Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

More accurate specification of water supply shows its importance for global crop production

Nature Food volume 3, pages 753–763 (2022)Cite this article

Subjects

Abstract

Warming temperatures tend to damage crop yields, yet the influence of water supply on global yields and its relation to temperature stress remains unclear. Here we use satellite-based measurements to provide empirical estimates of how root zone soil moisture and surface air temperature jointly influence the global productivity of maize, soybeans, millet and sorghum. Relative to empirical models using precipitation as a proxy for water supply, we find that models using soil moisture explain 30–120% more of the interannual yield variation across crops. Models using soil moisture also better separate water-supply stress from correlated heat stress and show that soil moisture and temperature contribute roughly equally to historical variations in yield. Globally, our models project yield damages of −9% to −32% across crops by end-of-century under Shared Socioeconomic Pathway 5-8.5 from changes in temperature and soil moisture. By contrast, projections using temperature and precipitation overestimate damages by 28% to 320% across crops both because they confound stresses from dryness and heat and because changes in soil moisture and temperature diverge from their historical association due to climate change. Our results demonstrate the importance of accurately representing water supply for predicting changes in global agricultural productivity and for designing effective adaptation strategies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Climate change alters the coupling of temperature, soil moisture and precipitation.
Fig. 2: Modelling soil moisture and temperature jointly better partitions the influence of water supply from that of temperature on global crop yields.
Fig. 3: Irrigation modifies the influences of climatic factors on crop yield and supports empirical separation of water-supply and temperature stresses.
Fig. 4: Projected influence of climate change on crop yield accounting for changes in water-supply and temperature stresses.

Similar content being viewed by others

Data availability

All data used in this analysis are from free, publicly available sources and are publicly accessible from Zenodo at https://doi.org/10.5281/zenodo.7041431.

Code availability

Replication code is publicly accessible from Zenodo at https://doi.org/10.5281/zenodo.7041431.

References

  1. Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  2. IPCC Food Security (eds Mbow, C. et al.) (IPCC, 2019).

  3. Schlenker, W. & Lobell, D. B. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).

    Article  ADS  Google Scholar 

  4. Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

    Article  CAS  PubMed  ADS  Google Scholar 

  5. Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Chang. 11, 306–312 (2021).

    Article  ADS  Google Scholar 

  6. Proctor, J., Hsiang, S., Burney, J., Burke, M. & Schlenker, W. Estimating global agricultural effects of geoengineering using volcanic eruptions. Nature 560, 480–483 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  7. Vogel, E. et al. The effects of climate extremes on global agricultural yields. Environ. Res. Lett. 14, 054010 (2019).

    Article  ADS  Google Scholar 

  8. Agnolucci, P. et al. Impacts of rising temperatures and farm management practices on global yields of 18 crops. Nat. Food 1, 562–571 (2020).

    Article  Google Scholar 

  9. Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci USA 114, 9326–9331 (2017).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  10. Buckley, T. N. How do stomata respond to water status? New Phytol. 224, 21–36 (2019).

    Article  PubMed  Google Scholar 

  11. Chaves, M. M. et al. How plants cope with water stress in the field. Photosynthesis and growth. Ann. Bot. 89, 907–916 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Siega, T. D. C., Bertoldo, E. & Vismara, L. D. S. Cavitation and embolism in plants: literature review. Aust. J. Basic Appl. Sci. 12, 1–4 (2018).

    CAS  Google Scholar 

  13. Waqas, M. A. et al. Thermal stresses in maize: effects and management strategies. Plants 10, 1–23 (2021).

    Article  Google Scholar 

  14. Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8, 1–9 (2017).

    Article  CAS  Google Scholar 

  15. Rajendra Prasad, V. B. et al. Drought and high temperature stress in sorghum: physiological, genetic, and molecular insights and breeding approaches. Int. J. Mol. Sci. 22, 9826 (2021).

    Article  CAS  Google Scholar 

  16. Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001 (2017).

    Article  ADS  CAS  Google Scholar 

  17. Jones, J. W. et al. Toward a new generation of agricultural system data, models, and knowledge products: state of agricultural systems science. Agric. Syst. 155, 269–288 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ewert, F. et al. Crop modelling for integrated assessment of risk to food production from climate change. Environ. Model. Softw. 72, 287–303 (2015).

    Article  Google Scholar 

  19. Boote, K. J., Jones, J. W., White, J. W., Asseng, S. & Lizaso, J. I. Putting mechanisms into crop production models. Plant Cell Environ. 36, 1658–1672 (2013).

    Article  CAS  PubMed  Google Scholar 

  20. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci Rev 99, 125–161 (2010).

    Article  CAS  ADS  Google Scholar 

  21. Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2002).

