Muhammad Babar1*,
Hidayat Ullah2, Khilwat Afridi3,
Haneef Raza4, Salman Ali4,
Mushtaq Ali1, Ghani Subhan5, Junaid Khan6 and Hassan Khan7
1Department of
Agriculture, The University of Swabi-Anbar, Swabi-Khyber Pakhtunkhwa, Pakistan
2Department of
Agriculture, Faculty of Sciences, The University
of Swabi-Anbar, Swabi-Khyber Pakhtunkhwa, Pakistan
3Cereal Crop Research Institute,
Pirsabak-Nowshera, Khyber Pakhtunkhwa, Pakistan
4Cereal Section, Agricultural Research Institute, Tarnab-Peshawar,
Khyber Pakhtunkhwa, Pakistan
5Department of Plant Breeding and
Genetics, The University of Agriculture, Peshawar, Pakistan
6Department of Horticulture, The University of
Swabi-Anbar, Swabi-Khyber Pakhtunkhwa
7Department of Soil and
Environmental Sciences, The University of Agriculture, Peshawar, Pakistan
*For correspondence: babarkhanuos@gmail.com
Received 16 October 2021; Accepted 14 January 2022; Published 15 June
2022
Abstract
Classical breeding has a
long-term foundation in the current era of molecular breeding, and molecular
marker applications are verified by classical breeding. The present research
was carried out on bread wheat to determine the genetic variability, combining
ability effects among F2 populations and gene action for yield
traits. Five lines Shafaq-06, SKD-1, TD-1, Benazir-13 and
Khyber-87 and three testers IBWSN-24, IBWSN-192, IBWSN-205 were initially crossed during spring 20172018 in a line × tester
fashion design. After advancing the generation the parental lines, testers and
their fifteen F2 populations were grown during the wheat season 2019-2020
in randomized complete block design with 3 replications. Significant (P ≤ 0.01) variation was recorded
among genotypes and lines for all the tested characters. Means squares for
lines × testers interactions/F2 populations and testers exhibited
significant differences (P ≤ 0.01)
for most traits except plant height. The studied parental lines Shafaq-06, TD-1, Benazir-13 and Khyber-87, testers IBWSN-192(1718) and IBWSN-205(1718) and F2 populations
Shafaq-06 × IBWSN-24(1718), Shafaq-06 × IBWSN-192(1718), SKD-1 × IBWSN-205(1718),
TD-1 × IBWSN-192(1718), Benazir-13 × IBWSN-192(1718) and Benazir-13 ×
IBWSN-205(1718) revealed significant (P ≤ 0.01) GCA and SCA effects and appeared as best general
and specific combiners for numerous traits. In percent contribution to the
total variance, the lines and L × T F2 populations had a maximum
share for most of the characters. Ratios of GCA to SCA variances and degree of
dominance showed that all the traits were managed by dominant gene action.
Non-additive gene action indicated the selection of desirable genotypes should
be postponed to later segregating generations for further improvement. The said
outstanding populations could be used in the future breeding program to develop
high production wheat varieties for commercial cultivation. İ 2022
Friends Science Publishers
Keywords: Combining ability; Gene action;
Yield productive traits; F2 population; Bread wheat
Introduction
Wheat (Triticum aestivum
L.) is a bisexual, self-pollinated crop with chromosomal numbers of 2n = 6x = 42
that belongs to the Poaceae family. Wheat is a major cereal crop in many
countries throughout the world and it is the most widely consumed cereal food
in Pakistan (Akram et al. 2008). Similarly, it is believed that wheat was primarily originated in the
Southeast regions of the Asian continent. It adds 8.9% to the agricultural
value-added and 1.6 percent to Pakistan's GDP (PBS 2018). Therefore, wheat
provides food to 36% of the global population and contributes 20% of the food
calories required for healthy growth (Khan and Naqvi 2011; Bhanu et al. 2018). Wheat is the most
significant and widely adapted grain crop in Pakistan. Wheat is produced as a
rain-fed crop on more than 52% of the land in Khyber Pakhtunkhwa. Its average
yield is comparably poor due to a lack of acceptable wheat cultivars for the
area's climatic conditions and a proper time of sowing (Ali and Akmal 2016).
Wheat
production on a global level must continue to increase 2% annually until 2030
to meet future demands of the growing population and maintain prosperity growth
(Anonymous 2015). The worldwide wheat cultivated area was 215.47 million
hectares which producing 731.28 million metric tons (USDA 2019). Similarly,
China, India, Russia, the United States, France, Canada, Germany and Pakistan
are indeed the world's top wheat producers, releasing 126, 95, 60, 55, 39, 29,
28 and 26 million metric tons of wheat annually (Food Outlook 2017). Pakistan
is one of the top ten wheat-producing nations on this planet, and the
fourth-largest producer in Asia. Bread wheat was sown on 8.74 million hectares
across the country in 201819, yielding 25.19 million tons with an average
yield of 2883 kg ha-1 (PBS 2018). Wheat crop in Pakistan grew at a
pace of 0.5 percent every year (Pakistan Economic Survey 20182019). Similarly,
in Khyber Pakhtunkhwa (KPK), bread wheat was grown on 0.74 million hectares and yielded 1.36
million tons with a total production of 1860 kg ha-1 (PBS 2017).
