Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features

A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha−1 conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (ECa), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m2 resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low ECa, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving ECa, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha−1 compared to the recommended uniform rate of 170 kg N ha−1, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.

[1]  Kenneth A. Sudduth,et al.  Spatially variable corn yield is a weak predictor of optimal nitrogen rate , 2006 .

[2]  Thomas A. Doerge.  Nitrogen Measurement for Variable‐Rate N Management in Maize , 2005 .

[4]  J. C. Taylor,et al.  Developing Strategies for Spatially Variable Nitrogen Application in Cereals, Part II: Wheat , 2003 .

[5]  O. Oenema,et al.  Does the crop or the soil indicate how to save nitrogen in maize production? Reviewing the state of the art , 2000 .

[6]  Bernd Huwe,et al.  FuN-Balance: a fuzzy balance approach for the calculation of nitrate leaching with incorporation of data imprecision , 2002 .

[7]  F. J. Pierce,et al.  GIS Applications in Agriculture , 2018 .

[8]  Donald G. Bullock,et al.  Relationship among Crop Grain Yield, Topography, and Soil Electrical Conductivity Studied with Cross‐Correlograms , 2003 .

[9]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[10]  Kenneth A. Sudduth,et al.  Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems , 2003 .

[11]  Nicolas Tremblay,et al.  Strategies to Make Use of Plant Sensors-Based Diagnostic Information for Nitrogen Recommendations , 2009 .

[12]  A. Thomsen,et al.  Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat , 2006, Precision Agriculture.

[13]  T. F. Morris,et al.  Disaggregating model bias and variability when calculating economic optimum rates of nitrogen fertilization for corn , 2007 .

[14]  John E. Sawyer,et al.  Using Relative Chlorophyll Meter Values to Determine Nitrogen Application Rates for Corn , 2007 .

[15]  E. M. Barnes,et al.  Fuzzy composite programming to combine remote sensing and crop models for decision support in precision crop management , 2000 .

[16]  Randal K. Taylor,et al.  Corn Yield Response to Nitrogen at Multiple In-Field Locations , 2002 .

[17]  John B. Solie,et al.  Identifying an In-Season Response Index and the Potential to Increase Wheat Yield with Nitrogen , 2003 .

[18]  Serge Guillaume,et al.  "Space, Time, Remote Sensing and Optimal Nitrogen Fertilization Rates: A Fuzzy Logic Approach" , 2011 .

[19]  David E. Clay,et al.  Using soil electrical conductivity to improve nutrient management , 2003 .

[20]  J H Assimakopoulos,et al.  A GIS-based fuzzy classification for mapping the agricultural soils for N-fertilizers use. , 2003, The Science of the total environment.

[21]  B. D. Kay,et al.  Spatial Variability of In‐Season Nitrogen Uptake by Corn Across a Variable Landscape as Affected by Management , 2006 .