opin. Environ. Sustain. 2014 6 138 147 10.1016/j.cosust.2013.12.003
Iiyama, M. et al. The potential of agroforestry in the provision of sustainable woodfuel in sub-Saharan Africa. Curr. Opin. Environ. Sustain. 6, 138–147 (2014).
36. Hellin J Fisher E The Achilles heel of climate-smart agriculture Nat. Clim. Change 2019 9 493 494 10.1038/s41558-019-0515-8
Hellin, J. & Fisher, E. The Achilles heel of climate-smart agriculture. Nat. Clim. Change 9, 493–494 (2019).
37. Chapman M Large climate mitigation potential from adding trees to agricultural lands Glob. Change Biol. 2020 26 4357 4365 10.1111/gcb.15121
Chapman, M. et al. Large climate mitigation potential from adding trees to agricultural lands. Glob. Change Biol. 26, 4357–4365 (2020).
38. Frank S How much multilateralism do we need? Effectiveness of unilateral agricultural mitigation efforts in the global context Environ. Res. Lett. 2021 16 104038 10.1088/1748-9326/ac2967
Frank, S. et al. How much multilateralism do we need? Effectiveness of unilateral agricultural mitigation efforts in the global context. Environ. Res. Lett. 16, 104038 (2021).
39. Golub A The opportunity cost of land use and the global potential for greenhouse gas mitigation in agriculture and forestry Resour. Energy Econ. 2009 31 299 319 10.1016/j.reseneeco.2009.04.007
Golub, A. et al. The opportunity cost of land use and the global potential for greenhouse gas mitigation in agriculture and forestry. Resour. Energy Econ. 31, 299–319 (2009).
40. Frank S Land-based climate change mitigation potentials within the agenda for sustainable development Environ. Res. Lett. 2021 16 024006 10.1088/1748-9326/abc58a
Frank, S. et al. Land-based climate change mitigation potentials within the agenda for sustainable development. Environ. Res. Lett. 16, 024006 (2021).
41. Gusti M An algorithm for simulation of forest management decisions in the Global Forest Model Artif. Intell. 2010 N4 45 49
Gusti, M. An algorithm for simulation of forest management decisions in the Global Forest Model. Artif. Intell. N4, 45–49 (2010).
42. Kindermann G Global cost estimates of reducing carbon emissions through avoided deforestation Proc. Natl Acad. Sci. USA 2008 105 10302 10307 10.1073/pnas.0710616105 18650377
Kindermann, G. et al. Global cost estimates of reducing carbon emissions through avoided deforestation. Proc. Natl Acad. Sci. USA 105, 10302–10307 (2008).18650377
43. Almaraz M Soil carbon sequestration in global working lands as a gateway for negative emission technologies Glob. Change Biol. 2023 29 5988 5998 10.1111/gcb.16884
Almaraz, M. et al. Soil carbon sequestration in global working lands as a gateway for negative emission technologies. Glob. Change Biol. 29, 5988–5998 (2023).
44. Ivezić, V., Yu, Y. & Van Der Werf, W. Crop yields in European agroforestry systems: a meta-analysis. Front. Sustain. Food Syst. 10.3389/fsufs.2021.606631 (2021).
45. Pardon P Effects of temperate agroforestry on yield and quality of different arable intercrops Agric. Syst. 2018 166 135 151 10.1016/j.agsy.2018.08.008
Pardon, P. et al. Effects of temperate agroforestry on yield and quality of different arable intercrops. Agric. Syst. 166, 135–151 (2018).
46. Fricko O The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century Glob. Environ. Change 2017 42 251 267 10.1016/j.gloenvcha.2016.06.004
Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road scenario for the 21st century. Glob. Environ. Change 42, 251–267 (2017).
47. O'Neill B A new scenario framework for climate change research: the concept of shared socioeconomic pathways Climatic Change 2014 122 387 400 10.1007/s10584-013-0905-2
O'Neill, B. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122, 387–400 (2014).
48. Hasegawa, T., et al. Land-based implications of early climate actions without global net-negative emissions. Nat. Sustain. 4, 1052–1059 (2021).
