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Nitrogen Revising of Rapeseed (Brassica napus L.) Phenology and Leaf Number Models

  • Hongxin CaoEmail author
  • Yan Liu
  • Wenyu Zhang
  • Yeping Zhu
  • Daokuo Ge
  • Yanbin Yue
  • Yongxia Liu
  • Jinying Sun
  • Zhiyou Zhang
  • Yuli Chen
  • Weixin Zhang
  • Kunya Fu
  • Na Liu
  • Chunhuan Feng
  • Taiming Yang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 452)

Abstract

The Decision-making System for Rapeseed Optimization-Digital Cultivation Based on Simulation Models, DSRODCBSM, is a dynamic model that describes the growth and development of winter rapeseed. In order to perfect rapeseed growth models, Ningyou16 (NY16), Ningyou 18 (NY18), and Ningza 19 (NZ19) were adopted as materials, and the field experiments with 2 cultivars and 2 nitrogen levels, and pot experiment with 3 cultivars and 2 nitrogen levels were conducted during 2007-2008, 2008-2009, and 2011-2012 in Nanjing, respectively. The experimental results showed that the phenology and leaf number in rapeseed models had obvious difference for the same cultivars under different nitrogen levels. Thus, the nitrogen effect factor, F (N), was put forward, used in the phenology sub-model in rapeseed growth models, and the verification of the leaf number sub-model can be done through model parameter adjusting. The simulated values before and after using F (N) and the observed values were compared, and the precision for the phenology sub-models in rapeseed growth models were raised further.

Keywords

nitrogen impact rapeseed (Brassica napus L.) phenology models leaf number models revising 

