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Rising atmospheric carbon dioxide concentrations increase gaps of rice yields between low- and middle-to-high-income countries

Nature Food volume 5, pages 754–763 (2024)Cite this article

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Abstract

The rising carbon dioxide concentrations are expected to increase future rice yields. However, variations in the CO2 fertilization effect (CFE) between rice subspecies and the influence of concurrent global warming introduce uncertainty in future global rice yield projections. Here we conducted a meta-analysis of rising carbon dioxide field experiments and employed crop modelling to assess future global rice yields for the top 14 rice producing countries. We found a robust parabolic relationship between rice CFE and temperature, with significant variations between rice subspecies. Our projections indicate that global rice production in the 2050s is expected to increase by 50.32 million tonnes (7.6%) due to CFE compared with historical production. Because low-income countries will experience higher temperatures, the gaps (difference of Δyield) between middle-to-high-income and low-income countries are projected to widen from the 2030s to the 2090s under elevated carbon dioxide. These findings underscore the critical role of CFE and emphasize the necessity to increase investments in research and technology for rice producing systems in low-income countries.

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Fig. 1: The rice CFE as a function of Tair.
Fig. 2: Projected future Tair in the rice growing season and CFE for every ten- or nine-year period during 2031–2099 in the main global rice planting regions.
Fig. 3: Projected changes in global rice yield and production under eCO2 concentrations under the SSP 5-85 scenario.
Fig. 4: Projected changes in rice yield under eCO2 concentrations for low-income and middle-to-high-income rice planting countries under the SSP 5-85 scenario.

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Data availability

The data supporting the findings of this study are available within the article and Supplementary Information, and the national-scale rice CFE, yield and production dataset are available via figshare at https://doi.org/10.6084/m9.figshare.26028958 (ref. 51). The historical harvested area, rice yield, production and rice subspecies for the 14 top rice producing countries can be acquired from FAO (http://www.fao.org/faostat/en/) and Chinese National Bureau of Statistics. The number of rice growing seasons and rice planting and harvesting dates of the RiceAtlas database can be downloaded from https://www.nature.com/articles/sdata201774. The SPAM rice harvesting area data can be downloaded from https://www.mapspam.info/. The CMIP6 data can be downloaded from the Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=overview). Source data are provided with this paper.

Code availability

The code files can be accessed via figshare at https://doi.org/10.6084/m9.figshare.26028958 (ref. 51).

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Acknowledgements

The work was supported by grants from the National Natural Science Foundation of China (32001191 to L.S., 31870423 to C.Z., 32171591 to C.C.), the Carbon Peaking and Carbon Neutrality Special Fund for Science and Technology from Nanjing Science and Technology Bureau (20221103 to C.Z.), the Key-Area Research and Development Program of Guangdong Province, China (number 2020B0202010006 to C.Z.), the Key and General projects of Jiangsu Province (BE2018402 to C.Z.), the Doctor of Mass Entrepreneurship and Innovation in Jiangsu Province (L.S.), Key Research and Development Program of Jiangsu Province (BE2022308 to C.Z.), the Major Science and Technology Project of Ordos City (2022EEDSKJZDZX010 to C.Z.), Science Foundation of the Chinese Academy of Sciences (ISSAS2413 to C.C.) and the Spanish Government grants (TED2021-132627B-I00 to J.P.), the Fundación Ramón Areces grant (CIVP20A6621 to J.P.), and the Catalan Government grant (SGR2021-1333 to J.P.).

Author information

Author notes

  1. These authors contributed equally: Lian Song, Ye Tao.

Authors and Affiliations

  1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, China

    Lian Song, Ye Tao, Jishuang Zhang, Chuang Cai, Chuanqi Ma, Xiaoyuan Yan, Dongming Wang, Yu Wang & Chunwu Zhu

  2. University of Chinese Academy of Sciences, Nanjing, China

    Ye Tao, Jishuang Zhang, Chuanqi Ma & Chunwu Zhu

  3. Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

    Kees Jan van Groenigen

  4. Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada

    Scott X. Chang

  5. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Josep Peñuelas

  6. CREAF, Cerdanyola del Vallès, Spain

    Josep Peñuelas

  7. Macro Agriculture Research Institute, College of Economics and Management, Huazhong Agricultural University, Wuhan, China

    Liangzhi You

  8. International Food Policy Research Institute (IFPRI), Washington, DC, USA

    Liangzhi You

  9. Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China, College of Agriculture, Nanjing Agricultural University, Nanjing, China

    Songhan Wang & Yu Jiang

  10. Tea Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Tea Biology and Resource Utilization, Ministry of Agriculture, Hangzhou, China

    Kang Ni

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Contributions

C.Z. and L.S. conceived the study. C.Z., L.S., Y.T., J.Z., C.C. and K.N. conducted the modelling. C.Z., L.S. and Y.T. performed the investigation. L.S., Y.T. and J.Z. curated the data. L.S., Y.T., K.J.v.G., S.X.C., J.P., L.Y., S.W. and C.M. carried out the formal analyses. C.Z. supervised the study. L.S., Y.T. and C.Z. drafted the original paper. L.S., Y.T., K.J.v.G., S.X.C., J.P., J.Z., L.Y., S.W., C.C., C.M., X.Y., C.Z., Y.W., K.N. and D.W. contributed to the review and editing process.

