# Narrowing uncertainties in the effects of elevated CO2 on crops

Andrea Toreti, Delphine Deryng, Francesco Tubiello, Christoph Müller, Bruce Kimball, Gerald Moser, Kenneth Boote, Senthold Asseng, Thomas A.M. Pugh, Eline Vanuytrecht, et al.

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Andrea Toreti, Delphine Deryng, Francesco Tubiello, Christoph Müller, Bruce Kimball, et al. Narrowing uncertainties in the effects of elevated CO2 on crops. Nature Food, 2020, 1 (12), pp.775-782. 10.1038/s43016-020-00195-4. hal-03093673

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# Narrowing the uncertainties in the effects of elevated CO₂ on crops

Toreti, Andrea; Deryng, Delphine; Tubiello, Francesco; Müller, Christoph; Kimball, Bruce; Moser, Gerald; Boote, Kenneth; Asseng, Senthold; Pugh, Thomas; Vanuytrecht, Eline; Pleijel, Hakan; Webber, Heidi; Durand, Jean-Louis; Dentener, Frank; Ceglar, Andrej; Wang, Xuhui; Badeck, Franz; Lecerf, Remi; Wall, Gerald; van den Berg, Maurits

**DOI:**
10.1038/s43016-020-00195-4

**License:**
None: All rights reserved

**Document Version**
Peer reviewed version

**Citation for published version (Harvard):**

Toreti, A, Deryng, D, Tubiello, F, Müller, C, Kimball, B, Moser, G, Boote, K, Asseng, S, Pugh, T, Vanuytrecht, E, Pleijel, H, Webber, H, Durand, J-L, Dentener, F, Ceglar, A, Wang, X, Badeck, F, Lecerf, R, Wall, G, van den Berg, M, Hoegy, P, Lopez-Lozano, R, Zampieri, M, Galmarini, S, O'Leary, G, Manderscheid, R, Mencos Contreras, E & Rosenzweig, C 2020, 'Narrowing the uncertainties in the effects of elevated CO₂ on crops', *Nature Food*, vol. 1, pp. 775–782. https://doi.org/10.1038/s43016-020-00195-4

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1 Narrowing the uncertainties in the effects of
2 elevated CO₂ on crops

3

4 Andrea Toreti¹, Delphine Deryng²,³, Francesco N. Tubiello⁴, Christoph Müller⁵, Bruce A. Kimball⁶, Gerald

5 Moser⁷, Ken Boote⁸, Senthold Asseng⁸, Thomas A. M. Pugh⁹,¹⁰, Eline Vanuytrecht¹¹,¹², Hakan Pleijel¹³,

6 Heidi Webber², Jean-Louis Durand¹⁴, Frank Dentener¹, Andrej Ceglar¹, Xuhui Wang¹⁵,¹⁶, Franz Badeck¹⁷,

7 Remi Lecerf¹, Gerard W. Wall⁶, Maurits van den Berg¹, Petra Hoegy¹⁸, Raul Lopez-Lozano¹⁹, Matteo

8 Zampieri¹, Stefano Galmarini¹, Garry J. O'Leary²⁰, Remy Manderscheid²¹, Erik Mencos Contreras²²,²³,

9 Cynthia Rosenzweig²²,²³

10 1 European Commission, Joint Research Centre (JRC), Ispra, Italy

11 2 Leibniz Centre for Agricultural Landscape Research (ZALF), 15374, Müncheberg, Germany

12 3 IRI THESys, Humboldt-Universität zu Berlin, 10099, Berlin, Germany

13 4 Statistics Division, Food and Agriculture Organization of the United Nations, Rome, Italy

14 5 Potsdam Institute for Climate Impact Research PIK, Member of the Leibniz Association, Potsdam, Germany

15 6 U.S. Arid-Land Agricultural Research Center USDA, Maricopa, USA

16 7 Justus-Liebig University of Giessen, Giessen, Germany

17 8 University of Florida, Gainesville, USA

18 9 School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK

19 10 Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK

20 11 Flemish Institute for Technological Research (VITO), Mol, Belgium

21 12 KU Leuven, Dept. of Earth and Environmental Science, Leuven, Belgium

22 13 University of Gothenburg, Göteborg, Sweden

23 14 INRAE, Lusignan, France

24 15 Laboratoire des Sciences du Climat et de l'Environment LSCE, CEA-CNRS-UVSQ, Gif-sur-Yvette, France