49. Riahi K Cost and attainability of meeting stringent climate targets without overshoot Nat. Clim. Change 2021 11 1063 1069 10.1038/s41558-021-01215-2
Riahi, K. et al. Cost and attainability of meeting stringent climate targets without overshoot. Nat. Clim. Change 11, 1063–1069 (2021).
50. Zomer RJ Global tree cover and biomass carbon on agricultural land: the contribution of agroforestry to global and national carbon budgets Sci. Rep. 2016 6 29987 10.1038/srep29987 27435095
Zomer, R. J. et al. Global tree cover and biomass carbon on agricultural land: the contribution of agroforestry to global and national carbon budgets. Sci. Rep. 6, 29987 (2016).27435095
51. Zomer RJ Global carbon sequestration potential of agroforestry and increased tree cover on agricultural land Circular Agric. Syst. 2022 2 1 10 10.48130/CAS-2022-0003
Zomer, R. J. et al. Global carbon sequestration potential of agroforestry and increased tree cover on agricultural land. Circular Agric. Syst. 2, 1–10 (2022).
52. Prestele R A spatially explicit representation of conservation agriculture for application in global change studies Glob. Change Biol. 2018 24 4038 4053 10.1111/gcb.14307
Prestele, R. et al. A spatially explicit representation of conservation agriculture for application in global change studies. Glob. Change Biol. 24, 4038–4053 (2018).
53. Henderson BB Greenhouse gas mitigation potential of the world’s grazing lands: modeling soil carbon and nitrogen fluxes of mitigation practices Agric. Ecosys. Environ. 2015 207 91 100 10.1016/j.agee.2015.03.029
Henderson, B. B. et al. Greenhouse gas mitigation potential of the world’s grazing lands: modeling soil carbon and nitrogen fluxes of mitigation practices. Agric. Ecosys. Environ. 207, 91–100 (2015).
54. Paustian K Climate-smart soils Nature 2016 532 49 57 10.1038/nature17174 27078564
Paustian, K. et al. Climate-smart soils. Nature 532, 49–57 (2016).27078564
55. Bai Y Cotrufo MF Grassland soil carbon sequestration: current understanding, challenges, and solutions Science 2022 377 603 608 10.1126/science.abo2380 35926033
Bai, Y. & Cotrufo, M. F. Grassland soil carbon sequestration: current understanding, challenges, and solutions. Science 377, 603–608 (2022).35926033
56. Bardgett RD Combatting global grassland degradation. Nat. Rev. Earth Environ. 2021 2 720 735 10.1038/s43017-021-00207-2
Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2, 720–735 (2021).
57. Krey, V. et al. MESSAGEix-GLOBIOM Documentation (International Institute for Applied Systems Analysis, 2020).
58. Herrero M Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems Proc. Natl Acad. Sci. USA 2013 110 20888 20893 10.1073/pnas.1308149110 24344273
Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).24344273
59. Smith P Soil carbon sequestration and biochar as negative emission technologies Glob. Change Biol. 2016 22 1315 1324 10.1111/gcb.13178
Smith, P. Soil carbon sequestration and biochar as negative emission technologies. Glob. Change Biol. 22, 1315–1324 (2016).
60. Roe S Contribution of the land sector to a 1.5 °C world Nat. Clim. Change 2019 9 817 828 10.1038/s41558-019-0591-9
Roe, S. et al. Contribution of the land sector to a 1.5 °C world. Nat. Clim. Change 9, 817–828 (2019).
61. Agricultural Policy Monitoring and Evaluation (OECD, 2022).
62. Zurek M Hebinck A Selomane O Climate change and the urgency to transform food systems Science 2022 376 1416 1421 10.1126/science.abo2364 35737771
Zurek, M., Hebinck, A. & Selomane, O. Climate change and the urgency to transform food systems. Science 376, 1416–1421 (2022).35737771
63. Patterson JJ Political feasibility of 1.5°C societal transformations: the role of social justice Curr. Opin. Environ. Sustain. 2018 31 1 9 10.1016/j.cosust.2017.11.002
Patterson, J. J. et al. Political feasibility of 1.5°C societal transformations: the role of social justice. Curr. Opin. Environ. Sustain. 31, 1–9 (2018).
