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Volume 30, No. (4), 2020 (August)
(Impact Factor 0.529; JCR 2018) |
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COMBINING ABILITY EFFECTS AND INHERITANCE OF MATURITY AND
YIELD ASSOCIATED TRAITS IN F2 POPULATIONS OF WHEAT
K.
Din1, N.U. Khan1,*, S. Gul1, S.U. Khan2,
I. Tahir1, Z.
Bibi3, S. Ali4, S.A. Khan4, N. Ali4,
I.A. Khalil5 and O.
Mumtaz1
1Department of
Plant Breeding and Genetics, The University of Agriculture, Peshawar, Pakistan
2Institute of Biotechnology and Genetic
Engineering, The University of Agriculture, Peshawar, Pakistan
3Department of Soil Science, Faculty of
Agriculture, Gomal University, Dera Ismail Khan, Pakistan
4Department of Plant Breeding and Genetics,
The University of Haripur, Haripur, Pakistan
5Cereal Crops Research Institute (CCRI),
Pirsabak – Nowshera, Pakistan
*Corresponding author email:
nukmarwat@yahoo.com, nukmarwat@aup.edu.pk
ABSTRACT
In existing era of
molecular breeding, conventional breeding has sustainable foundation, and
molecular marker applications are certified through classical breeding. The
present study was carried out with the objectives to determine the genetic
variability, combining ability effects among populations, and gene action for
maturity and yield traits in wheat. Six wheat lines i.e., IBWSN-177, IBWSN-52,
IBWSN131, SRN-09111, PR-107 and NR-21, and three testers i.e., Pakhtunkhwa-15,
Pirsabak-15 and Shahkar-13 were crossed during 2015-16 in a line by tester
matting design. After advancing the generation during 2016-17, the parental
lines and testers, and their eighteen F2 populations were grown
during 2017-18 in randomized complete block design with three replications. Significant
(p≤0.01) differences were observed among genotypes for all the traits. Line
by tester F2populations revealed significant (p≤0.01)
variations for all the traits. However, testers were significant (p≤0.01)
for days to maturity, spikelets per spike and 1000-grain weight. Parental lines
IBWSN-131 and IBWSN-52,testers Pakhtunkhwa-15 and Shahkar-13, and F2 populations IBWSN-131 × Pakhtunkhwa-15, SRN-09111 × Pakhtunkhwa-15 and PR-107 ×
Shahkar-13revealed significant (p≤0.01) GCA and SCA effects and acknowledged
as best general and specific combiners for various traits. High × high, high ×
low and low × high general combiners were involved in F2 populations
with promising SCA and best mean performance. In proportional contribution,F2 populations has maximum share to total variance for the majority of the traits.
The ratios of GCA to SCA, and average degree of dominance were smaller and
greater than unity, respectively, which revealed that non-additive gene effects
governed all the traits. Due to non-additive gene action, the selection of
promising populations in terms of maturity and yield traits should be delayed
to later segregating generations. The said promising populations could be used
in future breeding program to develop early maturing and high yielding wheat
genotypes.
Key
words: L
× T combining ability; gene action; earliness; yield related traits; F2 populations; Triticum aestivum L.
https://doi.org/10.36899/JAPS.2020.4.0114
Published online April 25, 2020
INTRODUCTION
Triticum
aestivum L.
is a highly self-pollinated and annual cereal crop. It is a member of family
Gramminae and genus Triticaceae, and a hexaploid having chromosomes number of
42. Wheat has perfect hermaphrodite flower and sexually autogamous crop. Its
domestication was started from the fertile areas of Middle East, and later
became one of the most important food crops of the human population of the
entire globe (Sabit et al., 2017). Wheat occupies a larger part of the
cultivated area and has maximum annual production than other crops, and
therefore, it termed as king of the cereals.
Being
major source of food and energy, it occupies a unique position in human life
with a large number of end use products like chapatti, bread, macaroni’s,
biscuits, pasta and is also a good source of animal feed (Afridi et al.,
2018; Sattar et al., 2018). Wheat is an elegant source of nutrients and
energy containing major constituents of the food i.e., vitamins particularly
riboflavin, thiamin, niacin and vitamin E. Wheat is enriched by protein and
carbohydrates and vital minerals such as phosphorus, magnesium, copper, iron
and zinc, and about 36% of the world human population uses wheat as staple food
(Bhanu et al., 2018). Gluten is made up of proteins that give strength,
structure, and texture to the different forms of the bread. At the time of crop
maturity, wheat grains contain 8 to 20% protein, while gluten proteins
constitute 80 to 85% of total wheat grain protein (Shewry et al., 1995;
Gautam et al., 2013).
Wheat
can tolerate the wide range of environmental conditions, however, dry, clear,
and cool environmental conditions guarantee the better crop production.
Although the crop is well adapted to climate between the latitudes of 30° and
60°N, and 27° and 40°S (Nuttonson, 1955). During 2017-18, the global wheat
production was estimated as 743 million tons (FAO, 2018). Globally top wheat producing countries are China, India, Russia, USA, France,
Canada, Germany and Pakistan. In Pakistan during 2018-19, wheat was cultivated
on an area of 8.740 million hectares, which produced 25.195 million tons of
grains with average production of 2883 kg ha-1 (PBS, 2018-19).Among
the important crops, the wheat and maize crops showed positive growth at the
rate of 0.5% and 6.9%, respectively in Pakistan (Pak. Economic Survey,
2018-19). However, other major crops witnessed with negative growth as
production of cotton, rice and sugarcane declined by 17.5%, 3.3% and 19.4%,
respectively. The increased population demands enhanced production of the major
staple food crops such as wheat and rice. Therefore, it is a dire obligation of
plant breeders to develop the wheat genotypes with earliness, rust resistance,
good yield and quality potential (Sharmin et al., 2015; Ahmed et al.,
2017).
Transgressive
segregation based on classification of the genotypes, have the ability of
transmitting genes of interest in specific genotypic combinations. Biometrical
techniques used for analysis of combining ability and other genetic parameters
are vital and helpful to the plant breeder in picking improved wheat genotypes
for existing environments and production system (Afridi et al., 2017,
2018). Grain yield is an unpredictable trait made up from interaction between
yield components and environmental effects (Hei et al., 2015; Ahmad et
al., 2017). Dependency of grain yield on yield contributing traits need
improvement in yield traits, which would eventually bring variation and improvement
in grain yield and could be used as selection criteria.
In
breeding programs, breeders are mostly interested in desirable genes and gene
complexes and selection of promising populations. Line by tester matting design
and its analysis is one of the vital breeding methods, which was developed by
Kempthorne (1957). It is used to estimate the general (GCA) and specific
combining ability (SCA) effects among parental lines, testers, and their line
bye tester interactions/populations, respectively. Mean performance of a
parental genotype in a series of cross combinations is known as general
combining ability which helps the breeder in selection of promising genotypes
for crossing program based on GCA effects and mean performance (Griffing, 1956;
Singh and Chaudhary, 1985). Performance of one specific parental genotype with
other parent in a specific cross combination is termed as specific combining
ability, which could help in developing of promising hybrids (Majeed et al.,
2011; Istipliler et al., 2015; Mandal and Madhuri, 2016).
Therefore,
the knowledge about GCA and SCA effects among parental genotypes their F1 and F2 populations, and gene action involved in various traits is
direly needed for the selection of potential parents for crossing and
development of cultivars and hybrids in wheat (Kruvadi, 1991; Rabbani et al.,
2009; Tiwari et al., 2017; Sattar et al., 2018). Earlier studies
on combining ability and genetic architecture in wheat by using the line ×
tester mating fashion enunciated that GCA and SCA effects were significant
among parental genotypes and their F1 populations, respectively for
grain yield and its associated traits in bread wheat (Inamullah et al.,
2006; Murugan and Kannan, 2017; Rahul, 2017; Hama-Amin and Towfiq, 2019). Past
findings indicated that variances due to lines, testers and line × tester F1 hybrids were significant for all the traits which were controlled by both
additive and non-additive gene effects in wheat (Abro et al., 2016;
Kumar et al., 2019). Grain yield and its component traits i.e., flag
leaf area, fertile tillers, spike traits, 1000-grain weight and grain yield
were controlled by additive gene action in wheat (Liu and Wei, 2006; Akram et al., 2008, 2009; Verma et al., 2016; Kumar et al., 2018; Sharma et al., 2019). However,
some investigations revealed that grain yield and other agronomic traits i.e.,
days to maturity, plant height and spike traits were managed by non-additive
gene action in wheat (Saeed and Khalil, 2017; Ingle et al., 2018; Farooq et al., 2019; Parveen et al., 2019).
