Fuzzy Logic Approach for Spatially Variable Nitrogen Fertilization of Corn Based on Soil, Crop and Precipitation Information

  • Yacine Bouroubi
  • Nicolas Tremblay
  • Philippe Vigneault
  • Carl Bélec
  • Bernard Panneton
  • Serge Guillaume
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6782)


A fuzzy Inference System (FIS) was developed to generate recommendations for spatially variable applications of nitrogen (N) fertilizer using soil, plant and precipitation information. Experiments were conducted over three seasons (2005-2007) to assess the effects of soil electrical conductivity (ECa), nitrogen sufficiency index (NSI), and precipitations received in the vicinity of N fertilizers application, on response to N measured at mid-season growth. Another experiment was conducted in 2010 to understand the effect of water supply (WS) on response to N, using a spatially variable irrigation set-up. Better responses to N were observed in the case of high ECa, low NSI and high WS. In the opposite cases (low ECa, high NSI or low WS), nitrogen fertilizer rates can be reduced. Using fuzzy logic, expert knowledge was formalized as a set of rules involving ECa, NSI and cumulative precipitations to estimate economically optimal N rates (EONR).


Variable nitrogen fertilization fuzzy inference systems soil electrical conductivity nitrogen sufficiency index precipitations water supply 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tremblay, N., et al.: Development and Validation of a Fuzzy Logic Estimation of Optimum N Rate for Corn Based on Soil and Crop Features. Precision Agriculture 11, 621–635 (2010)CrossRefGoogle Scholar
  2. 2.
    Welsh, J.P., et al.: Developing Strategies for Spatially Variable Nitrogen Application in Cereals. part II: Wheat. Biosystems Engineering: Special issue on Precision Agriculture - Managing Soil and Crop Variability for Cereals 84, 495–511 (2003)CrossRefGoogle Scholar
  3. 3.
    Scharf, P.C., et al.: Spatially Variable Corn Yield is a Weak Predictor of Optimal Nitrogen Rate. Soil Science of American Journal 70, 2154–2160 (2006)CrossRefGoogle Scholar
  4. 4.
    Derby, N.E., et al.: Interactions of nitrogen, weather, soil, and irrigation on corn yield. Agronomy Journal 97, 1342–1351 (2005)CrossRefGoogle Scholar
  5. 5.
    Tremblay, N.: Determining Nitrogen Requirements from Crops Characteristics. Benefits and Challenges. Recent Res. Devel. Agron. Hortic. 1, 157–182 (2004)Google Scholar
  6. 6.
    Kitchen, N.R., et al.: Soil Electrical Conductivity and Topography Related to Yield for Three Contrasting Soil-Crop systems. Agronomy Journal 95, 483–495 (2003)CrossRefGoogle Scholar
  7. 7.
    Van Es, H.M., Yang, C.L., Geohring, L.D.: Maize Nitrogen Response as Affected by Soil Type and Drainage Variability. Precision Agriculture 6, 281–295 (2005)CrossRefGoogle Scholar
  8. 8.
    Hong, L., et al.: Multispectral Reflectance of Cotton Related to Plant Growth, Soil Water and Texture, and Site Elevation. Agronomy Journal 93, 1327–1337 (2001)CrossRefGoogle Scholar
  9. 9.
    Xie, M., et al.: Weather Effects On Corn Response to in-Season Nitrogen Rates. In: A.S.A. (ed.) ASA, CSSA, and SSSA International Annual Meetings, Long Beach, CA, October 31-November 3 (2010)Google Scholar
  10. 10.
    Samborski, S.M., Tremblay, N., Fallon, E.: Strategies to Make Use of Plant Sensors-based Diagnostic Information for Nitrogen Recommendations. Agronomy Journal 101, 800–816 (2009)CrossRefGoogle Scholar
  11. 11.
    Berntsen, J., Thomsen, A., Schelde, K., Hansen, O., Knudsen, L., Broge, N., Hougaard, H., Hørfarter, R.: Algorithms for Sensor-based Redistribution of Nitrogen Fertilizer in Winter Wheat. Precision Agriculture 7, 65–83 (2006)CrossRefGoogle Scholar
  12. 12.
    Doerge, T.: Nitrogen Measurement for Variable-rate N Management in Maize. Communications in Soil Science and Plant Analysis 36, 23–32 (2005)CrossRefGoogle Scholar
  13. 13.
    Assimakopoulos, J.H., Kalivas, D.P., Kollias, V.J.: A GIS-based Fuzzy Classification for Mapping the Agricultural Soils for N-fertilizers use. Science of the Total Environment 309, 19–33 (2003)CrossRefGoogle Scholar
  14. 14.
    Jones, D., Barnes, E.M.: Fuzzy Composite Programming to Combine Remote Sensing and crop Models for Decision Support in Precision Crop Management. Agricultural Systems 65, 137–158 (2000)CrossRefGoogle Scholar
  15. 15.
    Molin, J.P., De Castro, C.N.: Establishing management zones using soil electrical conductivity and other soil properties by the fuzzy clustering technique. Scientia Agricola 65, 567–573 (2008)CrossRefGoogle Scholar
  16. 16.
    Yan, L., Zhou, S., Feng, L., Hong-Yi, L.: Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture 56, 174–186 (2007)CrossRefGoogle Scholar
  17. 17.
    Papageorgiou, E.I., Markinos, A.T., Gemtos, T.A.: Soft computing technique of fuzzy cognitive maps to connect yield defining parameters with yield in cotton crop production in central Greece as a basis for a decision support system for precision agriculture application. Studies in Fuzziness and Soft Computing 247, 325–362 (2010)CrossRefGoogle Scholar
  18. 18.
    Krysanova, V., Haberlandt, U.: Assessment of nitrogen leaching from arable land in large river basins. Part I. Simulation experiments using a process-based model. Ecological Modelling 150, 255–275 (2002)CrossRefGoogle Scholar
  19. 19.
    Ritchie, S.W., Hanway, J.J., Benson, G.O.: How a Corn Plant Grows. In: Report No. 48. Iowa State University of Science and Technology, Cooperative Extension Service, Ames (1992)Google Scholar
  20. 20.
    Tremblay, N., Bouroubi, M.Y., Panneton, B., Vigneault, P., Guillaume, S.: Space, Time, Remote Sensing and Optimal Nitrogen Fertilization Rates – A Fuzzy Logic Approach. In: GIS Applications in Agriculture Nutrient Management for Improved Energy Efficiency. CRC Press, Boca Raton (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yacine Bouroubi
    • 1
  • Nicolas Tremblay
    • 1
  • Philippe Vigneault
    • 1
  • Carl Bélec
    • 1
  • Bernard Panneton
    • 1
  • Serge Guillaume
    • 2
  1. 1.Horticulture Research and Development CentreAgriculture and Agri-Food CanadaSt-Jean-sur-RichelieuCanada
  2. 2.Cemagref, UMR ITAPMontpellierFrance

Personalised recommendations