Furthermore, wheat was cultivated on 0.38 million hectares in the rainfed area,
yielding 0.57 million tons, while the irrigated range produced 0.79 million
tons in KPK (Agriculture statistics Khyber Pakhtunkhwa, Peshawar 20162017). Similarly, Punjab supplied 76% of
total production, Sindh 16%, KP 5% and Baluchistan 3% (Laghari et al. 2016).
Selection of
best parents and choice of good mating design is the key to success of plant
breeding programs. In any crop, line × tester is used by plant breeders and
geneticists to make new populations and provide a base for additional selection
and develop new potential genotypes with desirable traits (Akbar et al.
2009). Line × tester analysis, also known as the
modified version of the top cross scheme, is an evaluation technique developed
by Kempthorne (1957). In the top cross, only one tester is used, whereas
several testers are used in line × tester mating. The first stage in
determining the efficacy of new lines is to cross them with a common parent
(tester) and compare their hybrid performance (test cross or top cross).
Knowing how combining ability (CA) influences yield and its components can help
you identify genotype variations and the nature and intensity of gene acts
(Fasahat et al. 2016). Previous studies on combining ability and genetic architecture in wheat
using the line × tester mating techniques revealed that parental
cultivars were substantial for specific combining ability (SCA) and general
combining ability (GCA) effects in grain yield and yield association traits in
bread wheat among and with their F2 population, respectively
(Murugan and Kannan 2017; Rahul 2017; Hama-Amin and Towfiq 2019).
Information of GCA and SCA influencing yield and its
components has become increasingly important for plant breeders to select
appropriate parents while, developing high-yielding synthetic and hybrid cultivars
(Aslam et al. 2014). Besides other available
techniques to improve the existing cultivar or develop a new line, Line ×
tester analysis is one of the most dominant tools for estimating the GCA of
parents and selecting right parents and crosses with high SCA (Rashid et al.
2007). Assessing the effects of GCA for yield components is very powerful in
parental selection of self-pollinated crops including wheat (Bhateria et al.
2006). Grain yield, like most attributes, has been reported to be influenced by
non-additive gene effects. (Sulayman and Akguni 2007). On the other hand, Tambe
et al. (2013) found that the number of tillers plant-1 is
controlled by additive gene action.
Given the
preceding, the current investigation was designed to identify possible parental
lines and testers, and their F2 populations by evaluating their GCA
and SCA effects. With all of these philosophies in mind, this study was
conducted for the estimation of genetic parameters with production traits in selected
lines and to identify general and specific combining ability of genotype for
future breeding program and to study the gene action and gene magnitude among
the selected lines and F2 populations.
Materials and Methods
Experimental site and conduct of
experiment
The study entitled "line × tester analysis
for combining ability and identification of gene action in F2 populations of
bread wheat"
was carried out at the Cereal Crops Research Institute (CCRI) Pirsabak,
Nowshera (Pakistan) located at 340 North Latitude, 720
East longitudes and 280 altitudes under normal conditions during
20192020. Five wheats (hereafter referred to as female
lines) i.e., SKD-1,
Khyber-87, Shafaq-06,
Benazir-13, TD-1 and three wheat genotypes (hereafter referred to
as male testers) i.e.,
IBWSN-24(1718), IBWSN-192(1718), IBWSN-205(1718), [acquired from International Bread Wheat
Screening Nursery (IBWSN), CIMMYT, Mexico], were initially
crossed during 20172018 in at CCRI in line × tester fashion. Therefore, during
20192020 wheat season, the five lines, three testers and 15 F2
populations were assessed under normal conditions. The preceding experimental
material, which included overall 23 genotypes (8 parents and 15 F2
populations) was sown in randomized complete block design having three
replications, every single genotype was sown in four rows of two meters length,
with spacing of 30 cm and 15 cm between rows and plants, respectively.
Cultural practices
All agronomic cultural procedures were completed before planting, and
the experimental area was properly irrigated for ideal seedbed conditions.
Therefore, the soil was loose, fine, levelled and crushed each time the
experimental sowing area was ploughed with a deep plough and then harrowed with
planking. The fertilizer was applied at the rate of 120:90:60 NPK kg ha-1.
Sowing was carried out during 3rd week of November by hand sowing
machine, after germination, thinning was done for maintaining plant to plant distance
while, dominant weeds and roughing (to maintain the variety purity) was done
manually without application of insecticide, pesticide during entire crop
growing season. Data was recorded on fourteen parameters by using ten randomly
selected plants in each plot.
Data recorded on
quantitative traits to be
measured
Ten plants were
selected from every single entry and data were noted on the consequent traits
at proper time. In almost all breeding or selection trials, prime
importance is given to the heading of wheat genotypes. This trait is important
for isolating the early, moderate or late genotypes for wide or narrow
adaptation. Days to heading was noted on or after the date of sowing till the
date of spike appearance in every single plot. The third important yield
related trait is the plant height or length of the tiller. Plant height was
measured in centimeters as the distance from the plant root of the soil surface
to the tip of the spike excluding awns, after detecting the physiological
maturity. Plant height is an equally significant for breeders and farmers as it
is an absolute indicator of whether to recommend the taller or dwarf wheat
variety for a specific area. Suppose for the dwarf varieties are good for
regions with heavy wind flow. Similarly, the tall genotypes were good for
preserving the moisture in the soil. Number of leaves and area of leaf is
equally important just like days to maturity, heading and height while dealing
with wheat crop. Flag leaf area is an indirect metric for identifying
photosynthetically active genotypes in a given area. Normally, the genotypes
with broader leaf area are selected and recommended for irrigated regions
whereas, the genotypes with narrow leaf area, assembled vertically are
recommended or selected for drought or low-rainfall regions. The following
formula was used to calculate the flag leaf area, as described by Francis et
al. (1969). Flag leaf area = Leaf length × Leaf width × 0.75.