Considering
the above facts, the present study was planned with the aim to determine the
potential parental lines and testers and their F2populations by
estimating their GCA and SCA effects, respectively and to estimate the nature and magnitude of gene action for
maturity and yield-related traits in a line ×
tester mating design in bread wheat.
MATERIALS AND METHODS
Breeding Material
and Procedure: The present investigations were carried out during cropping
seasons 2015-16
and 2017-18 at the Cereal Crops Research
Institute (CCRI), Pirsabak – Nowshera, Pakistan (situated between 34° N latitude and 72° E longitude, at an
altitude of 288 m).The soil of the experimental
site was sandy loam with pH (7.7), organic matter (0.33%), N (0.016%), P2O5 (4.00 ppm) and K2O (85.00 ppm). Six wheat lines i.e.,
IBWSN-177, IBWSN-52, IBWSN131, and SRN-09111 [obtained from International Bread Wheat Screening Nursery (IBWSN),
CIMMYT, Mexico], PR-107 (CCRI, Pirsabak – Nowshera, Pak.) and NR-21
[National Agriculture Research Center (NARC), Islamabad, Pak.] and three
testers i.e., Pakhtunkhwa-15, Pirsabak-15, and Shahkar-13 (CCRI, Pirsabak –
Nowshera, Pak.) were crossed during 2015-16 in a line by tester matting design
(Table 1). After advancing the generation during 2016-17, 18 F2 populations along their parental lines and testers were grown and evaluated
during 2017-18 in a randomized complete block design (RCBD) with three
replications. Each genotype was planted in four rows in each replication with
five meter length, having plants and rows spacing of 10 and 30 cm,
respectively.
Crop Husbandry: Before sowing, the
field was well irrigated to create conditions conducive for seedbed
preparation. The field was ploughed with deep plough then harrowed with
planking each time to make the soil loose, fine, leveled and pulverized. The
fertilizer was applied at the rate of 120:90:60 NPK kg ha-1. All P2O5,
K2O and half N were applied at sowing time and the remaining half N
was applied in two split doses with first and second irrigations. Sowing was
carried out during 2nd week of November. In parental genotypes and
their F2 populations the single seed per hill was planted. Overall,
four irrigations have been given to the crop. The dominant weeds were Avena fatua,
Chenopodium album, Chenopodium murale,
Convolvulus arvensis, Cynodon dactylon, Phalaris minor and Rumex dentatus.
The broad and narrow-leaved weeds were controlled with Buctril Super (750 ml ha-1)
and Puma Super (1250 ml ha-1), respectively. However, the left over
weed plants were removed manually.
Data Collection: Data were recorded
on days to maturity (days), spike length (cm), spikelets per spike (#),
1000-grain weight (g), biological and grain yield per plant (g) using randomly
selected 20 plants in parental genotypes and their F2 populations.
The biological yield of pre-selected plants was measured at maturity and after
proper sun drying; the plants were weighted using electric balance to obtain
the biological yield (g). The randomly selected plants were harvested on single
plant basis and used for the data recording separately after threshing with
single plant thresher. A descriptive sample of 1000 grains was used in each
entry/replication and weighed with an electric balance to record the 1000-grain
weight (g). By weighing the grains of 20 plants in parental cultivars and their
F2 populations in each genotype/replication, and then averaged for
getting grain yield per plant (g).
Biometrical
Analysis: Data
on all the traits were initially subjected to the analysis of variance (ANOVA)
to test the null hypothesis of no differences among various parental lines,
testers and their F2 populations (Steel et al., 1997). The
genotype means for each parameter were further separated and compared by the
least significant difference (LSD0.05) test. Upon significant
differences among the parental genotypes and their F2 populations,
the data were further subjected to line by tester combining ability analysis to
ascertain the variances and effects due to GCA and SCA among parental genotypes
and line by tester F2 populations, respectively, and gene action for
various traits (Kempthorne, 1957; Singh and Chaudhary, 1985).
Combining Ability
Effects: The
estimates of combining ability were computed by using line × tester analysis
(Kempthorne, 1957). The estimates of general combining ability (GCA) of lines
and testers, and specific combining ability (SCA) of the F2 populations
were calculated as under:
RESULTS
Estimation of GCA
Effects
(a)
Lines: gi = |
|
(b)
Testers: gt |
|
Where:
l = Number of lines (female parents)
t = Number of testers (male parents)
r = Number of replications
xi= Total of F2 resulting from crossing of ith lines with all the testers
x.j. = Total of all the
F2 crosses of jth testers with all lines
x… = Grand total of
all the crosses
Estimation of SCA
Effects
sij |
|
Where xij = Total of F2 resulting from crossing ith lines with jth testers
Standard Errors for Combining Ability Effects: Standard errors for combining ability effects were
calculated by the following equations:
S.E.
(GCA
S.E.
(GCA for lines) |
|
S.E.
(GCA for testers) |
|
S.E.
(SCA effects for L × T Interactions) |
|
S.E.
(gli - glj) between lines GCA effects |
|
S.E.
(gti - gtj) between testers GCA effects |
|
S.E.
(sij - skl) between SCA effects of two crosses |
|
Whereas;
S.E. =
Standard error
Me =
Error mean square |
|
The
distribution of populations in relation to GCA and SCA effects was worked out
by taking significant positive combining ability effects as high,
non-significant as average and significant negative as low for all the traits.
For days to maturity, the significant positive combining ability effects were
taken as low, non-significant as average and significant negative as high.
Genetic
Components: Genetic
components were calculated by following the Singh and Chaudhary (1985) as
follows:
Cov.
of half-sib (H.S) of line |
|
Cov.
of half-sib (H.S) of tester |
|
While,
Assuming
no epistasis, variances due to GCA (σ2GCA) and SCA
(σ2SCA) were calculated as follows.
Additive
and dominance genetic variances (σ2A and σ2D)
were calculated by taking inbreeding coefficient (F) equal to one i.e., F = 1
(Singh and Narayanan, 2004).
Gene
Action, and Degree of Dominance: When ratio of σ2GCA/σ2SCAwas
less than unity, were taken as preponderance of non-additive type of gene
action, greater than unity as additive and equal to unity were taken as equal
effects of additive and non-additive type of gene action (Singh and Chaudhary,
1985). Similarly, when ratio of (σ2D/σ2A)1/2 was less than unity, were taken as preponderance of additive gene effects,
greater than unity as non-additive, and equal to unity was taken as equality of
additive and non-additive effects.
Proportional
Contribution of Populations to Total Variance: Contribution of
lines, testers and L × T interactions/F2 populations to the total
variance were calculated in accordance with Singh and Chaudhary (1985).
Contribution
of Lines |
|
Contribution
of Testers |
|
Contribution
of L×T Interactions |
|
Analysis of
Variance: Analysis
of variance revealed significant (p≤0.01) differences among the parental
lines and testers, and their F2 populations for all the studied
parameters (Table 2).Results showed inclusive genetic variation was found in
the said breeding material which allows further assessment for general and
specific combining ability effects and gene action. According to combining
ability analysis of variance, lines revealed non-significant variations for all
the studied traits. Among testers, significant (p≤0.01) differences were
observed for days to maturity, spikelets per spike and 1000-grain weight.
However, line × tester interactions revealed significant
(p≤0.01)differences for all the variables.
Genetic
Variability in Lines, Testers and Line by Tester F2 Populations
Days to Maturity: In parental lines,
the days to maturity varied from 147 (SRN-09111) to 152.3 days (IBWSN-177),
testers ranging from 147.7 (Pirsabak-15) to 149 days (Pakhtunkhwa-15) (Table
3). In F2 populations, days to maturity ranged from 146.3 (NR-21 ×
Shahkar-13, IBWSN-131 × Shahkar-13) to 152.3 days (SRN-09111 × Pirsabak-15).
Overall, minimum and same days to maturity (146.3 days) were observed in two F2 populations i.e., IBWSN-131 × Shahkar-13 and NR-21 × Shahkar-13, followed by
IBWSN-177 × Shahkar-13 and SRN-09111 with same days to maturity (147.0 days).
However, maximum and same days to maturity (152.3 days) were observed in line
IBWSN-177 and F2 population SRN-09111 × Pirsabak-15, followed by F2 population PR-107 × Pakhtunkhwa-15 (152.0 days). The remaining lines, testers
and F2 populations manifested medium days to maturity. Therefore, F2 populations NR-21 × Shahkar-13, IBWSN-131 × Shahkar-13 and IBWSN-177 ×
Shahkar-13 could be used as source material for developing early maturing wheat
genotypes in future wheat breeding programs.
