# Delayed use of bioenergy crops might threaten climate and food security

Siqing Xu, Rong Wang, Thomas Gasser, Philippe Ciais, Josep Peñuelas, Yves Balkanski, Olivier Boucher, Ivan Janssens, Jordi Sardans, James Clark, et al.

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Siqing Xu, Rong Wang, Thomas Gasser, Philippe Ciais, Josep Peñuelas, et al. "Delayed use of bioenergy crops might threaten climate and food security." Nature, 2022, 609 (7926), pp.299-306. 10.1038/s41586-022-05055-8. hal-03779534

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Xu, Siqing, Wang, Rong, Gasser, Thomas et al. (13 more authors) (2022) *Delayed use of bioenergy crops might threaten climate and food security*. Nature. 299–306. ISSN: 0028-0836

https://doi.org/10.1038/s41586-022-05055-8

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1 **Delayed use of bioenergy crops might threaten climate and food security**

2 Siqing Xu¹, Rong Wang¹,²,³,¹³,¹⁴*, Thomas Gasser⁴, Philippe Ciais⁵,⁶, Josep Peñuelas⁷,⁸,

3 Yves Balkanski⁵, Olivier Boucher⁹, Ivan A. Janssens¹⁰, Jordi Sardans⁷,⁸, James H.

4 Clark¹,¹¹, Junji Cao¹², Xiaofan Xing¹, Jianmin Chen¹,²,³, Lin Wang¹,²,³, Xu Tang²,³,
5 Renhe Zhang²,³,¹³

6 ¹Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP³),
7 Department of Environmental Science and Engineering, Fudan University, Shanghai
8 200438, China.

9 ²IRDR International Center of Excellence on Risk Interconnectivity and Governance
10 on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai
11 200438, China.

12 ³Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China.

13 ⁴International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria.

14 ⁵Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette 91190, France.

16 ⁶Climate and Atmosphere Research Center (CARE-C) The Cyprus Institute 20
17 Konstantinou Kavafi Street, 2121, Nicosia, Cyprus.

18 ⁷CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08193 Bellaterra, Catalonia, Spain

19 ⁸CREAF, 08193 Cerdanyola del Vallès, Catalonia, Spain.

20 ⁹Institut Pierre-Simon Laplace, Sorbonne Université /CNRS, Paris 75252, France.

21 ¹⁰Department of Biology, University of Antwerp, Wilrijk B2610, Belgium.

22 ¹¹Green Chemistry Centre of Excellence, University of York, York YO105DD, UK.

23 ¹²Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,
24 China.

25 ¹³Shanghai Frontiers Science Center of Atmosphere-Ocean Interaction, Shanghai,
26 200438, China.

27 ¹⁴Institute of Eco-Chongming (IEC), 20 Cuiniao Rd., Chongming, Shanghai, 202162,
28 China.

29 *Correspondence: Rong Wang (rongwang@fudan.edu.cn).
30 Abstract

31 The potential of mitigation actions to limit global warming within 2 °C¹ might rely
32 on the abundant supply of biomass for large-scale bioenergy with carbon capture
33 and storage (BECCS) that is assumed to scale up significantly in the future²⁻⁵.

34 However, the detrimental effects of climate change on crop yields may reduce the
35 capacity of BECCS and threaten food security⁶⁻⁸, thus creating an unrecognized
36 positive feedback loop on global warming. We quantified the strength of this
37 feedback by implementing the responses of crop yields to increases in growing-
38 season temperature, atmospheric CO₂ concentration and intensity of nitrogen (N)
39 fertilization in a compact Earth system model⁹. Exceeding a threshold of climate
40 change would cause transformative changes in social-ecological systems by
41 jeopardizing climate stability and threatening food security. If global mitigation
42 alongside large-scale BECCS is delayed to 2060 when global warming exceeds
43 ~2.5 °C, then the yields of agricultural residues for BECCS would be too low to
44 meet the Paris goal of 2 °C by 2200. This risk of failure is amplified by the
45 sustained demand for food, leading to an expansion of cropland or intensification
46 of N fertilization to compensate for climate-induced yield losses. Our findings
47 thereby reinforce the urgency of early mitigation, preferably by 2040, to avoid
48 irreversible climate change and serious food crises unless other negative-emission
49 technologies become available in the near future to compensate for the reduced
50 capacity of BECCS.
One hundred and ninety-one parties responsible for 97% of global anthropogenic greenhouse-gas (GHG) emissions have joined the Paris Agreement with the objective to limit global warming by this century to 2 °C, while pursuing efforts to stay within warming of 1.5 °C¹. Global warming in 2021 is approaching 1.2 °C above the 1850–1900 average². Achieving all pledges under the nationally determined contributions may limit warming just below 2 °C, which requires steep emission reductions in the current decade¹⁰. Many mitigation scenarios nonetheless assume that climate change could be mitigated by negative-emission technologies such as bioenergy with carbon capture and storage (BECCS), which would be deployed in the second half of this century to benefit from technological advances³⁻⁵. However, large-scale deployment of BECCS faces biophysical, technical and social challenges¹¹,¹². An overreliance on BECCS could delay other decarbonizing technologies and fail to meet the Paris goal under overshoot scenarios¹³. Early actions are important to avoid irreversible climate change and drastic shifts in land use¹⁴. The USA, the EU and China, the three largest emitters of carbon dioxide (CO₂), aim to achieve carbon (C) neutrality by either 2050 or 2060¹. The effectiveness of these pledges depends largely on the remaining emissions in countries that have not yet made such pledges and on feedbacks in the carbon-climate systems¹⁵ that have not been fully recognized by current integrated assessment models (IAMs)².

Climate change is projected to be decelerated by dramatically abating CO₂ emissions from fossil fuels¹⁰, but large-scale negative-emission technologies at a global scale are required in most of the scenarios limiting global warming to 2 °C². Retrofitting coal-fired power plants to BECCS, which substitutes fossil fuels by generating electricity with biomass from lignocellulosic energy crops or residues and removes CO₂ from the atmosphere, is assumed to be a cost-effective option in IAMs¹⁶,¹⁷. Capturing CO₂ from the combustion of agricultural residues from food crops (e.g. maize and rice) or dedicated energy crops and storing it in geological sites are proposed to achieve the 2 or 1.5 °C target in the sixth assessments of the Intergovernmental Panel on Climate Change (IPCC)². Using the biomass from agricultural residues as feedstocks to generate electricity is more economical than growing dedicated energy crops (e.g. *Miscanthus*)¹⁸,¹⁹. Since the population and food demand from developing countries are both increasing²⁰, transferring residues of agricultural crops to BECCS would reduce the competition of new dedicated energy crops with food production for resources such as land, fertilizers and water²¹. Future crop yields, however, may decline due to the
detrimental effects of climate warming⁶⁻⁸ if strong mitigation actions are delayed, thereby reducing the capacity of BECCS for mitigation (Fig. 1). These feedbacks have not been considered in current IAMs²⁻⁴, which rely on the availability of agricultural residues¹⁸ or dedicated energy crops⁵ for BECCS at a large scale. The impacts of BECCS on the food-climate-energy nexus have been assessed in the literature (Table S1), but the feedbacks of reduced BECCS capacity to climate warming are unclear. Additional measures such as irrigation²², adaptation of crop cultivars²³ and conservation agriculture⁸ are helpful for increasing the productivity of cropland, but the widespread water scarcity due to the increasing frequency and intensity of droughts around the globe²⁴ may limit the potential of those adaptation measures for increasing crop yields. A quantification of the impact of reduced crop yields on climate change mitigation is needed for estimating the interactions between biological and techno-economic components²⁵ of the Earth system, recognizing the tipping points in social-ecological systems²⁶ and assessing the effectiveness of emission pledges to meet the 2 °C goal in the Paris Agreement¹.

