Authors:
Sumbangan Baja
1
;
Andi Ramlan
1
and
Muhammad Ramli
2
Affiliations:
1
Hasanuddin University, Indonesia
;
2
Ministry of Agriculture, Indonesia
Keyword(s):
Fuzzy set, Land suitability index, Spatial modeling, Corn development.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Complex Fuzzy Systems
;
Computational Intelligence
;
Fuzzy Systems
;
Fuzzy Systems Design, Modeling and Control
;
Pattern Recognition: Fuzzy Clustering and Classifiers
;
Soft Computing
Abstract:
The primary aim of this research is to develop and test fuzzy modeling procedures to assess spatial distribution of actual corn yields in the field in relation to land characteristics. This experiment implements a fuzzy set methodology to generate a land suitability index (LSI) for corn development. It also uses a direct yield record method in the fields, and utilizes geographic information systems (GIS) in spatial analysis, in synchrony with global positioning system (GPS). This study produced a set of spatial information on LSI on a cell-by-cell basis in the study area. A simple regression method was also employed to calculate spatial correlation between two sets of information (i.e., corn yield in kg/ha and fuzzy set-based LSI). Although the correlation coefficient (R2) is relatively low, the scatter points have shown a good indication that the higher the LSI the better yield can be produced in the area under consideration. Spatial interpolation was then undertaken to map predicte
d corn yields on a regional basis. Spatial segmentation of land area in form of a fuzzy-based land suitability index map can assist land managers or decision makers in allocating future corn cultivation area in the study region.
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