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Regional Environmental Change

, Volume 14, Issue 1, pp 61–74 | Cite as

Simulated impact of elevated CO2, temperature, and precipitation on the winter wheat yield in the North China Plain

  • Peng Yang
  • Wenbin Wu
  • Zhengguo Li
  • Qiangyi Yu
  • Masaru Inatsu
  • Zhenhuan Liu
  • Pengqin Tang
  • Yan Zha
  • Masahide Kimoto
  • Huajun TangEmail author
Original Article

Abstract

We studied the separate and interacting effects of changes on CO2, temperature, and precipitation on the growth and yield of winter wheat in five representative sites on the North China Plain using a crop yield simulation model, known as the Environmental Policy Integrated Climate (EPIC) model. The daily-maximum/minimum temperature and precipitation data obtained using a comprehensive climate model, that is, the Model for Interdisciplinary Research On Climate (MIROC), based on the scenario A1B for 2085–2100 were calibrated using a novel statistical algorithm and used as the climate change scenario in the EPIC model. The results indicated that an increase in the CO2 concentration of up to 680 ppm would increase the winter wheat yield by 24.8 and 43.1 % in irrigated and rainfed fields, respectively. Increases in the average maximum temperature of up to 4.9 °C and the average minimum temperature of up to 4.8 °C would increase the crop yield by 5.2 % in irrigated condition, but decrease it by 7.2 % in rainfed condition. By contrast, the yield of irrigated field decreased by 5.5 % when the annual precipitation increased by 317 mm, whereas that of rainfed field increased by 30.1 %. The interacting effects of simultaneous increases in the parameters were also simulated. With a constant CO2 level (370 ppm), the EPIC model predicted that the effects of temperature and precipitation on yield would be −0.9 and −1.9 % for irrigated and rainfed fields, respectively. When the CO2 level increased to 680 ppm, the interacting effect of elevated CO2, temperature, and precipitation increased the average yield by ca 23.1 % with the irrigated treatment and by ca 27.7 % with the rainfed treatment. The results also indicated that with a climate change scenario, the temperature-stress days decreased during the period of winter wheat growth whereas the nitrogen-stress days increased significantly in the North China Plain. These simulated separate and interaction simulations may be useful for identifying appropriate management or genotype adaptations of winter wheat to cope with a climate change scenario in the North China Plain.

Keywords

Agriculture Climate change Crop simulation model Crop yield Water use efficiency Winter wheat 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41171328 and 40930101) and the National Basic Research Program of China (973 Program) (Grant No. 2010CB951502), by the Frontier Research Consortium on Climate and Environment Applications of the University of Tokyo with the Itochu Co., Nippon Telegraph and Telephone Co., and Tokyo Marine & Nichido Fire Insurance Co., Ltd., and partly by the Innovative Program of Climate Change Projection for the 21st Century (KAKUSHIN program) and Data Integration and Analysis System, both of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.