## Scenarios of climate mitigation with BECCS

We examined how the benefits of ambitious mitigation with large-scale BECCS aimed at meeting climate and food targets could be offset due to reduction in crop yields under climate change (Fig. 1). We quantified the impact of climate change on crop yields in a set of scenarios, where global large-scale mitigation is initiated at the start of each decade from 2030 to 2100. When ambitious mitigation starts, we assumed that policy reduces fossil emissions from the baseline scenario of the Shared Socioeconomic Pathway (SSP) 5-8.5 to the lower-emission scenario of SSP2-4.5², while BECCS is deployed using agricultural residues globally (Fig. S1 and Methods). There are other decarbonizing technologies taking place from 2030 to meet emission pledges in the SSP2-4.5 scenario, but they imply a lack of negative emissions to be compliant with net-zero emissions² by 2100. SSP5-8.5 is worse than what seems to be “business-as-usual” emissions²⁷, but phasing out fossil fuels rapidly and deploying BECCS moves our projections close to the IPCC low-warming scenarios². Cumulative emissions during 2021–2050 in our scenario with mitigation starting in 2030 are 380 Gt C from fossil fuel reduction alone, with additional negative emissions of –120 Gt C from BECCS by 2050 (Fig. S2). These net emissions (260 Gt C) are higher than SSP 1-1.9 (150 Gt) but similar to SSP1-2.6 (250 Gt C)², which meets the Paris goal of 2 °C¹.
In our assumptions, the area of land converted from forests or marginal lands to cropland and the intensity of N fertilization depend on the food demand in 2030 (e.g. a higher food demand elicits more land conversion from forests or marginal lands to cropland). The impacts of transferring C associated with land-use change (LUC) from soils and vegetation to the atmosphere, and of the terrestrial emissions of methane and nitrous oxide (N₂O) on climate change, were simulated using the OSCAR Earth system model⁹. We estimated the average growing-season temperature for maize, rice and wheat by country based on global crop calendar data (Methods). We considered a scenario where half of cropland expansions from forests and marginal lands²⁸ were used to grow new energy crops and the other half were used to grow food crops with the residues used for BECCS. Since technologies increase crop yields, we considered two scenarios, where the N use efficiency would be enhanced globally²⁹ or the growing season was brought forward or delayed by one month to increase the crop yield by country. Negative emissions from BECCS were estimated based on the amount of C produced as biomass and an efficiency of capturing 90% of the CO₂ emitted by BECCS plants³⁰, while we examined the climate benefits for different types of bioenergy. Interactions between climate change and the global C cycle have been calibrated using the results of models in Coupled Model Intercomparison Project (CMIP)³¹ Phase 5 and 6. By running Monte Carlo simulations with OSCAR⁹, our results are representative of the CMIP ensembles³¹ and the variation in the yield-climate relationships.

## Relationships between crop yields and climate

We estimated the relationships between crop yields (Y) and the average growing-season temperature ($T_{atm}$), atmospheric CO₂ concentration ($X_{CO2}$) and N fertilization ($Z_{nit}$) using global data. First, crop yield peaks at an optimal temperature ($T_{opt}$) and decreases when temperatures increase beyond $T_{opt}$ due to increasing water loss by evapotranspiration and lower enzymatic activity in foliar photosynthesis when $T_{atm}$ exceeds a criterion⁷,³² (Fig. 2a,b). In our central case, we used a quadratic function to fit the yields of wheat and maize from field-warming experiments and local process-based or statistical crop models (Table S2 and Supplementary Data Set 1) by constraining $T_{opt}$ (Table S3). We considered that the yield of wheat would be reduced to 1% of its maximum value when $T_{atm}$ exceeded 29 °C ($T_{dam}$)³³ to represent the effect of heat exposure over the whole growing season. Short exposures to temperatures above 40 °C with low humidity may be lethal³⁴, but the effect of extreme heat events is not considered due to the lack of direct evidence. Following this, we examined the impact
of increasing $T_{opt}$ or $T_{dam}$ by 1 °C or using data from field warming experiments only, which altered the $Y-T_{atm}$ function moderately (Fig. S3). We examined the linear or nonlinear $Y-T_{atm}$ functions to fit the sensitivity of wheat yield to temperature for $T_{atm}$ ≤15 °C from field-warming experiments<sup>7</sup>, which led to a faster decline in crop yield for $T_{atm}$ <25 °C than our estimate (Fig. S3).

Second, elevated $X_{CO2}$ increases the rate of plant photosynthesis of C-3 crops and the yields of wheat and rice<sup>35</sup>. This effect saturates when $X_{CO2}$ exceeds 700 ppm (Fig. 2c), likely due to the co-limitation of soil nutrients and water<sup>36</sup>. We used a quadratic function to fit the saturating yield of wheat grown with ample water and nutrients at an optimal temperature in free-air CO₂-enrichment experiments<sup>37</sup> for $X_{CO2}$<700 ppm ($P$<0.001) and assumed a flat response for $X_{CO2}$>700 ppm. This empirical sensitivity of Y to $X_{CO2}$ is similar to the sensitivity obtained with crop models for wheat in the Netherlands and rice in Japan but is larger for maize as a C-4 crop in Tanzania that is exposed to higher temperatures<sup>38</sup> (Fig. S3). Third, N addition is beneficial for the growth of crops, but the effect decreases with excessive inputs<sup>39</sup>. We used a logarithmic function to fit the yields of rice, wheat, maize and soybeans<sup>40</sup> by region from 1961 to 2019 after adjusting for the impacts of $T_{atm}$, $X_{CO2}$ and precipitation (Fig. 2d and Fig. S4). The yield of rice increases by six folds when N fertilization increases from 5 to 100 kg ha⁻¹ but by 12% when it increases further from 100 to 150 kg ha⁻¹.

The yield-climate relationships are compared among five agriculturally important countries (Fig. S5). Crop yield is more sensitive to warming at lower latitudes and more sensitive to N inputs in the USA than in other countries<sup>38</sup>. We assumed that the dependencies of crop yield on air temperature, CO₂ concentration and N fertilization for a limited set of species could be generalized to energy crops due to the lack of consistent data for those specific cultivars. We adopted the parameters calibrated in a previous study⁹ to prescribe regional responses of yield to precipitation due to the lack of data to estimate the relationship between crop yield and precipitation. Similar to a previous study⁶, the impact of precipitation was estimated to be low in our model (Fig. S6), but the compound effect of temperature and precipitation on crop yield deserves attention<sup>7,20</sup>. Our yield model is different from previous studies (e.g. ref⁶) using national crop yield from the Food and Agriculture Organization (FAO) data set<sup>40</sup>. However, identifying the impact of climate change on national crop yield<sup>40</sup> can be prevented in some regions where the impact of historical climate change was not strong enough yet
186 to reduce crop yield significantly⁶. It is important to further improve our crop yield
187 model when data from field-warming experiments become available in a broader range
188 of countries or the regional impacts of climate change on crop yields are more
189 significant under global warming.

190 **Feedbacks of reduced BECCS capacity to climate change**

191 Our simulations indicated that global warming would reach 2.5 °C (2.3–2.9 °C as the
192 range of 90% uncertainty) in 2050, 2.7 °C (2.4–3.1 °C) in 2100 and 1.7 °C (1.2–2.6 °C)
193 in 2200 (Fig. 3a), if large-scale mitigation alongside BECCS was initiated in 2040
194 (Methods). Cropland area is expanded to meet the caloric target⁴¹ of 2 million calories
195 per day (Mcal d⁻¹) per capita in 2030 for countries where the supply is below this
196 threshold, and cropland area is maintained for other countries. Due to the detrimental
197 effects of climate change on crop yields, there is a decline in global average per capita
198 calories from 2.2 Mcal d⁻¹ in 2030 to 1.8 (1.6–2.0) and 2.1 (1.8–2.2) Mcal d⁻¹ in 2100
199 and 2200, respectively if the benefits of technology²⁹ were not considered (Fig. 3b). In
200 contrast, global warming is estimated to reach 3.4 and 4.2 °C in 2100, followed by a
201 decrease to 2.6 and 3.7 °C in 2200, if ambitious mitigation is delayed to 2050 and 2060,
202 respectively, because of a longer maintenance of fossil emissions and reduced biomass
203 feedstocks for BECCS. We provided the relationship between the quantity of bioenergy
204 from agricultural residues and the projected level of global warming in 2050, 2100 and
205 2200 (Fig. S7), which could be implemented into IAMs²⁻⁵.