Number of tillers plant-1 is utilized for
direct selection of bread wheat genotypes. More quantity of tillers plants-1
is directly proportional with the grain yield. Using ten arbitrary plants and
taking an average was count the quantity of productive tillers of every single
genotype. These ten arbitrarily selected plants were counted for numbers of
tillers plant-1 and the sample extent and accurate demonstrative of
the complete plot. Spike length is a direct selection criterion once more. A
long, extended spike with compacted grains or spikelets can hold a large number
of grains. As a result, the space between the bottom of the wheat plant spike
and the tip of the ending spikelet, excluding awns, was determined as the wheat
plant spike length of every single entry utilizing ten spikes and the mean
score value was determined. Grain yield is the end product, and in many
situations, breeders rely on grain yield to choose appropriate and fixed
genotypes due to a lack of funds. At harvest, each experimental entry was
threshed individually and grain yield was calculated in kilograms for each
variety. After that, the data was transformed to kg per hectare. Harvest index
is also regarded as a supplementary yield-contributing factor. The grain
fraction and biomass of bread wheat were determined using the harvest index
ratio. Harvest index was calculated by dividing grain yield by biological yield
for each genotype and then converting it to a percentage using the formula
below:
𝐻𝑎𝑟𝑣𝑒𝑠𝑡 𝑖𝑛𝑑𝑒𝑥 (%) = 𝐺𝑟𝑎𝑖𝑛 𝑦𝑖𝑒𝑙𝑑/𝐵𝑖𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑦𝑖𝑒𝑙𝑑 × 100
Statistical
analysis
The data collected on different agronomic and
yield-related characteristics was statistically evaluated using excel sheet, a
computer program designed for RCBD. To assess genetic progress the means were
separated using the LSD test at a 5% level of probability was estimated from
the mean squares of ANOVA.
Combining ability effects
To filter out significant differences, the data were run via an analysis of variance (Steel et al. 1997). Characters with
considerable variances were more likely to be subjected to Kempthorne (1957)
line × tester examination to approximate general and specific combining
ability.
Genetic components
Genetic components were
calculated, according to the Singh and Chaudhary (1985).
Gene action and degree of
dominance
When the value of general
combining ability variance to specific combining ability variance (σ2GCA/σ2SCA)
was smaller than one, were taken as preponderance of non-additive type of gene
action, and more than one as additive gene action. In addition, equivalent to
one were taken as equal effects of additive and non-additive type of gene
action (Singh and Chaudhary 1985). Likewise, inferior than one (σ2D/σ2A)1/2,
were taken as preponderance of additive gene effects, more than one as
non-additive, and equal to one was taken as equality of additive and
non-additive effects.
Proportional contribution of
populations to total variance
Singh and Chaudhary (1985) looked
at the proportional contribution of maternal lines, paternal testers and their
lines × testers interactions to overall variance in percentage.
Results
Analysis of Variance
The results obtained through
ANOVA stated that the variations observed enough for the successful selections
for suitable material used in this experiment because selection and variation
are the base pillars for improvement in plant breeding. The female
parent (lines) exhibited highly significant variations (P ≤ 0.01) for all the traits. Moreover, the male parent
(Tester) also showed highly significant differences (P ≤ 0.01) for flag leaf area, tillers plant-1,
spike length, grain yield and harvest index. Interactions of lines × Table 1: List of breeding materials of 23 wheat
genotypes were studied for selected morphological and production traits during
the year 20192020 at CCRI, Pirsabak Nowshera
S.
No |
Parental
genotypes |
S.
