Revista Científica UDO Agrícola Volumen 9.
Número 3. Año 2009. Páginas: 487-490
Character association in Chilli
(Capsicum annuum
L.)
Asociación
entre caracteres en pimentón (Capsicum annuum L.)
Nayeema JABEEN1, Parvaze A. SOFI
2 and Shafiq
A. WANI2
1Division of Olericulture, Sher-e-Kashmir
University of Agricultural Sciences and Technology (SKUAST), Shalimar, 191121,
India and 2Directorate of Research, SKUAST, Shalimar, 191121, India.
E-mail:
phdpbg@yahoo.com Corresponding author
|
Received: 01/29/2009 |
First
reviewing ending:
03/08/2009 |
|
First
review received:
09/04/2009 |
Accepted: 09/05/2009 |
ABSTRACT
The present
investigation was carried out in 2007-08 using 25 chilli
genotypes to elucidate the association of various yield attributing traits to
develop a reliable set of traits for indirect selection. The data were observed
from five randomly selected competitive plants from each replication for eight
quantitative traits. The genotypic coefficients were higher in the magnitudes
relative to corresponding estimates of phenotypic coefficients, which indicated
high heritability of the traits under study. The fruits yield/plant exhibited
highly significant correlation with number of fruits/plant, number of
branches/plant and height of the plant, indicating the usefulness of these
traits for improving upon fruit yield in chilli. Path
coefficient analysis revealed that the highest direct effect on fruit
yield/plant was exerted by average fruit weight followed by number of
fruits/plant, number of branches/plant and plant spread, while as highest
indirect effect on fruit yield/plant was exerted by number of branches/plant
through number of fruits/plant, fruit length and fruit breadth through average
fruit weight and plant height through number of fruits/plant. These traits can
be used to develop an optimally reliable selection index for realizing
improvements in fruit yield in chilli.
Key words: Chilli, character
association, correlation, path analysis
RESUMEN
La presente
investigación se llevó a cabo en 2007-2008 utilizando 25 genotipos de pimentón
para dilucidar la asociación de diversos componentes del rendimiento para
desarrollar un conjunto confiable de caracteres para la selección indirecta.
Los datos se observaron en cinco plantas bajo competencia y seleccionadas al
azar de cada replicación para ocho caracteres cuantitativos. Los coeficientes
genotípicos fueron mayores en magnitud relativa en comparación con las
estimaciones del coeficiente fenotípico,
lo cual indica una alta heredabilidad de los
caracteres bajo estudio. El rendimiento de frutos/planta exhibió una
correlación altamente significativa con el número de frutos/planta, número de
reamas/planta y altura, indicando la utilidad de estos caracteres para mejorar
el rendimiento de frutos en pimentón. El análisis de los coeficientes de
trayectoria reveló que el mayor efecto directo sobre el rendimiento de
frutos/planta fue ejercido por el peso promedio del fruto seguido por el número
de frutos/planta, número de reamas/planta y el dosel de la planta, mientras,
mientras que los mayores efectos indirectos sobre el rendimiento de frutos/planta
fue ejercido por el número de ramas/planta a través del número de
frutos/planta, longitud del fruto y ancho del fruto a través del peso promedio
del fruto y la altura de la planta a través del número de frutos/planta. Estos
caracteres pueden ser usados para desarrollar un índice de selección
óptimamente confiable para realizar mejoras en el rendimiento de frutos en
pimentón.
Palabras clave: Pimentón, asociación de
caracteres, correlación, análisis de trayectoria.
INTRODUCTION
Chilli
pepper (Capsicum annuum
L.) is one of the most important spice crops of India and finds a variety of
uses. India is the leading producer and exporter of chillies
followed by China, Indonesia, Korea, Pakistan, Turkey, Sri Lanka, Nigeria, Ghana,
Tunisia, Egypt, Mexico, the US, Yugoslavia, Spain, Romania, Bulgaria, Italy,
Hungary, Argentina, Peru and Brazil. Andhra Pradesh leads the country both in
acreage (49%) and production (49%). In J&K state, the chillies
occupy an area of 2.812 h with a production of 12.423 t (Anonymous, 2006). Some
chillies are used as colorants (capsanthin)
while some are used for pungency (capsaicin). Paprika, also known as Hungarian
pepper or pimento pepper, is a less pungent type of chillies
or sweet red pepper type for grinding used as colorant attributed to the
pigment oleoresin. It is native of South America, and though originally of
tropical origin, can grow in cooler climates also. In India, the available
Paprika types are not suitable for cultivation in all chilli
growing areas (Prasath and Ponnuswami,
2008). Therefore, there is an urgent need to develop location specific
cultivars for enhanced adaptability and productivity.
