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The critical benefits of snowpack insulation and snowmelt for winter wheat productivity

Abstract

How climate change will affect overwintering crops is largely unknown due to the complex and understudied interactions among temperature, rainfall and snowpack. Increases in average winter temperature should release cold limitations yet warming-induced reductions of snowpack thickness should lead to decreased insulation effects and more exposure to freezing. Here, using statistical models, we show that the presence of snowpack weakens yield sensitivity to freezing stress by 22% during 1999–2019. By 2080–2100, we project that reduced snow cover insulation will offset up to one-third of the yield benefit (8.8 ± 1.1% for RCP 4.5 and 11.8 ± 1.4% for RCP 8.5) from reduced frost stress across the United States. Furthermore, by 2080–2100 future decline in wheat growing season snowfall (source of snowmelt) will drive a yield loss greater than the yield benefit from increasing rainfall. Explicitly considering these factors is critical to predict the climate change impacts on winter wheat production in snowy regions.

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Fig. 1: Historical and projected changes in climate variables.
Fig. 2: Sensitivity of wheat yields to climate variables.
Fig. 3: Climate change effects on wheat yield by 2080–2100 under RCP 4.5 and RCP 8.5.
Fig. 4: Estimates of snow effects on wheat yields by process-based models differ from those by yield statistics.

Data availability

All data used in this study are from publicly available datasets. US winter wheat yield, planted area and production data at county level are available at https://quickstats.nass.usda.gov/. The PRISM daily spatial climate dataset is accessed through Google Earth Engine at ee.ImageCollection. Daily snow depth data are available at https://doi.org/10.5067/W9FOYWH0EQZ3. AgMIP phase 1 model outputs are available at http://www.rdcep.org/research-projects/ggcmi. Processed and extracted variables used directly in the analyses are available at https://doi.org/10.5281/zenodo.6209590

Code availability

The scripts and datasets used to run the regression and generate tables and figures are also available through Zenodo at https://doi.org/10.5281/zenodo.6209590.

References

  1. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  2. Sindelar, A. J. et al. Winter oilseed production for biofuel in the US Corn Belt: opportunities and limitations. GCB Bioenergy 9, 508–524 (2017).

    Article  CAS  Google Scholar 

  3. Stöckle, C. O. et al. Evaluating opportunities for an increased role of winter crops as adaptation to climate change in dryland cropping systems of the U.S. Inland Pacific Northwest. Clim. Change 146, 247–261 (2018).

    Article  CAS  Google Scholar 

  4. Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).

    Article  Google Scholar 

  5. Seifert, C. A., Azzari, G. & Lobell, D. B. Satellite detection of cover crops and their effects on crop yield in the Midwestern United States. Environ. Res. Lett. 13, 064033 (2018).

    Article  Google Scholar 

  6. Marcillo, G. S. & Miguez, F. E. Corn yield response to winter cover crops: an updated meta-analysis. J. Soil Water Conserv. 72, 226–239 (2017).

    Article  Google Scholar 

  7. Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).

    Article  Google Scholar 

  8. Mankin, J. S. & Diffenbaugh, N. S. Influence of temperature and precipitation variability on near-term snow trends. Clim. Dynam. 45, 1099–1116 (2015).

    Article  Google Scholar 

  9. Zhu, L., Radeloff, V. C. & Ives, A. R. Characterizing global patterns of frozen ground with and without snow cover using microwave and MODIS satellite data products. Remote Sens. Environ. 191, 168–178 (2017).

    Article  Google Scholar 

  10. Huning, L. S. & AghaKouchak, A. Global snow drought hot spots and characteristics. Proc. Natl Acad. Sci. USA 117, 19753–19759 (2020).

    Article  CAS  Google Scholar 

  11. Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).

    Article  Google Scholar 

  12. Trnka, M. et al. Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change 4, 637–643 (2014).

    Article  Google Scholar 

  13. Li, D., Wrzesien, M. L., Durand, M., Adam, J. & Lettenmaier, D. P. How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. 44, 6163–6172 (2017).

    Article  Google Scholar 

  14. Biemans, H. et al. Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nat. Sustain. 2, 594–601 (2019).

    Article  Google Scholar 

  15. Acevedo, E., Silva, P. & Silva, H. in Bread Wheat: Improvement and Production (eds Curtis, B. C. et al.) 39–70 (FAO Plant Production and Protection, 2002).

  16. Baker, J. T., Pinter, P. J., Reginato, R. J. & Kanemasu, E. T. Effects of temperature on leaf appearance in spring and winter wheat cultivars. Agron. J. 78, 605–613 (1986).

    Article  Google Scholar 

  17. Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl Acad. Sci. USA 112, 6931–6936 (2015).

    Article  CAS  Google Scholar 

  18. Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).

    Article  Google Scholar 

  19. Talukder, A. S. M. H. M., McDonald, G. K. & Gill, G. S. Effect of short-term heat stress prior to flowering and early grain set on the grain yield of wheat. Field Crops Res. 160, 54–63 (2014).