25 16 Sino-French Institute of Earth System Sciences, College of Urban and Environmental Sciences, Peking University, Beijing, China

26 17 Council for Agricultural Research and Agricultural Economics, Research Centre for Genomics and Bioinformatics, CREA-GB, Fiorenzuola d'Arda, Italy

27 18 University of Hohenheim, Stuttgart, Germany

28 19 INRAE, Avignon, France

29 20 Agriculture Victoria, Horsham, Australia

30 21 Thunen Institute of Biodiversity, Braunschweig, Germany

31 22 NASA Goddard Institute for Space Studies, New York, USA

32 23 Center for Climate Systems Research, Columbia University, New York, USA

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34 Joint 1st authors & corresponding authors: andrea.toreti@ec.europa.eu, delphine.deryng@mail.mcgill.ca

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37 40 Plant responses to rising atmospheric carbon dioxide (CO₂) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems.

38 41 Here, we review the current experimental evidence and crop models on the effects of elevated CO₂ concentrations. Recent concerted efforts have narrowed the uncertainties in CO₂-induced crop responses so that climate change impact simulations omitting CO₂ can now be eliminated.

39 42 To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO₂ and climate change on crops, future research should expand experiments on more crops
species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.

Many countries under the Paris Agreement have committed to increasing their resilience to climate risks through adaptation and mitigation policies in their agricultural sectors. The scientific community produce relevant scientific information for guiding the monitoring and evaluation of national climate policies and increasing their ambition as stipulated by the Global Stocktake component of the Paris Agreement².

Crop models are among the key tools to generate such scientific sources³. Process-based crop models account for the impact of biophysical, climatic and environmental factors, including elevated CO₂ concentration (*eCO₂*, hereafter) on plant growth processes⁴, crop yield quantity and quality. Yet, despite decades of experiments robustly demonstrating the effects of *eCO₂*⁴, climate change impact assessments have continued to use scenarios both with and without CO₂-fertilization effects⁵⁻⁷. Here we argue that this approach has produced more confusion than clarity, whereas current knowledge is sufficiently robust to make the *without CO₂-fertilization* scenario obsolete.

## Available experimental evidence of eCO₂ effects

The role of eCO₂ in stimulating crop growth has been documented since 1804, when De Saussure⁸ reported that peas exposed to eCO₂ grew better than control plants in ambient air. Since then, this effect has been exploited in commercial greenhouse production, while further scientific work has continued through many CO₂ enrichment experiments using greenhouses, growth chambers, gradient tunnels, open-top chambers (OTC), and Free-Air CO₂ Enrichment (FACE) techniques (Supplementary Tables S1 and S2). The understanding of eCO₂ effects on plant growth derived from those experiments has been synthesized in several topical and literature reviews as summarized below⁹⁻¹¹.
The effects of eCO₂ on crop productivity. Kimball et al.<sup>12</sup> assembled more than 70 reports and tabulated 430 prior observations of eCO₂-driven productivity changes in crops, concluding that yields of C₃ species under a full complement of water and nutrients significantly increase with a doubling of ambient CO₂ concentration (aCO₂; since that time, the CO₂ mixing ratio has increased from 340 ppm to 412 ppm, which affects the degree of response to an experimental doubling). However, crop responses to eCO₂ vary by species and growing conditions<sup>4</sup>. Elevation of CO₂ concentration in FACE experiments (from a CO₂ mixing ratio of 353 ppm to 550 ppm) with ample water and nutrients increased yields of C₃ grains (e.g., wheat, rice, barley) on average by 19%<sup>4</sup>. In contrast, the yield of C₄ crops (e.g., maize, sorghum) did not change significantly when the crops were grown under ample water supply conditions. Variation in CO₂ responsiveness across genotypes within species<sup>13-15</sup> has also been demonstrated in rice, soybean, and wheat<sup>16-17</sup>.