Since yield is a complex trait,
governed by a large number of component traits, it is imperative to know the
interrelationship between yield and its component traits to arrive at an
optimal selection index for improvement of yield. Wright (1921) was first to
propose the correlation and path analysis to organize the relationship between predictor
variables and the response variable. Correlation simply measures the
association between yield and other traits, while as path coefficient analysis
permits the separation of correlation into direct effects (path coefficient)
and indirect effects (effects exerted through other variables). It is basically
a standardized partial regression and deals with the closed set of variables
which are linearly related. Such an analysis provides for realistic basis of
allocation of appropriate weightage to various yield components. Since not many
studies have been conducted in case of paprika, the present study was
undertaken to study the association of various yield attributing traits to
develop a reliable set of traits for indirect selection.
MATERIALS AND METHODS
The present investigation was
carried out in 2007-2008 at Vegetable Research Farm of SKUAST-K, Shalimar. The
material consisted of 25 chilli genotypes namely P-2,
P-4, P-7, P-9, P-19, P-20, P-29, P-37, P-59, P-101, P-104, P-201, P-444,
P-1005, PL-7, LCA-436, LCA-443, KTPL-19, ACS-2001-01, ACS-2001-04, Arka Abhir, Bayadagi
Dabbi,
IVPBC-535, IVPBC-553 and Bayadagi Kaddi. Each entry was represented by two replications in a
randomized block design with a spacing of 60 x 40 cm. Recommended package of
practices was adopted to raise a good crop. The data was observed from five
randomly selected competitive plants from each replication for eight
quantitative traits viz,
plant height (cm), plant spread (cm), number of branches/plant, number of
fruits/plant, average fruit weight (g), fruit length (cm), fruit breadth (cm),
and fruit yield/plant (g). The data was statistically analyzed following Aljibouri et al
(1958) for estimation of correlation coefficient, while as path analysis was
done by method of Dewey and Lu (1959).
RESULTS AND DISCUSSION
The
results of correlation coefficients are presented in Table 1, which revealed
that genotypic coefficients were higher in magnitudes relative to corresponding
estimates of phenotypic coefficients, which indicates high heritability of the
traits under study. Moreover, it may be due to masking effect of environment
causing differential genotypic and phenotypic expression of these traits. The
fruits yield/plant exhibited highly significant correlation with no. of fruits /plant,
number of branches/plant and height, indicating the usefulness of these traits
for improving upon fruit yield in chilli. Similar
results have been reported in chillies by Palsudesai et al.
(2006), Hosamani and Shivkumar
(2008) and Ganeshreddy et al (2008), who have observed significant correlation of various
yield attributing traits with fruit yield. The present study also revealed
significant interrelationship among various yield components. Since the
component traits do not define the limit of yield by their direct effects only
but also indirect effects due to interrelationship between them.
|
Table 1. Genotypic (above
diagonal) and phenotypic (below diagonal) correlation coefficients for eight
quantitative traits in chilli (Capsicum annuum L.). |
||||||||
|
Trait |
Plant height (cm) |
Plant spread (cm) |
Number of branches/ plant |
Number of fruits/plant |
Average fruit weight (g) |
Fruit length (cm) |
Fruit breadth (cm) |
Fruit yield/ plant (g) |
|
Plant height |
1.000 |
0.093 |
0.311** |
0.379** |
-0.191 |
0.201 |
-0.108 |
0.286** |
|
Plant spread |
0.087 |
1.000 |
-0.277* |
-0.130 |
0.004 |
-0.088 |
-0.098 |
-0.152 |
|
Number
of branches/plant |
0.272* |
-0.263 |
1.000 |
0.478** |
0.117 |
-0.083 |
-0.137 |
0.449** |
|
Number
of fruits/plant |
0.368** |
-0.089 |
0.468** |
1.000 |
-0.076 |
-0.348** |
-0.061 |
0.557** |
|
Average
fruit weight |
-0.159 |
0.002 |
0.109 |
-0.070 |
1.000 |
0.207 |
0.296** |
0.219 |
|
Fruit length |
0.189 |
-0.073 |
-0.076 |
-0.344** |
0.199 |
1.000 |
-0.133 |
-0.143 |
|
Fruit breadth |
-0.093 |
-0.089 |
-0.133 |
-0.049 |
0.293** |
-0.122 |
1.000 |
0.233 |
|
Fruit yield/ plant |
0.468** |
-0.138 |
0.446** |
0.551** |
0.214 |
-0.135 |
0.214 |
1.000 |
|
** Significant (p ≤
0.01) and * Significant (p ≤ 0.05) |
||||||||
Path
coefficient analysis is a method of investigating such cause and effect
relationships through partitioning correlation into direct and indirect
effects. The perusal of path analysis (Table 2) revealed that the highest direct
effect on fruit yield/plant was exerted by average fruit weight followed by
number of fruits/plant, number of branches/plant and plant spread, while as the
highest indirect effect on fruit yield /plant was exerted by number of
branches/plant through number of fruits/plant, fruit length and fruit breadth
through average fruit weight and plant height through number of fruits/plant.