64. Lipper L Climate-smart agriculture for food security Nat. Clim. Change 2014 4 1068 1072 10.1038/nclimate2437
Lipper, L. et al. Climate-smart agriculture for food security. Nat. Clim. Change 4, 1068–1072 (2014).
65. Venier-Cambron, C. et al. Representing justice in global land-use scenarios can align biodiversity benefits with protection from land grabbing. One Earth10.1016/j.oneear.2024.03.006 (2024).
66. Giang A Equity and modeling in sustainability science: examples and opportunities throughout the process Proc. Natl Acad. Sci. USA 2024 121 e2215688121 10.1073/pnas.2215688121 38498705
Giang, A. et al. Equity and modeling in sustainability science: examples and opportunities throughout the process. Proc. Natl Acad. Sci. USA 121, e2215688121 (2024).38498705
67. Bradford MA Managing uncertainty in soil carbon feedbacks to climate change Nat. Clim. Change 2016 6 751 758 10.1038/nclimate3071
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
68. Arora VK Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models Biogeosciences 2020 17 4173 4222 10.5194/bg-17-4173-2020
Arora, V. K. et al. Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models. Biogeosciences 17, 4173–4222 (2020).
69. Beillouin D A global meta-analysis of soil organic carbon in the Anthropocene Nat. Commun. 2023 14 3700 10.1038/s41467-023-39338-z 37349294
Beillouin, D. et al. A global meta-analysis of soil organic carbon in the Anthropocene. Nat. Commun. 14, 3700 (2023).37349294
70. Frank D Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts Glob. Change Biol. 2015 21 2861 2880 10.1111/gcb.12916
Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).
71. Pinheiro FM Soil carbon stock and stability under eucalyptus-based silvopasture and other land-use systems in the Cerrado biodiversity hotspot J. Environ. Manage. 2021 299 113676 10.1016/j.jenvman.2021.113676 34526275
Pinheiro, F. M. et al. Soil carbon stock and stability under eucalyptus-based silvopasture and other land-use systems in the Cerrado biodiversity hotspot. J. Environ. Manage. 299, 113676 (2021).34526275
72. Pent, G. Over-yielding in temperate silvopastures: a meta-analysis. Agrofor. Syst.10.1007/s10457-020-00494-6 (2020).
73. Grosjean G Options to overcome the barriers to pricing European agricultural emissions Clim. Policy 2018 18 151 169 10.1080/14693062.2016.1258630
Grosjean, G. et al. Options to overcome the barriers to pricing European agricultural emissions. Clim. Policy 18, 151–169 (2018).
74. Wreford, A., Ignaciuk, A. & Gruère, G. Overcoming Barriers to the Adoption of Climate-Friendly Practices in Agriculture OECD Food, Agriculture and Fisheries Papers No. 145 (OECD, 2017).
75. Brown C Achievement of Paris climate goals unlikely due to time lags in the land system Nat. Clim. Change 2019 9 203 208 10.1038/s41558-019-0400-5
Brown, C. et al. Achievement of Paris climate goals unlikely due to time lags in the land system. Nat. Clim. Change 9, 203–208 (2019).
76. Owombo PT Idumah FO Determinants of agroforestry technology adoption among arable crop farmers in Ondo State, Nigeria: an empirical investigation Agrofor. Syst. 2017 91 919 926 10.1007/s10457-016-9967-2
Owombo, P. T. & Idumah, F. O. Determinants of agroforestry technology adoption among arable crop farmers in Ondo State, Nigeria: an empirical investigation. Agrofor. Syst. 91, 919–926 (2017).
77. Shyamsundar P Scaling smallholder tree cover restoration across the tropics Glob. Environ. Change 2022 76 102591 10.1016/j.gloenvcha.2022.102591
Shyamsundar, P. et al. Scaling smallholder tree cover restoration across the tropics. Glob. Environ. Change 76, 102591 (2022).
