MAIN EFFECT META PRINCIPAL COMPONENT ANALYSIS (ME-METAPCA) OF PLANT GROWTH REGULATOR TREATMENT EFFECT ON SIMULATED MULTIPLE APPLE DATA (CROSBI ID 654051)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija
Podaci o odgovornosti
Borut Bosančić, Marija Pecina, Nikola Mićić
engleski
MAIN EFFECT META PRINCIPAL COMPONENT ANALYSIS (ME-METAPCA) OF PLANT GROWTH REGULATOR TREATMENT EFFECT ON SIMULATED MULTIPLE APPLE DATA
Meta-Analysis as a statistical and analytical method for combining and synthesizing several independent studies and integrating results into a common result and conclusion has not been established in agricultural research yet. Moreover, combination of Meta-Analysis and Principal Components Analysis of main treatment effects (ME-MetaPCA) is a novel approach for analysis of experimental treatment effects. The aim of this paper is to introduce this approach through agricultural model, where it is required, in order to objectively and effectively summarize and generalize conclusions through multiple researches with multiple variables. Simulated data are modeled as real multiple fruit characteristics that define both yield quantity and fruit quality in apple, which is the case in most studies of agricultural crops. Treatment generally affects several fruit characteristics and its effect typically varies throughout different studies and varieties. Finding the real underlying treatment effect size and grouping the studied varieties accordingly would be of practical use for both agricultural researcher and producer. The simulated data were modeled as Plant Growth Regulator (PGR) treatment in several studies where multiple apple varieties were treated and multiple fruit characteristics measured. Results are displayed in form of forest plots related to Meta-Analysis for individual characteristics followed by graphical presentation in principal component space of main effect’s eigenvectors of measured characteristics in studied varieties. This leads to better and more objective understanding of the general rules regarding the effect of the PGR treatment and its influence over various measured fruit characteristics and studied varieties, their grouping and dispersion. Key Words: Meta-Analysis, PCA, Multivariate, Biometrics, Fruit Characteristics
Meta-Analysis, PCA, Multivariate, Biometrics, Fruit Characteristics
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Podaci o prilogu
51-51.
2016.
objavljeno
Podaci o matičnoj publikaciji
AgroReS 2016 Book of Abstracts
Đurić Gordana
Banja Luka: Poljoprivredni fakultet Univerziteta u Banjoj Luci
978-99938-93-37-0
Podaci o skupu
5th International Symposium on Agricultural Sciences AgroReS 2016
predavanje
29.02.2016-03.03.2016
Banja Luka, Bosna i Hercegovina