Spike Length: In lines, the
spike length varied from 12.1 (IBWSN-52) to 12.9 cm (IBWSN-131), testers ranged
in between 13.7 (Shahkar-13) and 14.9 cm (Pakhtunkhwa-15) (Table 3). In F2 populations, the spike length ranged from 10.9 (IBWSN-52 × Shahkar-13) to
14.3 cm (IBWSN-131 × Pakhtunkhwa-15). Overall, maximum spike length was
observed in tester Pakhtunkhwa-15 (14.9 cm), followed by F2 population IBWSN-131 × Pakhtunkhwa-15 and tester Pirsabak-15 with same spike
length (14.3 cm). However, minimum spike length was observed in F2 population IBWSN-52 × Shahkar-13(10.9 cm), followed by line SRN-09111 (11.8
cm). All other parental lines, testers and F2 populations revealed
medium values for spike length. Plants with maximum spike length are preferred
in wheat breeding programs because of its significant contribution in grain
yield. Therefore, the F2 population IBWSN-131 × Pakhtunkhwa-15 and
parental testers i.e., Pakhtunkhwa-15 and Pirsabak-15 could be used in future
wheat breeding programs to improve the spike length.
Spikelets per
Spike: In
lines, the spikelets per spike varied from 18.3 (IBWSN-52) to 20.5 (PR-107),
testers ranged from 20.5 (Pakhtunkhwa-15) to 21.5 (Shahkar-13) (Table 3).
However, in F2 populations the spikelets per spike differed from
19.0 (SRN-09111 × Pirsabak-15) to 23.0 (IBWSN-131 × Pakhtunkhwa-15). Overall,
the highest spikelets per spike were observed in F2 population
IBWSN-131 × Pakhtunkhwa-15 (23.0), followed by F2 populations
IBWSN-177 × Shahkar-13 (22.6) and SRN-09111 × Pakhtunkhwa-15 (22.0). However,
the lesser number of spikelets per spike was observed in line IBWSN-52 (18.3),
followed by F2 population SRN-09111 × Pirsabak-15 (19.0). The
remaining lines, testers and F2 populations showed medium number of
spikelets per spike. Genotypes with more spikelets per spike are preferred because
grain yield has significant (p≤0.01) positive correlation with spikelets
per spike. Therefore, F2 populations i.e., IBWSN-131 ×
Pakhtunkhwa-15, IBWSN-177 × Shahkar-13 and SRN-09111 × Pakhtunkhwa-15 could be
used in future breeding program to enhance the spikelets per spike and
eventually the grain yield.
1000-Grain Weight: In
lines, 1000-grain weight ranged from 37.6 (NR-21) to 45.7 g (IBWSN-52), testers
varied from 32.4 (Pirsabak-15) to 43.5 g (Pakhtunkhwa-15) (Table 3). In F2 populations the thousand grain weight varied from 35.5 (IBWSN-177 × Shahkar-13)
to 51.8 g (IBWSN-131 × Pakhtunkhwa-15). Overall, the maximum 1000-grain weight
was observed in F2 population IBWSN-131 × Pakhtunkhwa-15 (51.8 g),
followed by line IBWSN-52 (45.7 g) and F2 population PR-107 ×
Pakhtunkhwa-15 (45.5 g). However, minimum 1000-grain weight was observed in
tester Pirsabak-15 (32.4 g), followed by F2 population IBWSN-177 ×
Shahkar-13 (35.5 g). Other lines, testers and F2 populations showed
the medium values for 1000-grain weight. Genotypes with greater 1000-grain
weight are preferred for selection, to increase the grain yield per unit area,
therefore, the F2 populations i.e., IBWSN-131 × Pakhtunkhwa-15,
PR-107 × Pakhtunkhwa-15 and line IBWSN-52 could be used in future wheat
breeding programs to improve the seed index.
Biological Yield
per Plant: For
lines the biological yield per plant varied from 60.4 (SRN-09111) and 80.5 g
(IBWSN-131) while in testers it ranged from 59.4 (Pirsabak-15) to 73.7 g
(Shahkar-13) (Table 3). In F2 populations the biological yield per
plant ranged in between 61.7 (IBWSN-177 × Shahkar-13) and 88.3 g (PR-107 ×
Shahkar-13). Overall, the highest biological yield per plant was observed in F2 population PR-107 × Shahkar-13 (88.3 g), followed by SRN-09111 ×
Shahkar-13 (86.6 g) and line IBWSN-131 (80.5 g). However, the least biological
yield per plant was recorded in tester Pirsabak-15 (59.4 g) followed by line
SRN-09111 (60.4 g). The remaining genotypes showed medium values for biological
yield per plant. Genotypes with increased biological yield are preferred for
selection with aim to use in future breeding program to develop wheat genotypes
with increased fodder for livestock. Therefore, the F2 populations
PR-107 × Shahkar-13, SRN-09111 × Shahkar-13 and line IBWSN-131 could be used in
future breeding programs to enhance the fodder yield.
Grain Yield per
Plant: Grain
yield per plant is a complex trait which managed by yield contributing traits.
In parental lines, the grain yield per plant ranged from 21.9 (NR-21) to 29.5 g
(IBWSN-52), testers varied in between 19.2 (Pirsabak-15) and 27.6 g
(Pakhtunkhwa-15) (Table 3). In F2 populations, the grain yield per
plant differed from 22.7 (SRN-09111 × Pakhtunkhwa-15) to 30.4 g (IBWSN-131 ×
Pakhtunkhwa-15). Overall, the highest grain yield per plant was observed in F2 population IBWSN-131 × Pakhtunkhwa-15 (30.4 g), followed by F2 populations
IBWSN-131 × Shahkar-13 (30.0 g) and PR107 × Shahkar-13 (29.95 g). However, the
least grain yield per plant was observed in tester Pirsabak-15 (19.2 g),
followed by line NR-21 (21.9 g) and F2 population SRN-09111 ×
Pakhtunkhwa-15 (22.7 g). The remaining lines, testers and F2 populations exhibited medium values for grain yield per plant. Grain yield per
plant directly affects the overall grain yield; therefore, the F2 populations i.e., IBWSN-131 × Pakhtunkhwa-15, IBWSN-131 × Shahkar-13 and PR107
× Shahkar-13 were favored to select and to use in future wheat breeding
programs to enhance the final grain yield per unit area.
Overall,
the lines IBWSN-131 and IBWSN-52 performed better for spike length, 1000-grain
weight, grain yield and biological yield per plant with medium days to
maturity. Among the testers, Pakhtunkhwa-15 followed by Shahkar-13, revealed
best mean performance for spike length, 1000-grain weight, and grain yield per
plant. Among F2 populations, IBWSN-131 × Pakhtunkhwa-15, followed by
NR-21 × Shahkar-13 and PR-107 × Shahkar-13, manifested immense and desirable
mean values for spike length, spikelets per spike, 1000-grain weight, and grain
yield per plant. Results further revealed that F2 populations were
observed with improved performance than lines and testers for all the studies
traits. Therefore, these promising and best performing populations could be
used as gene source in future wheat breeding program to develop genotypes with
early maturity and good yield potential.
Combining Ability
Analysis: Greater
genetic variation in the said breeding material allowed further analysis and
partition of combining ability into its components i.e., general and specific
combining ability effects in lines, testers and line by tester interactions,
respectively (Table 2). According to GCA and SCA effects, the positive values
are found desirable for majority of the traits in crop plants i.e., growth and
yield related traits. However, negative GCA and SCA effects are enviable for
those traits where minimum values are required and pleasing i.e., early
flowering and maturity.
General and
Specific Combining Ability Effects: For days to maturity, in lines the GCA
effects ranged from -1.19 to 1.81. Three lines i.e., IBWSN-131, IBWSN-177 and
NR-121 showed negative and desirable GCA effects while the other three lines
i.e., IBWSN-52, SRN-09111 and PR-107 revealed positive GCA effects (Table 4).
Significant (p≤0.01) negative GCA effects were observed in line IBWSN-131
(-1.19) followed by NR-21 (-0.85), while maximum positive GCA effects were
observed in line SRN-09111 (1.81). In case of testers, the GCA effects ranged
from -1.13 to 1.04 for days to maturity. The tester Shahkar-13 showed
significant (p≤0.01) negative GCA effects while the remaining two testers
i.e., Pirsabak-15 and Pakhtunkhwa-15 manifested positive GCA effects.