78. Henderson, B. & Verma, M. Global Assessment of the Carbon Leakage Implications of Carbon Taxes on Agricultural Emissions OECD Food, Agriculture and Fisheries Papers No. 170 (OECD, 2021).
79. Lamboll, R. D. et al. Assessing the size and uncertainty of remaining carbon budgets. Nat. Clim. Change 13, 1360–1367 (2023).
80. Pe’er G A greener path for the EU Common Agricultural Policy Science 2019 365 449 451 10.1126/science.aax3146 31371602
Pe’er, G. et al. A greener path for the EU Common Agricultural Policy. Science 365, 449–451 (2019).31371602
81. Carlson, K. M. et al. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Change 7, 63–68 (2016).
82. Laborde D Agricultural subsidies and global greenhouse gas emissions Nat. Commun. 2021 12 2601 10.1038/s41467-021-22703-1 33972533
Laborde, D. et al. Agricultural subsidies and global greenhouse gas emissions. Nat. Commun. 12, 2601 (2021).33972533
83. Cohn AS Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation Proc. Natl Acad. Sci. USA 2014 111 7236 7241 10.1073/pnas.1307163111 24778243
Cohn, A. S. et al. Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation. Proc. Natl Acad. Sci. USA 111, 7236–7241 (2014).24778243
84. Spillias S Reducing global land-use pressures with seaweed farming Nat. Sustain. 2023 6 380 390 10.1038/s41893-022-01043-y
Spillias, S. et al. Reducing global land-use pressures with seaweed farming. Nat. Sustain. 6, 380–390 (2023).
85. The State of Food and Agriculture: Livestock in the Balance (FAO, 2009).
86. Wongpiyabovorn O Plastina A Crespi JM Challenges to voluntary Ag carbon markets Appl. Econ. Perspect. Policy 2023 45 1154 1167 10.1002/aepp.13254
Wongpiyabovorn, O., Plastina, A. & Crespi, J. M. Challenges to voluntary Ag carbon markets. Appl. Econ. Perspect. Policy 45, 1154–1167 (2023).
87. Klenert D Making carbon pricing work for citizens Nat. Clim. Change 2018 8 669 677 10.1038/s41558-018-0201-2
Klenert, D. et al. Making carbon pricing work for citizens. Nat. Clim. Change 8, 669–677 (2018).
88. Kahiluoto H Enabling food security by verifying agricultural carbon Nat. Clim. Change 2014 4 309 311 10.1038/nclimate2209
Kahiluoto, H. et al. Enabling food security by verifying agricultural carbon. Nat. Clim. Change 4, 309–311 (2014).
89. Thompson, N. M. et al. Opportunities and challenges associated with ‘carbon farming’ for US row-crop producers. Choices 37, 316-2022-1149 (2022).
90. Marland G McCarl BA Schneider U Soil carbon: policy and economics Clim. Change 2001 51 101 117 10.1023/A:1017575018866
Marland, G., McCarl, B. A. & Schneider, U. Soil carbon: policy and economics. Clim. Change 51, 101–117 (2001).
91. IBF-IIASA Global Biosphere Management Model (GLOBIOM) Documentation 2023 Version 1.0 (International Institute for Applied Systems Analysis, 2023).
92. Skalský, R. et al. GEO-BENE Global Database for Bio-physical Modeling Version 1.0—Concepts, Methodologies and Data. The GEO-BENE Database Report (International Institute for Applied Systems Analysis, 2008).
93. Williams, J. R. in Computer Models of Watershed Hydrology (ed. Singh, V. P.) 909–1000 (Water Resources, 1995).
94. Kindermann GE A global forest growing stock, biomass and carbon map based on FAO statistics Silva Fennica 2008 42 387 396 10.14214/sf.244
Kindermann, G. E. et al. A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica 42, 387–396 (2008).
95. Huppmann D The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): an open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development Environ. Model. Softw. 2019 112 143 156 10.1016/j.envsoft.2018.11.012
Huppmann, D. et al. The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): an open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. Environ. Model. Softw. 112, 143–156 (2019).