    Article  Google Scholar 

  20. Farooq, M., Bramley, H., Palta, J. A. & Siddique, K. H. M. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 30, 491–507 (2011).

  21. Cuadra, S. V., Kimball, B. A., Boote, K. J., Suyker, A. E. & Pickering, N. Energy balance in the DSSAT-CSM-CROPGRO model. Agric. For. Meteorol. 297, 108241 (2021).

    Article  Google Scholar 

  22. Harder, P., Helgason, W. D. & Pomeroy, J. W. Modeling the snowpack energy balance during melt under exposed crop stubble. J. Hydrometeorol. 19, 1191–1214 (2018).

    Article  Google Scholar 

  23. Barlow, K. M., Christy, B. P., O’Leary, G. J., Riffkin, P. A. & Nuttall, J. G. Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crops Res. 171, 109–119 (2015).

    Article  Google Scholar 

  24. Wang, W. et al. Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region. Cryosphere 10, 1721–1737 (2016).

    Article  Google Scholar 

  25. Seifert, C. A. & Lobell, D. B. Response of double cropping suitability to climate change in the United States. Environ. Res. Lett. 10, 024002 (2015).

    Article  Google Scholar 

  26. Pullens, J. W. M. et al. Risk factors for European winter oilseed rape production under climate change. Agric. For. Meteorol. 272–273, 30–39 (2019).

    Article  Google Scholar 

  27. Chopra, R. et al. Identification and stacking of crucial traits required for the domestication of pennycress. Nat. Food 1, 84–91 (2020).

    Article  Google Scholar 

  28. Crews, T. E., Carton, W. & Olsson, L. Is the future of agriculture perennial? Imperatives and opportunities to reinvent agriculture by shifting from annual monocultures to perennial polycultures. Glob. Sustain. 1, e11 (2018).

  29. Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. Meteorol. 282–283, 107862 (2020).

    Article  Google Scholar 

  30. Schierhorn, F., Hofmann, M., Gagalyuk, T., Ostapchuk, I. & Müller, D. Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages. Clim. Change 169, 39 (2021).

  31. Michel, S. et al. Improving and maintaining winter hardiness and frost tolerance in bread wheat by genomic selection. Front. Plant Sci. 10, 1195 (2019).

    Article  Google Scholar 

  32. Mahfoozi, S., Limin, A. E. & Fowler, D. B. Influence of vernalization and photoperiod responses on cold hardiness in winter cereals. Crop Sci. 41, 1006–1011 (2001).

    Article  Google Scholar 

  33. Dutra, E. et al. An improved snow scheme for the ECMWF land surface model: description and offline validation. J. Hydrometeorol. 11, 899–916 (2010).

    Article  Google Scholar 

  34. Ge, Y. & Gong, G. Land surface insulation response to snow depth variability. J. Geophys. Res. Atmos. 115, 8107 (2010).

    Article  Google Scholar 

  35. Hunt, J. R. et al. Early sowing systems can boost Australian wheat yields despite recent climate change. Nat. Clim. Change 9, 244–247 (2019).

    Article  Google Scholar 

  36. Sloat, L. L. et al. Climate adaptation by crop migration. Nat. Commun. 11, 1243 (2020) .

  37. Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Change Biol. 27, 27–49 (2021).

    Article  Google Scholar 

  38. Shimoda, S. et al. Effects of snow compaction ‘yuki-fumi’ on soil frost depth and volunteer potato control in potato–wheat rotation system in Hokkaido. Plant Prod. Sci. 24, 186–197 (2021).

    Article  CAS  Google Scholar 

  39. Luojus, K. et al. GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset. Sci. Data 8, 163 (2021)..

  40. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions Version 1 (NSIDC, 2008).

  41. Jing, Q. et al. Assessing the options to improve regional wheat yield in Eastern Canada using the CSM–CERES–wheat model. Agron. J. 109, 510–523 (2017).

    Article  Google Scholar 

  42. Vogel, F. A. & Bange, G. A. Understanding USDA Crop Forecasts (USDA, 1999).

  43. Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).

    Article  Google Scholar 

  44. Brown, R. D. & Brasnett, B. Daily Snow Depth Analysis Data Version 1 (Canadian Meteorological Centre, 2010).

  45. Brasnett, B. A global analysis of snow depth for numerical weather prediction. J. Appl. Meteorol. Climatol. 38, 726–740 (1999).

    Article  Google Scholar 

  46. Toure, A. M., Reichle, R. H., Forman, B. A., Getirana, A. & De Lannoy, G. J. M. Assimilation of MODIS snow cover fraction observations into the NASA catchment land surface model. Remote Sens. 10, 316 (2018).

    Article  Google Scholar 

  47. Snauffer, A. M., Hsieh, W. W. & Cannon, A. J. Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. J. Hydrol. 541, 714–726 (2016).

