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Climatic Change

, Volume 109, Supplement 1, pp 335–353 | Cite as

Effect of climate change on field crop production in California’s Central Valley

  • Juhwan LeeEmail author
  • Steven De Gryze
  • Johan Six
Article

Abstract

Climate change under various emission scenarios is highly uncertain but is expected to affect agricultural crop production in the 21st century. However, we know very little about future changes in specific cropping systems under climate change in California’s Central Valley. Biogeochemical models are a useful tool to predict yields as it integrates crop growth, nutrient dynamics, hydrology, management and climate. For this study, we used DAYCENT to simulate changes in yield under A2 (medium-high) and B1 (low) emission scenarios. In total, 18 climate change predictions for the two scenarios were considered by applying different climate models and downscaling methods. The following crops were selected: alfalfa (hay), cotton, maize, winter wheat, tomato, and rice. Sunflower was also selected because it is commonly included in rotations with the other crops. By comparing the 11-year moving averages for the period 1956 to 2094, changes in yield were highly variable depending on the climate change scenarios across times. Furthermore, yield variance for the crops increased toward the end of the century due to the various degrees of climate model sensitivity. This shows that future climate, suggested by each of the emission scenarios, has a broad range of impacts on crop yields. Nevertheless, there was a general agreement in trends of yield changes. Under both A2 and B1, average modeled cotton, sunflower, and wheat yields decreased by approximately 2% to 9% by 2050 compared to the 2009 average yields. The other crops showed apparently no decreases in yield for the period 2010–2050. In comparison, all crop yields except for alfalfa significantly declined by 2094 under A2, but less under B1. Under A2, yields decreased in the following order: cotton (25%) > sunflower (24%) > wheat (14%) > rice (10%) > tomato and maize (9%). Under A2 compared to B1, the crop yield further decreased by a range of 2% (alfalfa) to 17% (cotton) by 2094, with more variation in yield change in the southern counties than the northern counties. The CO2 fertilization effects were predicted to potentially offset these yield declines (>30%) but may be overestimated. Our results suggest that climate change will decrease California crop yields in the long-term, except for alfalfa, unless greenhouse gas emissions and resulting climate change is curbed and/or adaptation of new management practices and improved cultivars occurs.

Keywords

Crop Yield Emission Scenario Wheat Yield Yield Change Biogeochemical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

GCMs

Global circulation models

analog

Constructed analogues

bcsd

Bias correction and spatial downscaling

MSD

Mean squared deviation

SB

Squared bias

NU

Nonunity slope

LC

Lack of correlation

Notes

Acknowledgements

This work was funded by the California Energy Commission and Kearney Foundation of Soil Science.