206 If climate-induced feedbacks on crop yields are not considered by maintaining crop
207 yields and BECCS capacity at their levels simulated with current climatology in 2020,
208 global warming will decrease by 0.3, 0.6 and 0.8 °C in 2200 when ambitious mitigation
209 with BECCS is initiated in 2040, 2050 and 2060, respectively, relative to our central
210 cases (see Fig. S8 for the temporal evolutions of global warming and crop calories in
211 all scenarios). In addition, global warming will be lower than our central case, if 50%
212 of marginal lands are used to grow dedicated energy crops (e.g. *Miscanthus*) rather than
213 agricultural crops whose residues are used for BECCS, because energy crops produce
214 more bioenergy than do agricultural crops through the recovery of agricultural
215 residues³⁰. Further, if afforestation is considered in addition to BECCS by converting
216 marginal lands to forests, global warming will be lower than in the BECCS-only
217 scenarios without afforestation (Fig. 3). Lastly, if agricultural residues are used to
218 produce liquid bioethanol to replace vehicle oils without CCS or if the gas-fired power
219 plants were retrofitted for BECCS, the climate benefits of bioenergy would be lower
than retrofitting coal-fired power plants for BECCS, due to the higher CO₂ emissions incurred. If the biomass is used for liquid biofuel production with a high efficiency of energy conversion (47.5%)⁴³, then bioenergy at biorefineries generates less climate benefits than BECCS power plants if only 15% of CO₂ released at a high purity during the fermentation process to manufacture bioethanol is subject to CCS⁴³, but generates more benefits than BECCS power plants if 55% of CO₂ in the fermentation process can be captured⁴³. Given different types of bioenergy, the impact of the yield-climate feedback remains robust, which could lead to a failure of meeting the 2 °C goal¹ (Fig. S9).

After propagation of uncertainties, the probability of meeting the 2 °C goal¹ by 2200 would be reduced from 47 to 4% after considering agricultural feedbacks when mitigation is initiated in 2050. If mitigation is initiated in 2040, this probability only decreases from 93 to 75% by considering agricultural feedbacks. We examined the sensitivity of our results to the choice of yield-temperature functions fitted to experimental data only, fitting the Y-T_atm function to the sensitivity of crop yields to temperature⁷, increasing T_opt or T_dam by 1 °C when constraining the Y-T_atm function, and adopting the Y-X_CO₂ relationship for maize in Tanzania, wheat in the Netherlands or rice in Japan from crop models³⁸ (Fig. S3). The impact of feedbacks on failure to meet the 2 °C goal¹ due to delayed mitigation remains robust, but the crop caloric production could be increased or decreased using those alternative yield-climate relationships (Fig. 3). We did not account for all possible factors that could further limit BECCS capacity such as soil degradation¹² or imbalanced nitrogen-phosphorus supplies⁴⁴, so our model may be optimistic and meeting the Paris goals¹ may require even earlier or more ambitious mitigation than we estimated.

## Implications for food security

The previous section demonstrated a failure of delayed mitigation to meet the climate goal¹ of 2 °C as climate warming reduces crop yields and BECCS capacity, but the demand on crops for food need to be considered in addition to bioenergy production. We assessed whether enlarging cropland area by converting marginal lands and forests to cropland would ameliorate the conflict between food crops and BECCS by considering their impact on the global C cycle though LUC emissions. To do so, we assumed that first marginal lands and then forests are converted to cropland or that N fertilization is increased (see Fig. S10 for the spatial distributions of per capita cropland area and N fertilization in 2019) to meet higher caloric targets in 2030. The food supply
then depends on the responses of crop yields to climate change (Methods).

Global mitigation by 2050 is needed to match the increasing food demand in the face of decreasing crop yields (Fig. 4). Global warming will be higher in 2100 due to LUC emissions but lower in 2200 due to more BECCS negative emissions when mitigation is initiated earlier than 2050. We decomposed the changes in GHG emissions into its drivers. Total emissions during 2041–2200 to meet a reasonable per capita caloric target⁴¹ of 2 Mcal d⁻¹ would be 28 Gt C from the reduced terrestrial C sink, 10 Gt C from emissions induced by land-use change and 92 Gt C from terrestrial emissions of N₂O (converted to equivalent CO₂) (Methods) when mitigation is initiated in 2040 (Fig. S11). Converting marginal lands, rather than forests, to cropland will slow warming (see Fig. S12 for the difference between these scenarios) but increase the demand of fertilizers⁴⁴. In contrast, if mitigation is delayed to 2060, cropland expansion will accelerate global warming due to LUC and N₂O emissions, because the effect of cropland expansion to increase BECCS will be overcome by the reduction of BECCS capacity caused by global warming. The effect of intensifying N fertilization alone on slowing global warming is smaller than in the scenarios of increasing the area of cropland (Fig. S13) due to larger terrestrial emissions of N₂O (Fig. S11), saturation of N fertilization (Fig. 2d) and potential co-limitations by water and phosphorus⁴⁵.

## Impact of agricultural feedbacks on the C budget

The impact of deploying BECCS on allowable fossil emissions depends on the magnitude of agricultural feedbacks under climate change (Fig. 5). To meet the climate goal¹ of 2 °C in 2100 in our central estimate, allowable CO₂ emissions during 1850–2100 increases from 940 to 1400 Gt C by deploying BECCS without accounting for agricultural feedbacks, and to 1380 Gt C by including them. This negative emission service from BECCS (460 Gt C) agrees with previous model estimates (400–800 Gt C)⁴⁶, but requires that global mitigation actions are initiated by 2030. The impact of agricultural feedbacks on the global C budget is larger in 2200 than 2100. Allowable CO₂ emissions during 1850–2200 for meeting the target of 2 °C in 2200 increase from 1120 to 2040 Gt C by implementing large-scale BECCS when excluding agricultural feedbacks, but only to 1890 Gt C with them. The effects of agricultural feedbacks in reducing allowable CO₂ emissions will increase as the mitigation is delayed due to increasing feedbacks to climate warming. For example, agricultural feedbacks would reduce allowable CO₂ emissions by 150 and 270 Gt C to meet the targets of 2 and 3 °C in 2200, respectively. These reductions suggest that the ability to mitigate climate
288 change by BECCS will decrease as a result of delayed mitigation actions.

289 **Regional food gap under climate change**

290 Mitigating climate change requires global early actions through large-scale BECCS
291 implementation², but the impact of climate warming on crop yields varies among
292 regions. Based on the yield-climate relationships, warming increases yields of wheat
293 and maize over high-latitude regions with an average growing-season temperature
294 lower than 10 and 19 °C, covering 4 and 30% of the global cropland area, respectively
295 (Fig. S14). We define an index of food gap as one minus the ratio of per capita calories
296 to a minimum undernutrition level of 1.5 Mcal d⁻¹, where a higher positive food gap
297 indicates a larger shortage of food crops. The effect of a delay from 2040 to 2060 of
298 ambitious climate mitigation by deploying large-scale BECCS together with
299 decarbonizing technologies in the SSP2-4.5 scenario² would be that the food gap in
300 2100 will increase to >50% in India, Africa and Middle East without food trade (Fig.
301 6). Many developing countries are located at lower latitudes and exposed to higher
302 temperatures. Due to a delay of climate mitigation from 2040 to 2060, the number of
303 developing countries where the food gap is positive will increase from 81 to 90 in 2100.
304 In contrast, the food gap in 2100 remains negative in developed countries if ambitious
305 mitigation is delayed from 2040 to 2060.

306 The gap of food supply in low-latitude developing countries may be alleviated by
307 international trade of crops from temperate and northern countries to Central America,
308 Africa and the Middle East. Export of food crops (e.g. wheat, rice and maize) from
309 North America (417 Mt y⁻¹), Europe (385 Mt y⁻¹) and China (422 Mt y⁻¹) to the
310 remaining regions of the world is required to reduce the fraction of people with a
311 positive food gap in 2100 from 65% to 30% when mitigation starts in 2060 (Fig. S15).
312 The projected export of crops, however, would be 3, 2 and 80 times larger than the
313 current levels⁴⁰ in 2019 for these three regions, respectively, indicating a large and
314 likely implausible extent of increasing trade. Early climate mitigation¹⁰ or population
315 migration⁴⁷ may be the choice we have to make if the necessary food trade fails to occur.