    Article  Google Scholar 

  48. Census of Agriculture (USDA National Agricultural Statistics Service, 2017).

  49. Skinner, D. Z. & Mackey, B. Freezing tolerance of winter wheat plants frozen in saturated soil. Field Crops Res. 113, 335–341 (2009).

    Article  Google Scholar 

  50. Lollato, R. P. et al. Climate-risk assessment for winter wheat using long-term weather data. Agron. J. 112, 2132–2151 (2020).

    Article  Google Scholar 

  51. Siebers, M. H. et al. Heat waves imposed during early pod development in soybean (Glycine max) cause significant yield loss despite a rapid recovery from oxidative stress. Glob. Change Biol. 21, 3114–3125 (2015).

    Article  Google Scholar 

  52. Çakir, R. Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Res. 89, 1–16 (2004).

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Chen, M., Griffis, T. J., Baker, J., Wood, J. D. & Xiao, K. Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes. J. Geophys. Res. Biogeosci. 120, 310–325 (2015).

    Article  CAS  Google Scholar 

  55. Larson, K. M. & Small, E. E. Daily Snow Depth and SWE from GPS Signal-to-Noise Ratios Version 1 (NSIDC, 2017).

  56. Sturm, M. et al. Estimating snow water equivalent using snow depth data and climate classes. J. Hydrometeorol. 11, 1380–1394 (2010).

    Article  Google Scholar 

  57. McCabe, G. J. & Wolock, D. M. Recent declines in western U.S. snowpack in the context of twentieth-century climate variability. Earth Interact. 13, 1–15 (2009).

    Article  Google Scholar 

  58. Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2019).

    Article  Google Scholar 

  59. Qiao, S. et al. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  61. Xie, Y., Gibbs, H. K. & Lark, T. J. Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. Earth Syst. Sci. Data 13, 5689–5710 (2021).

    Article  Google Scholar 

  62. Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    Article  CAS  Google Scholar 

  63. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  Google Scholar 

  64. Elliott, J. et al. The global gridded crop model intercomparison: data and modeling protocols for phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).

    Article  Google Scholar 

  65. Li, X., Shen, Z., Harri, A. & Coble, K. H. Comparing survey-based and programme-based yield data: implications for the U.S. Agricultural Risk Coverage-County programme. Geneva Pap. Risk Insur. Issues Pract. 45, 184–202 (2020).

    Article  Google Scholar 

  66. Hawkins, E., Osborne, T. M., Ho, C. K. & Challinor, A. J. Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric. Meteorol. 170, 19–31 (2013).

    Article  Google Scholar 

  67. Ho, C. K., Stephenson, D. B., Collins, M., Ferro, C. A. T. & Brown, S. J. Calibration strategies: a source of additional uncertainty in climate change projections. Bull. Am. Meteorol. Soc. 93, 21–26 (2012).

    Article  Google Scholar 

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Acknowledgements

This work is partially supported by the USDA National Institute of Food and Agriculture Specialty Crop Research Initiative to Z.J. under award no. 2021-51181-35861. P.Z. and P.C. are supported by the CLAND project (grant no. 16-CONV-0003) and ISIPEDIA: The Open Inter-Sectoral Impacts Encyclopedia (grant no. ANR-17-ERA4-0006—ISIPEDIA). D.M. is supported by the CLAND project (grant no. 16-CONV-0003) and meta-program CLIMAE-INRAE. C.L. is supported by the MnDRIVE Informatics PhD Graduate Fellowship.

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Contributions

Z.J. and P.Z. conceived and designed the study. T.K., P.Z., Z.J. and C.L. processed the data and performed the analysis. X.W., P.C., N.M., A.A., J.H., D.Mulla and D.Makowski made suggestions to the analysis and helped interpret the results. P.Z. and Z.J. wrote the manuscript with edits from all other authors.

Corresponding author

Correspondence to Zhenong Jin.

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Nature Climate Change thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of yield sensitivity to rainfall and snowfall between crop models and yield statistics.

Comparison of estimated coefficients of rainfall and snowfall on wheat yield estimated based on process-based crop model simulations (seven different process-based crop models considered) and yield statistics (‘observation’). Each bar represents estimated coefficients derived from process-based crop models. Horizontal lines represent the estimated coefficients derived from this study.

Extended Data Fig. 2 Climate change impacts on wheat yield with and without growing season shift.

Yield changes due to changes in FDD, snow cover fraction, GDD, rainfall, and snowfall with no growing season shift (a), −15 days growing season shift (b) and +15 days growing season shift (c) in the future. Yield impacts of climate change were estimated as the ensemble mean of yield impacts projected with nine GCM and then weighted by county planting areas. Error bars indicate the 95% confidence interval of each estimation.

Supplementary information

Supplementary Information

Supplementary Tables 1–10, Figs. 1–5 and model summary.

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Zhu, P., Kim, T., Jin, Z. et al. The critical benefits of snowpack insulation and snowmelt for winter wheat productivity. Nat. Clim. Chang. 12, 485–490 (2022). https://doi.org/10.1038/s41558-022-01327-3

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