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Journal of Meteorological Research

, Volume 32, Issue 4, pp 636–647 | Cite as

Uncertainty in Simulating the Impact of Cultivar Improvement on Winter Wheat Phenology in the North China Plain

  • Dingrong Wu
  • Chunyi Wang
  • Fang Wang
  • Chaoyang Jiang
  • Zhiguo Huo
  • Peijuan Wang
Article
  • 31 Downloads

Abstract

The phenology model is one of the major tools in evaluating the impact of cultivar improvement on crop phenology. Understanding uncertainty in simulating the impact is an important prerequisite for reliably interpreting the effect of cultivar improvement and climate change on phenology. However, uncertainty induced by different temperature response functions and parameterization methods have not been properly addressed. Based on winter wheat phenology observations during 1986–2012 in 47 agro-meteorology observation stations in the North China Plain (NCP), the uncertainty of the simulated impacts caused by four widely applied temperature response functions and two parameterization methods were investigated. The functions were firstly calibrated using observed phenology data during 1986–1988 from each station by means of two parameterization methods, and were then used to quantify the impact of cultivar improvement on wheat phenology during 1986–2012. The results showed that all functions and all parameterization methods could reach acceptable precision (RMSE < 3 days for all functions and parameterization methods), however, substantial differences exist in the simulated impacts between different functions and parameterization methods. For vegetative growth period, the simulated impact is 0.20 day (10 yr)–1 [95% confidence interval:–2.81–3.22 day (10 yr)–1] across the NCP, while for reproductive period, the value is 1.50 day (10 yr)–1 [–1.03–4.02 day (10 yr)–1]. Further analysis showed that uncertainty can be induced by both different functions and parameterization methods, while the former has greater influence than the latter. During vegetative period, there is a significant positive linear relationship between ranges of simulated impact and growth period average temperature, while during reproductive period, the relationship is polynomial. This highlights the large inconsistency that exists in most impact quantifying functions and the urgent need to carry out field experiment to provide realistic impacts for all functions. Before applying a simulated effect, we suggest that the function should be calibrated over a wide temperature range.

Key words

observed phenology temperature response function parameterization method renewing cultivar 

