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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Author(s)
Virgílio Gavicho Uarrota1, Cileide Maria Medeiros Coelho2, Julhana Cristina Sponchiado1 and Clovis Arruda Souza1
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DOI:10.17265/2161-6264/2017.02.003
Affiliation(s)
1. Laboratory of Crop Plants, Department of Agronomy, Santa Catarina State University, Agroveterinary Science Center, Av. Luiz de Camões 2090, Zip Code 88520-000, Lages, SC, Brazil
2. Laboratory of Seed Analysis, Department of Agronomy, Santa Catarina State University, Agroveterinary Science Center, Av. Luiz de Camões 2090, Zip Code 88520-000, Lages, SC, Brazil
ABSTRACT
Selection of effective
agronomic and industrial parameters of oat cultivars is a decisive step in oat
breeding programs for development of new oat and elite cultivars. In this
study, a new approach was utilized to distinguish the most informative
agronomic and industrial parameters that are most affected with fungicide
application in oat cultivars. Four subsequent field experiments from 2007 to
2010 were conducted in completely randomized block design
(CRBD) with split plots. Total nine oat cultivars with or without fungicide application were evaluated
for plant height, sieve
yield, grain yield, lodging index, weight of hectoliter and de-hulling index. Soft
independent modeling of class analogy (SIMCA) was conducted as one-class and
multi-classes models to identify important variables that can be used to
discriminate samples. Results showed that SIMCA was effective, and lodging index, de-hulling
index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can
be used to easily discriminate some agronomic and quality parameters of oats.
KEYWORDS
Oat cultivars, SIMCA modeling, non-supervised techniques, fungicide application, growth, yield, principal component analysis, hierarchical cluster analysis, model classification.
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