316 **Implications**

317 Our results suggest that the negative impacts of climate change can reduce crop yields
318 and thus the BECCS capacity, leading our exceeding the 2 °C Paris goal¹ and
319 threatening food security. This process is absent in the future scenarios from current
320 IAMs relying on large-scale deployment of BECCS during the second half of this
321 century²⁻⁵,⁴⁸,⁴⁹. The capacity of BECCS could rapidly decrease after reaching a
threshold of climate warming. This would be the consequence of reduced biomass feedstocks in response to accelerated global warming due to a 20-year delay in mitigation from 2040 to 2060. The climate warming threshold, modeled here to occur in around 2050 when global warming exceeds 2.5 °C, is lower than many known 'tipping points' in the climate system that would lead to failure of the Paris goals¹, such as triggering the melting of the Greenland ice sheet or the collapse of the Atlantic thermohaline circulation⁵⁰. Exceeding the warming threshold above will jeopardize food security in the majority of developing countries, with a potential impact on developed countries. Accounting for these feedbacks improves our understanding of the food-climate-energy nexus, and reinforces the importance of early and ambitious mitigation¹⁰ to meet the Paris goals².

Delayed mitigation of CO₂ emissions inevitably requires a larger effort by deploying BECCS negative emissions, lasting for a longer time to offset the positive fossil emissions². Food crises due to a unprecedented climate change may also lead to a shift of the growing season⁷ and to population migration⁴⁷. As a caveat, our study may overestimate future food shortages because we did not consider all potential benefits of advancing technologies and optimizing managements⁵¹. As half of the N added to cropland is currently lost to the environment⁵² and in many countries N fertilization is already very high, food shortage could be alleviated by increasing the N use efficiency with better phosphorus and potassium fertilization so as to reach an adequate balance among these three fertilizers⁴⁴. For example, if the N use efficiency was increased following a recent projection²⁹ to increase N uptake by region and reduce N₂O emissions⁵³ for global croplands, per capita calories are projected to increase by 10% with a reduction of global warming by 0.2 °C in 2200 when mitigation is initiated in 2050 (Fig. S16). We also projected an increase in per capita calories by 11% and a reduction of global warming by 0.3 °C in 2200 if we bring forward or delay the growing season for each country to optimize the crop yield under future, warmer climatology. Assuming that humanity can moderate the rise of N fertilizers use and achieve a better N use efficiency (by crops taking up more N and getting more benefits from the N applied) by equilibrating fertilization⁴⁴, improving water use and developing new crop variates⁵¹, technologies will further alleviate the shortage of food and increase the capacity of BECCS. Even so, if ambitious mitigation of CO₂ emissions with a heavy reliance on BECCS is delayed, the impact of yield-climate feedbacks could still lead to a failure of meeting the 2 °C goal in the Paris Agreement¹ by considering the
interactions between crop yield and climate warming (Fig. S16). Accounting these
feedbacks substantially undermines the feasibility of high allowable fossil-fuel
emissions under overshoot scenarios¹³ of delayed mitigation relying heavily on BECCS
after 2050 to limit global warming below 2 °C²⁻⁵,⁴⁸,⁴⁹.

Our findings support the concerns of overshooting temperature targets by relying solely
on BECCS and the assumption that BECCS production would remain insensitive to
climate change³. They also indicate that irreversible climate change and serious food
crises should be best avoided by accelerating supply-side decarbonization⁵⁴ if the
reduced capacity of BECCS cannot be compensated by other negative-emission
technologies. Although biophysical and technological barriers of BECCS have been
widely recognized³,¹¹,¹²,¹⁴,⁴⁸,⁴⁹, our results underscore an unrecognized drawback of
BECCS due to agricultural feedbacks that limit BECCS capacity to mitigate climate
change in cases of delayed mitigation. If the climate benefits of BECCS were to be
attained, this technology should be deployed as early as possible, otherwise, the
decreasing biomass feedstocks will reduce the BECCS efficacy and lead to failure of
meeting the Paris goal of 2 °C¹ even by 2200. If the large-scale BECCS project cannot
be put into place in the near term, these feedbacks will inevitably reduce the allowable
emissions more than previously thought: demand-side decarbonization and other
negative-emission technologies should undergo a more rapid deployment for human
society to stay within the safe boundaries with regards to climate change.

376
377 References

378 1. United Nations Framework Convention on Climate Change (UNFCCC). Paris
379 Agreement - Status of Ratification. https://unfccc.int/process/the-paris-
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508
509 **Figure legends**

510 **Figure 1: Climate-yield feedbacks due to reduced biomass feedstocks of crop residues for bioenergy with carbon capture and storage (BECCS) and the potential impacts on food supply and land-use change (LUC).** This illustration shows the response of a social-ecological system relying on agricultural residues for bioenergy to a delay of mitigation with large-scale BECCS (from blue to red).

515 **Figure 2: Relationships between crop yield (Y), climate and land management.** **a,** **b,** A quadratic function of average growing-season atmospheric temperature ($T_{atm}$, °C) is used to fit the yields of wheat (**a**) and maize (**b**). The yields are derived from field warming experiments and process-based or statistical models from 13 countries worldwide (**Table S2**), where the yields are normalized to 1 at 25 °C for different studies. Six outliers are excluded ($P<0.005$). We adopted the optimal temperature ($T_{opt}$) for maize (19 °C) and wheat (9 °C) as an average in different countries or regions (**Table S3**) and assumed that the yield is reduced to 1% of its maximum value when $T_{atm}$ exceeds 29 °C ($T_{dam}$)$^{33,34}$. We used the yield-temperature functions fit to the local data to predict the crop yields by country if applicable and applied the functions fit to global data in the remaining regions of the world. The shaded area shows the 90% interval range of the fitted function, which is adopted in our Monte Carlo simulations. **c,** A quadratic function of atmospheric CO$_2$ concentration ($X_{CO2}$) is used to fit the wheat yield$^{37}$ for $X_{CO2}<700$ ppm. The yields are normalized to 1 at 350 ppm. A constant yield is predicted for $X_{CO2}\ge700$ ppm, where the correlation between Y and $X_{CO2}$ is not significant ($P=0.16$). **d,** A logarithmic function of N fertilization ($Z_{nit}$) is used to fit the yield of rice as an example (see **Fig. S4** for the yields of wheat, maize and soybeans)$^{40}$ in the nine regions of the OSCAR model from 1961 to 2019. The yields in (**d**) have been adjusted for the impacts of $T_{atm}$, $X_{CO2}$ and precipitation (**Methods**). The data used to fit the functions are listed in **Supplementary Data Set 1**. The arrow in each panel shows the range of $T_{atm}$, $X_{CO2}$ or $Z_{nit}$ in the OSCAR model.
**Figure 3: Impact of agricultural feedbacks on climate warming and food supply.**

Violin plots of global warming relative to 1850–1900 (a) and global average per capita calories (b) in 2100 or 2200 when ambitious mitigation is initiated in 2040 (blue), 2050 (yellow) or 2060 (orange), respectively by deploying large-scale BECCS together with decarbonizing technologies from the SSP2-4.5 scenario² after the year of mitigation onset. The results of scenarios without climate feedbacks on crop yields are obtained by maintaining the simulated capacity of BECCS for current climate (dashed violin plots). The results are estimated from Monte Carlo simulations combining uncertainties in the Y-T<sub>atm</sub> functions with uncertainties in the Earth system model (**Methods**). The horizontal line in each violin plot shows the median estimate. The Y-T<sub>atm</sub> function is derived from our central case, of which the sensitivity is examined to increasing T<sub>opt</sub> (I) or T<sub>dam</sub> by 1 °C (II), using experimental data only to fit the Y-T<sub>atm</sub> function (III) and fitting the sensitivity<sup>7</sup> of Y to T<sub>atm</sub> (s<sub>y,t</sub>) to a linear (IV) or nonlinear (V) function (**Fig. S3**). The Y-X<sub>CO2</sub> function is derived from our central case or crop models for maize in Tanzania (VI), wheat in the Netherlands (VII) and rice in Japan (VIII)<sup>38</sup>. We also consider a case with 50% of the cropland expanded from marginal lands for growing energy crops (*Miscanthus*) rather than food crops (IX) and a case with marginal lands converted to forests in afforestation (X). The difference between two neighbouring violin plots is examined (*** for P<0.001).