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References

  1. Braga, R. P., M. J. Cardoso, and J. P. Coelho, 2008: Crop model based decision support for maize (Zea mays L.) silage production in Portugal. Eur. J. Agron., 28, 224–233, doi: 10.1016/j.eja.2007.07.006.CrossRefGoogle Scholar
  2. Cassman, K. G., 1999: Ecological intensification of cereal production systems: Yield potential, soil quality, and precision agriculture. Proc. Natl. Acad. Sci. USA, 96, 5952–5959, doi: 10.1073/pnas.96.11.5952.CrossRefGoogle Scholar
  3. Chenu, K., J. R. Porter, P. Martre, et al., 2017: Contribution of crop models to adaptation in wheat. Trends Plant Sci., 22, 472–490, doi: 10.1016/j.tplants.2017.02.003.CrossRefGoogle Scholar
  4. China Meteorological Administration, 1993: Agricultural Meteorological Observation Specification (Volume 1). China Meteorological Press, Beijing, 4–18. (in Chinese)Google Scholar
  5. Chmielewski, F. M., A. Müller, and E. Bruns, 2004: Climate changes and trends in phenology of fruit trees and field crops in Germany, 1961–2000. Agric. Forest Meteor., 121, 69–78, doi: 10.1016/S0168-1923(03)00161-8.CrossRefGoogle Scholar
  6. Cleland, E. E., I. Chuine, A. Menzel, et al., 2007: Shifting plant phenology in response to global change. Trends Ecol. Evol., 22, 357–365, doi: 10.1016/j.tree.2007.04.003.CrossRefGoogle Scholar
  7. Danuso, F., G. Zanin, and I. Sartorato, 2012: A modelling approach for evaluating phenology and adaptation of two congeneric weeds (Bidens frondosa and Bidens tripartita). Ecol. Model, 243, 33–41, doi: 10.1016/j.ecolmodel.2012.06.009.CrossRefGoogle Scholar
  8. Ding, Y. H., G. Y. Ren, G. Y. Shi, et al., 2007: China’s national assessment report on climate change (I): Climate change in China and the future trend. Adv. Climate Change Res., 3, 1–5.Google Scholar
  9. Doi, H., M. Takahashi, and I. Katano, 2010: Genetic diversity increases regional variation in phenological dates in response to climate change. Global Change Biol., 16, 373–379, doi: 10.1111/j.1365-2486.2009.01993.x.CrossRefGoogle Scholar
  10. Dose, V., and A. Menzel, 2004: Bayesian analysis of climate change impacts in phenology. Global Change Biol., 10, 259–272, doi: 10.1111/j.1529-8817.2003.00731.x.CrossRefGoogle Scholar
  11. Estrella, N., T. H. Sparks, and A. Menzel, 2007: Trends and temperature response in the phenology of crops in Germany. Global Change Biol., 13, 1737–1747, doi: 10.1111/j.1365-2486.2007.01374.x.CrossRefGoogle Scholar
  12. Gao, L. Z., Z. Q. Jin, Y. Huang, et al., 1992: Rice clock model—a computer model to simulate rice development. Agric. Forest Meteor., 60, 1–16, doi: 10.1016/0168-1923(92)90071-B.CrossRefGoogle Scholar
  13. He, D., E. L. Wang, J. Wang, et al., 2017: Uncertainty in canola phenology modelling induced by cultivar parameterization and its impact on simulated yield. Agric. Forest Meteor., 232, 163–175, doi: 10.1016/j.agrformet.2016.08.013.CrossRefGoogle Scholar
  14. He, L., J. Cleverly, C. Chen, et al., 2014: Diverse responses of winter wheat yield and water use to climate change and variability on the semiarid Loess Plateau in China. Agron. J., 106, 1169–1178, doi: 10.2134/agronj13.0321.CrossRefGoogle Scholar
  15. Ibáñez, I., R. B. Primack, A. J. Miller-Rushing, et al., 2010: Forecasting phenology under global warming. Philos. Trans. Roy. Soc. Lond. B Biol. Sci., 365, 3247–3260, doi: 10.1098/rstb.2010.0120.CrossRefGoogle Scholar
  16. Jochner, S., T. H. Sparks, J. Laube, et al., 2016: Can we detect a nonlinear response to temperature in European plant phenology. Int. J. Biometeor., 60, 1551–1561, doi: 10.1007/s00484-016-1146-7.CrossRefGoogle Scholar
  17. Jones, J. W., G. Hoogenboom, P. W. Wilkens, et al., 2010: Decision Support System for Agrotechnology Transfer Version 4.0. Volume 4. DSSAT v4.5: Crop Model Documentation. University of Hawaii, Honolulu, HI.Google Scholar
  18. Li, K. N., X. G. Yang, H. Q. Tian, et al., 2015: Effects of changing climate and cultivar on the phenology and yield of winter wheat in the North China Plain. Int. J. Biometeor., 60, 21–32, doi: 10.1007/s00484-015-1002-1.CrossRefGoogle Scholar
  19. Liu, J., N. Yao, H. X. Lin, et al., 2016: Response mechanism and simulation of winter wheat phonology to soil water stress. Trans. Chin. Soc. Agric. Eng., 32, 115–124, doi: 10.11975/j.issn.1002-6819.2016.21.016. (in Chinese)Google Scholar