No |
F2
Populations |
1 |
Shafaq-06 |
4 |
SKD-1
× IBWSN-24(17-18) |
2 |
SKD-1 |
5 |
SKD-1
× IBWSN-192(17-18) |
3 |
TD-1 |
6 |
SKD-1
× IBWSN-205(17-18) |
4 |
Benazir-13 |
7 |
TD-1
× IBWSN-24(17-18) |
5 |
Khyber-87 |
8 |
TD-1
× IBWSN-192(17-18) |
Testers |
|
9 |
TD-1
× IBWSN-205(17-18) |
1 |
IBWSN-24(17-18) |
10 |
Benazir-13
× IBWSN-24(17-18) |
2 |
IBWSN-192(17-18) |
11 |
Benazir-13
× IBWSN-192(17-18) |
3 |
IBWSN-205(17-18 |
12 |
Benazir-13
× IBWSN-205(17-18) |
F2
Populations |
|
13 |
Khyber-87
× IBWSN-24(17-18) |
1 |
Shafaq-06
× IBWSN-24(17-18) |
14 |
Khyber-87
× IBWSN-192(17-18) |
2 |
Shafaq-06
× IBWSN-192(17-18) |
15 |
Khyber-87
× IBWSN-205(17-18) |
3 |
Shafaq-06
× IBWSN-205(17-18) |
|
|
Table 2: Mean squares
for various traits of 8 parents and 15 F2 populations, were
evaluated in lines 5 × 3 testers mating design of bread wheat during the year
20192020 at CCRI, Pirsabak Nowshera
Source of variation |
DF |
DH |
PH |
FLA |
Tp-1 |
SL |
GYLD |
HI |
Replications (r) |
2 |
4.45 |
35.49 |
0.21 |
0.45* |
0.12 |
9553.39 |
2.87 |
Genotypes (g) |
22 |
57.51** |
265.32** |
4.56** |
0.65** |
2.03** |
550399.65** |
10.87** |
Parents (p) |
7 |
12.42* |
97.76** |
4.39** |
0.53** |
0.90* |
428285.38** |
4.16** |
Parents vs. Crosses |
1 |
615.22** |
3873.14** |
26.57** |
2.48** |
4.46** |
3150680.39** |
21.68** |
Crosses (c) |
14 |
40.21** |
91.40** |
3.08** |
0.58** |
2.43NS |
425722.44** |
13.46** |
Lines (l) |
4 |
99.02** |
223.74** |
3.40** |
0.61** |
2.51** |
1132910.49** |
34.86** |
Testers (t) |
2 |
28.16* |
19.36 NS |
6.01** |
0.85** |
5.55** |
325140.43** |
3.87* |
Lines × Testers |
8 |
13.82* |
43.24 NS |
2.18** |
0.50** |
1.60** |
97273.92** |
5.15** |
Error |
44 |
5.16 |
24.30 |
0.53 |
0.14 |
0.27 |
19975.06 |
1.07 |
C.V.% |
|
1.97 |
5.44 |
2.40 |
3.44 |
4.51 |
5.32 |
3.08 |
*: significant at 0.05%; **: significant at 0.01%; NS:
Non-significant at 5 and 1% level of significance
testers accounted highly significant variations (P ≤ 0.01) for traits i.e.,
flag leaf area, tillers plant-1, spike length, grain yield and
harvest index of the characters except plant height while, bearing of heading
on the other hand, showed significant (P ≤
0.05) variations (Table 1 and 2).
Genetic changeability among parental
genotypes and line × tester F2 populations
No. of days to heading
In parental lines, the days to heading ranged between 107.00 days
(Shafaq-06) to 112.33 days (Benazir-13), testers ranged between 111.67 days
(IBWSN-24(1718)) to 113.33 days (IBWSN-192(1718)) while in F2
population ranged between 112.67 days (SKD-1) × (IBWSN-24(1718)) to 124.00
days (Khyber-87) × (IBWSN-192(1718)). Among all the tested wheat genotypes,
the maximum numbers of days to heading (124.00) were detected in following two
F2 populations i.e., (Khyber-87) × (IBWSN-192(1718)) and
(Khyber-87) × (IBWSN-205(1718)). However, in lines, minimum numbers of days to
heading (107.00) was observed in (Shafaq-06), in tester least numbers of days
to heading (IBWSN-192(1718)) (111.67 days). Overall, the F2
population means ranged is high than the parental genotypes (Table 3).
Plant height
In maternal lines, the plant height ranged between 71.67 cm (Shafaq-06)
to 84.67 cm (Benazir-13) and paternal testers ranged between 80.00 cm
(IBWSN-24(1718) to 86.00 cm (IBWSN-205(1718)). In F2 populations,
for plant height ranged between 86.67 cm (Shafaq-06) × (IBWSN-24(1718)) to
106.67 cm (Khyber-87) × (IBWSN-205(1718)). In female lines i.e., short
stature genotype was Shafaq-06 (71.67 cm) and TD-1 (72.67 cm). In F2 generations
minimum plant height was recorded by (Shafqat-06) × (IBWSN-24(1718)) (86.67
cm), followed by (Shafqat-06) × (IBWSN-192(1718)) (89.33 cm) (Table 3).
Flag leaf area
In parental lines, mean value for flag leaf area ranged between 27.35 cm2
(SKD-1) to 31.28 cm2 (Khyber-87), testers ranged between 28.68
cm2 (IBWSN-24(1718)) to 30.65 cm2 (IBWSN-192(1718))
(Table 3). And the F2 populations the average value for flag leaf
area ranged between 28.59 cm2 (Shafaq-06) × (IBWSN-192(1718)) and
32.25 cm2 (Khyber-87) × (IBWSN-24(1718)). However, the highest
average area was observed in F2 populations i.e., (Khyber-87)
× (IBWSN-24(1718)) (32.25 cm2) accompanied by F2
populations (Benazir-13) × (IBWSN-205(1718)) (31.66 cm2). Moreover,
the minimum average area was observed in maternal line (SKD-1) (27.35 cm2)
for flag leaf area (Table 3).