Beyond stimulating photosynthesis and growth, eCO₂ also causes reduced stomatal conductance by 19% to 22%<sup>12,18-19</sup> and reduced crop transpiration<sup>4,20</sup>. This leads to lower crop evapotranspiration (ET), as demonstrated by the average 10% ET reduction in FACE experiments for all investigated crops<sup>4,21</sup> (Supplementary Material S.1.1). Improved water-use efficiency under eCO₂ can enable crops to be more drought tolerant compared to crops grown in aCO₂. This effect is particularly important for C₄ crops, for which yield increases have been reported under water-limiting conditions in eCO₂. For example, FACE-sorghum<sup>22-23</sup> and FACE-maize<sup>24</sup> experiments had average yield increases of 15% and 41%, respectively.

While under ample water and nutrient conditions, yields of most C₃ crops increase by 10% to 30% under eCO₂ in experiments, yield stimulation due to eCO₂ is generally smaller or insignificant when nutrients are limiting. Nutrient deficiencies, such as nitrogen (N) and probably also phosphorus (P) deficiency, can minimize eCO₂ effects on crop productivity<sup>4,25</sup>. While eCO₂ improves water-use efficiency, the eCO₂ growth stimulus, which accelerates leaf
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growth and may increase leaf area and root biomass, can lead to higher water use and nutrient limitation later in the growing season²⁶. The modulating effects of N and seasonal rainfall on plant responses to eCO₂ have recently been demonstrated for a temperate C₃-C₄ grassland²⁷.

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**The effects of eCO₂ on crop quality.** While eCO₂ has the potential to partly offset (and in some cases and conditions even compensate for) the negative effects of climate change on crop productivity (especially for C₃ crops such as wheat, rice, and soybean²⁸), a substantial body of work has shown that a CO₂-rich atmosphere also results in lowering food quality and potential affecting nutrition security²⁹⁻⁴³ (Supplementary Material S.1.2).

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A meta-analysis³³ of 228 pairs of experimental observations on barley, potato, rice, and wheat reported reductions in protein concentrations ranging on average from -15.3% to -9.8% under eCO₂, while the reduction was relatively small (-1.4%) in soybean³³. A larger meta-analysis⁴³ done on 7,761 pairs of observations covering 130 species and cultivars reported an average 8% decline in mineral concentrations (except for Mn) and high agreement between FACE and non-FACE experiments. N fertilization and climate conditions may play a role in modulating the eCO₂-response in protein and mineral (Fe and Zn) concentrations⁴¹⁻⁴², entailing that processes such as mineralization should be taken into account to better understand this modulating role⁴².

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Declines in B vitamins (ranging from -30% to -13% for rice cultivars) under eCO₂ have been identified as well³⁰ (Supplementary Material S.1.2). These changes in rice quality under eCO₂ may affect the nutrient status of about 600 million people³⁰ around the world.

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Global-scale declines in mineral, such as Ca, Mg, protein concentrations, and carotenoids under eCO₂ have been reported for many C₃ plants in general, including non-staple crops and vegetables⁴³⁻⁴⁵. A meta-analysis⁴⁶ on legumes and leafy vegetables found no changes in Fe,

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vitamin C, and flavonoid concentrations under eCO₂; whereas antioxidant concentration tended to increase (although with high uncertainty). In another study, significant decreases in Fe concentration under eCO₂ were reported for leafy vegetables (-31%), fruit (-19.2%), and root vegetables (-8.2%), together with decreases in Zn concentration (-10.7% in stem vegetables, -18.1% in both fruit and root vegetables)⁴⁴. Conversely, eCO₂ favors higher total antioxidant capacity in leafy vegetables (72.5%) but not in fruit vegetables (-14.4%)⁴⁴.

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## Future directions to improve experimental coverage

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Although the overall number of eCO₂-experiments is large and the findings of the main effects on crops are unequivocal, more experimental work is still needed to improve the spatial
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(geographical) representativeness, temporal (timing and duration) distribution, numbers of
crops and cultivars, and analyze components besides yield (e.g., water use and nutrient
concentrations).

