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Authors: Serge Guillaume 1 ; Terry Bates 2 ; Jean-Luc Lablée 1 ; Thom Betts 3 and James Taylor 1

Affiliations: 1 ITAP, Univ Montpellier, INRAE, Montpellier SupAgro, Montpellier, France ; 2 Cornell Lake Erie Research and Extension Laboratory, Cornell University, New-York, U.S.A. ; 3 Betts Vineyard LLC, New-York, U.S.A.

Keyword(s): Fusion, Multicriteria, Preference, Decision.

Abstract: This paper presents an application of Fuzzy Logic, well known for its linguistic modeling ability, in a multi-criteria decision making framework applied to spatial data sets. The Fuzzy Logic is integrated in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual spatial information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented in an open source software called GeoFIS. The potential of the method is illustrated using a typical farming decision: the design of a nitrogen fertilization map within a vineyard. The vineyard is a Concord (Vitis labrusca) juice grape vineyard in the Lake Erie region of New York state. The vineyard manager and a local research/extension viticulturist both used the tool to generate a prescription nitrogen map based on their knowledge and s patial crop and soil information. The process captured different preferences between the two users (industry vs. research) and generated different prescription maps that reflected their differing objectives, knowledge and risk perception in vine management. Although applied to vineyard data, this decision tool has a wide potential application to agri-environmental (and other) spatial data sets. (More)

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Paper citation in several formats:
Guillaume, S.; Bates, T.; Lablée, J.; Betts, T. and Taylor, J. (2020). Combining Spatial Data Layers using Fuzzy Inference Systems: Application to an Agronomic Case Study. In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM, ISBN 978-989-758-425-1; ISSN 2184-500X, pages 62-71. DOI: 10.5220/0009356000620071

@conference{gistam20,
author={Serge Guillaume. and Terry Bates. and Jean{-}Luc Lablée. and Thom Betts. and James Taylor.},
title={Combining Spatial Data Layers using Fuzzy Inference Systems: Application to an Agronomic Case Study},
booktitle={Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM,},
year={2020},
pages={62-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009356000620071},
isbn={978-989-758-425-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM,
TI - Combining Spatial Data Layers using Fuzzy Inference Systems: Application to an Agronomic Case Study
SN - 978-989-758-425-1
IS - 2184-500X
AU - Guillaume, S.
AU - Bates, T.
AU - Lablée, J.
AU - Betts, T.
AU - Taylor, J.
PY - 2020
SP - 62
EP - 71
DO - 10.5220/0009356000620071