These traits can be used to develop an optimally reliable selection index for
realizing improvements in fruit yield in chilli. Thus
an ideal plant type should have higher values for these traits. Similar results
have been reported by Khader and Jose (2002) and Ganeshreddy et al
(2008).
|
Table 2. Direct (diagonal)
and indirect effects for eight
quantitative traits in chilli (Capsicum annuum L.). |
||||||||
|
Trait |
Plant height (cm) |
Plant spread (cm) |
Number of branches/ plant |
Number of fruits/plant |
Average fruit weight (g) |
Fruit length (cm) |
Fruit breadth (cm) |
Effect on fruit yield/ plant (g) |
|
Plant height |
0.109 |
0.014 |
0.036 |
0.337 |
0.079 |
-0.049 |
-0.039 |
0.486 |
|
Plant spread |
-0.114 |
0.227 |
-0.062 |
-0.008 |
-0.134 |
-0.040 |
0.026 |
-0.152 |
|
Number
of branches/plant |
0.009 |
-0.121 |
-0.263 |
0.512 |
0.047 |
0.073 |
0.192 |
0.449 |
|
Number
of fruits/ plant |
0.061 |
-0.139 |
-0.201 |
0.483 |
0.002 |
0.317 |
0.014 |
0.557 |
|
Average
fruit weight |
-0.007 |
0.023 |
-0.048 |
-0.167 |
0.538 |
-0.139 |
0.018 |
0.219 |
|
Fruit length |
-0.103 |
0.032 |
-0.193 |
-0.214 |
0.446 |
-0.079 |
-0.112 |
-0.143 |
|
Fruit breadth |
-0.068 |
0.133 |
-0.241 |
0.036 |
0.379 |
-0.044 |
-0.195 |
0.233 |
The
conventional path analysis, as carried out in present investigation, suffers from
non-independence of predictor variables leading to high multicolinearity.
Thus the multiple regression based path analysis can be improved by stepwise
removal of no-significant predictor variables in a sequential manner as
proposed by Samonte et al (1998). Moreover, the number pf
predictor variables in such studies need to be enhance to decrease the residual
effects in such analyses.
LITERATURE CITED
Al-Jibouri, H. A.; P. A. Miller and H. F.
Robinson 1958. Genotypic and environmental variances and covariances
in an upland cotton cross of interspecific origin. Agron. J.
50: 633-637.
Anonymous. 2006. Department of Agriculture, J&K Government. India.
Dewey, D. and K. Lu. 1959. A correlation and path analysis for components
of crested wheat grass seed production. Agron J. 51:
515-518.
Ganeshreddy, M.; H. Kumar and P. Salimath. 2008. Correlation and path analysis in chilli. Karnatka J. Agric. Sci. 21: 259-261.
Hosamani, R. M. and Shivkumar. 2008. Correlation and path analysis in chilli. Ind. J. Hort. 65: 349-352.
Khader, K. and L. Jose. 2002.
Correlation and path coefficient analysis in chilli.
Capsicum Eggplant Newsletter. 21: 56-59.
Pasudesai, M.; V. Bendale,
S. Bhave, S. Sawant and S.
Desai, S. 2006. Association analysis for fruit yield and its components in chilli. Crop Res. 31: 291-294.
Prasath, D and V. Ponnuswami.
2008. Heterosis and combining ability for
morphological, yield and quality traits in paprika type chillies.
Ind. J. Hort. 63: 441-445.
Samonte, S.; L. Wilson and A.
McClung. 1998. Path analysis for yield and yield traits in fifteen diverse
genotypes of rice. Crop Sci. 38: 1130-1136.
Wright, S. 1921.
Correlation and causation. J. Agric. Res. 20: 557-587.
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