References

  1. Adams RM, Rosenzweig C, Peart RM, Ritchie JT, McCarl BA, Glyer JD, Curry RB, Jones JW, Boote KJ, Allen LH Jr (1990) Global climate change and US agriculture. Nature 345:219–224CrossRefGoogle Scholar
  2. Ainsworth EA, Ort DR (2010) How do we improve crop production in a warming world? Plant Physiol 154:526–530CrossRefGoogle Scholar
  3. Ainsworth EA, Leakey ADB, Ort DR, Long SP (2008) FACE-ing the facts: inconsistencies and interdependence among field, chamber and modeling studies of elevated [CO2] impacts on crop yield and food supply. New Phyto 179:5–9CrossRefGoogle Scholar
  4. Anderson J, Chung F, Anderson M, Brekke L, Easton D, Ejeta M, Peterson R, Snyder R (2008) Progress on incorporating climate change into management of California’s water resources. Clim Change 89:91–108CrossRefGoogle Scholar
  5. Bachelet D, Gay CA (1993) The impacts of climate change on rice yield: a comparison of four model performances. Ecol Model 65:71–93Google Scholar
  6. Bunce JA (1995) Long-term growth of alfalfa and orchard grass plots at elevated carbon dioxide. J Biogeogr 22:341–348CrossRefGoogle Scholar
  7. California Agricultural Statistics Service (2008) Agricultural overviews. In: California agricultural statistics 2007 crop year. USDA-NASS, Sacramento, CA, USAGoogle Scholar
  8. Cassman KG (1999) Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. PNAS 96:5952–5959CrossRefGoogle Scholar
  9. Cayan DR, Maurer EP, Dettinger MD, Tyree M, Hayhoe K (2008) Climate change scenarios for the California Region. Clim Change 87(Supplement 1):21–42CrossRefGoogle Scholar
  10. Cure JD, Acock B (1986) Crop responses to carbon-dioxide doubling – a literature survey. Agric For Meteorol 38:127–145Google Scholar
  11. Dai A, Trenberth KE, Karl TR (1998) Global variations in droughts and wet spells: 1900–1995. Geophys Res Lett 25:3367–3370CrossRefGoogle Scholar
  12. De Graaff MA, van Groenigen KJ, Six J, Hungate BA, van Kessel C (2006) Interactions between plant growth and soil nutrient cycling under elevated CO2: a meta-analysis. Glob Change Biol 12:2077–2091CrossRefGoogle Scholar
  13. De Gryze S, Albarracin MV, Catala-Luque R, Howitt RE, Six J (2009) Modeling shows that alternative soil management can decrease greenhouse gases. Cal Ag 63:84–90CrossRefGoogle Scholar
  14. Del Grosso S, Ojima D, Parton W, Mosier A, Peterson G, Schimel D (2002) Simulated effects of dryland cropping intensification on soil organic matter and greenhouse gas exchanges using the DAYCENT ecosystem model. Environ Pollut 116:S75–S83CrossRefGoogle Scholar
  15. Easterling WE, Weiss A, Hays CJ, Mearns LO (1998) Spatial scales of climate information for simulating wheat and maize productivity: the case of the US Great Plains. Agr For Meteorol 90:51–63CrossRefGoogle Scholar
  16. Gauch HG Jr, Hwang JTG, Fick GW (2003) Model evaluation by comparison of model-based predictions and measured values. Agron J 95:1442–1446CrossRefGoogle Scholar
  17. Giorgi F, Mearns LO (1991) Approaches to the simulation of regional climate change: a review. Rev Geophys 29:191–216CrossRefGoogle Scholar
  18. Hansen JW, Challinor A, Ines A, Wheeler T, Moron V (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33:27–41CrossRefGoogle Scholar
  19. Howell TA, Steiner JL, Schneider AD, Evett SR, Tolk JA (1997) Seasonal and maximum daily evapotranspiration of irrigated winter wheat, sorghum, and corn - Southern High Plains. Trans ASEA 40:623–634Google Scholar
  20. Howitt RE, Catala-Luque R, De Gryze S, Wicks S, Six J (2009) Realistic payments could encourage farmers to adopt practices that sequester carbon. Cal Ag 63:91–95CrossRefGoogle Scholar
  21. Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007: mitigation. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds) Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  22. Janzen HH (2004) Carbon cycling in earth systems: a soil science perspective. Agr Ecosyst Environ 104:399–417CrossRefGoogle Scholar
  23. Johnson JMF, Allmaras RR, Reicosky DC (2006) Estimating source carbon from crop residues, roots and rhizodeposits using the national grain-yield database. Agron J 98:622–636CrossRefGoogle Scholar
  24. Jones PD, Mann ME (2004) Climate over past millennia. Rev Geophys 42:RG2002CrossRefGoogle Scholar
  25. Joyce BA, Mehta VK, Purkey DR, Dale LL, Hanemann M (2009) Climate change impacts on water supply and agricultural water management in California’s Western San Joaquin Valley, and potential adaptation strategies. California Energy Commission, CEC-500-2009-051-FGoogle Scholar
  26. Kim SH, Sicher RC, Bae H, Gitz DC, Baker JT, Timlin DJ, Reddy VR (2006) Canopy photosynthesis, evapotranspiration, leaf nitrogen, and transcription profiles of maize in response to CO2 enrichment. Glob Change Biol 12:588–600CrossRefGoogle Scholar
  27. Lobell DB, Field CB, Cahill KN, Bonfils C (2006) Impacts of future climate change on California perennial crop yields: model projections with climate and crop uncertainties. Agr Fort Meteorol 141:208–218CrossRefGoogle Scholar
