Climatic Change

, Volume 130, Issue 2, pp 247–260 | Cite as

The effects of extremely wet planting conditions on maize and soybean yields

  • Daniel W. Urban
  • Michael J. Roberts
  • Wolfram Schlenker
  • David B. Lobell
Article

Abstract

Short durations of very high spring soil moisture can influence crop yields in many ways, including delaying planting and damaging young crops. The central United States has seen a significant upward trend in the frequency and intensity of extreme precipitation in the 20th century, potentially leading to more frequent occurrences of saturated or nearly saturated fields during the planting season, yet the impacts of these changes on crop yields are not known. Here we investigate the yield response to excess spring moisture for both maize and soybean in the U.S. states of Illinois, Iowa, and Indiana, and the impacts of historical trends for 1950–2011. We find that simple measures of extreme spring soil moisture, derived from fine-scale daily moisture data from the Variable Infiltration Capacity (VIC) hydrologic model, lead to significant improvements in statistical models of yields for both crops. Individual counties experience up to 10 % loss in years with extremely wet springs. However, losses due to historical trends in excess spring moisture measures have generally been small, with 1–3 % yield loss over the 62 year study period.

Supplementary material

10584_2015_1362_MOESM1_ESM.docx (1.6 mb)
ESM 1(DOCX 1676 kb)