Tillers per plant
In maternal lines, mean values for tillers plant-1 ranged Table 3: Mean
performance of lines, testers, and F2 population for various traits
were evaluated in lines 5 × 3 testers mating design of bread wheat during the
year 20192020 at CCRI, Pirsabak Nowshera
Lines, testers
and F2 populations |
DH |
PH |
PL |
FLA |
Tp-1 |
SL |
GYLD |
HI |
Lines |
|
|
|
|
|
|
|
|
Shafaq-06 |
107.00 |
71.67 |
31.33 |
28.99 |
10.17 |
11.07 |
1972.22 |
32.84 |
SKD-1 |
111.67 |
78.00 |
27.68 |
27.35 |
10.10 |
11.32 |
1972.22 |
34.96 |
TD-1 |
110.00 |
72.67 |
31.63 |
29.08 |
11.40 |
10.91 |
2294.44 |
32.55 |
Benazir-13 |
112.33 |
84.67 |
34.32 |
29.50 |
10.20 |
11.14 |
2283.33 |
32.96 |
Khyber-87 |
112.00 |
84.33 |
34.46 |
31.28 |
10.20 |
10.17 |
2277.78 |
31.87 |
Means |
110.60 |
78.27 |
31.88 |
29.24 |
10.41 |
10.92 |
2160.00 |
33.03 |
Testers |
|
|
|
|
|
|
|
|
IBWSN-24(17-18) |
111.67 |
80.00 |
29.53 |
28.68 |
10.40 |
11.59 |
2244.44 |
34.13 |
IBWSN-192(17-18) |
113.33 |
85.00 |
29.76 |
30.65 |
10.53 |
11.14 |
2802.78 |
32.10 |
IBWSN-205(17-18) |
113.00 |
86.00 |
32.98 |
28.95 |
10.53 |
12.07 |
3047.22 |
31.44 |
Means |
112.67 |
83.67 |
30.76 |
29.43 |
10.49 |
11.60 |
2698.15 |
32.56 |
F2
populations |
|
|
|
|
|
|
|
|
Shafaq-06 ×
IBWSN-24(17-18) |
114.33 |
86.67 |
31.59 |
31.03 |
10.50 |
11.24 |
2430.56 |
30.78 |
Shafaq-06 ×
IBWSN-192(17-18) |
115.67 |
89.33 |
35.06 |
28.59 |
10.03 |
11.73 |
2866.67 |
31.58 |
Shafaq-06 ×
IBWSN-205(17-18) |
118.00 |
94.00 |
37.23 |
30.59 |
10.97 |
11.27 |
2650.00 |
31.77 |
SKD-1 ×
IBWSN-24(17-18) |
112.67 |
97.33 |
39.59 |
28.92 |
10.20 |
11.16 |
2308.33 |
34.42 |
SKD-1 ×
IBWSN-192(17-18) |
117.00 |
100.67 |
37.41 |
29.60 |
10.33 |
10.57 |
2458.33 |
33.47 |
SKD-1 ×
IBWSN-205(17-18) |
117.00 |
92.67 |
38.63 |
31.51 |
11.47 |
13.57 |
2550.00 |
34.61 |
TD-1 ×
IBWSN-24(17-18) |
117.33 |
95.00 |
31.91 |
31.28 |
11.37 |
11.08 |
2280.56 |
36.32 |
TD-1 ×
IBWSN-192(17-18) |
112.67 |
94.67 |
34.47 |
29.84 |
10.77 |
10.34 |
3061.11 |
33.68 |
TD-1 ×
IBWSN-205(17-18) |
114.00 |
92.67 |
33.62 |
30.45 |
11.17 |
11.83 |
2519.44 |
34.43 |
Benazir-13
× IBWSN-24(17-18) |
117.33 |
98.67 |
33.45 |
31.01 |
11.17 |
12.69 |
2975.00 |
31.17 |
Benazir-13
× IBWSN-192(17-18) |
118.33 |
97.00 |
32.15 |
30.40 |
10.87 |
12.31 |
2970.83 |
34.39 |
Benazir-13
× IBWSN-205(17-18) |
122.33 |
90.67 |
36.51 |
31.66 |
10.53 |
12.50 |
3136.11 |
33.82 |
Khyber-87 ×
IBWSN-24(17-18) |
120.00 |
99.33 |
35.46 |
32.25 |
11.33 |
11.31 |
3240.28 |
36.52 |
Khyber-87 ×
IBWSN-192(17-18) |
124.00 |
105.00 |
38.43 |
31.00 |
10.87 |
11.24 |
3305.56 |
35.11 |
Khyber-87 ×
IBWSN-205(17-18) |
124.00 |
106.67 |
38.41 |
31.06 |
11.03 |
12.82 |
3404.17 |
38.40 |
Means |
117.64 |
96.02 |
35.59 |
30.61 |
10.84 |
11.71 |
2810.46 |
34.03 |
Over all
mean |
115.46 |
90.55 |
34.16 |
30.16 |
10.70 |
11.52 |
2654.41 |
33.62 |
LSD0.05 |
3.74 |
8.11 |
2.46 |
1.19 |
0.61 |
0.86 |
232.57 |
1.70 |
between 10.10 (SKD-1) to 11.40 (TD-1), besides paternal testers mean
values ranged 10.40 (IBWSN-24(1718)) to 10.53 (IBWSN-192(1718)). In F2 populations for tillers plant-1
average value ranged between 10.03 (Shafaq-06) ×
(IBWSN-192(1718)) to 11.47 (SKD-1) × (IBWSN-205(1718)). The F2
populations showed more numbers of tillers plant-1 (SKD-1) ×
(IBWSN-205(1718)) followed by (TD-1) × (IBWSN-24(1718)) (11.37). Over all
means, of tillers plant-1 in F2 population is greater
than both lines and tester (Table 3).