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As shown in Figure 1a, eCO₂ experiments have been concentrated in Europe and the U.S., with some significant multi-year, large-scale FACE studies in South America, Asia (Japan, China and India), and Australia. There have been no eCO₂ experiments in Africa, where agriculture provides significant livelihoods. Furthermore, Figure 1b highlights the need for more experiments in order to achieve a better coverage of the diverse climatic conditions around the world. There is also a lack of multiple-year eCO₂-experiments, which are important for grasslands and perennials, especially tree crops, and for understanding long-term effects on soils and microbiota. A few long-term experiments have confirmed the ability of agro-ecosystems to acclimate (i.e., reduced photosynthetic activity response compared to the initial response, known as down-regulation) to a CO₂-rich environment⁵⁵ (Supplementary Material S.1.4). Their results suggest that eCO₂-induced effects in grasslands and perennial crops are highly dependent on climatic conditions and that acclimation may take more than 3-5 years⁵⁶-⁵⁹. Although acclimation is of less relevance for the main food crops, it is still an important factor considering that it may act on shorter time scale and also looking at recent studies on perennial grains⁶⁰ and the amplification of eCO₂ positive effects through crop generations⁶¹.

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Other types of experiments – including OTC, mini-FACE, climate control chambers and enclosures – can be cheaper and faster. These experiments can significantly reduce uncertainties by providing larger number of replicates and sample sizes, covering a larger range of eCO₂ well above 550ppm, and thus complementing and further supporting the evidence provided by the more expensive and time-consuming FACE experiments. OTC and mini-FACE may also help in addressing the role of eCO₂ at night⁶², as many FACE experiments only enrich during daylight hours.

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<figure><img src="image_2.png" /><figcaption>**Figure 1. Overview of the eCO₂ experiments.** a). Global distribution of eCO₂ experiments on crops and grasslands. The distribution is derived from an updated version of the CLIMMANI Networking Group database (https://climmani.org, access date: October 2018; Table S2 in Supplementary Material) and other studies⁴³.

Colors indicate different agricultural crops: green – grassland/forages, ochre – cereals (barley, maize, sorghum, wheat), purple – woody crops (cotton, grape), light blue – natural ecosystems, red – other crops (apple, banana, cassava, coffee, cucumber, lemon, orange, pea, peach, potato, radish, spinach), gold – artificial crops (single or multiple species mixtures without agricultural use). b). The mean annual temperature vs annual precipitation⁶³ (1981-2010) of the experimental sites and of the global cropland⁶⁴ (grey area). The grey color gets darker according to the cropland area falling into the temperature/precipitation bin.</figcaption></figure>

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## Approaches for modeling primary production

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Crop growth models are key tools for scaling-up experimental evidence and assessing regional and global crop. We distinguish four basic types of approaches for modeling primary⁶⁵: complex with a biochemical basis; semi-complex involving leaf-level photosynthesis; radiation-use efficiency (RUE)-based; and transpiration-efficiency based⁶⁶. The choice of these modeling approaches largely determines how CO₂ responsiveness is implemented in crop models, either as simple response functions that scale productivity, or as components of the underlying mechanisms such as Rubisco kinetics⁶⁷ (Supplementary Material S.2).
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While existing crop models include CO₂ responses in the simulation of primary production, they differ in the representation of transpiration and abiotic responses such as N stress<sup>66</sup>.

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Many crop models have been tested against observations conducted with eCO₂ up to 600 ppm (FACE) and beyond (OTC). At the field scale under experimental conditions, crop models performed reasonably well<sup>68</sup> in reproducing the main effects of eCO₂ under both ample and limited water and N supplies, of higher temperatures on growth, harvestable yield, leaf area, water uptake, and of N dynamics for wheat<sup>69-71</sup>, rice<sup>72</sup>, maize<sup>73</sup>, cotton<sup>74</sup>, potatoes<sup>75-76</sup>, and pasture<sup>77</sup>. Figure 2 shows two examples of eCO₂ effects on yield of wheat and maize as simulated by crop models and measured in two dedicated experiments under different water and climatic conditions<sup>24,70,73,78</sup>. Overall, good performance characterizes the modeling simulations, although some discrepancies remain (e.g. in the case of maize under dry conditions).