Significant (p≤0.01) negative GCA effects were shown by tester Shahkar-13
(-1.13) while the maximum positive GCA effects were exhibited by Pakhtunkhwa-15
(1.04). Overall in parental lines and testers, the highest negative and
desirable GCA effects were recorded in line IBWSN-131 and tester Shahkar-13and
were identified as best general combiners for days to maturity.
For
days to maturity, in F2 populations the SCA effects ranged from
-2.15 to 1.96 (Table 5). Ten out of eighteen F2 populations showed
negative and desirable SCA effects ranging from -2.15 to -0.4 while the other eight
F2 populations revealed positive SCA effects (0.02 to 1.96) for days
to maturity. The significant (p≤0.01)negative SCA effects were observed
in F2 population SRN-09111 × Pakhtunkhwa-15 (-2.15), followed by two
other F2 populations i.e., PR-107 × Pirsabak-15 (-1.76) and NR-21 ×
Pirsabak-15 (-1.22), and identified as best specific combiners. Results further
revealed that low × low general combiners were involved in these F2 populations with promising SCA for the said trait. However, the maximum positive
SCA effects were observed in F2 population PR-107 × Pakhtunkhwa-15
(1.96), followed by two other F2 populations i.e., SRN-09111 ×
Pirsabak-15 (1.80) and IBWSN-177 × Pakhtunkhwa-15 (0.52).
For
spike length, in lines the GCA effects ranged in between -0.83 and 0.59 (Table
4). Three lines (IBWSN-177, IBWSN-131 and PR-107) showed positive and desirable
GCA effects while the other three lines i.e., IBWSN-52, SRN-09111, and NR-21
showed negative GCA effects. Significant (p≤0.01) positive GCA effects were
observed in line IBWSN-131 (0.59) while maximum negative GCA effects were
recorded in line IBWSN-52 (-0.83). In testers, the GCA effect varied from -0.38
to 0.28 for spike length. Two testers i.e., Pakhtunkhwa-15 and Pirsabak-15
showed the positive GCA effects while the tester Shahkar-13revealed negative
GCA effects. Significant (p≤0.01) positive GCA effects governed by tester
Pirsabak-15 (0.28) while the maximum negative GCA effects shown by Shahkar-13
(-0.38). Overall, in parental genotypes the highest and desirable GCA effects
were recorded in line IBWSN-131 (0.59) followed by tester Pirsabak-15 (0.28)
and identified as best general combiners for spike length.
For
spike length, in F2 populations the SCA effects ranged from -0.73 to
0.83 (Table 5). The 50% of the F2 populations revealed positive and
desirable SCA effects ranging from 0.16 to 0.83 while other 50% showed negative
SCA effects (-0.73 to -0.05). Significant (p≤0.01) positive SCA effects
were observed in F2 population IBWSN-131 × Pakhtunkhwa-15 (0.83)
followed by SRN-09111 × Shahkar-13 (0.59), and were considered as best specific
cross combinations. Results further revealed that high× low and low × low
general combiners were engaged in the F2 populations with promising
SCA for spike length. However, the maximum negative SCA effects were found in F2 population PR-107 × Pakhtunkhwa-15 (-0.73), followed by F2 population IBWSN-52 × Shahkar-13 (-0.68).
For
spikelets per spike, the parental lines GCA effects ranged from -0.85 to 1.07
(Table 4).Four lines i.e., IBWSN-177, IBWSN-52, IBWSN-131 and NR-21 showed
positive and desirable GCA effects while the other two lines (SRN-09111,
PR-107) showed negative GCA effects. Significant (p≤0.01) positive GCA
effects were observed in line IBWSN-177 (1.07) while maximum negative GCA
effects were noted in line PR-107 (-0.85). In testers, for spikelets per spike
the GCA effects were ranging in between -0.68 and 0.79. The tester
Pakhtunkhwa-15 showed positive GCA effects while other two testers (Pirsabak-15
and Shahkar-13) showed negative GCA effects. significant (p≤0.01)
positive GCA effects were observed in Pakhtunkhwa-15 (0.79) while maximum
negative GCA effects were observed in Pirsabak-15 (-0.68). Overall, in parental
genotypes the maximum positive and desirable GCA effects were observed in line
IBWSN-177 (1.07) followed by tester Pakhtunkhwa-15 (0.79), and considered as
best general combiners for spikelets per spike.
For
spikelets per spike, in F2 populations the SCA effects ranged from
-1.03 to 1.21 (Table 5). Nine out of eighteen F2 populations showed
revealed and desirable SCA effects ranging from 0.06 to 1.21 while the other nine
F2 populations exhibited negative SCA effects (-1.03 to -0.25) for
spikelets per spike. Significant (p≤0.05) positive SCA effects were
observed in F2 population IBWSN-131 × Pakhtunkhwa-15 (1.21),
followed by SRN-09111 × Pakhtunkhwa-15 (0.97). However, maximum negative SCA
effects were observed in F2 population PR-107 × Pakhtunkhwa-15
(-1.03), followed by IBWSN-131 × Shahkar-13 (-0.96). Spikelets per spike are
positively correlated with grain yield, and therefore, genotypes having
positive combining ability for spikelets per spike are preferred for selection.
Therefore, the F2 populations IBWSN-131 × Pakhtunkhwa-15, SRN-09111
× Pakhtunkhwa-15 and IBWSN-177 × Shahkar-13 were considered as the best
specific combiners for spikelets per spike. Results further revealed that low ×
high and low × low general combiners were involved in the management of best F2 populations with promising SCA.
For
1000-grain weight, the lines GCA effects varied from -3.50 to 1.89 (Table 4).
Four lines i.e., IBWSN-52, IBWSN-131, SRN-09111 and PR-107 enunciated positive
and desirable GCA effects while the other two lines (IBWSN-177 and NR-21)
showed negative GCA effects for 1000-grain weight. Significant (p≤0.01)
positive GCA effects were observed in line PR-107 (1.89), followed by IBWSN-131
(1.76) and SRN-09111 (1.23), while maximum negative GCA effects were recorded
in line NR-21 (-3.50). In testers, the GCA effects ranged from -2.11 to 2.46
for 1000-grain weight. Results further revealed that tester Pakhtunkhwa-15
showed positive GCA effects while the remaining two testers (Pirsabak-15 and
Shahkar-13) showed negative GCA effects. Significant (p≤0.01) positive
GCA effects were observed in tester Pakhtunkhwa-15 (2.46) while maximum
negative GCA effects were exhibited by tester Shahkar-13 (-2.11). Overall, in
parental genotypes, maximum positive GCA effects were found in tester
Pakhtunkhwa-15, followed by lines i.e., PR-107and IBWSN-131, and were
identified as best general combiners.
For
1000-garin weight, in F2 populations the SCA effects ranged from
-4.19 to 5.27 (Table 5). Ten populations showed positive SCA effects ranging
from 0.02 to 5.27 while the remaining eight populations showed negative SCA
effects (-4.19 to -1.09). Significant (p≤0.01) positive SCA effects were
recorded in F2 population IBWSN-131 × Pakhtunkhwa-15 (5.27),
followed by two other F2 populations i.e., IBWSN-177 ×
Pakhtunkhwa-15 (3.19) and IBWSN-52 × Shahkar-13 (2.91). However, maximum negative
SCA effects were found in F2 population IBWSN-131 × Shahkar-13
(-4.19), followed by IBWSN-52 × Pakhtunkhwa-15 (-3.64). The positive combining
ability is more important for 1000-grain weight because it has significant
positive correlation with grain yield and it can be used as selection criteria
in wheat breeding program. Present results revealed that parental tester and
lines i.e., Pakhtunkhwa-15, PR-107 and IBWSN-131 could be used as best general
combiners for 1000-grain weight while F2 populations IBSWN-131 ×
Pakhtunkhwa-15, IBWSN-177 × Pakhtunkhwa-15 and IBWSN-52 × Shahkar-13 could be
used as best specific combiners in future wheat breeding program. Results
further revealed that high × high and low × high general combiners were
involved in the F2 populations with promising SCA.