96. Landsberg JJ Waring RH A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning For. Ecol. Manag. 1997 95 209 228 10.1016/S0378-1127(97)00026-1
Landsberg, J. J. & Waring, R. H. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For. Ecol. Manag. 95, 209–228 (1997).
97. Forrester DI Tang X Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model Ecol. Model. 2016 319 233 254 10.1016/j.ecolmodel.2015.07.010
Forrester, D. I. & Tang, X. Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model. Ecol. Model. 319, 233–254 (2016).
98. Augustynczik ALD Yousefpour R Assessing the synergistic value of ecosystem services in European beech forests Ecosyst. Serv. 2021 49 101264 10.1016/j.ecoser.2021.101264
Augustynczik, A. L. D. & Yousefpour, R. Assessing the synergistic value of ecosystem services in European beech forests. Ecosyst. Serv. 49, 101264 (2021).
99. Viskari T Calibrating the soil organic carbon model Yasso20 with multiple datasets Geosci. Model Dev. 2022 15 1735 1752 10.5194/gmd-15-1735-2022
Viskari, T. et al. Calibrating the soil organic carbon model Yasso20 with multiple datasets. Geosci. Model Dev. 15, 1735–1752 (2022).
100. Andrén O Kätterer T ICBM: the introductory carbon balance model for exploration of soil carbon balances Ecol. Appl. 1997 7 1226 1236 10.1890/1051-0761(1997)007[1226:ITICBM]2.0.CO;2
Andrén, O. & Kätterer, T. ICBM: the introductory carbon balance model for exploration of soil carbon balances. Ecol. Appl. 7, 1226–1236 (1997).
101. Hengl T SoilGrids250m: global gridded soil information based on machine learning PLoS ONE 2017 12 e0169748 10.1371/journal.pone.0169748 28207752
Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).28207752
102. Fick SE Hijmans RJ WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas Int. J. Climatol. 2017 37 4302 4315 10.1002/joc.5086
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
103. Booth TH Eucalypt plantations and climate change For. Ecol. Manag. 2013 301 28 34 10.1016/j.foreco.2012.04.004
Booth, T. H. Eucalypt plantations and climate change. For. Ecol. Manag. 301, 28–34 (2013).
104. Trotsiuk V Hartig F Forrester DI r3PG–an r package for simulating forest growth using the 3‐PG process‐based model Meth. Ecol. Evol. 2020 11 1470 1475 10.1111/2041-210X.13474
Trotsiuk, V., Hartig, F. & Forrester, D. I. r3PG–an r package for simulating forest growth using the 3‐PG process‐based model. Meth. Ecol. Evol. 11, 1470–1475 (2020).
105. Cubbage F Comparing silvopastoral systems and prospects in eight regions of the world Agrofor. Syst. 2012 86 303 314 10.1007/s10457-012-9482-z
Cubbage, F. et al. Comparing silvopastoral systems and prospects in eight regions of the world. Agrofor. Syst. 86, 303–314 (2012).
106. Jose S Walter D Mohan Kumar B Ecological considerations in sustainable silvopasture design and management Agrofor. Syst. 2019 93 317 331 10.1007/s10457-016-0065-2
Jose, S., Walter, D. & Mohan Kumar, B. Ecological considerations in sustainable silvopasture design and management. Agrofor. Syst. 93, 317–331 (2019).
107. Tonucci RG Soil carbon storage in silvopasture and related land-use systems in the Brazilian cerrado J. Environ. Qual. 2011 40 833 841 10.2134/jeq2010.0162 21546669
Tonucci, R. G. et al. Soil carbon storage in silvopasture and related land-use systems in the Brazilian cerrado. J. Environ. Qual. 40, 833–841 (2011).21546669
108. Rockwood, D. L. et al. in New Vistas in Agroforestry: A Compendium for 1st World Congress of Agroforestry (eds Nair, P. K. R. et al.) 51–63 (Springer, 2004).
109. Hamilton, J. Silvopasture Establishment and Management Principles for Pine Forests in the Southeastern United States 72 (USDA National Agroforestry Center, 2008).