References

  1. Coumou D, Rahmstorf S (2012) A decade of weather extremes. Nat Clim Chang 2:491–496. doi:10.1038/nclimate1452 Google Scholar
  2. Easterling WE, Chen X, Hays C, Brandle JR, Zhang H (1996) Improving the validation of model-simulated crop yield response to climate change: an application to the EPIC model. Clim Res 06:263–273CrossRefGoogle Scholar
  3. Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074. doi:10.1126/science.289.5487.2068 CrossRefGoogle Scholar
  4. Groisman PY, Knight RW, Easterling DR, Karl TR, Hegerl GC, Razuvaev VN (2005) Trends in intense precipitation in the climate record. J Clim 18:1326–1350. doi:10.1175/JCLI3339.1 CrossRefGoogle Scholar
  5. Hansen JW, Jones JW (2000) Scaling-up crop models for climate variability applications. Agric Syst 65:43–72. doi:10.1016/S0308-521X(00)00025-1 CrossRefGoogle Scholar
  6. Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53. doi:10.1016/j.agrformet.2006.03.009 CrossRefGoogle Scholar
  7. Ines AVM, Das NN, Hansen JW, Njoku EG (2013) Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sens Environ 138:149–164. doi:10.1016/j.rse.2013.07.018 CrossRefGoogle Scholar
  8. Karl TR, Knight RW (1998) Secular trends of precipitation amount, frequency, and intensity in the United States. Bull Am Meteorol Soc 79:231–241. doi:10.1175/1520-0477(1998)079<0231:STOPAF>2.0.CO;2 CrossRefGoogle Scholar
  9. Kucharik CJ (2006) A multidecadal trend of earlier corn planting in the central USA. Agron J 98:1544–1550. doi:10.2134/agronj2006.0156 CrossRefGoogle Scholar
  10. Kunkel KE (2003) North American trends in extreme precipitation. Nat Hazards 29:291–305. doi:10.1023/A:1023694115864 CrossRefGoogle Scholar
  11. Kunkel KE, Andsager K, Easterling DR (1999) Long-term trends in extreme precipitation events over the conterminous United States and Canada. J Clim 12:2515–2527. doi:10.1175/1520-0442(1999)012<2515:LTTIEP>2.0.CO;2 CrossRefGoogle Scholar
  12. Kunkel, K.E., Easterling, D.R., Redmond, K., Hubbard, K. (2003) Temporal variations of extreme precipitation events in the United States: 1895–2000. Geophysical Research Letters 30, n/a–n/a. doi:10.1029/2003GL018052
  13. Lenderink G, van Meijgaard E (2008) Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat Geosci 1:511–514. doi:10.1038/ngeo262 CrossRefGoogle Scholar
  14. Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologically based dataset of land surface fluxes and States for the conterminous United States. J Clim 15:3237CrossRefGoogle Scholar
  15. Min S-K, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470:378–381. doi:10.1038/nature09763 CrossRefGoogle Scholar
  16. Pall P, Allen MR, Stone DA (2007) Testing the clausius–clapeyron constraint on changes in extreme precipitation under CO2 warming. Clim Dyn 28:351–363. doi:10.1007/s00382-006-0180-2 CrossRefGoogle Scholar
  17. Pryor SC, Barthelmie RJ, Schoof JT (2013) High-resolution projections of climate-related risks for the Midwestern USA. Clim Res 56:61–79. doi:10.3354/cr01143 CrossRefGoogle Scholar
  18. Raes D, Geerts S, Kipkorir E, Wellens J, Sahli A (2006) Simulation of yield decline as a result of water stress with a robust soil water balance model. Agric Water Manag 81:335–357. doi:10.1016/j.agwat.2005.04.006 CrossRefGoogle Scholar
  19. Riha SJ, Wilks DS, Simoens P (1996) Impact of temperature and precipitation variability on crop model predictions. Clim Chang 32:293–311. doi:10.1007/BF00142466 CrossRefGoogle Scholar
  20. Rosenzweig C, Iglesias A, Yang XB, Epstein PR, Chivian E (2001) Climate change and extreme weather events; implications for food production, plant diseases, and pests. Global Chang Hum Health 2:90–104. doi:10.1023/A:1015086831467 CrossRefGoogle Scholar
  21. Rosenzweig C, Tubiello FN, Goldberg R, Mills E, Bloomfield J (2002) Increased crop damage in the US from excess precipitation under climate change. Glob Environ Chang 12:197–202. doi:10.1016/S0959-3780(02)00008-0 CrossRefGoogle Scholar
  22. Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. PNAS 106:15594–15598. doi:10.1073/pnas.0906865106 CrossRefGoogle Scholar
  23. Sillmann J, Kharin VV, Zhang X, Zwiers FW, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res Atmos 118:1716–1733. doi:10.1002/jgrd.50203 CrossRefGoogle Scholar
  24. Trenberth KE (2011) Changes in precipitation with climate change. Clim Res 47:123–138CrossRefGoogle Scholar
  25. Urban D, Roberts M, Schlenker W, Lobell D (2012) Projected temperature changes indicate significant increase in interannual variability of U.S. maize yields. Clim Chang 112:525–533. doi:10.1007/s10584-012-0428-2 CrossRefGoogle Scholar
  26. Van der Velde, M., Tubiello, F., Vrieling, A., Bouraoui, F., 2011. Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data. Climatic Change 1–15. doi:10.1007/s10584-011-0368-2
  27. VanToai TT, Bolles CS (1991) Postanoxic injury in soybean (glycine max) seedlings. Plant Physiol 97:588–592. doi:10.1104/pp. 97.2.588 CrossRefGoogle Scholar
  28. Wuebbles DJ, Hayhoe K (2004) Climate change projections for the United States Midwest. Mitig Adapt Strateg Glob Chang 9:335–363. doi:10.1023/B:MITI.0000038843.73424.de CrossRefGoogle Scholar
  29. Yan B, Dai Q, Liu X, Huang S, Wang Z (1996) Flooding-induced membrane damage, lipid oxidation and activated oxygen generation in corn leaves. Plant Soil 179:261–268. doi:10.1007/BF00009336 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Daniel W. Urban
    • 1
  • Michael J. Roberts
    • 2
  • Wolfram Schlenker
    • 3
  • David B. Lobell
    • 1
  1. 1.Department of Earth System Science and Center on Food Security and the EnvironmentStanford UniversityStanfordUSA
  2. 2.Department of EconomicsUniversity of Hawaii at ManoaHonoluluUSA
  3. 3.School of International and Public AffairsColumbia UniversityNew YorkUSA

Personalised recommendations