For
biological yield per plant, the lines GCA effects varied from -5.37 to 8.34
(Table 4). Two lines i.e., SRN-09111 and PR-107 revealed positive GCA effects
while the remaining four lines (IBWSN-177, IBWSN-52, IBWSN-131 and NR-21)
showed negative GCA effects. Significant (p≤0.01) positive GCA effects
were observed in line SRN-09111 (8.34), followed by PR-107 (3.83), while
maximum negative GCA effects were noted in line IBWSN-52 (-5.37). In testers,
the GCA effects varied from -3.29 to 5.45 for biological yield per plant. The
tester Shahkar-13exhibited positive GCA effects while the remaining two testers
(Pakhtunkhwa-15 and Pirsabak-15) showed negative GCA effects. Significant
(p≤0.01) positive GCA effects were observed in tester Shahkar-13 (5.45)
while maximum negative GCA effects were manifested by tester Pirsabak-15
(-3.29). Overall, in parental genotypes, the highest positive and desirable GCA
effects were recorded in line SRN-09111 (8.34), followed by tester Shahkar-13
(5.45), and were considered as best general combiners for the said trait.
For
biological yield per plant, in F2 populations the SCA effects ranged
from -11.45 to 11.49 (Table 5). Half of the F2 populations showed
positive and desirable SCA effects (0.94 to 11.49) while the remaining half
showed negative SCA effects (-11.45 to -0.04). The F2 population
IBSWN-177 × Pakhtunkhwa-15 (11.49) showed significant (p≤0.01) positive SCA
effects, followed by PR-107 × Shahkar-13 (7.55), and were identified as best
specific combiners for biological yield. Results further revealed that low ×
high and low × low general combiners were occupied in the F2 populations
with promising SCA. However, maximum negative SCA effects were observed in F2 population IBWSN-177 × Shahkar-13 (-11.45), followed by PR-107 × Pakhtunkhwa-15
(-4.91).
For
grain yield per plant, the lines GCA effects varied from -1.15 to 2.54 (Table
4). Two lines i.e., IBWSN-52 and IBWSN-131 exhibited positive and desirable GCA
effects while remaining four lines (IBWSN-177, SRN-09111, PR-107 and NR-21)
showed negative GCA effects. Significant (p≤0.01) positive GCA effects
were observed in line IBWSN-131 (2.54) while maximum negative GCA effects were
observed in line SRN-09111 (-1.15). In testers, the GCA effects varied from
-0.62 to 1.19 for grain yield per plant. The tester Shahkar-13 showed the
positive GCA effects while the remaining two testers i.e., Pakhtunkhwa-15 and
Pirsabak-15 presumed with negative GCA effects. Significant (p≤0.01)
positive GCA effects were found in tester Shahkar-13 (1.19) while the maximum
negative GCA effects were observed in tester Pakhtunkhwa-15 (-0.62). Overall,
in parental genotypes, maximum positive and desirable GCA effects were recorded
in line IBWSN-131 and tester Shahkar-13, and were identified as best general
combiners for grain yield.
For
grain yield per plant, among F2 populations the SCA effects ranged
from -3.02 to 2.81 (Table 5). Eight F2 populations were found with
positive and desirable SCA effects ranging from 0.25 to 2.81 while the
remaining ten F2 populations exhibited negative SCA effects (-3.02
to -0.10). Significant (p≤0.01) positive SCA effects were found in F2 population PR-107 × Shahkar-13 (2.81), followed by two other F2 populations i.e., IBWSN-131 × Pakhtunkhwa-15 (2.01) and IBWSN-177 × Pirsabak-15
(1.93), which could be further studied as best specific combiners for improving
the grain yield in wheat. Results further revealed that low × high and low ×
low general combiners were implied in the F2 populations with
promising SCA. Maximum negative SCA effects were observed in F2 population IBWSN-177 × Shahkar-13 (-3.02), followed by SRN-09111 ×
Pakhtunkhwa-15 (-2.03). Grain yield is a complex trait and its variance managed
by different yield attributing traits.
Present
study revealed that in parental genotypes, the line IBWSN-131 proved to be the
best general combiner for days to maturity, spike length 1000-grain weight, and
grain yield per plant. Among testers, the cultivar Shahkar-13 observed to be
the best general combiner for days to maturity, biological yield and grain
yield per plant, while Pakhtunkhwa-15 manifested best GCA effects for spikelets
per spike and 1000-grain weight. Overall, the lines (IBWSN-131 and IBWSN-177),
testers (Shahkar-13 and Pakhtunkhwa-15), and their F2 populations
i.e., IBWSN-131 × Pakhtunkhwa-15, SRN-0911 × Shahkar-13 and IBWSN-177 ×
Pirsabak-15, were found as best general and specific combiners,
respectively
and performed better for maturity and yield traits. High × high, low ×high and
high × low general combiners were involved in the presentation of F2 populations with promising SCA and best mean performance.
Gene
Action, and Degree of Dominance: Overall, the variances due to general
combining ability (σ2GCA) were lower than variances
of specific combining ability (σ2SCA) for all the
studies traits, suggesting the preponderance of non-additive gene effects which
controlled these characters (Table 6). The values of dominance genetic variance
were greater than additive for all traits. These results were also supported by
the ratios of variances of GCA to SCA(σ2GCA/σ2SCA)
which were found smaller than unity while the rations of degree of dominance
(σ2D/σ2A)1/2 were
greater than unity for all the traits. Therefore, it appeared that the
inheritance of all the traits was controlled by non-additive gene action. The
differed ratio of GCA and SCA variances (σ2GCA/σ2SCA)
always based on frequencies of alleles found in parental genotypes. The diverse
parental genotypes had favorable ratio of GCA and SCA variances because of
their high GCA effects.
Proportional
Contribution of Populations to Total Variance: In proportional
contribution to total variance, the L × T interactions/F2 populations have shown maximum share and contribution to total variance for
majority of the traits i.e., spike length (42.72%), spikelets per spike
(34.67%), 1000-grain weight (39.11%), biological yield per plant (40.63%) and
grain yield per plant (47.63%), followed by lines (Table 7). For days to
maturity (35.66%), the share of lines was leading as compared to testers and L
× T interactions. Results further revealed that line × tester interactions and
parental lines played an important role in managing the variation in the
studied traits.
Table 1. Parental
genotypes (lines & testers) and their line by tester F2 populations
used in the studies.
S. No. |
Parental
genotypes |
S.