    MATH  Google Scholar 

  22. Zeppetello, L. R., Tetreault-Pinard, E., Battisti, D. S. & Baker, M. B. Identifying the sources of continental summertime temperature variance using a diagnostic model of land-atmosphere interactions. J. Clim. 33, 3547–3564 (2020).

    Article  ADS  Google Scholar 

  23. Carter, E. K., Melkonian, J., Riha, S. J. & Shaw, S. B. Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize. Environ. Res. Lett. 11, 094012 (2016).

    Article  ADS  Google Scholar 

  24. Ortiz-Bobea, A., Wang, H., Carrillo, C. M. & Ault, T. R. Unpacking the climatic drivers of US agricultural yields. Environ. Res. Lett. 14, 064003 (2019).

    Article  ADS  Google Scholar 

  25. Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).

    Article  Google Scholar 

  26. Zhu, P. & Burney, J. Untangling irrigation effects on maize water and heat stress alleviation using satellite data. Hydrol. Earth Syst. Sci. 26, 827–840 (2021).

    Article  ADS  Google Scholar 

  27. Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang. 6, 1023–1027 (2016).

    Article  CAS  ADS  Google Scholar 

  28. Liu, L. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 1–9 (2020).

    CAS  ADS  Google Scholar 

  29. Wijewardana, C. et al. Quantifying soil moisture deficit effects on soybean yield and yield component distribution patterns. Irrig. Sci. 36, 241–255 (2018).

    Article  Google Scholar 

  30. Lesk, C. et al. Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields. Nat. Food 2, 683–691 (2021).

    Article  Google Scholar 

  31. Dorigo, W. et al. ESA CCI soil moisture for improved Earth system understanding: state-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).

    Article  ADS  Google Scholar 

  32. Gruber, A., Scanlon, T., Van Der Schalie, R., Wagner, W. & Dorigo, W. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data 11, 717–739 (2019).

    Article  ADS  Google Scholar 

  33. CPC Global Unified Gauge-Based Analysis of Daily Precipitation (NOAA, 2020); https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html

  34. FAOSTAT Crops and Livestock Products (FAO, 2020); https://www.fao.org/faostat

  35. Lesk, C., Coffel, E. & Horton, R. Net benefits to US soy and maize yields from intensifying hourly rainfall. Nat. Clim. Chang. 10, 819–822 (2020).

    Article  ADS  Google Scholar 

  36. Hsiang, S. M. Climate econometrics. Annu. Rev. Resource Econ. 8, 43–75 (2016).

    Article  Google Scholar 

  37. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  ADS  Google Scholar 

  38. Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Vol. 2 (Springer, 2009).

  39. Sánchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Change Biol. 20, 408–417 (2014).

    Article  ADS  Google Scholar 

  40. Stone, L. R. & Schlegel, A. J. Yield–water supply relationships of grain sorghum and winter wheat. Agron. J. 98, 1359–1366 (2006).

    Article  Google Scholar 

  41. Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Chang. 3, 497–501 (2013).

    Article  ADS  Google Scholar 

  42. Hirasawa, T. & Hsiao, T. C. Some characteristics of reduced leaf photosynthesis at midday in maize growing in the field. Field Crops Res. 62, 53–62 (1999).

    Article  Google Scholar 

  43. Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).

    Article  ADS  Google Scholar 

  44. Ashraf, M. & Habib-ur-Rehman. Interactive effects of nitrate and long-term waterlogging on growth, water relations, and gaseous exchange properties of maize (Zea mays L.). Plant Sci. 144, 35–43 (1999).

  45. Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Change Biol. 25, 2325–2337 (2019).

    Article  ADS  Google Scholar 

  46. Rosenzweig, C., Tubiello, F. N., Goldberg, R., Mills, E. & Bloomfield, J. Increased crop damage in the US from excess precipitation under climate change. Glob. Environ. Change 12, 197–202 (2002).

    Article  Google Scholar 

  47. Voesenek, L. A. & Bailey-Serres, J. Flood adaptive traits and processes: an overview. New Phytol. 206, 57–73 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111, 3239–3244 (2014).

    Article  CAS  PubMed  ADS  Google Scholar 

  49. Myhre, G. et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9, 2–11 (2019).

    Article  CAS  Google Scholar 

  50. Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 236–244 (2017).

    Article  ADS  Google Scholar 

  51. Lorenz, D. J., Nieto-Lugilde, D., Blois, J. L., Fitzpatrick, M. C. & Williams, J. W. Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD. Sci. Data 3, 1–19 (2016).

    Article  Google Scholar 

  52. Douville, H., Raghavan, K. & Renwick, J. Water cycle changes. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the IPCC (eds Masson-Delmotte, V. et al.) 1055–1210 (Cambridge University Press, 2021).

  53. Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang. 6, 317–322 (2015).

