Space, Time, Remote Sensing, and Optimal Nitrogen Fertilization Rates: A Fuzzy Logic Approach

• Published in: GIS Applications in Agriculture, Volume Two pp. 119 - 140
• Main Authors: Tremblay, N., Bouroubi, M.Y., Panneton, B., Vigneault, P., Guillaume, S., Clay, D., Shanahan, J.
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2011; Taylor & Francis Group
• Subjects: Agricultural science; Agriculture & related industries; ENERGY TECHNOLOGY & ENGINEERING; Environmental Sciences; Fuzzy Sets; Copy Paste; Absolute NDVI; Fertilization Recommendations; Conceptual Menu; High ECa; Fuzzy Inference System; Mouse Cursor; Spatial Analyst; Gamma Design Software; Ri Ga; Tiff Format; FIS; Raster Calculator; Mid-season Growth; Map Le; Layers Label; Output NDVI; Raw NDVI; Calculation Layer; Fuzzy Logic Approach; Mathematical Expression; Temporary Layer; PRECISION FARMING; TOPOGRAPHY; VARIABLE-RATE TECHNOLOGY; APPARENT ELECTRICAL CONDUCTIVITY; FUZZY INFERENCE SYSTEMS
• ISBN: 1420092707; 9781420092707
• DOI: 10.1201/b10600-11

Fuzzy logic inference systems (FISs) can help provide within-eld nitrogen (N) fertilization recommendations by combining critical plant-and soil-based spatial information. This chapter describes how, based on spatially distributed information, FIS can be used to develop in-season N recommendations. A sample problem is provided. Soil and plant information considered in this analysis included apparent soil electrical conductivity (ECa), elevation (ELE), and the remote sensing-based N sufciency index6.1 Executive Summary ... 101 6.2 Introduction ... 102 6.3 Materials and Methods ... 1036.3.1 Extracting the Field Parameters ... 103 6.3.2 NDVI and Calculating the NSI ... 104 6.3.3 Background Knowledge about Soil and Plant Status Neededto Determine N Needs ... 105 6.3.4 FIS for Estimating Spatial N Needs ... 1066.3.4.1 Design of the FIS ... 108 6.3.5 Step-by-Step Exercises Using ArcGIS 9.2 ... 1096.4 Results ... 118 6.5 Conclusions ... 119 Acknowledgment ... 119 References ... 119(NSI = NDVIsample/NDVIwell-fertilized reference). Expert knowledge for formulating fuzzy rules was developed from corn growth data following an in-season N application. The best mid-season growth response to in-season N occurred in areas of low ECa and high ELE. Under these favorable soil conditions, maximum mid-season growth was obtained without in-season N irrespective of the NSI values. Where soil conditions were less favorable (i.e., high ECa and low ELE), mid-season growth beneted from high in-season N rate only when NSI was low. These relationships were modeled using a simple FIS having three inputs (ECa, ELE, and NSI) fuzzied with only two sets (low and high), an output (optimum N rate) with three fuzzy sets (low, medium, and high) and a set of eight simple rules. The FIS appeared to be a useful and handy tool for incorporating expert knowledge into spatially variable N recommendations. An example describing a basic implementation of the FIS in ArcGIS is included.