**Figure 4: The nexus of bioenergy, climate warming and food security. a, b, Global warming in 2100 (a) and 2200 (b) relative to 1850–1900 when cropland area is increased by first converting marginal lands and then forests to cropland to meet the caloric targets of 1.5–2.5 Mcal d⁻¹ in 2030. Climate mitigation is initiated in 2040, 2050 or 2060 by deploying large-scale BECCS with other decarbonizing technologies in the SSP2-4.5 scenario². The higher caloric targets show the impact of larger cropland areas that increases not only BECCS negative emissions but also N₂O emissions and CO₂ emissions due to land-use change (LUC). c, d, Global C budget with (unhatched) or without (hatched) feedbacks of reduced BECCS capacity due to**
reduced crop yields when cropland area is expanded to meet the caloric target of 2 Mcal d⁻¹ in 2030 and when global mitigation with large-scale BECCS is initiated in 2040 (c) or 2060 (d). The cascading bars show a decomposition of the C budget into fossil-fuel (FF) emissions, emissions due to land-use change (LUC) and terrestrial emissions of N₂O, BECCS, LUC emissions due to BECCS (LUC-B) and N₂O emissions due to BECCS (N₂O-B) from 1750 to 2200.

**Figure 5: Agricultural feedbacks impact the relationship between warming and cumulative CO₂ emissions.** Global warming in 2100 (a) or 2200 (b) relative to 1850–1900 is plotted against the cumulative CO₂ emissions by 2100 (a) or 2200 (b), respectively. Historical emissions are identical before 2020, but global climate mitigation starts in different years to deploy large-scale BECCS together with other decarbonizing technologies from the SSP2-4.5 scenario². Global warming in these scenarios without agricultural feedbacks by maintaining the capacity of BECCS (orange line) is compared with the result with them (green line). The relationship between global warming and cumulative CO₂ emissions in IPCC-AR6² is indicated by the purple lines. The shaded area indicates the range of 90% uncertainty in Monte Carlo simulations varying climate parameters and yield-climate relationships (**Methods**).

**Figure 6: Contribution of climate mitigation to reduce the regional food gap.** a, Regional food gap, defined as one minus the ratio of per capita calories to a minimum undernutrition level of 1.5 Mcal d⁻¹, in 2100. A higher food gap indicates a larger shortage of food crops. Ambitious mitigation is initiated in 2040 (solid line) or 2060 (dotted line) by deploying large-scale BECCS together with other decarbonizing technologies from the SSP2-4.5 scenario². The area of pie chart is proportional to current crop caloric production in 2019. Inserts show the food gap in 2100 when mitigation is initiated in different years. **b**, Food gap in 2100 when global climate mitigation starts in 2040. **c**, **d**, Plots of the food gap in 2100 when mitigation starts in 2040 (**c**) and the change in food gap when the timing of mitigation is advanced from 2060 to 2040 (**d**) against current per capita GDP in 2019 for developed (blue) and developing (red) countries, respectively.
593 **Methods**

594 **Earth system model**

595 We used a compact Earth system model, OSCAR 2.2, to simulate climate change during
596 historical and future periods driven by emissions of greenhouse gases (GHGs) from
597 human activities. Detailed descriptions of this model are provided by Li *et al.*⁵⁵, Gasser
598 *et al.*⁹,⁵⁶ and Fu *et al.*⁵⁷. The interactions between climate change and the carbon (C)
599 cycle in terrestrial systems were calibrated using the CMIP models³¹. In this study, we
600 implemented the yield-climate relationships into the OSCAR model to simulate the
601 interactions between climate change and agricultural development in assumed
602 scenarios of cropland expansion and intensified N fertilization and to evaluate the
603 impact of agriculture feedbacks on climate change under temperature overshoots¹³.

604 Total anthropogenic CO₂ emissions from fossil-fuel combustion and cement production
605 before 2010 were obtained from the CDIAC data set⁵⁸; anthropogenic emissions of
606 methane (CH₄), nitrous oxide (N₂O), nitrogen oxides (NOₓ), carbon monoxide (CO),
607 volatile organic compounds (VOCs), sulfur dioxide (SO₂), ammonia (NH₃), 11
608 hydrofluorocarbons (HFCs), eight perfluorocarbons (PFCs) and 16 ozone-depleting
609 substances were obtained from the EDGAR inventory⁵⁹; anthropogenic and natural
610 emissions of organic carbon (OC) and black carbon (BC) were obtained from the
611 ACCMIP inventory⁶⁰ and the GFED v3.1 inventory⁶¹ and emissions of CO₂ and non-
612 CO₂ GHGs due to land-use change (LUC) were obtained from the LUH1.1 data set⁶².

613 Forcing data after 2010 were compiled from the Shared Socioeconomic Pathway (SSP)
614 5-8.5 and SSP 2-4.5 (excluding the contribution of negative emissions)², including data
615 for anthropogenic emissions of CO₂, CH₄, N₂O, NOₓ, CO, VOCs, BC, OC, SO₂, NH₃,
616 11 HFCs, eight PFCs and 16 ozone-depleting substances.

617 The model was run with active interactions and feedbacks between various Earth
618 elements⁶³, where the elements interacting with each other in the Earth system
619 represented the responses of the climatic system to anthropogenic perturbations such as
620 GHG emissions from industrial processes, cropland expansion, LUC and intensified N
621 fertilization. Changes in global C budgets and GHG emissions were modeled using the
622 terrestrial C sink, LUC emissions and the terrestrial emissions of N₂O. This model
623 configuration allowed us to simulate the feedbacks of both climate change to
624 agricultural activities and of agricultural yields to climate change. Calculations of the
625 changes in atmospheric concentrations of CO₂, tropospheric and stratospheric
626 chemistry, surface albedo, terrestrial C sinks, LUC emissions, air-sea gas exchanges
and the regional responses of atmospheric temperature and precipitation to the climatic forcers in the OSCAR model were identical to those in previous studies⁹,⁵⁵⁻⁵⁷ with a limit to the simulated concentrations of N₂O and CH₄ (420 ppb for N₂O and 2200 ppb for CH₄).

## Net primary production in cropland

The net primary production for cropland (NPP, g C y⁻¹) in year t was represented by a function of crop yield (Yᵢt, g biomass ha⁻¹ y⁻¹) and cropland area (Aᵢt, ha):

$$NPP_t = \sum_{i=1}^{8} \frac{A_{it}Y_{it}\mu_i f_i}{v_i I_i} \quad (1)$$

where i is the crop, vᵢ is the fraction of shoots in the biomass, μᵢ is the fraction of dry biomass, fᵢ is the fraction of C in the dry biomass and Iᵢ is a harvest index, defined as the ratio of the mass of the harvested yield to aboveground biomass. We divided all crops into eight categories: cereals, roots and tubers, beans, oil crops, fiber crops, sugar crops, primary fruits and primary vegetables. The values of the parameters μᵢ, vᵢ, fᵢ and Iᵢ for these categories are listed in Table S4.

