Skip to main content
SpringerLink
Log in

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

  • Regular Articles
  • Published:
Journal of Meteorological Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • China Meteorological Administration, 1993: Agricultural Meteorological Observation Specification (Volume 1). China Meteorological Press, Beijing, 4–18. (in Chinese)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Menzel, A., and P. Fabian, 1999: Growing season extended in Europe. Nature, 397, 659, doi: 10.1038/17709.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China

    Dingrong Wu, Chunyi Wang, Fang Wang, Chaoyang Jiang, Zhiguo Huo & Peijuan Wang

  2. Hainan Meteorological Service, Haikou, 570203, China

    Chunyi Wang

Authors
  1. Dingrong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chaoyang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiguo Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peijuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunyi Wang.

Additional information

Supported by the Project of Basic Scientific Research and Operating Expenses of Chinese Academy of Meteorological Sciences (2016Y009) and National Natural Science Foundation of China (31771672).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, D., Wang, C., Wang, F. et al. Uncertainty in Simulating the Impact of Cultivar Improvement on Winter Wheat Phenology in the North China Plain. J Meteorol Res 32, 636–647 (2018). https://doi.org/10.1007/s13351-018-7139-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13351-018-7139-1

Key words

Search

Navigation