Ranking Rice (Oryza sativa L.) Genotypes Using Multi-Criteria Decision Making, Correlation and Path Coefficient Analysis.
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Date
2012-10
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Abstract
The evaluation of selection criteria using correlation coefficients, multiple regression and
path analysis was carried out for a period of two years on sixteen genotypes of rice
(Oryza sativa L.).These genotypes were studied during 2008 and 2009 summer seasons
at EDduim and Kosti locations in randomized complete block design with three
replications per each location. The field experiment is directed to study character
association; contribution of various yield influencing traits on rice for establishment of
appropriate plant attributes to select and improve the grain yield, and accordingly select
the most suitable genotype.
Combined analysis of variance revealed highly significant effects of locations, seasons,
genotypes and their interactions for most of the studied traits indicating that these
genotypes are highly variable. Genotypes differed significantly in grain yield, (NERICA 4,
NERICA 14, NERICA 15, YUNLU 33 and WAB-1-38-19-14-P2-HB) were higher yielding
genotypes giving 3.78, 4.03, 3.24, 3.55 and 3.51 t/ha respectively. These genotypes
presented a valuable source of diversity which can be used for breeding programs. Correlation analysis in both seasons indicated that grain yield was positively and
significantly correlated with plant height, number filled grains/ panicle and 1000-grain
weight, while it was negatively correlated with percentage of unfilled grains/panicle. Path
coefficient analysis indicated that among yield components number of filled grains/
panicle, number of panicles/m2 and 1000-grain weight showed a positive direct effect on
grain yield and therefore, may be considered as selection criteria for the improvement of
grain yield.
Multi-objective decision-making model was employed to rank the studied genotypes
according to the measured various yield influencing traits and the degree of association of
each trait on yield. For determination of criteria weight this article considers the analysis of
correlation that is used frequently in to quantify the degree of association between a
response variable, and some explanatory variable. Consequently, we propose new
weighted information criteria to be used to guide the selection of the “best” genotype
based on determining correlation coefficient. As a result, compromise programming
analysis is in agreement with analysis of variance and indicated that genotypes can be
ranked in a descending order as: N12, N14, Y30, WAB8, WAB19, N4, Y33, Y26, N15,
N17 and Y24.
Description
Keywords
Rice genotypes, Multi-criteria decision making, path coefficient analysis, correlation coefficient
Citation
Mohamed Khalid A, Idris Atif Elsadig, Mohammed Hassan Ibrahim, Adam Khalid Abdalla Osman. Ranking Rice (Oryza sativa L.) Genotypes Using Multi-Criteria Decision Making, Correlation and Path Coefficient Analysis. British Biotechnology Journal. 2012 Oct; 2(4): 211-228.