In our model, the crop yield (Yᵢt) in year t was predicted:

$$Y_{it} = Y_{i0} \frac{F^C(C_t) F^T(T_t) F^Z(Z_t) F^P(P_t)}{F^C(C_0) F^T(T_0) F^Z(Z_0) F^P(P_0)} \quad (2)$$

where Yᵢ₀ (g biomass ha⁻¹ y⁻¹) is the yield in 2019 and Cᵢ, Tᵢ, Zᵢ and Pᵢ denote atmospheric CO₂ concentration, average temperature during the growing season, cropland intensity of nitrogen (N) fertilization and precipitation in a future year t, respectively. F^C, F^T, F^Z and F^P were estimated from the relationships between observed crop yields and atmospheric CO₂ concentration (Cᵢ, ppm), atmospheric mean growing-season temperature (Tᵢ, °C), intensity of N fertilization (Zᵢ, kg N ha⁻¹) and precipitation (Pᵢ, mm y⁻¹), respectively:

$$F^C(C_t) = \beta^C C_t^2 + \gamma^C C_t + \alpha^C \quad (3)$$

$$F^T(T_t) = \beta^T T_t^2 + \gamma^T T_t + \alpha^T \quad (4)$$

$$F^Z(Z_t) = \gamma^Z \ln(Z_t) + \alpha^Z \quad (5)$$

$$F^P(P_t) = \exp(\gamma^P \Delta P_t) \quad (6)$$

where the coefficients α^C, β^C, γ^C, α^T, β^T, γ^T, α^Z and γ^Z were determined by fitting these functions to data (Supplementary Data Set 1). We compiled the yield data for maize and wheat from both field-warming experiments and local process-based or statistical models (Table S2). After excluding data with a narrow range of growing-season temperature or without controlling the impact of confounding variables, our data set
covers 13 countries globally distributed in Africa, East Asia, South Asia, West Asia, North America, South and Central America, where the average growing-season temperature ranges from 12 to 34 °C. As the environments for these experiments are different, it is necessary to normalize the variance of the yields between different studies. This is done by dividing the yields by the average yields measured around 25 °C using 10% of data. To constrain the yield-temperature functions, we compiled the optimal growing temperature ($T_{opt}$) for maize and wheat growing in different countries or regions (Table S3). We fit the yield-temperature functions to the local data in the USA, India, Sudan, Mexico, China, Pakistan and Africa using the local $T_{opt}$ if applicable or using the average $T_{opt}$ (Fig. S3), and we fit the global yield-temperature functions to all data applying the average $T_{opt}$ (Fig. 2).

In our Earth system model, we used the yield-temperature functions fit to the local data to predict the future crop yields in these countries if applicable and used the yield-temperature functions fit to the global data in the remaining regions of the world. We did not find long-term data for other crops and assumed that the yield-temperature function for other crops is similar to that of wheat. We estimated uncertainties in the fitted functions (Fig. 2 and Fig. S3), which were considered in our Monte Carlo Earth system model simulations to estimate the climate impact of deploying BECCS. We performed additional experiments to examine the sensitivity of the yield-temperature relationship to using only experimental data, increasing the optimal growing temperature ($T_{opt}$) or the dampening temperature ($T_{dam}$) by 1 °C, using a linear or nonlinear function to fit the sensitivity of wheat yield to temperature change⁷ (Fig. S3), which are considered to examine the sensitivity of the climate benefits of BECCS to these factors (Fig. 3).

The fitted parameters $\alpha^T, \beta^T, \gamma^T$ using all data and the fitted $\alpha^C, \beta^C, \gamma^C, \alpha^Z$ and $\gamma^Z$ are listed by region in Table S5. Different from the parameters in the response of crop yields to changes in temperature, atmospheric CO₂ and intensity of N fertilization, the parameter $\gamma^P$ in the response of crop yield to change in precipitation was determined by a previous study⁹. In that study, crop yield was simulated using seven Earth system models³¹,⁶³ in a case using a fully coupled configuration with an increase of atmospheric CO₂ of +1% yr⁻¹, in a case using the fixed climate and in a case using the fixed carbon cycle, respectively. For each region, an exponential function was used to fit the simulated crop yields based on the decadal moving averages of the relevant variables in the seven models, where the best fit returned the parameter $\gamma^P$ in the response of crop yield to
precipitation in each region. As a caveat, $γ^P$ was not determined as other parameters due to the lack of field experiments measuring the response of crop yield to precipitation change, but, similar to a previous study$^6$, the impact of precipitation on crop yields in the future was estimated at a lower magnitude than temperature, atmospheric CO$_2$ and intensity of N fertilization in our model (Fig. S6).

For future scenarios, we predicted the yields of eight crops (cereals, roots and tubers, beans, oil crops, fiber crops, sugar crops, primary fruits and primary vegetables) ($Y_t$) based on the yield of each crop for the year 2019 from the Food and Agriculture Organization (FAO) data set$^{40}$ and the changes in N fertilization, CO$_2$ concentrations and the average growing-season temperature and precipitation over croplands from 2019 to a future year during 2020–2200 by country. The crop yields ($Y_{2019}$), N fertilization ($Z_{2019}$), CO$_2$ concentration ($C_{2019}$), and the average growing-season temperature and precipitation over cropland ($T_{2019}$ and $P_{2019}$) for 167 countries in 2019 are listed in Supplementary Data Set 2. For dedicated energy crops, the average yield (8.5 t ha$^{-1}$) in 2020 was derived from a previous study$^{64}$ as a conservative estimate. The yield of dedicated energy crops under climate change is predicted by Eqs. 2–6 using the functions of atmospheric CO$_2$ concentration, atmospheric surface temperature, N fertilization and precipitation as of wheat crop.

## Terrestrial C sink

The terrestrial C sink, which is one of the drivers of changes in atmospheric CO$_2$ concentration, responds to changes in atmospheric CO$_2$ concentration and other environmental changes. The OSCAR model$^9$ divided global land into five categories: bare soil, forest, grassland and shrubland, cropland and pasture. The change in the terrestrial C sink ($ΔE_{↓land}$, Gt C y$^{-1}$) for each biome relative to the preindustrial period (1850–1900) was estimated:

$$ \Delta E_{↓land} = (\Delta e_t^{fire} + \Delta rh_t^{litter} + \Delta rh_t^{soil} - \Delta NPP_t)(A_0 + \Delta A_t) \quad (7) $$

where $A_0$ is the preindustrial area for this biome, $ΔA_t$ is the change in area relative to the preindustrial period, $Δe_t^{fire}$ is the change in the flux of C from biomass burnt in wildfires, $Δrh_t^{litter}$ is the change in the flux of C from biomass to the atmosphere when C in litter is oxidized by heterotrophic respiration, $Δrh_t^{soil}$ is the change in the flux of C from soil to the atmosphere when soil C is oxidized by heterotrophic respiration and $ΔNPP_t$ is the intensive change in net primary production. $Δe_t^{fire}$ was calculated as a function of the fire intensity and the amount of living biomass, where the fire intensity was represented
as a function of surface air temperature, precipitation and atmospheric CO₂ concentration⁹. Δrhᵢ⁹ was calculated as a function of the litter C concentration, annual mean atmospheric temperature and precipitation⁵⁷. Δrhᵢ⁵⁷ was calculated as a function of the soil C concentration, annual mean atmospheric temperature and precipitation³⁵. ΔNPPᵢ was calculated for cropland using Eq. 1 and for other biomes as a function of atmospheric CO₂ concentration, annual mean atmospheric temperature and precipitation⁹.

## LUC emissions of CO₂

The conversion of marginal lands first and then forests to cropland to meet the increasing food targets leads to additional LUC emissions of CO₂ by affecting the stock of C in living biomass, litter and soil C pools and harvested wood products. LUC emissions (ΔE<sub>LUC</sub>) depend on the changes in C stocks in different pools:

$$ \Delta E_{LUC} = -\frac{d}{dt}(\Delta C_{veg} + \Delta C_{litt} + \Delta C_{soil} + \sum_{p} \Delta C_{hwp}^{p}) \quad (8) $$

where p is the use of a wood product (1 for fuel wood, 2 for pulp-based products and 3 for hardwood-based products) and ΔC<sub>veg</sub>, ΔC<sub>litt</sub>, ΔC<sub>soil</sub> and ΔC<sub>hwp</sub> indicate the stocks of C in living biomass, litter, soil and harvested wood products, respectively. ΔC<sub>veg</sub>, ΔC<sub>litt</sub>, ΔC<sub>soil</sub> and ΔC<sub>hwp</sub> were calculated based on the changes in the area from one biome to another biome and on the C concentration in each pool. The C concentration in each pool was simulated using the dynamic scheme that is calibrated by the flux of C in the CMIP5 model⁶³. The total LUC emissions from 1800 to 2020 are estimated of 137 Gt C, which is in the range of the estimates since 1800 (100–180 Gt) by Erb et al.⁶⁵.