References

  1. Adejuwon JO (2006) Food crop production in Nigeria. II. Potential effects of climate change. Clim Res 32(3):229–245CrossRefGoogle Scholar
  2. Chen C, Lei C, Deng A, Qian C, Hoogmoed W, Zhang W (2011) Will higher minimum temperatures increase corn production in Northeast China? An analysis of historical data over 1965–2008. Agr Forest Meteorol 151(12):1580–1588CrossRefGoogle Scholar
  3. David SB (2009) Historical warning of future food insecurity with unprecedented seasonal heat. Science 323(240):240–244Google Scholar
  4. Domenico V, Monia C, Marco M, Michele R, Marco B (2012) Agronomic adaptation strategies under climate change for winter durum wheat and tomato in southern Italy: irrigation and nitrogen fertilization. Reg Environ Change 12(3):407–419CrossRefGoogle Scholar
  5. 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. Agric Forest Meteorol 90(1–2):51–63CrossRefGoogle Scholar
  6. Ewert F (2012) Adaptation: opportunities in climate change? Nature Clim Change 2:153–154CrossRefGoogle Scholar
  7. Ewert F, Porter JR, Rounsevell MDA (2007) Crop models, CO2, and climate change. Science 315:459–460CrossRefGoogle Scholar
  8. Gholipoor M (2007) Potential effects of individual versus simultaneous climate change factors on growth and water use in chickpea. Int J Plant Prod 1(2):189–204Google Scholar
  9. IPCC (2007a) In: Solomon S, Qin D, Manning M, et al. (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 PressGoogle Scholar
  10. IPCC (2007b) In: Parry ML, Canziani OF, Palutikof JP, et al. (Eds.), Climate change 2007: Impacts, adaptation and vulnerability. contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  11. Izaurralde RC, Rosenberg NJ, Brown RA, Thomson AM (2003) Integrated assessment of Hadley Center (HadCM2) climate-change impacts on agricultural productivity and irrigation water supply in the conterminous United States: Part II. Regional agricultural production in 2030 and 2095. Agric Forest Meteorol 117(1–2):97–122CrossRefGoogle Scholar
  12. K-1 model developers. K-1 coupled GCM (MIROC) description. In: Hasumi H, Emori S (eds) K-1 Technical Report. 2004Google Scholar
  13. Kimball BA (1983) Carbon dioxide and agricultural yield: an assemblage an analysis of 430 prior observations. Agron J 75:779–788CrossRefGoogle Scholar
  14. Kimoto M (2005) Simulated change of the East Asian circulation under global warming scenario. Geophys Res Lett 32 (16). doi: 10.1029/2005GL023383
  15. Krishnan P, Swain DK, Chandra Bhaskar B, Nayak SK, Dash RN (2007) Impact of elevated CO2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies. Agric Ecosyst Environ 122(2):233–242CrossRefGoogle Scholar
  16. 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. Agric Forest Meteorol 141(2–4):208–218CrossRefGoogle Scholar
  17. Long SP, Ainsworth EA, Leakey ADB, Nösberger J, Ort DR (2006) Food for thought: lower-than-expected crop yield simulation with rising CO2 concentrations. Science 312:1918–1921CrossRefGoogle Scholar
  18. Luo QY, Bellotti W, Williams M, Bryan B (2005) Potential impact of climate change on wheat yield in South Australia. Agric Forest Meteorol 132(3–4):273–285CrossRefGoogle Scholar
  19. Mearns LO, Mavromatis T, Tsvetsinskaya E, Hays C, Easterling W (1999) Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios. J Geophys Res Atmos 104(D6): 6623–6646Google Scholar
  20. Mera RJ, Niyogi D, Buol GS, Wilkerson GG, Semazzi FHM (2006) Potential individual versus simultaneous climate change effects on soybean (C-3) and maize (C-4) crops: an agro-technology model based study. Global Planet Change 54(1–2):163–182Google Scholar
  21. Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Centeno GS, Khush GS, Cassman KG (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci 101(27):9971–9975CrossRefGoogle Scholar
  22. Rik L, Bas E, Bart S, Strengers B, Bouwman L, Schaeffer M (2002) The consequences of uncertainties in land use, climate and vegetation responses on the terrestrial carbon. Sci China Ser C 45(s1):126–141Google Scholar
  23. Rosenzweig C, Tubiello FN (2007) Adaptation and mitigation strategies in agriculture: an analysis of potential synergies. Mitig Adapt Strat Glob Change 12(5):855–873CrossRefGoogle Scholar
  24. Roudier P, Sultan B, Quirion P, Berg A (2011) The impact of future climate change on West African crop yields: what does the recent literature say? Global Environ Change 21(3):1073–1083CrossRefGoogle Scholar