No. |
F2 Populations |
Lines |
4 |
IBWSN-52 ×
Pakhtunkhwa-15 |
1 |
IBWSN-177 |
5 |
IBWSN-52 ×
Pirsabak-15 |
2 |
IBWSN-52 |
6 |
IBWSN-52 ×
Shahkar-13 |
3 |
IBWSN-131 |
7 |
IBWSN-131 ×
Pakhtunkhwa-15 |
4 |
SRN-09111 |
8 |
IBWSN-131 ×
Pirsabak-15 |
5 |
PR-107 |
9 |
IBWSN-131 ×
Shahkar-13 |
6 |
NR-21 |
10 |
SRN-09111 ×
Pakhtunkhwa-15 |
Testers |
11 |
SRN-09111 ×
Pirsabak-15 |
1 |
Pakhtunkhwa-15 |
12 |
SRN-09111 ×
Shahkar-13 |
2 |
Pirsabak-15 |
13 |
PR-107 ×
Pakhtunkhwa-15 |
3 |
Shahkar-13 |
14 |
PR-107 ×
Pirsabak-15 |
F2 Populations |
15 |
PR-107 ×
Shahkar-13 |
1 |
IBWSN-177 ×
Pakhtunkhwa-15 |
16 |
NR-21 ×
Pakhtunkhwa-15 |
2 |
IBWSN-177 ×
Pirsabak-15 |
17 |
NR-21 × Pirsabak-15 |
3 |
IBWSN-177 ×
Shahkar-13 |
18 |
NR-21 ×
Shahkar-13 |
Table
2. Mean squares for various traits among line by tester populations in wheat.
Source
of variation |
d.f. |
Days to maturity |
Spike length |
Spikelets spike-1 |
1000-grain weight |
Biological yield plant-1 |
Grain yield plant-1 |
Replications |
2 |
0.94 |
0.13 |
2.70 |
1.66 |
79.96 |
4.08 |
Genotypes |
26 |
8.45** |
2.05** |
3.64** |
47.03** |
177.95** |
22.48** |
Parents |
8 |
8.90* |
3.46** |
2.82** |
52.01** |
143.17** |
33.85** |
Parents
vs. Crosses |
1 |
7.14* |
0.77* |
9.48** |
56.13** |
112.18* |
55.78** |
Crosses |
17 |
8.31** |
1.47** |
3.68** |
44.15** |
198.18** |
15.17** |
Lines |
5 |
10.07NS |
2.03NS |
4.24NS |
53.09NS |
237.25NS |
19.29NS |
Testers |
2 |
21.24* |
2.07NS |
9.84* |
95.76* |
407.01NS |
19.28NS |
Lines
vs. Testers |
10 |
4.84** |
1.07** |
2.17** |
29.35** |
136.88** |
12.28** |
Error |
52 |
1.49 |
0.15 |
0.65 |
1.84 |
18.40 |
2.21 |
C.v.% |
- |
0.82 |
3.03 |
3.90 |
3.25 |
6.01 |
5.74 |
|
Table
3. Mean performance of lines, testers and line by tester F2 populations for various traits in wheat.
Lines,
Testers
and
F2 Populations |
Days
to maturity (Days) |
Spike
length (cm) |
Spikelets
spike-1 |
1000-grain
weight (g) |
Biological
yield plant-1 (g) |
Grain
yield plant-1 (g) |
Lines |
|
|
|
|
|
|
IBWSN-177 |
152.3 |
12.4 |
19.9 |
44.4 |
72.5 |
23.4 |
IBWSN-52 |
151.7 |
12.1 |
18.3 |
45.7 |
73.9 |
29.5 |
IBWSN-131 |
148.7 |
12.9 |
19.3 |
43.3 |
80.5 |
26.3 |
SRN-09111 |
147.0 |
11.8 |
19.8 |
38.5 |
60.4 |
23.9 |
PR-107 |
149.3 |
12.3 |
20.5 |
39.5 |
64.9 |
28.2 |
NR-21 |
149.0 |
12.3 |
20.2 |
37.6 |
66.4 |
21.9 |
Testers |
|
|
|
|
|
|
Pakhtunkhwa-15 |
149.0 |
14.9 |
20.5 |
43.5 |
68.7 |
27.6 |
Pirsabak-15 |
147.7 |
14.3 |
21.1 |
32.4 |
59.4 |
19.2 |
Shahkar-13 |
148.7 |
13.7 |
21.5 |
40.0 |
73.7 |
22.7 |
F2 Populations |
|
|
|
|
|
|
IBWSN-177
× Pakhtunkhwa-15 |
150.0 |
13.2 |
22.2 |
45.2 |
77.1 |
25.9 |
IBWSN-177
× Pirsabak-15 |
148.3 |
13.4 |
21.0 |
38.0 |
64.4 |
26.7 |
IBWSN-177
× Shahkar-13 |
147.0 |
12.2 |
22.6 |
35.5 |
61.7 |
23.6 |
IBWSN-52
× Pakhtunkhwa-15 |
149.7 |
12.4 |
21.1 |
42.4 |
63.3 |
26.5 |
IBWSN-52
× Pirsabak-15 |
148.3 |
12.5 |
20.7 |
44.0 |
66.2 |
27.7 |
IBWSN-52
× Shahkar-13 |
148.0 |
10.9 |
21.3 |
44.4 |
68.7 |
28.2 |
IBWSN-131
× Pakhtunkhwa-15 |
148.3 |
14.3 |
23.0 |
51.8 |
63.9 |
30.4 |
IBWSN-131
× Pirsabak-15 |
147.7 |
13.1 |
20.1 |
42.6 |
69.3 |
26.7 |
IBWSN-131
× Shahkar-13 |
146.3 |
12.7 |
19.9 |
37.8 |
77.5 |
30.0 |
SRN-09111
× Pakhtunkhwa-15 |
149.3 |
12.4 |
22.0 |
43.7 |
80.3 |
22.7 |
SRN-09111
× Pirsabak-15 |
152.3 |
12.7 |
19.0 |
44.4 |
72.5 |
25.8 |
SRN-09111
× Shahkar-13 |
149.7 |
12.9 |
19.7 |
42.4 |
86.6 |
27.6 |
PR-107
× Pakhtunkhwa-15 |
152.0 |
12.3 |
19.8 |
45.5 |
68.2 |
24.4 |
PR-107
× Pirsabak-15 |
147.3 |
13.6 |
19.8 |
43.9 |
69.3 |
23.5 |
PR-107
× Shahkar-13 |
147.7 |
12.9 |
20.5 |
43.1 |
88.3 |
29.95 |
NR-21
× Pakhtunkhwa-15 |
148.7 |
12.4 |
21.8 |
39.8 |
62.9 |
25.4 |
NR-21
× Pirsabak-15 |
148.3 |
12.9 |
20.5 |
38.8 |
67.2 |
25.1 |
NR-21
× Shahkar-13 |
146.3 |
12.7 |
20.6 |
37.8 |
78.5 |
26.9 |
LSD0.05 |
2.00 |
0.64 |
1.32 |
2.22 |
7.03 |
2.44 |
Table
4. General combining ability effects among lines and testers for various traits
in wheat.
Parental
genotypes |
Days to maturity |
Spike length |
Spikelets spike-1 |
1000-grain weight |
Biological yield plant-1 |
Grain yield plant-1 |
Lines |
|
|
|
|
|
|
IBWSN-177 |
-0.19 |
0.16 |
1.07** |
-2.71** |
-3.70* |
-1.11* |
IBWSN-52 |
0.04 |
-0.83** |
0.19 |
1.33** |
-5.37** |
0.96 |
IBWSN-131 |
-1.19** |
0.59** |
0.14 |
1.76** |
-1.21 |
2.54** |
SRN-09111 |
1.81** |
-0.08 |
-0.65* |
1.23** |
8.34** |
-1.15* |
PR-107 |
0.37 |
0.21 |
-0.85** |
1.89** |
3.83* |
-0.55 |
NR-21 |
-0.85* |
-0.05 |
0.10 |
-3.50** |
-1.88 |
-0.70 |
S.E. |
0.41 |
0.13 |
0.27 |
0.45 |
1.43 |
0.50 |
CD0.05 |
0.83 |
0.26 |
0.54 |
0.92 |
2.90 |
1.01 |
CD0.01 |
1.11 |
0.35 |
0.73 |
1.23 |
3.90 |
1.35 |
Testers |
|
|
|
|
|
|
Pakhtunkhwa-15 |
1.04** |
0.09 |
0.79** |
2.46** |
-2.16** |
-0.62 |
Pirsabak-15 |
0.09 |
0.28** |
-0.68** |
-0.34 |
-3.29** |
-0.57 |
Shahkar-13 |
-1.13** |
-0.38** |
-0.10 |
-2.11** |
5.45** |
1.19** |
S.E. |
0.29 |
0.09 |
0.19 |
0.32 |
1.01 |
0.35 |
CD0.05 |
0.58 |
0.19 |
0.39 |
0.65 |
2.05 |
0.71 |
CD0.01 |
0.78 |
0.25 |
0.52 |
0.87 |
2.76 |
0.96 |
**, * : Significant at 1% and 5%
level of probability, S.E.: Standard error, C.D.: Critical difference |
Table
5. Specific combining ability effects among line by tester F2 populations
for various traits in wheat.
F2 Populations |
Days to maturity |
Spike length |
Spikelets spike-1 |
1000-grain weight |
Biological yield plant-1 |
Grain yield plant-1 |
IBWSN-177
× Pakhtunkhwa-15 |
0.52 |
0.19 |
-0.52 |
3.19** |
11.49** |
1.09 |
IBWSN-177
× Pirsabak-15 |
-0.20 |
0.16 |
-0.25 |
-1.25 |
-0.04 |
1.93* |
IBWSN-177
× Shahkar-13 |
-0.31 |
-0.35 |
0.77 |
-1.94* |
-11.45** |
-3.02** |
IBWSN-52
× Pakhtunkhwa-15 |
-0.04 |
0.42 |
-0.70 |
-3.64** |
-0.57 |
-0.38 |
IBWSN-52
× Pirsabak-15 |
-0.43 |
0.25 |
0.37 |
0.73 |
3.43 |
0.85 |
IBWSN-52
× Shahkar-13 |
0.46 |
-0.68** |
0.33 |
2.91** |
-2.85 |
-0.47 |
IBWSN-131
× Pakhtunkhwa-15 |
-0.15 |
0.83** |
1.21* |
5.27** |
-4.20 |
2.01* |
IBWSN-131
× Pirsabak-15 |
0.13 |
-0.52 |
-0.25 |
-1.09 |
2.34 |
-1.78* |
IBWSN-131
× Shahkar-13 |
0.02 |
-0.31 |
-0.96* |
-4.19** |
1.86 |
-0.24 |
SRN-09111
× Pakhtunkhwa-15 |
-2.15** |
-0.35 |
0.97* |
-2.23** |
2.65 |
-2.03 |
SRN-09111
× Pirsabak-15 |
1.80* |
-0.24 |
-0.53 |
1.26 |
-4.02 |
1.02 |
SRN-09111
× Shahkar-13 |
0.35 |
0.59* |
-0.44 |
0.97 |
1.37 |
1.01 |
PR-107
× Pakhtunkhwa-15 |
1.96** |
-0.73** |
-1.03* |
-1.11 |
-4.91 |
-0.94 |
PR-107
× Pirsabak-15 |
-1.76* |
0.39 |
0.47 |
0.02 |
-2.64 |
-1.87* |
PR-107
× Shahkar-13 |
-0.20 |
0.34 |
0.56 |
1.08 |
7.55** |
2.81** |
NR-21
× Pakhtunkhwa-15 |
-0.15 |
-0.35 |
0.06 |
-1.49 |
-4.46 |
0.25 |
NR-21
× Pirsabak-15 |
-1.22* |
0.46 |
-0.05 |
0.19 |
0.33 |
0.94 |
NR-21
× Shahkar-13 |
-0.32 |
-0.31 |
0.40 |
-0.25 |
1.16 |
3.53 |
S.E. |
0.57 |
0.70 |
0.22 |
0.46 |
0.78 |
2.48 |
C.D0.05 |
1.15 |
1.43 |
0.46 |
0.94 |
1.59 |
5.03 |
C.D0.01 |
1.55 |
1.92 |
0.61 |
1.27 |
2.13 |
6.75 |
**, *: Significant at 1% and 5%
level of probability, S.E.: Standard error, CD: Critical difference |
Table
6. Genetic components among line by tester populations for various traits in
wheat.