Spike length
Minimum (10.17 cm) and maximum values (11.32
cm) for spike length was found in lines Khyber-87 and (SKD-1), paternal testers
average value for spike length ranged between (IBWSN-192(1718)) (11.14 cm) to
(IBWSN-205(1718)) (12.07 cm). In F2 generations the mean value for
spike length starting from 10.34 cm (TD-1) × (IBWSN-192(1718)) to 13.57 cm (SKD-1) × (IBWSN-205(1718)). F2 populations
had the longest average value for spike length i.e., (SKD-1) ×
(IBWSN-205(1718)) (13.57 cm) and (Khyber-87) × (IBWSN-205(1718)) (12.82 cm).
In the future the F2 populations (Khyber-87) × (IBWSN-205(1718))
and (SKD-1) × (IBWSN-205(1718)) will be used to improve spike length in wheat
breeding projects (Table 3).
In maternal lines, the mean
value for grain yield ranged between Shafaq-06 and SKD-1 (1972.22 kg ha-1)
to TD-1 (2294.44 kg ha-1) while, the paternal testers ranged between
2244.44 kg ha-1 (IBWSN-24(1718) to 3047.22 kg ha-1
(IBWSN-205(1718)) (Table 3). In F2 populations, the mean value for
grain yield ranged between 2280.56 kg ha-1 (TD-1) × (IBWSN-24(1718))
to 3404.17 kg ha-1 (Khyber-87) × (IBWSN-205(1718)). In F2
populations, the maximum mean for grains yield were observed in F2
generations i.e., (Khyber-87) × (IBWSN-205(1718)) (3404.17 kg ha-1)
followed by (Khyber-87) × (IBWSN-192(1718)) (3305.56 kg ha-1).
Therefore, the F2 populations, i.e., (Khyber-87) ×
(IBWSN-192(1718)) and (Khyber-87) × (IBWSN-205(1718)) were recommended for
selection and use in next wheat breeding initiatives aimed at increasing grain
yield.
Harvest index
Harvest index of wheat parental genotypes i.e., In lines the
average for harvest index ranged between 31.87% (Khyber-87) to 34.96% (SKD-1)
while, the testers ranged between 31.44% (IBWSN-205(1718)) to 34.13%
(IBWSN-24(1718)). In F2 populations, the average for harvest index
ranged between 30.78% (Shafaq-06) × (IBWSN-24(1718)) and 38.40% (Khyber-87) ×
(IBWSN-205(1718)). F2 populations, the highest average for harvest
index was observed in F2 populations i.e., (Khyber-87) ×
(IBWSN-205(1718)) (38.40%) followed by (Khyber-87) × (IBWSN-24(1718)) (Table
3).
Combining ability analysis
Greater genetic changes in the mating material allows for a more
detailed examination and partitioning of combining ability into its components,
such as general and specific combining ability impacts in lines, testers, and
line by tester interactions (Table 1 and 2). According to GCA and SCA impacts,
Positive values are desirable for most crop plants characteristics, such as
growth and yield yield-related attributes. Negative GCA and SCA impacts, on the
other hand, are desirable for characters where minimum values are essential and
appealing, such as early flowering.
General and specific combining
ability effects
In maternal lines, for days to heading GCA effects ranged between -2.09
to 5.02. As a good general combiner, maternal line (TD-1) (-2.98) had highly
significant (P ≤ 0.01) and
highest negative desired GCA effects, and female line (SKD-1) -2.09 had
significant (P ≤ 0.05) negative
GCA effects. Similarly, highly significant (P
≤ 0.01) and highest positive GCA effects were found in line
(Khyber-87) (5.02) followed by line (Benazir-13) (1.69). The GCA effects for
days to heading ranged from -1.31 to 1.42 in the situation of paternal testers.
Therefore, significant (P ≤ 0.05)
maximum positive GCA effects was detected in male tester (IBWSN-205(1718))
(1.42) and the maximum negative GCA effects were exhibited in the following
paternal testers i.e., (IBWSN-24(1718)) (-1.31) and (IBWSN-192(1718)) (-0.11)
(Table 4).
In the F2
population, the SCA impacts for days to heading ranged from -2.09 to 3.98.
Sixty percent of the F2 populations showed negative and desirable
SCA effects ranged between -2.09 to -0.09 while, the
other six F2 populations revealed positive SCA effects ranged
between 0.02 to 3.98 for days to maturity. The
significant (P ≤ 0.05) positive
SCA effects were detected in F2 population (TD-1) × (IBWSN-24(1718))
(3.98*). Therefore, the following F2 population had the highest
negative and desirable SCA effects i.e., (TD-1) × (IBWSN-205(1718))
(-2.09) followed by (TD-1) × (IBWSN-192(1718)) (-1.89) (Table 5).
For plant
height, in lines the GCA effects ranged between -6.02 and 7.64. Therefore, the
highly significant negative and desirable GCA impacts were recognized in female
line (Shafaq-06) (-6.02**), whereas highly significant and highest positive GCA
impacts were detected in maternal line (Khyber-87) with the value of 7.64**. In
state of testers, the GCA effects ranged between -0.69 and 1.31 for plant
height. In three testers, there are no significant GCA effects were observed
for plant height. As a result, in maternal lines and paternal testers, the
maximum negative value and desirable GCA effects were distinguished in line
(Shafaq-06) because highly significant which is more contributed and recognized
as good general combiners for plant height (Table 4).
