Alternative for the evaluation of coffee seedlings using Fisher's discriminant analysis
| Autor: | Augusto Ramalho de Morais, Katia Alves Campos, Crysttian Arantes Paixão |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Multivariate statistics
Multivariate analysis Variable selection 020209 energy Soil Science Data transformation (statistics) 02 engineering and technology 010501 environmental sciences Horticulture 01 natural sciences Data transformation Block design Multivariate analysis of variance Seleção de variável Statistics 0202 electrical engineering electronic engineering information engineering Análise multivariada Analysis of variance lcsh:Agriculture (General) 0105 earth and related environmental sciences Mathematics Análise de variância Univariate Coffea arabica Linear discriminant analysis lcsh:S1-972 Agronomy Transformação de dados Agronomy and Crop Science |
| Zdroj: | Revista Ciência Agronômica, Vol 47, Iss 2, Pp 299-306 (2016) Revista Ciência Agronômica v.47 n.2 2016 Revista ciência agronômica Universidade Federal do Ceará (UFC) instacron:UFC |
| ISSN: | 1806-6690 |
| Popis: | One of the applications of Fisher's linear discriminant function (FDF) is its use in transforming multivariate data into a new univariate variable. This then makes possible a new option for the variance analysis of multivariate data, in addition to the multivariate analysis of variance (MANOVA). The aim of this work was to select groups of seven characteristics of quality in coffee seedlings using six criteria for selection, to use the FDF to transform such groupings of characteristics into a new variable, and then to compare interpretation of the results obtained from the univariate and multivariate analyses of variance of the characteristics and this new variable, with a view to its use in evaluating coffee seedlings. A randomised block design was used to assess the effect of organic fertiliser on the formation of seedlings in coffee cv. Catuaí Vermelho IAC-44, evaluating the following characteristics: seedling height, diameter, root length, dry weight of shoots and roots, leaf area, number of leaves and total dry weight. According to the selection criteria used, different subsets of the selected characteristics are possible. The use of the FDF is shown to be viable in discriminating between treatments. Univariate analysis of the new variable obtained with the FDF and multivariate analysis (MANOVA) was able to detect differences between the treatments, however, it is simpler to apply FDF methodology. |
| Databáze: | OpenAIRE |
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