  25. Semenov MA (2007) Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric Forest Meteorol 144(1–2):127–138CrossRefGoogle Scholar
  26. Solman S, Nuñez M (1999) Local estimates of global climate change: a statistical downscaling approach. Int J Climatol 19:835–861CrossRefGoogle Scholar
  27. Stafford N (2007) The other greenhouse effect. Nature 448:526–528CrossRefGoogle Scholar
  28. Stockle CO, Williams JR, Rosenberg NJ, Jones CA (1992a) Estimation of the effects of CO2 induced climate change on growth and yield of crops, Part I: modification of the EPIC model for climate change analysis. Agr Syst 38:225–238CrossRefGoogle Scholar
  29. Stockle CO, Dyke PT, Williams JR, Jones CA, Rosenberg NJ (1992b) Estimation of the effects of CO2 induced climate change on growth and yield of crops, Part II: sensitivity analysis at three sites in the Midwestern USA. Agr Syst 38:239–256CrossRefGoogle Scholar
  30. Sun L, Rosenberg NJ, Izaurralde RC (2001) Assessment of climate change impacts on agriculture in the northern part of the North China Plain. PNNL Technical Report 13546. Pacific Northwest National Laboratory, Richland, WA, 35Google Scholar
  31. Sun J, Zhou G, Sui X (2012) Climatic suitability of the distribution of the winter wheat cultivation zone in China. Eur J Agron 43:77–86CrossRefGoogle Scholar
  32. Thomson AM, Izaurralde RC, Rosenberg NJ, He X (2006) Climate change impacts on agriculture and soil carbon sequestration potential in the Huang-Hai Plain of China. Agric Ecosyst Environ 114:195–209CrossRefGoogle Scholar
  33. Tubiello FN, Amthor JS, Boote KJ, Donatelli M, Easterling W, Fischer G, Gifford RM, Howden M, Reilly J, Rosenzweig C (2007) Crop response to elevated CO2 and world food supply: a comment on “Food for Thought…” by Long et al (2006) Science 312: 1918–1921. Eur J Agron 26 (3): 215–223Google Scholar
  34. Welch JR, Vincent JR, Auffhammer M, Moya PF, Dobermann A, Dawe D (2010) Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc Natl Acad Sci 107(33):14562–14567CrossRefGoogle Scholar
  35. Williams JR, Jones CA, Kiniry JR (1989) The EPIC crop growth model. Trans ASAE 32:497–511CrossRefGoogle Scholar
  36. Wu W, Shibasaki R, Yang P, Tan G, Matsumura K, Sugimoto K (2007) Global-scale modelling of future changes in sown area of major crops. Ecol Model 208(2–4):378–390CrossRefGoogle Scholar
  37. Xiong W, Holman I, Lin ED, Conway D, Jiang J, Xu Y, Li Y (2010) Climate change, water availability and future cereal production in China. Agric Ecosyst Environ 135:58–69CrossRefGoogle Scholar
  38. Yang P (2005) Linking multi-temporal remotely sensed data, field observations and GIS-based crop growth model to estimate winter wheat yield in North China Plain. Doctoral dissertation, Graduate school of Engineering, The University of Tokyo, JapanGoogle Scholar
  39. Yang P, Shibasaki R, Wu W, Zhou Q, Chen Z, Zha Y, Shi Y, Tang H (2007) Evaluation of MODIS land cover and LAI products in cropland of North China Plain using in situ measurements and Landsat TM images. IEEE T Geosci Remote 45(10):3087–3097CrossRefGoogle Scholar
  40. Yang X, Liu Z, Chen F (2011) The possible effect of climate warming on northern limits of cropping system and crop yield in China. Agric Sci China 10(4):585–594CrossRefGoogle Scholar
  41. Yao FM, Xu YL, Lin ED, Yokozawa M, Zhang J (2007) Assessing the impacts of climate change on rice yields in the main rice areas of China. Climatic Change 80(3–4):395–409CrossRefGoogle Scholar
  42. Yu Q, Wu W, Yang P, Li Z, Xiong W, Tang H (2012) Proposing an interdisciplinary and cross-scale framework for global change and food security researches. Agric Ecosyst Environ 156:57–71CrossRefGoogle Scholar
  43. Zhang XC (2005) Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agric Forest Meteorol 135(1–4):215–229CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peng Yang
    • 1
    • 2
  • Wenbin Wu
    • 1
  • Zhengguo Li
    • 1
  • Qiangyi Yu
    • 1
  • Masaru Inatsu
    • 3
  • Zhenhuan Liu
    • 1
  • Pengqin Tang
    • 1
  • Yan Zha
    • 1
  • Masahide Kimoto
    • 2
  • Huajun Tang
    • 1
    Email author
  1. 1.Key Laboratory of Agri-Informatics, Ministry of Agriculture, Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijingChina
  2. 2.Atmosphere and Ocean Research InstituteThe University of TokyoChibaJapan
  3. 3.Graduate School of ScienceHokkaido UniversityHokkaidoJapan

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