Genetic
Components |
Days to maturity |
Spike length |
Spikelets spike-1 |
1000-grain weight |
Biological yield plant-1 |
Grain yield plant-1 |
σ2GCA |
0.10 |
0.01 |
0.05 |
0.44 |
1.84 |
0.09 |
σ2SCA |
1.12 |
0.30 |
0.50 |
9.17 |
39.50 |
3.35 |
σ2A |
0.21 |
0.02 |
0.09 |
0.89 |
3.67 |
0.17 |
σ2D |
1.12 |
0.30 |
0.50 |
9.17 |
39.50 |
3.35 |
σ2GCA/σ2SCA |
0.09 |
0.04 |
0.08 |
0.04 |
0.04 |
0.02 |
(σ2D/σ2A)1/2 |
2.32 |
3.55 |
2.36 |
3.21 |
3.28 |
4.40 |
σ2GCA:
GCA variance, σ2SCA: SCA variance, σ2A:
Additive genetic variance, σ2D: Dominant genetic
variance, Ratio of GCA and SCA variances: σ2GCA/σ2SCA,
Ratio of additive and dominance genetic variances: (σ2D/σ2A)1/2 |
Table 7.
Proportional contribution of lines, tester and line by tester populations.
Parameters |
Lines (%) |
Testers (%) |
Line × Tester interactions (%) |
Days
to maturity |
35.66 |
30.07 |
34.27 |
Spike
length |
40.66 |
16.62 |
42.72 |
Spikelets
spike-1 |
33.90 |
31.43 |
34.67 |
Biological
yield plant-1 |
35.21 |
24.16 |
40.63 |
1000-grain
weight |
35.37 |
25.52 |
39.11 |
Grain
yield plant-1 |
37.41 |
14.96 |
47.63 |
DISCUSSION
General
and specific combining ability variances were estimated with a view to
interpret the genetic design of the maturity and yield related traits of L x T
F2populations in wheat. The enormity and tendency of combining
ability effects provides the strategy for the utilization of potential parental
lines and testers in hybridization programs. Combining ability illustrates the
breeding value of parental lines and testers to produce F1 and F2 populations in a breeding program and to exercise the selection in terms
of maturity and yield related traits in wheat (Romanus et al., 2008).
Present
results revealed that significant (p≤0.01) differences were observed
among the parental lines, testers and their F2 populations for all
the traits. Breeding material with comprehensive genetic variation allowed
further assessment for general and specific combining ability effects
(Kempthorne, 1957). In combining ability analysis of variance, testers revealed
significant (p≤0.01) differences for majority of the traits. However,
mean squares due to line × tester interactions were significant (p≤0.01)
for all the traits. Earlier studies reported significant differences among the
wheat populations for the concerned traits, predict sufficient genetic
variation which provides open choice for the selection of best individual
genotypes in wheat (Ahmad et al., 2017; Ahmed et al., 2017;
Rahul, 2017; Farooq et al., 2019).
Past
studies of on line × tester combining ability analysis revealed significant
differences among the parental genotypes and their F1 and F2 populations for maturity and yield related traits in wheat (Esmail, 2007; Ingle et al., 2018; Kumar et al., 2018). Ahmad et al. (2017)
estimated the GCA and SCA effects and heritability through L × T combining
ability, and observed significant differences among the parental lines, testers
and line by tester populations for maturity, morphological and yield traits in
wheat. Past studies about line × tester combining ability revealed similar
pattern of significance and inheritance for various traits in wheat populations
(Akram et al., 2008, 2009; Fellahi et al., 2013; Tabassum et
al., 2017). Bibi et al. (2013) findings revealed that parental
lines, testers and their F1 populations showed significant
differences for spike length, spikelets per spike 1000-grain weight and grain
yield per plant in L × T combining ability analysis in wheat.
Combining
ability of parental genotypes (lines and testers) and their line × tester F2 populations were studied through line by tester analysis to estimate the
ability of parental genotypes to combine their favorable genes in F2 populations after hybridization. Two kinds of combining abilities (GCA and SCA)
were studied in the present research. Generally, the GCA effects are manifested
due to additive gene effects while SCA effects are due to dominant or epistatic
gene effects (Griffing, 1956; Kempthorne, 1957). Parental genotypes (lines and
testers) having desirable GCA effects were considered as best parental
genotypes and good general combiners for the said trait in wheat breeding
(Afridi et al., 2017, 2018; Parveen et al., 2019). However, F2 populations having desirable SCA effects were considered as best specific
combiners for the concerned trait.
The
F2 populations i.e., NR-21 × Shahkar-13, IBWSN-131 × Shahkar-13 and
IBWSN-177 × Shahkar-13 were identified as early maturing genotypes on the basis
of mean performance and desirable negative SCA effects. There promising
populations could be used in future wheat breeding programs for the development
of the wheat genotypes with early maturity and good yield potential. In wheat
breeding, early maturity is preferred because the early maturing genotypes
could escape from biotic and abiotic stresses, save the inputs, vacate the soil
early for the following crop and earlier access of the crop produce to the
market to fetch best prices (Faisal et al., 2005; Afridi, 2016). Previous
studies reported that lines, testers and their hybrid populations were found to
have significant negative GCA and SCA effects and took less day to maturity in
wheat (Esmail, 2007; Akram et al., 2008). Past findings also revealed
that wheat genotypes with minimum days to maturity were more preferable from
farmer and end user point of view (Akram et al., 2009; Afridi et al.,
2018).
In
wheat crop, the increased spike length and number of spikelets are preferred
because it contributes significantly to the final grain yield. Therefore, the F2 population IBWSN-131 × Pakhtunkhwa-15, and parental line and tester i.e.,
IBWSN-177 and Pakhtunkhwa-15, respectively involved in the said cross
combination performed better for spike traits with desirable GCA and SCA effects.
These promising populations could be used in future breeding programs to
increase the spike length and spikelets per spike in wheat. Spike length and
spikelets per spike are important components of grain yield and have
significant positive correlation with grain yield; hence, these spike traits
affects the grain yield directly in wheat. Present results revealed that wheat
genotypes with maximum spike length were found more vigorous and produce
greater grain yield, which got support from the past findings in wheat (Farooq et
al., 2019; Parveen et al., 2019). Wheat genotypes revealed
significant differences for spike length and spikelets per spike, and increased
spike length also leads to enhanced grain yield (Yucel et al., 2009;
Cifci and Yagdi, 2010; Kumar et al., 2019). Earlier study of line ×
tester combining ability reported that F1 population 9738 ×
Chakwal-50 manifested the significant and desirable SCA effects for spike
related characters, while 9740 × Chakwal-50 was found as best specific combiner
for the grain yield (Sattar et al., 2018).
Earlier
studies revealed that F1 hybrids with increased spike length and
spikelets were found more productive and also have greater grain yield than
their parental genotypes in wheat (Afridi, 2016; Afridi et al., 2017).
