view ofshodhganga.inflibnet.ac.in/bitstream/10603/1008/9/09...leaf size, number of leaves per plant...
TRANSCRIPT
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VIEW OF
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2. REVIEW OF LITERATURE
An effective breeding programme aimed at improving the yielding ability of a
crop species requires information on the nature and magnitude of variability. The
phenotypic variation exhibited by the plant is the combination of both genotypic as
well as environmental components. The genotypic variability is important. The
extent to which the variability of quantitative characters is transferable to the
progeny is referred to as heritability. The heritability and genetic variability are the
pre-requisites for any selection programme.
2.1. Variability, heritability and genetic advance of quantitative traits:
Estimation of components of variation was studied by Das and
Krishnaswamy (1969) in 256 mulberry strains and they found that the heritability
and genetic coefficient of variation for leaf yield were higher than the
corresponding estimates for height of the plant and number of branches per plant.
Variability of leaf area and weight was studied by Das and Prasad (1974) in
tetraploid and triploid mulberry genotypes, which indicated that seasonal influence
on these genotypes was significant. Genetic variability of 161 accessions
comprising 60 germplasm strains and 101 elite F l plants of desired parents have
been studied by Dandin et a/. (1983) for 10 yield components. For all the
characters, range of variability and average variation were studied. Based on the
variability, three genotypes were recommended for selection of six characters.
Genetic variation pattern of six metric traits viz., length of primary branches per
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plant, number of nodes per meter length of a branch, length of secondary branches
per plant, number of nodes per meter length of a secondary branch, single leaf
area and leaf yield per plant were studied by Bindroo et a/. (1990). They
suggested that number of primary branches and leaf area were important traits for
leaf yield improvement.
Twenty mulberry clones were studied for root growth parameters by Bhat
and Hittalmani (1992) which revealed significant differences for shoot length, root
length, number of roots per plant and shoot to root ratio by length and dry weight.
The genetic parameters estimated viz., phenotypic and genotypic co-efficient of
variability, heritability (broad sense) and genetic advance as percent over mean,
indicated that shoot to root ratios by length and dry weight, number of roots per
plant and volume of roots per plant are the best characters for selecting mulberry
genotypes for further improvement. Prakas et a/. (1992) studied variability in some
crosses of mulberry and the variability was found to be high in number of
branches, total length of branches, leaf area and leaf yield. Coefficient of variation
both at phenotypic (PCV) and genotypic (GCV) levels was studied by Raju et a/.
(1992), which revealed that the PCV of 11 quantitative traits were higher than
corresponding GCV. Further, high variability was reported for single leaf area, total
length of branches per plant, number of leaves per plant, plant height and leaf yield
per plant. Variability was studied in 50 mulberry genotypes that included exotic
and indigenous genotypes maintained at Central Sericultural Research and
Training Institute, Mysore by Susheelamma et a/. (1997) and found wide variability
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among the collections. Maximum exotic genotypes failed to show better
phenotypic performance under Indian environment.
Phenotypic, genotypic and environmental variability of quantitative
characters were studied by Bari et a/. (q988b) in 60 open pollinated and 4 parental
lines of mulberry. Significant interaction between genotypes and season was
observed. Except for total length of primary branches per plant and inter-nodal
distance, high heritability values with high genetic advance were observed for all
the characters, indicating that the variations were genetically controlled and that
selection for these characters would be effective for mulberry improvement.
Masilamani and Kamble (1998) recommended certain characters including number
of nodes per meter length of a branch, weight of 100 leaves, single leaf area and
leaf yield per plant for selection as these characters had high heritability estimates
and high magnitude of genetic advance in mulberry.
Genetic parameters in relation to leaf yield in mulberry were studied by Patil
et a/. (2000). They indicated that weight of shoot had low heritability with low
genetic advance; leaf area had high heritability with high genetic advance and
number of nodes had high heritability with low genetic advance. Heritability value
of 15 quantitative traits was studied in six genotypes of mulberry by Patil et al.
(2002), which indicated that the quantitative traits like fresh and dry weight of
100 leaves, lamina weight and leaf area were influenced by additive gene effects.
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2.2. Correlation of agronomic traits influencing leaf yield:
Knowledge of association among components of economic importance can
help in improving the efficiency of selection. The final level of yield, quality and
crop performance are often governed by a series of genetic traits and cumulative
effect of multiple factors (Dandin and Kumar, 1989). In order to understand the
multiple factors controlling and limiting the growth and leaf output of mulberry,
genetic traits and their associations need to be identified and measured. In
selecting traits, priority of a trait has to be decided depending upon the need for
which correlations are helpful in determining the component characters of a
complex entity.
Hamada (1959) observed a strong association of mulberry leaf yield with
total length of shoots and leaf weight per unit length of shoot. Das and
Krishnaswamy (1969) investigated the interrelations among three characters like
leaf yield, plant height and average number of branches per plant and reported that
mutual correlation both at phenotypic and genotypic levels was positive and
significant. The study concluded that average plant height and average number of
branches per plant were dependable auxiliary parameters in selection of superior
genotypes. Susheelamma and Jolly (1986) evaluated morpho-physiological
parameters, associated with drought resistance in mulberry. The correlation was
found to be positive and highly significant between leaf thickness and cuticle
thickness; length of the root and dry weight of the root; dry weight of the root and
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moisture retention capacity of the detached leaves. Correlation study made by
Sarkar et a/. (1987) found that number of leaves per meter length of a branch
negatively correlated with the leaf yield and the same was confirmed by
Masilarnani ef a/. (1996~).
Simple correlation of morphological characters in open pollinated progenies
and parental lines of mulberry was studied by Bari et a/. (1988a). They suggested
that leaf yield was a complex character that was directly and/or indirectly affected
by a number of components, therefore, selection criteria need to be carefully
devised.
Susheelamma ef a/. (1988) studied the correiation of agronomic characters
associated with leaf yield, which revealed that number of leaves per meter length
of a shoot and moisture percentage had positive correlation with leaf yield while
length of shoot and weight of 100 leaves showed negative correlation with leaf
yield under stress conditions. Moisture percentage had negative correlation with
the leaf yield under non-stress conditions, which had a positive correlation under
stress conditions. Bari et.al. (1989) reported that number of branches per plant,
leaf size, number of leaves per plant and shoot weight per plant were strongly
associated with leaf yield per plant. While screening mulberry genotypes for
higher rooting, it was observed by Baksh et a/. (1992) that genotypes did not show
any significant correlation between rooting ability and leaf yield. Sarkar et a/.
(1992) reported that leaf yield was significantly correlated with total shoot weight
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per plant, total length of all branches and weight of 100 leaves. Correlations of leaf
weight and total length of branches were found to be statistically significant with
the leaf yield (Rahman et a/. 1994). Studies made by Prasad et a/. (4995) on the
juvenile-mature correlation indicated that total length of all branches after first
pruning had a very high significant correlation with future performance of the adult
plants. Correlations of five agronomic traits with the leaf yield in mulberry were
studied by Masilamani et a/. (1996~) and a conclusion was that the height of the
plant, weight of 100 leaves, number of leaves and number of branches had
positive and highly significant correlations with leaf yield and that they were
suitable criteria for selection. Vijayan et a/. (1997) reported that number of primary
branches per plant, annual aerial biomass and inter nodal length are to be
considered for selecting high yielding mulberry genotypes as they showed a
positive and significant association with the leaf yield.
The correlation coefficients of seven metric traits with leaf yield were studied
by Fotadar (2002) which revealed that leaf yield was positively correlated with total
shoot length, height of the plant, weight of 100 leaves and leaf area. The shoot
length was significantly correlated with height of the plant, inter nodal distance and
leaf area. Height of the plant had positive and significant correlation with weight of
100 leaves, inter nodal distance and leaf area. Both weight of I00 leaves and inter
nodal distance had positive and significant correlations with leaf area.
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Interrelations among yield components and leaf yield in mulberry were
studied by Singhvi (2002); they found positive association between leaf yield and
total length of branches, number of branches, leaf area, which could be exploited
by the breeder while selecting superior genotypes in breeding programmes.
Tikader and Rao (2002) studied simple correlations among 1 1 growth
characters of mulberry and confirmed the observation made by Sarkar et. a/.
(1987), Bari et a/. (1989), Tikadar (1997) and Vijayan et a/. (1997). They
concluded that number of branches had high significant positive correlation with
leaf yield and total shoot length per plant. Longest shoot length was highly
associated with inter-nodal distance, total shoot length, leaf yield per plant and
total biomass weight.
2.3. Path analysis of component characters:
Yield is a complex character and is associated with a number of component
characters which may be interrelated among themselves. Such inter-dependence
of the contributing factors often affects their direct relationship with yield, thereby
making correlation coefficients unreliable as selection indices. Path coefficient
analysis permits the separation of direct effects from the indirect effects through
other related characters by partitioning the correlation coefficients. Path coefficient
analysis helps not only to identify the cause and effect relationship between yield
and component characters but also the relative importance of each, as they affect
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the leaf yield both directly and indirectly. Perusal of literature indicated that in
mulberry, unlike in other agricultural crops, not many studies have been reported.
Susheelamma et a/. (1988) studied the path analysis of important leaf yield
components under stress and non-stress conditions. It was suggested that
number of primary branches, number of leaves per meter length of a shoot and
moisture percentage of leaf are important traits contributing to leaf yield under
stress conditions whereas under non-stress conditions, number of primary
branches, number and weight of leaves per meter length of a shoot were found to
be important traits, having major direct effects on leaf yield in mulberry.
The study by Sarkar ef a/. (1992) on indirect selection indicated that total
length of all branches, total weight of all branches and 100 leaf weight could be
considered in the selection process to improve the leaf productivity of mulberry.
Path coefficient analysis studies by Rahman et a/. (1994) revealed that total weight
and length of all branches and 100 leaf weight had high positive direct effect on
leaf yield. Studies made by Masilamani et a/. (1996c, 1998, 2000b) indicated that
height of the plant, number of branches per plant and weight of 100 leaves are
more important characters, which had maximum direct effect on leaf yield. Unlike
an earlier study by Susheelamma et at, (1988), characters like number and weight
of leaves exerted maximum indirect effects through other component traits on leaf
yield and a recommendation was to use indirect selection. Path coefficient studies
have been carried out Singhvi et at. (1998, 2001 b) which furnished a realistic basis
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for association of weightage to each contributing character for deciding suitable
selection criteria. Length of longest shoot, stem yield, number of branches, and
leaf area had higher positive direct effect on leaf yield. The path coefficient
analysis of seven metric traits on leaf yield was done by Fotadar (2002), who
concluded that total shoot length was the best criterion for assessing the leaf yield
potentiality of a genotype. The other characters viz., height of the plant, weight of
100 leaves, inter-nodal distance and leaf area also exhibited positive and high
indirect effects through total shoot length and hence, can be given more priority.
2.4 Genotype x Environment interactions (g x e) and stability:
Plant breeders aim at selecting cultivars that perform well in a wide range of
diverse environments. To identify such well buffered cultivars, detailed study about
the phenotypic response of cultivars to a change in environments is essential. This
response is mainly due to genotype x environment (g x e) interactions.
A brief review of literature pertaining to different objectives of research are
presented under the following headings:
i. g x e interactions.
ii. Measurement of stability
... 111. g x e interactions in other crops.
iv. g x e interactions in mulberry.
v. GGE biplot technique
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i. g x e interactions:
The studies on g x e interactions have been made in a number of field
crops. Comstock and Moll (1963) made a critical study on g x e interactions and
suggested methods of analysis and ways to reduce g x e effect due to macro
environmental factors such as soil type, rainfall and temperature. Frey (1964)
observed the interplay of genetic and non-genetic effects on the phenotypic
expression and stated that it could be indicated by the failure of a genotype to give
the same phenotypic response in different environments. The occurrence of g x e
interaction poses a major problem for complete understanding of the genetic
control of variability (Breese, 1969) and the differential response of genotypes to
different environments (Saini et a/. 1974). Subsequently plant breeders paid
attention to the study of g x e interactions in the development of improved varieties.
It was observed in parental lines, single or double cross hybrids, top crosses and si
lines (Eberhart and Russell, 1966; Satish Rao, 1989). The "si lines" refers to the
specific combining ability effects of a line when crossed as a parent to the single
cross hybrid (Singh and Narayanan, 1993).
ii. Measurement of stability:
Different authors have variously defined stability. A stable variety is one that is
able to produce a high mean over a wide range of climatic conditions (Finlay &
Wilkinson, 1963). The conventional analysis of genotype-environment interaction
cannot detect the theoretically ideal genotype, which has been defined as the one
with relatively low sensitivity in poor environment and high sensitivity in favourable
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environments. No inference can be derived from such an analysis to measure the
response of individual genotypes in terms of their stability under different
environments.
Lewis (1954) suggested a simple measure of phenotypic stability and
named it stability factor, which is expressed as:
Mean yield in a high yielding environment Stability factor (S-F) = -----------------------------------------------------
Mean yield in a low yielding environment
A value of S.F = 1 indicates the maximum phenotypic stability. The greater
the deviation from unity, lesser is the stability of the phenotype.
The stability models proposed by various workers represent three different
concepts of stability (Lin ef a/. 1986):
Type-I: A genotype is considered stable if its among-environment variance is
small. Francis and Kannenberg (1978) used the conventional coefficient of
variation (CV%) of each genotype as a stability measure. This type of stability did
not give any information on yield parameters and parallel to the concept of
homeostasis (Becker, 1981). However, a breeder would like to find cultivars not
only with good stability but also with high yield. Type-1 stability is often associated
with a relatively poor response and low yield in environments that are high yielding
for other cultivars.
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Type 2: A genotype is considered to be stable if its response to environments is
parallel to the mean response of all genotypes in the trial. Accordingly four models
have been developed:
i ) Model-1: Plaisted and Peterson (1959) conducted a combined analysis of
variance over all locations for each pair of varieties and estimated 2 g l for each
pair and each variety. The stable variety was the one with the smallest mean
value. This process is more complicated, involving the increase in the number
of genotypes requiring g (gl) analysis.
ii) Model-2: Wricke (1962) developed another stability parameter and named it
the Ecovalence (Wi) of genotype (g) given under varying environments (n).
The ecovalence (Wi) is the contribution of each genotype to total genotype-by-
environment interaction sum of squares. Shukla's (1972) stability variance (02i)
is a coded value of ecovalence. Their values have a rank correlation of 1.00
always (Kang et al. 1987).
iii) Model-3: Finlay and Wilkinson's (1963) regression coefficient (bi). The
observed values are regressed on environmental indices, defined as the
difference between the marginal mean of the environments and the overall
mean. The regression coefficient for each genotype is taken as its stability
parameter.
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Type-2 stability is a relative measure depending on the genotypes included in
the test so its scope of inference is confined to the test set and should not be
generalized.
Type-3: Stability model proposed by Perkins and Jinks' (1968) is similar to Finlay
and Wilkinson's (1963) regression coefficient except that the observed values are
adjusted for location effects before the regression. The model proposed by
Eberhart and Russell (1966) is a relatively new concept and simple. Breese (1969)
and Luthra et a/. (1974) strongly advocated its use for the reasons that the
variability of any genotype with respect to environment can be subdivided into a
predictable part corresponding to regression and an un-predictable part
corresponding to deviation Mean Square. These reasons were appealing and
received a wide acceptance as shown by number of publications in other
agricultural crops.
iii. g x e interaction in other crops:
The g x e interaction is known to exist in different agronomic traits in
sugarcane (Nagarajan, 1983; Kang and Miller, 1984; Satish Rao, 1989), sorghum
(Yue et al. 1990; Khanure, 1999), barley (Ceccarelli, 1994), pigeon pea (Sunil
Holkar et a/. 1991), cotton (Rajarathinam and Subbaraman, 1997), potato (Tai,
1971), rice (Manual ef a/. 1997), wheat (Yue et at. 1990) and so on.
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iv. g x e interaction in mulberry:
Leaf yield potential and quality of mulberry leaves are greatly influenced by
the genotypes (Susheelamma et a/. 1992b; Bongale and Chaluvachari, 1993).
Studies on the nature and extent of interaction of genotypes with different
environmental conditions in mulberry are rather few.
Sarkar et a/. (1986) studied the response of 20 mulberry genotypes under
varying environments in West Bengal using the procedure suggested by Finlay and
Wilkinson (1963). Accordingly, the cultivars were ranked and recommended for
poor and favourable environments.
Multilocation trial conducted with 15 mulberry genotypes in 8 locations
covering 5 states ir, North India by Prasad (1989) revealed that triploid genotypes
viz., TR-4 and TRIO had higher leaf yield than the rest of the genotypes. Also
triploids were found to have many superior traits like adaptability and higher leaf
yield. Leaf yield performance of 6 open pollinated hybrids and 2 improved
cultivars were studied under 4 environments and mulberry genotypes suitable for
different environments in Bangladesh were recommended (Bari et al. 1990).
Phenotypic stability of mulberry was measured for leaf yield (both fresh and
dry weight), number of primary branches and its weight using Eberhart and Russell
(1966) model and their variation coefficients between the seasons using Francis
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and Kannenberg (1978) model and genotypes were recommended for different
environments in Brazil (Almeida, etal. 1991).
Ravi et a/. (1992a & 1992b) studied A2 mulberry genotypes including 9
crosses to evaluate their relative stability response to the environmental
fluctuations and found that linear and non-linear component of g x e interactions
were found to be important for leaf yield and its attributing parameters.
Susheelarnma et a/. (1992a) evaluated 11 mulberry cultivars in 4 seasons at 3
locations using a stability model of Eberhart and Russell (1966) and recommended
a drought tolerant genotype (DTS-14) suitable for all the 3 locations studied.
Prakash et a/. (1994) suggested that the variable population should be tested in
both poor and rich environments depending on the objectives of the experiment.
The testing on rich environment will be for potentiality and the selection under poor
environment will be for adaptability.
Masilamani et al. (1996b & 2000a) studied the stability of leaf yield in 12
mulberry genotypes using Eberhart and Russell (1966) model and confirmed the
observation made by Ravi et a/. (1992a & 1992b), identifying genotypes suitable
for poor or unfavourable environment, rich or favourable environment and stable
ones recommended for all environments. Das et a/. (2003) studied the adaptation
of mulberry genotypes and recommended a region specific genotype (C-1730) for
red laterite soils of West Bengal.
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v. GGE biplot technique:
Studies have been made by different workers across the world to
demonstrate the application of the recently developed GGE biplot methodology in
visualizing agronomic research data. The concept of biplot was first proposed by
Gabriel (1971). Later, Yan (1999) and Yan et al. (2000) proposed a GGE biplot
that allows visual examination of the GE interaction pattern of multiple environment
trial (MET) data. The GGE biplot emphasizes two concepts. First, although the
measured yield is the combined effect of genotype (G), environment (E), and
genotype-by-environment interaction (GE), only G and GE are relevant, and must
be considered simultaneously in cultivar evaluation, and hence the term "GGE"
(Yan and Kang, 2003). Second, the biplot technique developed by Gabriel (1971)
was employed to approximate and display the GGE of a MET, hence the term
"GGE biplot".
The GGE biplot was constructed by the first two principal components (PC1
and PC2, also referred to as primary and secondary effects, respectively) derived
from subjecting environment-centered yield data, i.e., the yield variation due to
GGE, to singular value decomposition (SVD) (Yan, 1999; Yan et al., 2000). This
GGE biplot was shown to effectively identify the GE interaction pattern of the data.
It clearly shows which cultivar won in which environments, and thus facilitates
mega-environment identification.
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Multiple Environment Trials (METs) are conducted annually throughout the
world by various breeding institutions and seed companies. The primary goal is
usually to identify superior cultivars for the target region, besides understanding of
the target region or environments. Yan and Rajcan (2002) studied genotype main
effect plus genotype-by-environment interaction effect (GGE) biplot analysis for
soybean [Glycine max (L.) Merr.]. Yield data for 2800 crop heat unit area of
Ontario for MET during 1994-1999 revealed yearly crossover genotype by site
interactions.
Trethowan et al. (2003) used GGE biplot technique to evaluate
environments and its association for international bread wheat yield, covering a 20
years trial. Rubio et al., (2004) have used GGE biplot analysis and studied the trait
relations of white lupin in Spain. Similarly, heterotic pattern in hybrids involving
cultivar-group of summer squash, Cucurbita pep0 L. was studied by Anido et al.
(2004), using the technique of GGE biplot. Ma et al. (2004) studied hard red spring
wheat (Triticum aestivum L.) in eastern Canada to determine the effect of seasons
on wheat yield by demonstrating the application of GGE biplot.
Perusal of literature indicated that in mulberry, as on today, no study has
been reported using GGE biplot analysis.
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2.5. OBJECTIVES:
The information available on biometrical parameters of mulberry grown in
hill areas is quite incomplete. Hence, the present study was undertaken with the
following objectives:
0:. To study the extent of variability, heritability and genetic advance for
various metric traits, influencing leaf yield in mulberry.
03 To determine the association of characters between leaf yield and its
contributing attributes in mulberry.
*:* To determine the direct and indirect effects of different leaf yield
components in mulberry, grown in the hill areas.
*:* To study the genotype x environment interactions and stability
performances of elite mulberry genotypes for leaf yield improvement
in the hill areas.