In F2
populations, SCA effects calculated for plant height varied from -4.09 to 4.69.
Therefore, 46% F2 generations exhibited negative and desirable SCA
effects exhibited ranged between -4.09 and -0.76. For plant height, the
remaining eight F2 generations had positive SCA effects between 0.02
and 4.69. For the said character, there are no substantial SCA consequences.
However, in F2 populations the negative SCA impacts on plant height
were seen in (Benazir-13) × (IBWSN-205(1718)) with
highest SCA effects value of -4.09; whereas, the highest positive SCA effects
were manifested in F2 population (Shafaq-06) × (IBWSN-205(1718))
with highest SCA effects value of 4.69 for plant height (Table 5).
The general
combine ability (GCA) influences on flag leaf area in (females) lines ranged
from -0.58 to 0.83. Maternal line (SKD-1) (-0.58*) showed significant negative
GCA consequences whereas, maternal line (Khyber-87) (0.83**) showed highly
significant (P ≤ 0.01)
beneficial GCA effects for flag leaf area. In state of paternal testers, the
GCA impacts for flag leaf area starting from -0.73 to 0.45. Therefore, highly
significant (P ≤ 0.01) negative
GCA impacts were manifested by paternal tester IBWSN-192(1718) (-0.73**)
whereas (IBWSN-205(1718)) (0.45*) displayed the maximum significant (P ≤ 0.05) positive GCA impacts for
for flag leaf area. Overall, female line Khyber-87 and IBWSN-205(1718) was
known as the good general connecters for the said character (Table 4).
Specific
combination ability (SCA) effects on flag leaf area ranged from -1.38 to 1.05
in F2 populations. 33% F2 generations revealed negative
SCA impacts ranged between -1.38 and -0.83 whereas, the eight F2
generations had positive SCA impacts on flag leaf area (0.04 to 1.05).
Therefore, in the F2 generation (SKD-1) × (IBWSN-24(1718))
(-1.38**) was seen highly significant (P ≤
0.01) adverse SCA effects. Furthermore, the significant (P ≤ 0.05) and highest positive SCA
impacts was recorded in F2 generation (SKD-1) × (IBWSN-205(1718))
(1.05*) and identified as good specific combiner for flag leaf area (Table 5).
In parental
lines, for tillers plant-1 the general combine ability (GCA) effects
ranged between -0.34 and 0.26. Highly significant (P ≤ 0.01) negative GCA impacts were attained by maternal line
(Shafaq-06) (-0.34**). Whereas, the significant (P ≤ 0.05) and maximum positive useful GCA impacts were noted
maternal line (TD-1) (0.26*) and (Khyber-87) (0.24*) for tillers plant-1.
In state of paternal testers, for tillers plant-1 the GCA impacts
ranged between -0.27 and 0.19. The paternal tester (IBWSN-192(1718)) (-0.27**)
achieved highly significant (P ≤ 0.01)
negative GCA influences whereas was paternal tester (IBWSN-205(1718)) (0.19*)
achieved significant maximal positive GCA influences for tillers plant-1.
Overall, female lines (TD-1), (Khyber-87) and male tester (IBWSN-205(1718))
considered to be a major general combiner for boosting tiller capacity (Table
4).
In F2 generations, for tillers plant-1 the
specific combine ability (SCA) impacts ranged starting from -0.54 to 0.61.
Therefore, for tillers plant-1 the positive SCA effects varied from
0.06 to 0.61 in the seven F2 populations. The highly significant
negative SCA impacts were exhibited in F2 populations (SKD-1) ×
(IBWSN-24(1718)) (-0.54**). Table 4: General combine ability (GCA) effects among lines and
testers for various traits were evaluated in lines 5 × 3 testers mating design
of bread wheat during the year 201920 at CCRI, Pirsabak Nowshera
Parental
genotypes |
DH |
PH |
FLA |
Tp-1 |
SL |
GYLD |
HI |
Lines |
|
|
|
|
|
|
|
Shafaq-06 |
-1.64 |
-6.02** |
-0.54 |
-0.34** |
-0.28* |
-161.51** |
-2.65** |
SKD-1 |
-2.09* |
0.87 |
-0.58* |
-0.17 |
0.04 |
-371.51** |
0.14 |
TD-1 |
-2.98** |
-1.91 |
-0.10 |
0.26* |
-0.62** |
-190.07** |
0.77 |
Benazir-13 |
1.69 |
-0.58 |
0.39 |
0.02 |
0.78** |
216.82** |
-0.90* |
Khyber-87 |
5.02** |
7.64** |
0.83** |
0.24* |
0.08 |
506.27** |
2.65** |
S.E. |
0.89 |
1.90 |
0.28 |
0.11 |
0.12 |
45.72 |
0.38 |
S.E (gi -
gj) lines |
1.26 |
2.68 |
0.39 |
0.15 |
0.17 |
64.66 |
0.54 |
CD0.05 |
2.59 |
5.50 |
0.81 |
0.31 |
0.35 |
132.55 |
1.11 |
CD0.01 |
3.48 |
7.41 |
1.09 |
0.42 |
0.47 |
178.46 |
1.49 |
Testers |
|
|
|
|
|
|
|
IBWSN-24(17-18) |
-1.31 |
-0.62 |
0.28 |
0.07 |
-0.21* |
-163.44** |
-0.19 |
IBWSN-192(17-18) |
-0.11 |
1.31 |
-0.73** |
-0.27** |
-0.47** |
122.02** |
-0.38 |
IBWSN-205(17-18) |
1.42* |
-0.69 |
0.45* |
0.19* |
0.69** |
41.42 |
0.57 |
S.E. |
0.69 |
1.47 |
0.22 |
0.08 |
0.09 |
35.42 |
0.30 |
S.E (gTi -
gTj) testers |
0.98 |
2.08 |
0.31 |
0.12 |
0.13 |
50.09 |
0.42 |
CD0.05 |
2.00 |
4.26 |
0.63 |
0.24 |
0.27 |
102.68 |
0.86 |
CD0.01 |
2.70 |
5.74 |
0.84 |
0.33 |
0.36 |
138.24 |
1.16 |
S.E (gi - gj)
lines = Standard error between lines, S.E (gTi - gTj) testers = Standard error
between testers, and CD = Critical
difference.