  20. Liu, L. L., E. L. Wang, Y. Zhu, et al., 2012: Contrasting effects of warming and autonomous breeding on single-rice productivity in China. Agric. Ecosyst. Environ., 149, 20–29, doi: 10.1016/j.agee.2011.12.008.CrossRefGoogle Scholar
  21. Liu, L. L., E. L. Wang, Y. Zhu, et al., 2013: Effects of warming and autonomous breeding on the phenological development and grain yield of double-rice systems in China. Agric. Ecosyst. Environ., 165, 28–38, doi: 10.1016/j.agee.2012.11.009.CrossRefGoogle Scholar
  22. Liu, L. L., D. Wallach, J. Li, et al, 2018: Uncertainty in wheat phenology simulation induced by cultivar parameterization under climate warming. Eur. J. Agron., 94, 46–53, doi: 10.1016/j. eja.2017.12.001.CrossRefGoogle Scholar
  23. Liu, Y., E. L. Wang, X. G. Yang, et al., 2010: Contributions of climatic and crop varietal changes to crop production in the North China Plain, since 1980s. Global Change Biol., 16, 2287–2299, doi: 10.1111/j.1365-2486.2009.02077.x.CrossRefGoogle Scholar
  24. Liu, Z. J., K. G. Hubbard, X. M. Lin, et al., 2013: Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China. Global Change Biol., 19, 3481–3492, doi: 10.1111/gcb.12324.Google Scholar
  25. McMaster, G. S., and W. W. Wilhelm, 2003: Phenological responses of wheat and barley to water and temperature: Im-proving simulation models. J. Agric. Sci, 141, 129–147, doi: 10.1017/S0021859603003460.CrossRefGoogle Scholar
  26. McMaster, G. S., W. W. Wilhelm, and J. A. Morgan, 1992: Simulating winter wheat shoot apex phenology. J. Agric. Sci., 119, 1–12, doi: 10.1017/S0021859600071483.CrossRefGoogle Scholar
  27. Menzel, A., and P. Fabian, 1999: Growing season extended in Europe. Nature, 397, 659, doi: 10.1038/17709.CrossRefGoogle Scholar
  28. Mo, F., M. Sun, X. Y. Liu, et al., 2016: Phenological responses of spring wheat and maize to changes in crop management and rising temperatures from 1992 to 2013 across the Loess Plateau. Field Crop. Res., 196, 337–347, doi: 10.1016/j.fcr.2016.06. 024.CrossRefGoogle Scholar
  29. Porter, J. R., and M. Gawith, 1999: Temperatures and the growth and development of wheat: A review. Eur. J. Agron., 10, 23–36, doi: 10.1016/S1161-0301(98)00047-1.CrossRefGoogle Scholar
  30. Ray, D. K., N. Ramankutty, N. D. Mueller, et al., 2012: Recent patterns of crop yield growth and stagnation. Nat. Commun., 3, 1293, doi: 10.1038/ncomms2296.CrossRefGoogle Scholar
  31. Richardson, A. D., T. A. Black, P. Ciais, et al., 2010: Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. Roy. Soc. Lond. B. Biol. Sci., 365, 3227–3246, doi: 10.1098/rstb.2010.0102.CrossRefGoogle Scholar
  32. Ruml, M., and T. Vulić, 2005: Importance of phenological observations and predictions in agriculture. J. Agric. Sci., 50, 217–225, doi: 10.2298/jas0502217r.Google Scholar
  33. Siebert, S., and F. Ewert, 2012: Spatio-temporal patterns of phenological development in Germany in relation to temperature and day length. Agric. Forest Meteor., 152, 44–57, doi: 10.1016/j.agrformet.2011.08.007.CrossRefGoogle Scholar
  34. Supit, I., A. A., Hooijer, and C. A. van Diepen, 1994: System description of the WOFOST 6.0 Crop Growth Simulation Model implemented in CGMS. Joint Research Centre, Commission of the European Communities, Brussels, Luxembourg.Google Scholar
  35. Tao, F. L., M. Yokozawa, Y. L. Xu, et al., 2006: Climate changes and trends in phenology and yields of field crops in China, 1981–2000. Agric. Forest Meteor., 138, 82–92, doi: 10.1016/j. agrformet.2006.03.014.CrossRefGoogle Scholar
  36. Tao, F. L., S. Zhang, Z. Zhang, et al., 2014: Maize growing duration was prolonged across China in the past three decades under the combined effects of temperature, agronomic management, and cultivar shift. Global Change Biol., 20, 3686–3699, doi: 10.1111/gcb.12684.CrossRefGoogle Scholar
  37. van Oort, P. A. J., T. Y. Zhang, M. E. De Vries, et al., 2011: Correlation between temperature and phenology prediction error in rice (Oryza sativa L.). Agric. Forest Meteor., 151, 1545–1555, doi: 10.1016/j.agrformet.2011.06.012.CrossRefGoogle Scholar