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<figure><img src="image_3.png" /></figure>

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**Figure 2.** Yield responses (g/m²) to eCO₂ as measured in two FACE experiments<sup>24,78</sup> and simulated by crop models<sup>70,73</sup>. a): maize yield responses to eCO₂ from a mixing ratio of 387 ppm to 550 ppm measured in the 2007-8 Braunschweig-FACE experiment<sup>24</sup> (northern Germany) under two levels of water supply: dry and irrigated. Uncertainty in measured crop yield response (given by replicates performed in the FACE experiment) is
represented by grey solid lines. Uncertainty of the simulations, given by a 21-member ensemble of models<sup>73</sup>, is represented by grey dotted lines. b): wheat grain yield responses to eCO<sub>2</sub> from a mixing ratio of 365 ppm to 550 ppm measured in the 2007-9 Horsham-FACE experiment<sup>78</sup> (south-eastern Australia) under different water supply conditions (dry and supplemental irrigation). Uncertainty in measured crop yield responses (given by replicates performed in the FACE experiment) is represented by grey solid lines. Uncertainty of the simulations, given by a 6-member ensemble of models<sup>70</sup>, is represented by grey dotted lines.

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Concerning the effects of N limitation in modulating the impacts of eCO₂, crop models in general reproduce how the lack of adequate N reduces yield gains induced by eCO₂, although uncertainties tend to be greater (Supplementary Figure S1). In most cases, crop models also tend to underestimate yield gains induced by eCO₂ when N is adequate under experimental conditions (Supplementary Figure S1).

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## Scaling-up crop simulations from field experiments

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The high costs of running eCO₂ and climate change field experiments have prohibited the study of a representative sample with respect to the crop genetics (G), environmental (E) conditions and management (M) regimes (G×E×M) in which farmers produce crops. Process-based crop models constitute an affordable solution to explore crop responses across a range of G×E×M combinations and at any scale of interest. More than twenty global-scale crop models<sup>79</sup> have been developed and many of them have been used in multi-model assessments<sup>28,80-82</sup>. These global crop models follow the same dynamic process approaches of field-based models and have been increasingly used in economic and climate impact studies<sup>5-7</sup> that contribute to policy formulation<sup>7,83</sup>. Large-scale crop simulations introduce additional uncertainty compared to field-scale crop models due to lack of complete spatial and temporal data coverage on relevant agronomic information. Simulation and scenario approaches are used to fill current data gaps<sup>84</sup>.

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$^{89}$, and relevant global data are being marshalled to address these challenges. Trust in crop
modeling capacity has been gained over the past five decades since models were first
developed$^{28}$ based on widespread comparison of simulated yields and other variables against
available field data and from multi-model comparisons$^{91-93}$.

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The effects of eCO₂ in crop model simulations

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Past climate change assessments have routinely presented crop yield ‘with and without’ the effects of eCO₂ ⁷,⁹⁴⁻⁹⁵, under the implicit assumption that the no-eCO₂-effects scenario represented an acceptable lower limit of the uncertainty range (Supplementary Table S3). That extremely cautious approach has, however, generated unnecessary misunderstanding of uncertainty regarding the current knowledge of eCO₂ on crops within climate change scenarios.
As a result, some studies⁹⁶⁻⁹⁷ have used crop modelling results based on both ‘with’ and ‘without’ CO₂ simulations indistinguishably, potentially leading to misinterpretation of the ensemble median, range, and causes for model (dis)agreement.

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We demonstrate the issues in comparing crop model simulations with these different key
settings (i.e., with and without eCO₂) with global wheat and maize simulations under projected
climate changes (Supplementary Figure 2). The high uncertainties induced by the ‘without
CO₂’ lower bound ultimately reduce trust in the underlying crop models, whereas experimental
knowledge on the eCO₂ effect, as well as crop models’ ability to reproduce it, is substantial.

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The large and growing body of experimental evidence has shown that current crop modeling
approaches are increasingly able to capture the main effects of eCO₂ on crop growth and yield
under a wide range of growing conditions at field scale. Hence, we argue that these effects
should be included by default in climate change impact assessments: there is no longer a

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scientifically valid reason for expanding the range of model uncertainties to include a 'without eCO₂' scenario (other than quantifying the isolated effect). Under optimal growing conditions, 'with eCO₂' simulations should represent the upper bound of the uncertainty range. For the lower bound, rather than using a 'without eCO₂' scenario, levels responding to observed interactions of eCO₂ with abiotic stresses affecting crop growth, e.g., soil N and water availability<sup>72</sup>, temperature and O<sub>3</sub><sup>98-99</sup> should be assessed.

