Study the Spatial-Temporal Variation of Wheat Growth Under Different Site-Specific Nitrogen Fertilization Approaches

  • Bei Cui
  • Wenjiang HuangEmail author
  • Xiaoyu Song
  • Huichun Ye
  • Yingying Dong
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)


Many variable fertilization approaches based on ‘real-time’ crop N status were developed for making N fertilizer management in precision agriculture. Unfortunately, to date, only few papers reported the effect of variable fertilization algorithms on the spatial and temporal variability of crop parameters. Based on these problems, this study designed three different variable fertilization algorithms based on vegetation index (Y), SPAD (S) and crop growth model (Z), respectively, with uniform fertilization and no fertilization as controls. Results showed that wheat growth had strong spatial dependence, which become stronger after fertilization. Wheat yield also had strong spatial dependence. There were some similar spatial distribution between NDVIs, soil TN and yield, indicating that spatial variability of yield had strong relationship with crop growth status and soil TN content. The site-specific fertilization treatment based on crop growth model (Z) had the best adjustment capacity to promote crop growth and yield, and reduce their spatial variation, compared with other fertilization treatments.


Site-specific N fertilization Winter wheat Spatial-temporal variation 


Funding Information

This study was supported by National Key R&D Program of China (2016YFD0300601), National Natural Science Foundation of China (41501468, 41601466), the Agricultural Science and Technology Innovation of Sanya (2015KJ04), the Natural Science Foundation of Hainan Province, China (20164179, 2016CXTD015), the Technology Research, Development and Promotion Program of Hainan Province, China (ZDXM2015102), the Hainan Provincial Department of Science and Technology under Grant (ZDKJ2016021).


