Fuzzy logic approach for spatially variable nitrogen fertilization of corn based on soil, crop and precipitation information

  • Authors:

  • Yacine Bouroubi;Nicolas Tremblay;Philippe Vigneault;Carl Bélec;Bernard Panneton;Serge Guillaume

  • Affiliations:

  • Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, QC., Canada;Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, QC., Canada;Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, QC., Canada;Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, QC., Canada;Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, QC., Canada;Cemagref, UMR, ITAP, Montpellier, France

  • Venue:
  • ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
  • Year:
  • 2011

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Abstract

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).