## N₂O emissions

N₂O was treated as a well-mixed GHG in the OSCAR model. Anthropogenic sources of N₂O include direct and indirect emissions from agriculture, energy production, industry, waste and wildfires⁵⁹,⁶⁶,⁶⁷. Natural sources of N₂O include emissions from tropical soils⁶⁸ and emissions from the application of N fertilizers⁶⁹. N₂O in the atmospheric is mainly removed by stratospheric photolysis, the rate of which is a function of the stratospheric N₂O concentration due to the autocatalytic feedback of N₂O by reducing the concentration of stratospheric ozone⁷⁰. For the future simulations, we modeled the agricultural practice of N fertilization with the average length of growing season (153 d)⁶⁹. N₂O emissions were converted to equivalent CO₂ emissions using a constant ratio of 81.3 g C to 1 g N₂O⁶⁹. For the future scenarios, N₂O emissions converted to equivalent CO₂ emissions (ΔE<sub>N₂O-fertilizer</sub>, t C y⁻¹) due to agricultural N
759 fertilization in cropland were represented by an exponential function⁶⁹:

$$760 \qquad \Delta E_{N2O-fertilizer} = 4.93 \cdot D \cdot A \cdot \sigma_{N2O} \cdot \exp(0.0134 \cdot Z) \qquad (9)$$

761 where Z is the intensity of N fertilization in the cropland (kg ha⁻¹), D is the duration of 762 N fertilization, A is the area of cropland and σN₂O is the coefficient for converting N₂O 763 emissions to equivalent CO₂ emissions.

764 **Average growing-season temperature in cropland**

765 We used the OSCAR model to simulate the average atmospheric temperature (Tjt) in 766 cropland in region j in year t during the growing season based on the preindustrial 767 temperature for cropland in region j during the growing season (Tj0) and degree of 768 global warming relative to the preindustrial period (1850–1900) (ΔTjt):

$$769 \qquad T_{jt} = T_{j0} + \omega_j \Delta T_t \qquad (10)$$

770 where j is the region (1 for North America, 2 for South and Central America, 3 for 771 Europe, 4 for the Middle East and northern Africa, 5 for tropical Africa, 6 for the former 772 Soviet Union, 7 for China, 8 for southern and southeastern Asia and 9 for the developed 773 Pacific region) and ωj is the ratio of regional to global warming, calibrated for each 774 region from an ensemble of CMIP models³¹. Atmospheric surface temperature differs 775 between cropland and other land types and between the growing and non-growing 776 seasons in a region, so we assumed that the change in atmospheric growing-season 777 temperature was homogeneous in a region. We estimated the average growing-season 778 temperature by country based on global crop calendar data⁷¹ (Supplementary Data Set 779 3).

780 The degree of global warming (ΔTt) was simulated as a function of anthropogenic 781 radiative forcing (ΔRF) of GHGs, ozone precursors, aerosols and aerosol precursors 782 and the natural forcings caused by various anthropogenic activities:

$$783 \qquad \tau \frac{d}{dt} \Delta T_t = \lambda \Delta RF - \Delta T_t - \theta (\Delta T_t - \Delta D_t) \qquad (11)$$

784 where τ is the temporal inertia of global mean atmospheric temperature, λ is the 785 equilibrium climate sensitivity, θ is the coefficient determining exchange of energy 786 between the Earth surface and deep oceans and ΔD is the change in temperature of deep 787 oceans. These parameters are identical to those determined by previous studies⁵⁵⁻⁵⁷. In 788 the OSCAR model, we calibrated the preindustrial surface air temperature in the 789 growing season over cropland (Tj0) in country j using the observed average temperature 790 in the growing season in cropland for 2016–2019 (Tj,2016–2019) in country j and the 791 simulated change in atmospheric surface temperature in this country in 2019 relative
the average of 1850–1900 (Δ*T*<sub>j,1900–2019</sub>). Atmospheric temperature in the growing season in cropland for 2016–2019 by country (*T*<sub>j,2016–2019</sub>) was estimated from the global gridded daily temperature re-analysis data set of the Global Forecast System released by the National Centers for Environmental Prediction<sup>72</sup>.

## Global data of crop yields, cropland area and N fertilization

We compiled the yields of crops by country for 1961–2019 from the FAO global agricultural data set<sup>40</sup>. We simulated the national crop yields for 2020–2200 using Eqs. 2–6 based on the simulated atmospheric CO<sub>2</sub> concentration, the simulated average growing-season temperature, the simulated precipitation and the targeted intensity of N fertilization. We compiled the national areas of cropland growing cereals, roots and tubers, beans, oil crops, fiber crops, sugar crops, primary fruits and primary vegetables for 1961–2019 from the FAO global agricultural data set of cropland area<sup>40</sup>. The area of marginal lands is derived from a previous study<sup>73</sup>. We applied the per capita cropland area in 2020 to the period from 2020 to 2200 as a constant in the scenario without cropland expansion. In the scenarios of cropland expansion, we increased the per capita cropland area in 2020 to a specific area (0.16, 0.17, ..., 0.24 ha) to meet the caloric targets of 1.5–2.5 Mcal d<sup>-1</sup> in 2030 in countries where the cropland area is below this threshold, while the cropland area is maintained at the 2020 level for countries above this threshold. We assumed that first marginal lands and then forests in the expansion of cropland were converted to cropland<sup>74</sup>. We estimated the impact of a higher per capita food demand by adopting the national population in 2020<sup>75</sup> to estimate the total area of croplands based on the per capita cropland area by country for years after 2020, so we took population as a control variable to estimate the impact of increasing per capita food demand on cropland area<sup>76</sup>. We estimated the amount of synthetic N fertilizer applied to the cropland in 167 countries for 1961–2019 by subtracting the amount of synthetic N fertilizer applied to pastures<sup>77</sup> from the amount of synthetic N fertilizer applied to both pastures and cropland from the FAO data set of fertilizers<sup>78</sup>. In the future scenarios of intensified N fertilization, we considered that the intensity of N fertilization increases to a specific level (100, 110, ..., 300 kg ha<sup>-1</sup>) during 2020–2030 in countries where the intensity is below this threshold, while N fertilization is maintained at the 2020 level for countries above this threshold.

## Calculation of calories in crops

We calculated the calories in cereal crops based on the production of wheat, rice and maize in the OSCAR model. We estimated the calories in a crop (*L*) based on the crop
yield ($Y_i$) and the cropland area ($A_i$):

$$L = \sum_{i=1}^{3} \chi A_i Y_i \eta_i (1 - \omega_i) E_i \quad (12)$$

where $i$ is a crop, $\chi$ is the fraction of food loss and waste (56% for developed countries and 44% for developing countries)⁷⁹, $\eta_i$ is a factor for converting the agricultural product produced to the part that is edible⁸⁰, $\omega_i$ is the fraction of crops used for animal feed and other non-food purposes and $E_i$ is the caloric content by weight for each crop. The fraction of crops used for animal feed and other non-food purposes was derived from the FAO global food-balance data set⁸¹. Caloric contents were compiled for wheat, rice and maize from the Calories data set⁸². For each country, we considered the calories provided by the animal products compiled from the FAO global food-balance data set⁷⁵ as a constant, which were added to the calories provided by crops. The parameters $\chi$, $\eta_i$, $\omega_i$ and $E_i$ by crop are listed in Table S6.

## Negative emissions from BECCS

We estimated the negative emissions from BECCS based on the quantity of agricultural residues that is harvested from crop production. Negative emissions from BECCS included the reduction in CO₂ emissions by substituting coal to produce the same amount of electricity in power plants and the sequestration of C in biomass to geological repositories¹⁹. We assumed that BECCS was deployed by retrofitting coal-fired power plants. We estimated the negative emissions from BECCS as a function of crop yield ($Y_i$, g biomass ha⁻¹ y⁻¹) and cropland area ($A_i$, ha) at an efficiency of C capture and storage of 90%:

$$\Delta E_{\text{BECCS}} = - \left[ \sum_{i=1}^{8} Y_i A_i \mu_i f_i \frac{(1-I_i)}{I_i} \cdot 90\% + Y_i A_i \mu_i V_i \frac{(1-I_i)}{I_i} \frac{\eta_{\text{bio}}}{V_{\text{coal}}\eta_{\text{coal}}} \xi \right] \quad (13)$$

where $i$ is a crop (i.e. cereals, roots and tubers, beans, oil crops, fiber crops and sugar crops), $\mu_i$ is the fraction of dry biomass, $f_i$ is the concentration of C in dry biomass, $I_i$ is the harvest index, defined as the ratio of the mass of the harvested yield to total aboveground biomass, $V_i$ is the ratio of bioenergy to dry biomass (5 MWh (g biomass)⁻¹)⁸³, $V_{\text{coal}}$ is the energy content of coal (7.44 MWh (g coal)⁻¹)⁸⁴, $\xi$ is the emission factor of coal (0.67 g C (g coal)⁻¹)⁸⁵, and $\eta_{\text{coal}}$ and $\eta_{\text{bio}}$ are the efficiencies of power generation in coal-fired power plants (39.3%) and BECCS plants (27.8%), respectively⁸⁶. The parameters $\mu_i$, $f_i$ and $I_i$ are listed by crop in Table S4.