  1. 1.
    Wang, Z., Li, S.: Effects of nitrogen and phosphorous fertilization on plant growth and nitrate accumulation in vegetables. J. Plant Nutr. 27, 539–556 (2004)CrossRefGoogle Scholar
  2. 2.
    Albornoz, F., Lieth, J.H.: Over fertilization limits lettuce productivity because of osmotic stress. Chilean J. Agric. Res. 75, 284–290 (2015)CrossRefGoogle Scholar
  3. 3.
    Francisco, A.: Crop responses to nitrogen over fertilization: a review. Sci. Hortic. 205, 79–83 (2016)CrossRefGoogle Scholar
  4. 4.
    Montemurro, F.: Different nitrogen fertilization sources, soil tillage, and crop rotations in winter wheat: effect on yield, quality, and nitrogen utilization. J. Plant Nutr. 32, 1–18 (2009)CrossRefGoogle Scholar
  5. 5.
    Meyer-Aurich, A., Weersink, A., Gandorfer, M., Wagner, P.: Optimal site-specific fertilization and harvesting strategies with respect to crop yield and quality response to nitrogen. Agr. Syst. 103, 478–485 (2010)CrossRefGoogle Scholar
  6. 6.
    Arne, M.R., Henning, K.: Predicting the site specific soil N supply under winter wheat in Germany. Nutr. Cycl. Agroecosys. 110, 1–11 (2017)Google Scholar
  7. 7.
    Griepentrog, H.W., Kyhn, M.: Strategies for site specific fertilization in a highly productive agricultural region. In: The 5th International Conference on Precision Agriculture, Minneapolis, USA, July 2000Google Scholar
  8. 8.
    Delin, S., Lindén, B., Berglund, K.: Yield and protein response to fertilizer nitrogen in different parts of a cereal field: potential of site-specific fertilization. Eur. J. Agron. 22, 325–336 (2005)CrossRefGoogle Scholar
  9. 9.
    Cui, Z.L., et al.: On-farm evaluation of an in-season nitrogen management strategy based on soil Nmin test. Field Crops Res. 105, 48–55 (2008)CrossRefGoogle Scholar
  10. 10.
    Li, F., et al.: In-season optical sensing improves nitrogen-use efficiency for winter wheat. Soil Sci. Soc. Am. J. 73, 1566–1574 (2009)CrossRefGoogle Scholar
  11. 11.
    Colac, A. F.¸ Molin, J. P.: Variable rate fertilization in citrus: a long term study. Precis. Agric.18, 169–191 (2017)Google Scholar
  12. 12.
    Mariangela, D., Pietro, R., Francesco, M.: Precision nitrogen management of wheat. a review. Agron. Sustain. Dev. 33(1), 219–241 (2013)CrossRefGoogle Scholar
  13. 13.
    Clevers, J.G.P.W., Kooistra, L.: Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(2), 574–583 (2012)CrossRefGoogle Scholar
  14. 14.
    Clevers, J.G.P.W., Gitelson, A.A.: Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int. J. Appl. Earth Obs. Geo-Inf. 23, 344–351 (2013)CrossRefGoogle Scholar
  15. 15.
    Li, F., et al.: Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany. Field Crop. Res. 138, 21–32 (2012)CrossRefGoogle Scholar
  16. 16.
    Wang, W., et al.: Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crop. Res. 129, 90–98 (2012)CrossRefGoogle Scholar
  17. 17.
    Ladha, J.K., Pathak, H., Krupnik, T.J., Six, J., Kesse, C.V.: Efficiency of fertilizer nitrogen in cereal production: retrospects and prospects. Adv. Agron. 87, 85–156 (2005)CrossRefGoogle Scholar
  18. 18.
    Montemurro, F., Maiorana, M., Ferri, D., Convertini, G.: Nitrogen indicators, uptake and utilization efficiency in a maize and barley rotation cropped at different levels and sources of N fertilization. Field Crop Res. 99, 114–124 (2006)CrossRefGoogle Scholar
  19. 19.
    Cartelat, A., et al.: Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crop Res. 91, 35–49 (2005)CrossRefGoogle Scholar
  20. 20.
    Ehlert, D., Schmerler, J., Voelker, U.: Variable rate nitrogen fertilization of winter wheat based on a crop density sensor. Precis. Agric. 5, 263–273 (2004)CrossRefGoogle Scholar
  21. 21.
    Godwin, R.J., Richards, T.E., Wood, G.A., Welsh, J.P., Knight, S.M.: An economic analysis of the potential for precision farming in UK cereal production. Biosyst. Eng. 84, 533–545 (2003)CrossRefGoogle Scholar
  22. 22.
    Morris, K.B., et al.: Mid-season recovery from nitrogen stress in winter wheat. J. Plant Nutr. 29, 727–745 (2006)CrossRefGoogle Scholar
  23. 23.
    Singh, B., et al.: Assessment of the nitrogen management strategy using an optical sensor for irrigated wheat. Agron. Sustain. Dev. 31(3), 589–603 (2011)CrossRefGoogle Scholar
  24. 24.
    Thomason, W.E., Phillips, S.B., Davis, P.H., Warren, J.G., Alley, M.M., Reiter, M.S.: Variable nitrogen rate determination from plant spectral reflectance in soft red winter wheat. Precis. Agric. 12, 666–681 (2011)CrossRefGoogle Scholar
  25. 25.
    Gao, X.Z., et al.: Spatial variability of soil nutrients and crop yield and site-specific fertilizer management. Scientia Agriculture Sinica 35(6), 660–666 (2002). (in Chinese with English Abstract)Google Scholar
  26. 26.
    Fensholt, R., Proud, S.R.: Evaluation of earth observation based global long term vegetation trends-comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 119, 131–147 (2012)CrossRefGoogle Scholar
  27. 27.
    Wen, L., Saintilan, N., Yang, X., Hunter, S., Mawer, D.: MODIS NDVI based metrics improve habitat suitability modelling in fragmented patchy floodplains. Remote Sens. Appl.: Soc. Environ. 1, 85–97 (2015)Google Scholar
  28. 28.
    Gerardo, E.S., Christian, G.P.H., Ingo, J.H., Amanda, D.R., Pablo, M.V.: Tree senescence as a direct measure of habitat quality: linking red-edge vegetation indices to space use by Magellanic woodpeckers. Remote Sens. Environ. 193, 1–10 (2017)CrossRefGoogle Scholar
  29. 29.
    Bruno, B., Costanza, F., Davide, C., Urs, S.: Variable rate nitrogen fertilizer response in wheat using remote sensing. Precis. Agric. 17, 1–15 (2015)Google Scholar
  30. 30.
    Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring vegetation systems in the great plains with ERTS. In: Proceedings of the 3rd ERTS Symposium, pp. 1309–1317. U.S. Government Printing Office, Washington D.C. (1974)Google Scholar
  31. 31.
    Ding, X.D.: Comparisons among the methods of handling outliers. Comput. Tech. Geophys. Geochem. Explor. 18(1), 71–77 (1996). (in Chinese with English Abstract)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Bei Cui
    • 1
    • 2
  • Wenjiang Huang
    • 1
    • 2
    Email author
  • Xiaoyu Song
    • 3
  • Huichun Ye
    • 1
    • 2
  • Yingying Dong
    • 1
    • 2
  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.Key Laboratory of Earth Observation, Hainan ProvinceSanyaPeople’s Republic of China
  3. 3.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry SciencesBeijingPeople’s Republic of China

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