  38. Wang, E. L., and T. Engel, 1998: Simulation of phenological development of wheat crops. Agric. Syst., 58, 1–24, doi: 10.1016/S0308-521X(98)00028-6.CrossRefGoogle Scholar
  39. Wang, E. L., P. Martre, Z. G. Zhao, et al., 2017: The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants, 3, 17102, doi: 10.1038/nplants. 2017.102.CrossRefGoogle Scholar
  40. Wang, J., E. L. Wang, X. G. Yang, et al., 2012: Increased yield potential of wheat–maize cropping system in the North China Plain by climate change adaptation. Climatic Change, 113, 825–840, doi: 10.1007/s10584-011-0385-1.CrossRefGoogle Scholar
  41. Wang, J., E. L. Wang, L. P. Feng, et al., 2013: Phenological trends of winter wheat in response to varietal and temperature changes in the North China Plain. Field Crop Res., 144, 135–144, doi: 10.1016/j.fcr.2012.12.020.CrossRefGoogle Scholar
  42. Wang, N., J. Wang, E. L. Wang, et al., 2015: Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming. Eur. J. Agron., 71, 19–33, doi: 10.1016/j.eja.2015.08.005.CrossRefGoogle Scholar
  43. Wang, Z., J. Chen, Y. Li, et al., 2016: Effects of climate change and cultivar on summer maize phenology. Int. J. Plant Prod., 10, 509–526, doi: 10.22069/ijpp.2016.3046.Google Scholar
  44. Wang, Z. Y., Y. H. Ding, J. H. He, et al., 2004: An updating analysis of the climate change in china in recent 50 years. Acta Meteor. Sinica, 62, 228–236. (in Chinese)Google Scholar
  45. White, M. A., K. M. De Beurs, K. Didan, et al., 2009: Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biol., 15, 2335–2359, doi: 10.1111/j.1365-2486.2009.01910.x.CrossRefGoogle Scholar
  46. Wilczek, A. M., L. T. Burghardt, A. R. Cobb, et al., 2010: Genetic and physiological bases for phenological responses to current and predicted climates. Philos. Trans. Roy. Soc. B Biol. Sci., 365, 3129–3147, doi: 10.1098/rstb.2010.0128.CrossRefGoogle Scholar
  47. Willmott, C. J., 1982: Some comments on the evaluation of model performance. Bull. Amer. Meteor. Soc., 63, 1309–1369, doi: 10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2.CrossRefGoogle Scholar
  48. Wu, D. R., Q. Yu, C. H. Lu, et al., 2006: Quantifying production potentials of winter wheat in the North China Plain. Eur. J. Agron., 24, 226–235, doi: 10.1016/j.eja.2005.06.001.CrossRefGoogle Scholar
  49. Wu, L., L. P. Feng, Y. Zhang, et al., 2017: Comparison of five wheat models simulating phenology under different sowing dates and varieties. Agron. J., 109, 1280–1293, doi: 10.2134/agronj2016.10.0619.CrossRefGoogle Scholar
  50. Xiao, D. P., and F. L. Tao, 2014: Contributions of cultivars, management and climate change to winter wheat yield in the North China Plain in the past three decades. Eur. J. Agron., 52, 112–122, doi: 10.1016/j.eja.2013.09.020.CrossRefGoogle Scholar
  51. Xiao, D. P., Y. Q. Qi, Y. J. Shen, et al., 2015: Impact of warming climate and cultivar change on maize phenology in the last three decades in North China Plain. Theor. Appl. Climatol., 124, 653–661, doi: 10.1007/s00704-015-1450-x.CrossRefGoogle Scholar
  52. Yin, X. Y., M. J. Kropff, G. Mclaren, et al., 1995: A nonlinear model for crop development as a function of temperature. Agric. Forest Meteor., 77, 1–16, doi: 10.1016/0168-1923(95) 02236-Q.CrossRefGoogle Scholar
  53. Zhang, T. Y., T. Li, X. G. Yang, et al., 2016: Model biases in rice phenology under warmer climates. Sci. Rep., 6, 27355, doi: 10.1038/srep27355.CrossRefGoogle Scholar
  54. Zhang, W.Y., B. Y. Wang, B. H. Liu, et al., 2016: Performance of new released winter wheat cultivars in yield: A case study in the North China plain. Agron. J., 108, 1346–1355, doi: 10. 2134/agronj2016.02.0066.CrossRefGoogle Scholar
  55. Zhao, J., X. G. Yang, S. W. Dai, et al., 2015: Increased utilization of lengthening growing season and warming temperatures by adjusting sowing dates and cultivar selection for spring maize in Northeast China. Eur. J. Agron., 67, 12–19, doi: 10.1016/j. eja.2015.03.006.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Dingrong Wu
    • 1
  • Chunyi Wang
    • 1
    • 2
  • Fang Wang
    • 1
  • Chaoyang Jiang
    • 1
  • Zhiguo Huo
    • 1
  • Peijuan Wang
    • 1
  1. 1.State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijingChina
  2. 2.Hainan Meteorological ServiceHaikouChina

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