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Correspondence to Chunwu Zhu.

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Nature Food thanks Liyin He, Irakli Loladze and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Distribution of e[CO2] experiments included in the analysis.

The circle size indicates the number of experiments at the same location. The background data indicates the distribution of the global rice-planting region using the global rice-harvest area downloaded from the Spatial Production Allocation Model: https://www.mapspam.info/.

Extended Data Fig. 2 The rice CO2 fertilization effect (CFE) as a function of different potential drivers.

a, Variations of the difference in the corrected Akaike information criterion (ΔAICc) of the most competitive regression models for the impact of rice growing season average temperature (Tair), subspecies, phosphatic fertilizer (P), potash fertilizer (K), plant density (PD) and nitrogen fertilizer (N) on CFE. b, Relative importance of potential drivers regulating the effects of CO2 increase on rice yield.

Extended Data Fig. 3 Responses of rice CFE (Carbon dioxide Fertilization Effect) to changes in [CO2].

Responses of rice CFE (Carbon dioxide Fertilization Effect) to changes in [CO2] (Δ[CO2]), based on all the data sets of FACE (Free-Air CO2 Enrichment) and OTC (Open-Top Chamber) experiments collected in this study. a, Box plots of the response of rice CFE to Δ[CO2] bins with intervals of 50 μmol mol-1 (50-100, 100-150, 150-200, 200-250, 250-300, and ≥300 μmol mol-1). Horizontal lines are medians, empty squares are means, boxes show the 25%-75% interquartile range (IQR), error bars show the full range excluding outliers defined as being more than ±1.5IQR outside the box. b, Scatter plots between average CFE across Δ[CO2] bins and Δ[CO2]. The line indicates the nonrectangular hyperbolic regression.

Extended Data Fig. 4 Box plots of CFE (Carbon dioxide Fertilization Effect) normalized to e[CO2] at 200 μmol mol-1.

Box plots of CFE (Carbon dioxide Fertilization Effect) normalized to e[CO2] at 200 μmol mol-1 for indica (a) and japonica (b) for each rice growing season average temperature (Tair) classes, including FACE (Free-Air CO2 Enrichment) and OTC (Open-Top Chamber) experiments. Each dot represents one independent experiment result. Horizontal lines are medians, empty squares are means, boxes show the 25%-75% interquartile range (IQR), error bars show the full range excluding outliers defined as being more than ±1.5IQR outside the box.

Extended Data Fig. 5 Predicted future changes in [CO2] (Δ[CO2]) compared to the average [CO2] during 2009-2018 under the SSPs (Shared Socioeconomic Pathways) 2-4.5 and 5-8.5 scenario.

The changes are averaged for each 10 or nine-year period during 2031-2099 (10-year periods for 2031-2090, and a nine-year period for 2091-2099).

Extended Data Fig. 6 Projected future mean daily air temperature (Tair) in the rice growing season for every 10- or nine-year period during 2031-2099.

Projected future mean daily air temperature (Tair) in the rice growing season for every 10- or nine-year period during 2031-2099 in the main global rice-planting regions under the “medium forcing middle-of-the-road pathway” scenario (SSP2-45). The shaded error bars represent standard deviation (SD) for Tair within the 10- or nine-year period, and the solid curve is the mean value of Tair.

Extended Data Fig. 7 Global distribution of predicted uncertainty of indica CFE (Carbon dioxide Fertilization Effect) under the “high-end forcing pathway” scenario (SSP5-85).

Global distribution of predicted uncertainty of indica CFE (Carbon dioxide Fertilization Effect) under the “high-end forcing pathway” scenario (SSP5-85) for each 10- or nine-year period during 2031-2099 (10-year periods of 2031-2040, 2041-2050, 2051-2060, 2061-2070, 2071-2080, 2081-2090, and a nine-year period of 2091-2099). The grey areas fall outside the Tair limits of this analysis.

Extended Data Fig. 8 Global distribution of predicted uncertainty of japonica CFE (Carbon dioxide Fertilization Effect) under the “high-end forcing pathway” scenario (SSP5-85).

Global distribution of predicted uncertainty of japonica CFE (Carbon dioxide Fertilization Effect) under the “high-end forcing pathway” scenario (SSP5-85) for each 10- or nine-year period during 2031-2099 (10-year period of 2031-2040, 2041-2050, 2051-2060, 2061-2070, 2071-2080, 2081-2090, and a nine-year period of 2091-2099). The grey areas fall outside the Tair limits of this analysis.

Extended Data Fig. 9 Projected future CFE (Carbon dioxide Fertilization Effect) for indica and japonica in the rice growing season.

Projected future CFE (Carbon dioxide Fertilization Effect) for indica and japonica in the rice growing season for every 10- or nine-year period during 2031-2099 in the main global rice-planting regions under the “medium forcing middle-of-the-road pathway” scenario (SSP2-45). The shaded error bars represent standard deviation (SD) of CFE within the 10- or nine-year period for indica and japonica, and the solid curves are the mean values of CFE.

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Song, L., Tao, Y., van Groenigen, K.J. et al. Rising atmospheric carbon dioxide concentrations increase gaps of rice yields between low- and middle-to-high-income countries. Nat Food 5, 754–763 (2024). https://doi.org/10.1038/s43016-024-01021-x

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