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Plant Gene and Trait, 2013, Vol.4, No.3, 9
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environment. In addition, grain yield also related with
other characters such as plant type, growth duration and
yield components. The low heritability of grain yield
characters made selection for high yielding varieties
usually use secondary traits associated with yield. Thus,
information on contribution of each plant character to
grain yield is important to make selection process
more efficient. Causal relatio- nship between predictor
variables and response variable can be defined by path
analysis. Using path analysis, correlation coefficient is
partitioned into two components, which are direct effect
of a predictor variable on its response variable and
indirect effect of that predictor variable on the
response variable through other related variables. Path
analysis has been intensively used to estimate
contribution of yield related traits to grain yield of rice
and assisted breeders to determine selection criteria to
improve yield. However, information on relationship
of agronomic traits and grain yield in the breeding
program for specific environment particularly for
different plant density levels is very limited.
2 Materials and methods
Grain amaranthus genotypes were obtained from the
germplasm collection of NBPGR maintained at the
University of Agricultural Sciences, Bangalore and
Forestry College and Research Institute, Mettupalayam,
India. Plants were grown from November-February,
2007, in a Randomized Complete Block Design with
three replications. The soil was a well-drained sandy
loam, pH above 6. The soil was prepared by
cultivation three times to obtain a loose, friable, soil.
Cow manure was applied along with urea, diammonium
phosphate and muriate of potash as per TNAU crop
production guide (2005). Irrigations were at a 7 day
interval during the growing season. The insecticides
chloriphyriphos or dimethoate were applied at 1.5 mL/L.
Genotypes were grown in bed of 2 m×1.5 m. Seed
were sown in a single line in the middle of the bed.
Plants were thinned 15 days after sowing to maintain
very high (30 cm×20 cm), high (30 cm×30 cm),
normal (45 cm×20 cm) and low (45 cm×30 cm)
densities. Observations were recorded from five
randomly selected plants of each genotype in each
replication and population density for plant height,
leaf area at 50% flowering, weight of the inflorescence,
number of rachis per inflorescence, rachis length per
inflorescence, number of secondary branches per
inflorescence, grain yield per plant, grain yield per
plot, and total carbohydrate and protein contents. For
quality traits, composite samples drawn from five
random plants of genotypes under population densities
were used for analysis. The association between yield
and component traits and intercorrelation among
component traits was computed based on
per se
perfo-
rmance of the genotypes as genotypic correlation
coefficient (Goulden, 1952). The variance and covari-
ance components were utilized to calculate genotypic
correlation coefficient as outlined by Al - Jibour et al.
(1958). Path analysis was adopted to partition the
genotypic correlation coefficient into direct and
indirect effects as suggested by Dewey and Lu (1959).
The path coefficients were ranked on the scales given
below (Lenka and Misra, 1973).
3 Results and discussion
Information on the strength and direction of
component characters with seed yield and also interco
rrelation among themselves would be very useful in
formulating an effective selection criteria for improve-
ment of yield. A simple measure of correlation of
characters with yield is inadequate, as it will not
reflect the direct influence of component characters on
the yield. Thus, it is necessary to split the correlation
coefficients into direct and indirect effects (Dewey
and Lu, 1959). This would help to identify with
certainty the component traits to be relied upon during
selection to improve seed yield. Such an attempt was
made in the present study. Genotypic correlation
coefficients were calculated through variance and