We assumed that BECCS was used for retrofitting coal-fired power plants (e.g., that is to substitute up to 57%, 83% and 85% of electricity generated by coal in Asia, Europe and North America, respectively in 2030) before retrofitting oil-fired and gas-fired
power plants. We considered four scenarios to examine the impacts of alternative bioenergy applications (Fig. S9). First, we considered that BECCS was used for substituting oil or gas rather than coal, where less emissions were abated due to a higher power generation efficiency (41% and 47% for oil and gas⁸⁷, respectively, versus 39% for coal⁸⁶) and a lower CO₂ emission factor (0.7 and 0.4 tCO₂ MWh⁻¹ for oil and gas⁸⁸, respectively, versus 0.85 tCO₂ MWh⁻¹ for coal⁸⁵) in power plants. Second, there are technological and market barriers for using bioenergy in transportation⁸⁹,⁹⁰, which make it difficult to equip CCS on vehicles⁹¹. We considered a scenario where biomass produces bioethanol with a 16% of energy loss in production⁹² to substitute vehicle oils without CCS. Third, we considered a scenario, where the efficiency of energy conversion was increased from 27.8% for BECCS power plants in our central case to 47.5% in biorefinery plants⁴³, but 15% of CO₂ released at a high purity during the fermentation process can be captured⁴³. Lastly, we considered an optimistic scenario where the efficiency of energy conversion was improved from 27.8% to 47.5% in biorefinery plants, but 55% of CO₂ released during the fermentation process in gasification can be captured at a high purity⁴³.

Our method for estimating the quantity of agricultural residues for BECCS differed from those in previous studies (e.g. ref⁹³) based on crop NPP, which scaled as the assumed fraction of agricultural residues that can be harvested in the field. We derived the quantity of agricultural residues from the quantity of the harvested grain using the crop-specified straw-to-grain ratio for above-ground biomass (excluding the difficult-to-obtain biomass like roots). The quantity of the collected agricultural residues for bioenergy ($q_{straw}$) could be computed: $q_{straw} = x_{straw} \cdot \eta_{straw} = [x_{grain} \cdot (1-I_i)/I_i] \cdot \eta_{straw} = [q_{grain} / \eta_{grain} \cdot (1-I_i)/I_i] \cdot \eta_{straw} = [q_{grain} \cdot (1-I_i)/I_i] \cdot (\eta_{straw} / \eta_{grain})$, where $x_{straw}$ is the quantity of agricultural residues from all crops growing in the field, $\eta_{straw}$ is the fraction of agricultural residues that can be harvested for use as bioenergy, $I_i$ is the harvest index, defined as the ratio of the mass of the harvested grain to total aboveground biomass (Table S4), $q_{grain}$ is the quantity of harvested grain and $\eta_{grain}$ is the fraction of grown grain that can be harvested for food. In the literature, $\eta_{grain}$ varies from 80 to 95%⁹⁴,⁹⁵ and $\eta_{straw}$ varies from 83 to 90%⁹⁶,⁹⁷, which both depend on the locations, type of crop and technology of pretreatment. We considered that the pretreatment of straw can improve $\eta_{straw}$ (e.g., by reducing the volume of straw⁹⁶), while the emissions of CO₂ from diesel in the pretreatment estimated in our previous study¹⁹ have been considered in this study. Therefore, we converted the quantity of harvested grain ($q_{grain}$) to the
quantity of harvested residue ($q_{straw}$) by assuming that it is possible to be equally efficient in harvesting grain and residue. However, this calculation may lead to an upper estimate of the effect of BECCS in mitigation, because sustaining a high $\eta_{straw}$ for long time may reduce soil fertility and require more fertilizer applications, which deserves attention$^{98}$.

## Uncertainty analyses

We estimated the uncertainty in global warming and crop calories by running valid Monte Carlo simulations 1000 times using the OSCAR model$^9$, randomly drawing parameters from their uncertainty distributions$^{99}$. Parameters that varied in the Monte Carlo simulations were: (i) anthropogenic emissions of CO$_2$, methane and N$_2$O, LUC emissions of CO$_2$, emissions of halogenated compounds, ozone precursors (NO$_x$, CO), VOCs, aerosols (BC, OC, sulfate and nitrate) and aerosol precursors (SO$_2$, NO$_x$, O$_3$, NH$_3$), (ii) natural radiative forcings, (iii) parameters governing the processes in oceans, biospheres, wildfires, land uses, hydroxyl groups, wetlands, photolysis, tropospheric ozone, stratospheric ozone, sulfate formation, nitrate formation, secondary organic aerosols, direct and indirect radiative forcings of aerosols, changes in surface albedo, temperature changes, precipitation and ocean acidification and (iv) the fitted coefficients $\alpha^C, \beta^C, \gamma^C, \alpha^T, \beta^T, \gamma^T, \alpha^Z$ and $\gamma^Z$ in the relationships between crop yields and atmospheric growing-season temperature, atmospheric CO$_2$ concentration and intensity of N fertilization. The standard deviations of these fitted coefficients as normal distributions were derived from the regression models, which are listed in Table S5.
We used the interquartile range and the range of 90% uncertainty from Monte Carlo simulations to indicate the uncertainties in the simulated global warming, crop production and per capita calories.
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1029
1030 Acknowledgements

1031 RW appreciates the provision of funds from the National Natural Science Foundation
1032 of China (41877506) and the Chinese Thousand Youth Talents Program. RHZ, TX,
1033 JMC and RW acknowledges support from the Shanghai International Science and
1034 Technology Partnership Project (21230780200). XT and RW acknowledges support
1035 from the Fudan-Sinar Mas Think Tank Fund (JGSXK2014). PC acknowledges support
1036 from the ANR CLAND Convergence Institute 16-CONV-0003. JP and JS acknowledge
1037 the financial support from the Catalan Government grants SGR 2017-1005 and
1038 AGAUR-2020PANDE00117, the Spanish Government grant PID2019-110521GB-I00,
1039 and the Fundación Ramón Areces grant ELEMENTAL-CLIMATE. T.G. acknowledges
1040 support from the Austrian Science Fund (FWF) under grant agreement P31796-N29
1041 (ERM project).

1042 Author contributions

1043 R.W. conceived the research, designed the study and wrote the first version of
1044 manuscript. S.Q.X. compiled data, performed the research and prepared graphs. T.G.
1045 provided the OSCAR model; P.C., T.G., J.P., Y.B., O.B., I.A.J., J.S., J.H.C., J.J.C.,
1046 R.H.Z. provided tools analyzing the relationship between climate change and food
1047 security. J.P., P.C., I.A.J., J.S. provided tools analyzing the ecological impact of using
1048 bioenergy. J.H.C. provided tools analyzing the measures of using green energy. J.J.C.,
1049 J.M.C., L.W., X.T., R.H.Z. provided tools analyzing the impact of climate change on
1050 the agronomy. All coauthors interpreted the results and contributed to the writing.

1051 Declaration of Interests

1052 The authors declare no competing interests.

1053 Availability of data and material

1054 Additional material is available in Supplementary Materials. Code and data used for
1055 our analyses are available on the GitHub repository: https://github.com/rongwang-
1056 fudan/OSCAR_Agriculture_Global.

1057 Correspondence and requests for materials should be addressed to R.W.

1058 Reprints and permissions information is available at http://www.nature.com/reprints.