110. Smith MM Silvopasture in the USA: a systematic review of natural resource professional and producer-reported benefits, challenges, and management activities Agric. Ecosyst. Environ. 2022 326 107818 10.1016/j.agee.2021.107818
Smith, M. M. et al. Silvopasture in the USA: a systematic review of natural resource professional and producer-reported benefits, challenges, and management activities. Agric. Ecosyst. Environ. 326, 107818 (2022).
111. Quinkenstein A Ecological benefits of the alley cropping agroforestry system in sensitive regions of Europe Environ. Sci. Policy 2009 12 1112 1121 10.1016/j.envsci.2009.08.008
Quinkenstein, A. et al. Ecological benefits of the alley cropping agroforestry system in sensitive regions of Europe. Environ. Sci. Policy 12, 1112–1121 (2009).
112. Havlík P Global land-use implications of first and second generation biofuel targets Energy Policy 2011 39 5690 5702 10.1016/j.enpol.2010.03.030
Havlík, P. et al. Global land-use implications of first and second generation biofuel targets. Energy Policy 39, 5690–5702 (2011).
113. Di Fulvio F Spatially explicit assessment of roundwood and logging residues availability and costs for the EU28 Scand. J. For. Res. 2016 31 691 707 10.1080/02827581.2016.1221128
Di Fulvio, F. et al. Spatially explicit assessment of roundwood and logging residues availability and costs for the EU28. Scand. J. For. Res. 31, 691–707 (2016).
114. Panoutsou, C. Modeling and Optimization of Biomass Supply Chains: Top-Down and Bottom-Up Assessment for Agricultural, Forest and Waste Feedstock (Academic Press, 2017).
115. Eggelston, H. S. et al. (eds) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme (IGES, 2006).
116. Smith P Greenhouse gas mitigation in agriculture Philos. Trans. R. Soc. B 2008 363 789 813 10.1098/rstb.2007.2184
Smith, P. et al. Greenhouse gas mitigation in agriculture. Philos. Trans. R. Soc. B 363, 789–813 (2008).
117. Lal R Enhancing crop yields in the developing countries through restoration of the soil organic carbon pool in agricultural lands. Land Degrad. Dev. 2006 17 197 209 10.1002/ldr.696
Lal, R. Enhancing crop yields in the developing countries through restoration of the soil organic carbon pool in agricultural lands. Land Degrad. Dev. 17, 197–209 (2006).
118. Homagain K Life cycle cost and economic assessment of biochar-based bioenergy production and biochar land application in Northwestern Ontario, Canada For. Ecosyst. 2016 3 21 10.1186/s40663-016-0081-8
Homagain, K. et al. Life cycle cost and economic assessment of biochar-based bioenergy production and biochar land application in Northwestern Ontario, Canada. For. Ecosyst. 3, 21 (2016).
119. Lauri P Impact of the 2°C target on global woody biomass use For. Policy Econ. 2017 83 121 130 10.1016/j.forpol.2017.07.005
Lauri, P. et al. Impact of the 2°C target on global woody biomass use. For. Policy Econ. 83, 121–130 (2017).
120. Holmatov B Can crop residues provide fuel for future transport? Limited global residue bioethanol potentials and large associated land, water and carbon footprints Renew. Sustain. Energy Rev. 2021 149 111417 10.1016/j.rser.2021.111417
Holmatov, B. et al. Can crop residues provide fuel for future transport? Limited global residue bioethanol potentials and large associated land, water and carbon footprints. Renew. Sustain. Energy Rev. 149, 111417 (2021).
121. Scarlat N Martinov M Dallemand J-F Assessment of the availability of agricultural crop residues in the European Union: potential and limitations for bionenergy use Waste Manage. 2010 30 1889 1897 10.1016/j.wasman.2010.04.016
Scarlat, N., Martinov, M. & Dallemand, J.-F. Assessment of the availability of agricultural crop residues in the European Union: potential and limitations for bionenergy use. Waste Manage. 30, 1889–1897 (2010).
122. Deckungsbeiträge und Daten für die Betriebsplanung 2008 (BMLFUW, 2008).