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## Knowledge gaps in model development

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Under complex growth-limiting environmental conditions, interactive processes are less well understood. A recent experiment on maize indicated that crop model results corresponded well to the observations under irrigated conditions<sup>73,100</sup>. Nevertheless, some models had poor performance under certain drought conditions (due to underestimation of eCO₂ water savings), and therefore underestimated the associated crop yield stimulation<sup>73</sup>. Other nutrients, such as phosphorus (P) and potassium (K), are often neither considered in crop models nor fully measured or controlled in experiments, even though P is known to be a main limiting crop nutrient in many soils, particularly in Africa<sup>101-103</sup>.

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A serious gap in crop modeling tools is the scarcity of models for fruits and vegetables<sup>66</sup>. This situation is now improving, but models for many more fruits and vegetables with the full range of eCO₂ responses are needed. In addition, most existing crop models do not account for nutritional aspects other than protein concentration<sup>69,104</sup>, while recent work on the socio-economic impacts<sup>54,105</sup> of reduced Fe and Zn concentration highlights the importance of including other key nutritional aspects, such as mineral concentrations. Finally, the upper range of projected CO₂ concentration by the end of the 21st century (e.g., up to a CO₂ mixing ratio of 936 ppm in RCP8.5) greatly exceeds eCO₂ in current experiments. As the rate of C₃ crop
291 responses declines with eCO₂ approaching 600 ppm¹⁰⁶, and considering that the current 292 atmospheric concentration is currently about 412 ppm and increasing by 2-3 ppm per year, key 293 performance of crop models for long-term assessments will depend on the representation of 294 this saturating response in interaction with other environmental variables, especially 295 temperature,¹⁸ and possible physiological limitations¹⁰⁷.

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297 **Key criteria for improving modeling protocols**

298 We argue that research and assessment should better focus on critical issues in projecting the 299 interactions of eCO₂ and climate change on crops. To this end, key criteria for selecting crop 300 models for climate change impact assessments should advance the representation as listed 301 below.

302 1. Concurrent and interactive effects of eCO₂, temperature, water and nitrogen (CTWN) on crop processes;

304 2. Evaluation of simulated responses to CTWN variation compared to a range of observations 305 from experiments (including at least crop cycle length, leaf area index, harvestable yield, 306 evapotranspiration) for C₃ and C₄ crops including staple grains, fruits, and vegetables;

307 3. Comparison with observations to identify systematic biases in simulated baseline (i.e., aCO₂) 308 crop yields, which should then be either bias-corrected or excluded from the crop model 309 ensemble.

310 The results of these evaluation tests should be made available as metadata in impact 311 assessments, and crop models should be assessed in standardized evaluation exercises¹⁰⁸. The 312 proposed criteria-based model could improve the robustness of multi-model impact 313 assessments.

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315 **Roadmap to advance future research on eCO₂**
We outline here the main priorities for future research and point to existing barriers that must be addressed urgently to further improve scientific assessments of the effects of eCO₂ and climate change on crop productivity and quality (Table 1). We propose that scientific community through international initiatives, such as the Agricultural Model Intercomparison and Improvement Project (AgMIP¹), plays an important role in delivering scientific resources that helps assess the potential biophysical and socio-economic consequences to support national and international agricultural policies.