  28. Lobell DB, Cahill KN, Field CB (2007) Historical effects of temperature and precipitation on California crop yields. Clim Change 81:187–203CrossRefGoogle Scholar
  29. Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD, Falcon WP, Naylor RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319:607–610CrossRefGoogle Scholar
  30. Long SP, Ainsworth EA, Leakey ADB, Morgan PB (2005) Global food insecurity. Treatment of major food crops with elevated carbon dioxide or ozone under large-scale fully open-air conditions suggests recent models may have overestimated future yields. Philos Trans R Soc B Biol Sci 360:2011–2020CrossRefGoogle Scholar
  31. Long SP, Ainsworth EA, Leakey ADB, Nosberger J, Ort DR (2006) Food for thought: lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 312:1918–1921CrossRefGoogle Scholar
  32. Luo YQ, Jackson RB, Field CB, Mooney HA (1996) Elevated CO2 increases belowground respiration in California grasslands. Oecologia 108:130–137CrossRefGoogle Scholar
  33. Maurer EP, Hidalgo HG (2008) Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrol Earth Syst Sci 12:551–563CrossRefGoogle Scholar
  34. Mearns LO, Easterling W, Hays C, Marx D (2001) Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part I. The uncertainty due to spatial scale. Clim Change 51:131–172CrossRefGoogle Scholar
  35. Medellín-Azuara J, Harou JJ, Olivares MA, Madani K, Lund JR, Howitt RE, Tanaka SK, Jenkins MW, Zhu T (2008) Adaptability and adaptations of California’s water supply system to dry climate warming. Clim Change 87:S75–S90CrossRefGoogle Scholar
  36. Mitchell JP, Klonsky K, Shrestha A, Fry R, DuSault A, Beyer J, Harben R (2007) Adoption of conservation tillage in California: current status and future perspectives. Aust J Exp Agric 47:1383–1388CrossRefGoogle Scholar
  37. Moen TN, Kaiser HM, Riha SJ (1994) Regional yield estimation using a crop simulation model: concepts, methods, and validation. Agr Syst 46:79–92CrossRefGoogle Scholar
  38. Ogle SM, Breidt FJ, Paustian K (2006) Bias and variance in model results associated with spatial scaling of measurements for parameterization in regional assessments. Glob Change Biol 12:516–523CrossRefGoogle Scholar
  39. Olesen JE, Bindi M (2002) Consequences of climate change for European agricultural productivity, land use and policy. Eur J Agron 16:239–262CrossRefGoogle Scholar
  40. Paruelo JM, Lauenroth WK (1996) Relative abundance of plant functional types in grasslands and shrublands of North America. Ecol Appl 6:1212–1224CrossRefGoogle Scholar
  41. Perez-Quezada JF, Pettygrove GS, Plant RE (2003) Spatial-temporal analysis of yield and soil factors in two four-crop-rotation fields in the Sacramento Valley, California. Agron J 95:676–687CrossRefGoogle Scholar
  42. Poorter H, Van Berkel Y, Baxter R, Den Hertog J, Dijkstra P, Gifford RM, Griffin KL, Roumet C, Roy J, Wong SC (1997) The effect of elevated CO2 on the chemical composition and construction costs of leaves of 27 C3 species. Plant Cell Environ 20:472–482CrossRefGoogle Scholar
  43. Porter JR, Semenov MA (2005) Crop responses to climatic variation. Philos T Roy Soc B 360:2021–2035CrossRefGoogle Scholar
  44. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
  45. Saxton KE, Rawls WJ, Romberger JS, Papendick RI (1986) Estimating generalized soil-water characteristics from texture. Soil Sci Soc Am J 50:1031–1036CrossRefGoogle Scholar
  46. Schimel DS, Emanuel W, Rizzo B, Smith T, Woodward FI, Fisher H, Kittel TGF, McKeown R, Painter T, Rosenbloom N, Ojima DS, Parton WJ, Kicklighter DW, McGuire AD, Melillo JM, Pan Y, Haxeltine A, Prentice C, Sitch S, Hibbard K, Nemani R, Pierce L, Running S, Borchers J, Chaney J, Neilson R, Braswell BH (1997) Continental scale variability in ecosystem processes: models, data, and the role of disturbance. Ecol Monogr 67:251–271CrossRefGoogle Scholar
  47. Smit B, Ludlow L, Brklacich M (1988) Implications of a global climate warming for agriculture: a review and appraisal. J Environ Qual 17:519–527CrossRefGoogle Scholar
  48. Stehfest E, Heistermann M, Priess JA, Ojima DS, Alcamo J (2007) Simulation of global crop production with the ecosystem model DayCent. Ecol Model 209:203–219CrossRefGoogle Scholar
  49. Stewart WM, Dibb DW, Johnston AE, Smyth TJ (2005) The contribution of commercial fertilizer nutrients to food production. Agron J 97:1–6CrossRefGoogle Scholar
  50. Tyler HH (1994) Fertilizer use and price statistics, 1960–93. Resources and Technology Division, Economic Research Service, USDA, Statistical Bull. No. 893Google Scholar
  51. Tubiello FN, Rosenzweig C, Goldberg RA, Jagtap S, Jones JW (2002) Effects of climate change on US crop production: simulation results using two different GCM scenarios. Part I: wheat, potato, maize, and citrus. Clim Res 20:259–270CrossRefGoogle Scholar
  52. Wilks DS, Pitt RE, Fick GW (1993) Modeling optimal alfalfa harvest scheduling using short-range weather forecasts. Agric Syst 42:277–305CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Plant SciencesUniversity of CaliforniaDavisUSA

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