    Article  ADS  Google Scholar 

  54. Shortridge, J. Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture. Clim. Change 157, 429–444 (2019).

    Article  ADS  Google Scholar 

  55. Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).

    Article  ADS  Google Scholar 

  56. Proctor, J. Atmospheric opacity has a nonlinear effect on global crop yields. Nat. Food 2, 166–173 (2021).

    Article  Google Scholar 

  57. Taylor, C. A. & Schlenker, W. Environmental Drivers of Agricultural Productivity Growth: CO2 Fertilization of US Field Crops, Working Paper Series No. 29320 (National Bureau of Economic Research, 2021).

  58. Feldman, A. et al. Satellites capture soil moisture dynamics deeper than a few centimeters and are relevant to plant water uptake. Preprint at https://doi.org/10.1002/essoar.10511280.1 (2022).

  59. Ford, T. W., Harris, E. & Quiring, S. M. Estimating root zone soil moisture using near-surface observations from SMOS. Hydrol. Earth Syst. Sci. 18, 139–154 (2014).

    Article  ADS  Google Scholar 

  60. CPC Global Daily Temperature (NOAA, 2020); https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html

  61. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, 1–19 (2008).

    Article  CAS  Google Scholar 

  62. Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeog. 19, 607–620 (2010).

    Google Scholar 

  63. Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 06.1, Technical Report (Earth Observation Data Centre for Water Resources Monitoring, 2021).

  64. Stefan, S., Verena, H., Karen, F. & Burke, J. AQUASTAT Global Map of Irrigation Areas version 5 (FAO, 2013).

  65. Ray, D. K., Gerber, J. S., Macdonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 1–9 (2015).

    Article  CAS  Google Scholar 

  66. Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food 2, 873–885 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

We thank seminar participants at the University of California Santa Barbara, Harvard University, the University of British Columbia, the Woods Hole Oceanographic Institution, the Gordon Research Conference on Climate Engineering, the American Geophysical Union, the Planetary Health Colloquium, and the Seminar on Planetary Management for useful comments. Funding for this project was provided by the Harvard Google Data+Climate Project.

Author information

Authors and Affiliations

  1. Center for the Environment and Data Science Initiative, Harvard University, Cambridge, MA, USA

    Jonathan Proctor

  2. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

    Angela Rigden & Peter Huybers

  3. Department of Earth System Science, University of California Irvine, Irvine, CA, USA

    Angela Rigden

  4. Department of Physical Oceanography, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Duo Chan

Authors
  1. Jonathan Proctor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angela Rigden
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Duo Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Huybers
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors designed the study and interpreted results. J.P., A.R. and D.C. collected the data. J.P. performed the analysis. J.P. and P.H. wrote the paper with input from all authors.

Corresponding author

Correspondence to Jonathan Proctor.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Food thanks Adriaan J. Teuling, Corey Lesk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Average yields of maize, soybeans, millet and sorghum.

Average of yield data (2007–2018, hg/ha) from the Food and Agricultural Organization of the United Nations.

Extended Data Fig. 2 Reconstructions of U.S. yield anomalies using a model accounting for water supply and temperature stress.

Observed yield anomalies from the trend, along with predicted climate-induced yield anomalies from the trend using the TS model. The individual temperature and soil moisture components of this prediction are also plotted. 95% confidence intervals around the predictions, relative to a growing season with average conditions, are shown as dotted lines.

Extended Data Fig. 3 The influence of temperature, soil moisture and precipitation on barley, oats and wheat yield.

Identical to Fig. 2 but estimated for barley, oats and wheat. These crops were omitted from the primary analysis due to them commonly being planted as either a spring or winter crop, as the FAO does not differentiate yield by winter versus spring planting. The findings from these three crops are consistent with those of maize, soybeans, sorghum and millet. Barley, oats and wheat also show moderated effects of extreme heat and improved performance in the TS model relative to the TP model. Out-of-sample within-R2 is 0.125 for barley, 0.172 for oats, and 0.122 for wheat in the TS model and 0.095 for barley, 0.154 for oats and 0.106 for wheat in the TP model.

Extended Data Fig. 4 Correlation of daily temperature, soil moisture and precipitation during the maize, soybean, sorghum and millet growing season over cropped areas.

The Pearson’s correlation coefficient is calculated weighting daily observations across crops by cropped fraction for each 50km x 50km pixel with harvest area greater than 100 hectares.

Extended Data Fig. 5 Omitting measures of water supply biases estimated effects of temperature on crop yield in simulation.