Previous studied also exhibited that lines Faisalabad-85 and Faisalabad-83 and
tester PBW-65 were found as best general combiners for spike length and
spikelets per spike while F2 populations Faisalabad-83 × PBW-65 and
Faisalabad-85 × PBW-502 were considered as best specific combiners for spike
traits in wheat (Akbar et al., 2009).
Seed
index is an important yield component which has direct positive impact on grain
yield and genotypes with maximum 1000-grain weight significantly enhanced the
grain yield (Hei et al., 2015; Mandal and Madhuri, 2016; Murugan and
Kannan, 2017). In present study, the genotypes with greater 1000-grain weight
were preferred to select for increased grain yield, therefore, based on mean
performance and desirable SCA and GCA effects, the F2 populations
i.e., IBWSN-131 × Pakhtunkhwa-15, IBWSN-177 × Pakhtunkhwa-15, IBWSN-52 ×
Shahkar-13, line IBWSN-52 and tester Pakhtunkhwa-15 could be used in future
wheat breeding programs. Past studies reported that wheat genotypes revealed
significant differences for seed index and the grain yield primarily depends on
the 1000-grain weight (Awan et al., 2005; Akbar et al., 2009).
Khan et al. (2007) findings enunciated that some parental genotypes and
specific cross combinations were identified as best general and specific
combiners with significant GCA and SCA effects for 1000-grain weight in wheat.
In other studies, both lines and testers showed positive GCA effects and most
of their F1/F2populations exhibited positive SCA effects
with best mean performance for 1000-grain weight and spike traits in wheat
(Ajmal et al., 2004; Farooq et al., 2006; Saeed and Khalil,
2017).
Wheat
genotypes with greater vegetative growth, more foliage and eventually increased
biological yield will provide more fodder and to help in food security of the
livestock. In present studies, the F2 populations i.e., PR-107 ×
Shahkar-13, IBSWN-177 × Pakhtunkhwa-15, SRN-09111 × Shahkar-13, line IBWSN-131
and tester Shahkar-13 were found as promising populations based on best mean
performance for biological yield with desirable GCA and SCA effects, and which
can be used for development of the genotypes with enhanced biological and
fodder yield in future wheat breeding programs. Previous studies revealed that
F1 and F2 populations with desirable SCA effects revealed
greater performance than parental genotypes for biological yield in wheat (Khan et al., 2007; Khattab et al., 2010). Wheat genotypes revealed
significant differences for biological yield and the genotypes with increased
biological yield were found more desirable from fodder point of view (Kumar and
Anil, 2011). Previous studied revealed that lines and testers were observed
with significant positive GCA effects while their some of the F2 populations were also identified as best specific combiners for biological
yield in wheat (Jain and Sastry, 2012). Similar variation for tillers per plant
was observed among the wheat testers, lines, F1 and F2 populations and their studies further revealed that genotypes with maximum tillers
have greater biological and grain yield (Ahmad et al., 2010;
Abd-El-Mohsen et al., 2012). Past studies about line by tester analyses
revealed significant differences among lines, testers and line × tester
interactions for plant height, tiller per plant, and the populations revealed
greater genetic variability for morphological and yield traits (Nour et al.,
2011; Ahtisham et al., 2014; Tripathi et al., 2015). Past studies
revealed that lines and testers and their L x T populations revealed desirable
GCA and SCA effects and suggested as best general and specific combiners for
biological yield in wheat (Araus and Cairns,
2016; Murugan and Kannan, 2017).
Grain
yield per plant directly affects the overall grain yield; therefore, the F2 populations IBWSN-131 × Pakhtunkhwa-15, IBWSN-131 × Shahkar-13, IBWSN-177 ×
Pirsabak-15 and PR107 × Shahkar-13 are favored to select based on best mean
performance and desirable GCA and SCA effects and to utilize in future wheat
breeding programs. The lines IBWSN-52 and IBWSN-131 and tester Shahkar-13 also
performed well individually and in production of above promising F2 populations. Previous studies reported that grain yield was primarily affected
and managed by several traits i.e., tillers per plant, spike length, spikelets
per spike and 1000-grain weight, and grain yield could be indirectly enhanced
by improving these yield attributing traits in wheat (Ingle et al.,
2018). Significant differences were observed among parental genotypes and their
F1 hybrids, and the F1 populations showed increased
grain yield than parental genotypes in wheat (Cifci and Yagdi, 2010; Jatav et al., 2014). Line by tester analyses revealed that F1 and F2 populations, and their parental lines and testers performed better for yield
and yield contributing traits with desirable GCA and SCA effects (Fellahi et
al., 2013; Istipliler et al., 2015). Past findings revealed that
parental genotypes i.e., Mexicali-75 and Kunduru showed desirable GCA effects
while the population Belfugitu × Alifian exhibited significant positive SCA
effects for grain yield and spike traits in wheat (Gorjanovic and Balalic,
2004). Past study revealed that line BRF1 and tester ZM04 were found as best
general combiners while F1 populations B4N11 × PS08 and BRF1 × ZM04
were identified as best specific combiners to be used in future breeding
program to improve the grain yield in wheat (Saeed and Khalil, 2017).
According to gene action,
the variances due to specific combining ability were found greater than general
combining ability variances for all traits, suggesting the predominance of
non-additive gene effects for maturity and yield traits. The values of
dominance genetic variances were also greater than additive for all traits.
Ratios of variances of general to specific combining ability and degree of
dominance were less and more than unity, respectively which also authenticated
non-additive gene effects. Such type of gene action needs that selection of
superior populations in terms of maturity, morphological and yield related traits
should be delayed to the later segregating generation to improve these traits
in wheat. The ratio of GCA and SCA variances varies depending on the allele
frequencies between parental lines and testers (Reif et al., 2007;
Longin et al., 2013). The parental genotypes selected from different
gene pools had encouraging ratio because of their high GCA effects (Labate et al., 1997). Past findings revealed that variances due general and
specific combining abilities and their ratio reported control of non-additive
gene action for majority of the traits in wheat (Saeed and Khalil, 2017; Farooq et al., 2019). The ratios of GCA to SCA variances indicated that the
inheritance was controlled by non-additive genes for earliness, maturity,
morphological and yield attributing traits in wheat (Akbar et al., 2009;
Ingle et al., 2018; Hama-Amin and Towfiq, 2019). Line by tester
combining ability indicated that additive gene effects were less important than
non-additive gene effects, and non-additive gene effects have greater role in
the inheritance of all the traits in wheat (Nour et al., 2011; Parveen et
al., 2019; Sharma et al.,
2019).
In proportional
contribution of populations to total variance, the L × T interactions/F2 populations have shown maximum share (as compared to lines and testers) for
majority of the traits i.e., spike length, spikelets per spike, 1000-grain
weight, biological yield per plant and grain yield per plant. However, for days
to maturity the contribution of lines was commendable and leading as compare to
testers and L × T interactions. Results revealed that line × tester
interactions and lines brought much variation in the expression of all the
studied traits. In earlier studies on line × tester combining ability, the contribution
of line by tester interaction to total variance was found much greater than
lines and testers individually in wheat (Istipliler et al., 2015). Past
studies also reported that line × tester contribution to total variance was
leading as compare to lines and testers and managed the variances for majority
of the maturity, morphological and yield associated traits in wheat (Akbar et
al., 2009; Fellahi et al., 2013; Sattar et al., 2018).
Conclusion: Parental lines
i.e., IBWSN-52 and IBWSN-131, testers (Shahkar-13 and Pakhtunkhwa-15), and
their F2 populations viz., IBWSN-131 × Pakhtunkhwa-15, SRN-0911 ×
Shahkar-13, and IBWSN-177 × Pirsabak-15 were found as best general and specific
combiners, respectively and performed better for maturity and yield traits.
High × high, low × high and high × low general combiners were involved in the
production of F2 populations with promising SCA and best mean
performance. The ratios of variances of GCA to SCA were smaller than unity
while values of degree of dominance were greater than unity which confirmed
that all the traits were controlled by non-additive gene effects. Non-additive
gene action suggested that selection of populations in terms of maturity and
yield related traits should be delayed to later segregating generations for
further improvement in these traits of wheat. Therefore, these populations
could be used in future breeding programs to develop early maturing and high
yielding wheat genotypes.
Acknowledgements: Authors
acknowledged with thanks the Director, and Wheat Breeding Section, Cereal Crops
Research Institute (CCRI), Pirsabak – Nowshera, Khyber Pakhtunkhwa, Pakistan
for providing the breeding material and land to carry out the present
investigations.
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