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References

  1. 1.
    National Bureau of Statistics of China (2009), http://www.stats.gov.cn/tjsj/qtsj/gjsj/ (September 26, 2013)
  2. 2.
    Kiniry, J.R., Major, D.J., Izaurralde, R.C., et al.: EPIC model parameters for cereal, oilseed, and forage crop in the north Great Plain region. Canadian Journal of Plant Science 63, 1063–1065 (1983)CrossRefGoogle Scholar
  3. 3.
    Petersen, C.T., Svendsen, H., Hansen, S., et al.: Parameter assessment for simulation of biomass production and nitrogen uptake in winter rape. Europe Journal of Agronomy 4, 77–89 (1995)CrossRefGoogle Scholar
  4. 4.
    Habekotté, B.: A model of the penological development of winter oilseed rape (Brassica napus L.). Field Crop Research 54, 137–153 (1997)CrossRefGoogle Scholar
  5. 5.
    Gabrielle, B., Denoroy, P., Gosse, G., et al.: Development and evaluation of a CERES-type model for winter oilseed rape. Field Crop Research 57, 95–111 (1998)CrossRefGoogle Scholar
  6. 6.
    Farré, M.J., Robertson, G.H., Walton, S.A.: Simulating response of canola to sowing data in Western Australia. In: Proceedings of the 10th Australia Agronomy Conference, Hobart, pp. 36–40 (2001)Google Scholar
  7. 7.
    Husson, F., Wallach, D., Vandeputte, A.: Evaluation of CECOL, a model of winter rape (Brassica napus L.). Europe Journal of Agronomy 8, 205–214 (1998)CrossRefGoogle Scholar
  8. 8.
    Liu, H., Jin, Z.: The simulation models of rape development dynamic. Journal of Application Meteorology 14(5), 634–640 (2003) (in Chinese with English Abstract)Google Scholar
  9. 9.
    Liu, T., Hu, L., Zhao, Z., et al.: A mechanistic of phasic and phenological development in rape. I. Description of the model. Chinese Journal of Oil Crop Sciences 26(1), 27–30 (2004)Google Scholar
  10. 10.
    Zhang, C., Li, G., Cao, H., et al.: Simulating growth and development of winter rape in Yangtze river valley. In: Proceedings of 11th International Rapeseed Congress, Copenhagen, Denmark, July 6-10, p. 835 (2003)Google Scholar
  11. 11.
    Cao, H., Zhang, C., Li, G., et al.: Researches of Decision-making System for Rape Optimization-Digital Cultivation Based on Simulation Models. In: The Third International Conference on Intelligent Agricultural Information Technology, pp. 285–292. China Agricultural Sciences and Technology Press, Beijing (2005)Google Scholar
  12. 12.
    Cao, H., Zhang, C., Li, G., et al.: Researches of Simulation Models of Rape (Brassica napus L.) Growth and Development. Acta Agronomica Sinica 32(10), 1530–1536 (2006) (in Chinese with English Abstract)Google Scholar
  13. 13.
    Cao, H., Zhang, C., Li, G., et al.: Researches of Optimum Leaf Area Index Dynamic Models for Rape (Brassica napus L.). In: Zhao, C., Li, D. (eds.) Computer and Computing Technologies in Agriculture II, Volume 3. IFIP AICT, vol. 295, pp. 1585–1594. Springer, Boston (2009)CrossRefGoogle Scholar
  14. 14.
    Cao, H., Zhang, C., Li, G., et al.: Researches of Optimum Shoot and Ramification Number Dynamic Models for Rapeseed (Brassica napus L.). In: World Automation Congress (WAC), pp. 129–135 (2010)Google Scholar
  15. 15.
    Tang, L.: Rapeseed growth simulation and decision-making support system. Dissertation for Ph.D. of Nanjing Agricultural University (2006)Google Scholar
  16. 16.
    Tang, L., Zhu, Y., Liu, T., et al.: A Process-Based Model for Simulating Phenological Development in Rapeseed. Scientia Agricultura Sinica 41(8), 2493–2498 (2008) (in Chinese with English Abstract)Google Scholar
  17. 17.
    Gao, L., Jin, Z., Huang, Y., et al.: Rice cultivational simulation-optimization-decision making system. Chinese Agricultural Sciences and Technology Publication House, Beijing (1992) (in Chinese with English Abstract)Google Scholar
  18. 18.
    Gao, L., Jin, Z., Zheng, G., et al.: Wheat cultivational simulation-optimization-decision making system. Journal of Jiangsu Agriculture 16, 65–72 (2000) (in Chinese with English Abstract)Google Scholar
  19. 19.
    Cao, H., Hanan, J.S., Liu, Y., et al.: Comparison of crop model validation methods. Journal of Integrative Agriculture (Formerly Agricultural Sciences in China) 11(8), 1274–1285 (2012)Google Scholar
  20. 20.
    Chen, Y.: Modeling of nitrogen accumulation and partitioning in plant and yield formation for protected cultivated tomato (Lycopersicon esculentum Mill.). M.D Paper of Nanjing Agricultural University (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Hongxin Cao
    • 1
    Email author
  • Yan Liu
    • 1
  • Wenyu Zhang
    • 1
  • Yeping Zhu
    • 2
  • Daokuo Ge
    • 1
  • Yanbin Yue
    • 3
  • Yongxia Liu
    • 4
  • Jinying Sun
    • 5
  • Zhiyou Zhang
    • 6
  • Yuli Chen
    • 1
  • Weixin Zhang
    • 1
  • Kunya Fu
    • 1
  • Na Liu
    • 7
  • Chunhuan Feng
    • 1
  • Taiming Yang
    • 8
  1. 1.Institute of Agricultural Economics and Information, Engineering Research Center for Digital AgricultureJiangsu Academy of Agricultural SciencesNanjingP.R. China
  2. 2.Institute of Agricultural InformationChina Academy of Agricultural SciencesBeijingP.R. China
  3. 3.Institute of Agricultural Sci-tech InformationGuizhou Academy of Agricultural SciencesGuiyangP.R. China
  4. 4.Institute of Banana and Plantain/Haikou Experimental StationChinese Academy of Tropical Agricultural SciencesHaikouP.R. China
  5. 5.Agricultural Technological Extensive Station of Luntai County in XinjiangLuntaiP.R. China
  6. 6.Institute of Agricultural Sci-tech InformationHunan Academy of Agricultural SciencesChangshaP.R. China
  7. 7.Center for China Meteorological InformationChina Meteorological BureauBeijingP.R. China
  8. 8.Institute of Agricultural MeteorologyAnhui Provincial Meteorological BureauHefeiP.R. China

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