(A) Simulated daily temperature and soil moisture. (B) Estimated temperature response curves where simulated yields are a linear function of daily growing season temperature and soil moisture. The prescribed true response (black) is shown along with estimated responses modelling temperature (red), temperature and precipitation (yellow), and temperature and soil moisture (blue). (C) Similar response curves to (A) except that yield is simulated and modelled as a quadratic, rather than linear, function of temperature. (D) Similar to (C) except that instead of soil moisture having a linear influence on yield, soil moisture has a nonlinear influence whereby increasing soil moisture at low values increases yield but increasing soil moisture at high values has little effect. This produces larger bias at high temperatures than at low temperatures in the models that omit soil moisture, which is consistent with what we observe in Fig. 2. (E) Similar to (A) but with a stronger simulated relationship between daily soil moisture and temperature in hot and dry conditions. (F) Similar to (D) but with additional bias at high temperatures relative to low temperatures due to the strengthened soil-moisture temperature relationship in (E).

Extended Data Fig. 6 Irrigation modifies the influences of climatic factors on crop yield and validates empirical separation of water supply and temperature stresses.

Identical to Fig. 3 except that responses are estimated separately for each of the four crops rather than pooled. Grey backgrounds indicate models where either allowing for heterogeneity with respect to irrigation does not improve performance or where the response function is not significantly different from the null of zero effect at the p < 0.10 level (see Extended Data Table 1 for model performance when neither T nor S is interacted with irrigation or when both T and S are; when only T is interacted with irrigation, within-R2 = 0.097 for maize, 0.141 for soybeans, 0.034 for sorghum and 0.028 for millet; when only S is interacted with irrigation, R2 = 0.106 for maize, 0.136 for soybeans, 0.054 for sorghum and 0.022 for millet). Outliers are winsorized for display to their 0.5 and 99.5 percentiles for temperature and soil moisture and 1 and 99 percentiles for precipitation.

Extended Data Fig. 7 Estimated influence of soil moisture and temperature on global crop yields allowing for interaction effects.

Response surfaces show the influence on yield of changing a day in the growing season from 25 ∘C and 0.2 cm3cm−3 to a given temperature and soil moisture. (A) Shows the estimated response surface, allowing the nonlinear influence of temperature and soil moisture to depend nonlinearly on the level of the other (Methods). (B) Shows the estimated response surface allowing for nonlinear effects of temperature and soil moisture, but no interaction between the two (that is the same model as in Fig. 2). Black contours show the cropped-area-weighted distribution of daily growing season values across crops in the pooled surface and for each crop in their respective plots. Response functions are shown over the 95th percentile contour of the underlying distribution of daily values. White lines show contours of the estimated response surfaces to aid display. P-values for the null of no interaction effect are 0.738 for the pooled model, 0.295 for maize, 0.461 for soybeans, 0.956 for sorghum, and 0.141 for millet. See Supplementary Discussion 4.

Extended Data Fig. 8 Projected influence of climate change on maize, soybean, sorghum and millet yields due to changes in temperature and soil moisture.

(A) Projected influence of changes in temperature stress due to climate change on crop yield using the TS model (SSP5-8.5, 2015–2035 to 2080–2100, averaged across six CMIP6 models, see Methods). (B) Projected influence of changes in water-supply stress due to climate change on crop yield using the TS model. Locations where five out of six of the models agree on the sign are not stippled.

Extended Data Fig. 9 Distribution of observed (black) and CMIP6 simulated (coloured) daily growing season temperature and soil moisture over cropped areas before and after debiasing.

Observations have been winsorized to 10∘C and 55∘C and to 0.5 cm3cm−3 for display. Light colours show near-future (2015-2035) values and dark colours show end of century (2080-2100) values. Middle and right columns show distributions after pixel-wise mean and mean and variance debiasing approaches were applied (Methods).

Extended Data Table 1 Model fit for a range of specifications. Within-R2 values are calculated out-of-sample using a 10-fold cross-validation procedure (Methods). Yield and explanatory climate factors are demeaned and detrended by country according to the model fixed effects prior to cross-validation. Column names indicate what combination of temperature (T), precipitation (P) and soil moisture (S) are included in the model. The model and resolution columns describe how nonlinear model features are calculated and at what temporal resolution the nonlinearities are applied. Restricted cubic splines calculated at the daily resolution are the primary specification (Methods, Eqn. (1)). The irrigation model (Methods, Eqn. (3)) allows for heterogeneous effects in irrigated and non-irrigated cropland (Extended Data Fig. 6) and the interaction model (Methods, Eqn. (5)) allows the level of temperature to alter the influence of soil moisture and vice versa (Extended Data Fig. 7)
Full size table

Supplementary information

Supplementary Information

Supplementary Discussion 1–5, Tables 1 and 2, and Figs. 1–9.

Reporting Summary

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Proctor, J., Rigden, A., Chan, D. et al. More accurate specification of water supply shows its importance for global crop production. Nat Food 3, 753–763 (2022). https://doi.org/10.1038/s43016-022-00592-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43016-022-00592-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing