VIEW OF

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

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

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.

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

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

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.

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

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

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

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

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.

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.

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.

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

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.

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.

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.

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.