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<table><caption>Table 1 Knowledge gaps, recommendations, and requirements for research progress on eCO₂ and climate change</caption><thead><tr><td>Data gaps and modeling inconsistencies</td><td>Recommendations</td><td>Main requirements to address</td></tr></thead><tbody><tr><td>Data gap on crop nutritional quality, beyond N/protein</td><td>Include measurement of crop quality in experimental design.</td><td>Funding</td></tr><tr><td>Data gap on crop types and cropping systems</td><td>Expand FACE, mini-FACE, OTC, climate control chambers, and enclosures experiments to other crops and beyond high-input systems</td><td>Funding, Expertise, Infrastructure</td></tr><tr><td>Data gap in many agro-climatic regions of the world, especially Africa</td><td>Set up experiments in unstudied regions, especially in Africa</td><td>Funding, Expertise, Infrastructure</td></tr><tr><td>Data gap on interactions of eCO₂ effects, weather conditions and extreme events</td><td>More long-term (&gt;10 years) FACE studies incorporating climate variables</td><td>Funding; Infrastructure</td></tr><tr><td>Disparities in data measurements</td><td>Harmonization of measurement methods</td><td>Research method development</td></tr><tr><td>Limited sample sizes for testing experimental evidence</td><td>Increase replicates of experiments, especially non-FACE ones and those focused on nutrients.</td><td>Funding, Infrastructure</td></tr><tr><td>Lack of access to data</td><td>Set up and maintain an open-access data repository, e.g. within Copernicus and AgMIP</td><td>Funding, Communication, Database development</td></tr><tr><td>modeling uncertainty</td><td><ul><li>Use multi-model ensembles</li><li>Harmonization of variables and input data for modeling intercomparison exercises</li><li>Display and discuss additional measures other than the ensemble median</li><li>Use evaluation and validation criteria for inclusion of specific models</li></ul></td><td>Research method, Communication</td></tr><tr><td>Large uncertainty across scales</td><td><ul><li>Harmonize available input data sets</li><li>Identify an optimal set of global data to be used as input for large scale model runs</li><li>Create a common input data repository</li><li>Develop time-varying dataset of the main input parameters</li></ul></td><td>Research method, Funding, Infrastructure, Communication</td></tr><tr><td>Misleading scenarios using without eCO₂ as plausible</td><td>For policy purpose, use results that fully include eCO₂ effects (as well as N limitation) and are validated against recent eCO₂ experiments</td><td>Research method, Communication</td></tr><tr><td>Effects on crop quality in modelling assessment are overlooked</td><td><ul><li>Development of modeling components to simulate protein and mineral concentrations</li><li>Set up AgMIP multi-modelling inter-comparison activity for coordinated</li></ul></td><td>Funding, Expertise, Research method</td></tr></tbody></table>
First, new eCO₂ experiments are needed for important crops in all agricultural regions of the world, particularly for cropping systems and agro-climatic regions in Africa, in order to capture the full diversity of responses. More experimental evidence on changes in crop quality and nutrition is needed for a wider range of crops to represent the threat for human health. All new studies describing results from specific CO₂-enrichment experiments should provide comprehensive and detailed weather, soil and management information to be easily integrated and used for crop model evaluation.

Synchronization of field experiments and modeling outputs should be enhanced to steadily improve crop models. Building connections among scientific disciplines will contribute to better access and use of experimental data to encourage continuous development of impact modeling tools.

Secondly, crop model improvements should focus with high priority on capturing the complex interactions of eCO₂, N, O₃, and varying climate/weather conditions, especially extreme events, and nutritional aspects. This crop model development will be fostered by an international initiative to be launched within AgMIP, but urgently requires research funding as well.

Thirdly, in addition to the inclusion of eCO₂ by default in impact assessments, the use of multi-model ensembles should be strongly encouraged to better capture modeling uncertainties⁸³.

Bias-correction techniques¹⁰⁹ should be applied to deal with potential biases in crop yield baseline simulations²⁸

Finally, we propose to build an open-access web-repository (which could be hosted, for example, in the Copernicus C3S data store in conjunction with AgMIP and other agricultural modeling and data groups), containing information in standardized formats of experiments,
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model metadata, and model simulations that are suitable for use in impact assessments, and to
be made accessible to stakeholders across the science and policy spheres.

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This roadmap will contribute to further narrowing the uncertainties that have long hampered
actions on climate change mitigation and adaptation in agriculture, and facilitate major
improvements in the conduct and use of climate change impact assessments in the agricultural
sector.

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## Acknowledgements

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We thank EC-JRC for hosting the ‘CO₂ Effects on Crops: Current Understanding, Modeling Needs, and Challenges’ Workshop 8-10 October 2018 held in Ispra (Italy) co-sponsored by AgMIP.

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AT and DD coordinated this community effort. All the authors contributed in writing, reviewing and interpreting the available literature. SA acknowledges support by the CGIAR research program on wheat agri-food systems (CRP WHEAT) and the CGIAR Platform for Big Data in Agriculture. TP acknowledges the Birmingham Institute of Forest Research. CR acknowledges the AgMIP Coordination Unit at Columbia University Earth Institute. FNT acknowledges funding from the FAO regular programme. The views expressed in this publication are those of the authors and do not necessarily reflect the views or policies of FAO and other organisations.

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The authors declare no competing interests.

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