Table 5: Specific combining ability effects among line × tester/F2
populations for various traits were evaluated in lines 5 × 3 testers
mating design of bread wheat during the year 201920 at CCRI, Pirsabak Nowshera
F2
populations |
DH |
PH |
FLA |
Tp-1 |
SL |
GYLD |
HI |
Shafaq-06 ×
IBWSN-24(17-18) |
-0.36 |
-2.71 |
0.67 |
-0.07 |
0.05 |
-54.89 |
-0.40 |
Shafaq-06 ×
IBWSN-192(17-18) |
-0.22 |
-1.98 |
-0.75 |
-0.20 |
0.76** |
95.64 |
0.59 |
Shafaq-06 ×
IBWSN-205(17-18) |
0.58 |
4.69 |
0.07 |
0.27 |
-0.81** |
-40.76 |
-0.19 |
SKD-1 ×
IBWSN-24(17-18) |
1.07 |
-1.38** |
-0.54** |
-0.37 |
33.11 |
0.44 |
|
SKD-1 ×
IBWSN-192(17-18) |
2.47 |
0.33 |
-0.07 |
-0.73** |
-102.69 |
-0.33 |
|
SKD-1 ×
IBWSN-205(17-18) |
0.02 |
-3.53 |
1.05* |
0.61** |
1.10** |
69.58 |
-0.12 |
TD-1 ×
IBWSN-24(17-18) |
3.98* |
1.51 |
0.46 |
0.19 |
0.22 |
-176.33* |
1.68* |
TD-1 ×
IBWSN-192(17-18) |
-0.76 |
0.04 |
-0.07 |
-0.28 |
318.53** |
-0.73 |
|
TD-1 ×
IBWSN-205(17-18) |
-2.09 |
-0.76 |
-0.50 |
-0.13 |
0.06 |
-142.20 |
-0.95 |
Benazir-13
× IBWSN-24(17-18) |
-0.69 |
3.84 |
-0.29 |
0.24 |
0.38 |
111.11 |
-1.76* |
Benazir-13
× IBWSN-192(17-18) |
-0.89 |
0.24 |
0.09 |
0.28 |
0.29 |
-178.36* |
1.64* |
Benazir-13
× IBWSN-205(17-18) |
-4.09 |
0.21 |
-0.52* |
-0.68** |
67.24 |
0.12 |
|
Khyber-87 ×
IBWSN-24(17-18) |
-3.71 |
0.54 |
0.18 |
-0.28 |
87.00 |
0.03 |
|
0.02 |
0.29 |
0.06 |
-0.04 |
-133.13 |
-1.17 |
||
Khyber-87 ×
IBWSN-205(17-18) |
3.69 |
-0.83 |
-0.24 |
0.32 |
46.13 |
1.14 |
|
S.E. |
1.55 |
3.29 |
0.48 |
0.19 |
0.21 |
79.19 |
0.66 |
S.E (sij -
skl) |
2.19 |
4.65 |
0.68 |
0.26 |
0.30 |
112.00 |
0.94 |
CD0.05 |
4.48 |
9.53 |
1.40 |
0.54 |
0.61 |
229.59 |
1.92 |
CD0.01 |
6.03 |
12.83 |
1.88 |
0.73 |
0.82 |
309.11 |
2.59 |
S.E. = Standard error for SCA effects, S.E (sij - skl) =
Standard error between Crosses SCA effects, CD = Critical difference
However,
highly significant and highest positive SCA impacts were recorded in F2
generation (SKD-1) × (IBWSN-205(1718)) (0.61**) and identified as the best
specific combiner, which is more contributed and for enhancement in tillers
plant-1 (Table 5).
In maternal
lines, for spike length, the general combine ability (GCA) influences starting
from -0.62 to 0.78. Therefore, significant (P
≤ 0.01) and highest positive GCA influences were exhibited by maternal
line (Benazir-13) (0.78**), whereas the highly significant and maximum negative
GCA influences were recorded by maternal line (TD-1) (-0.62**) for spike
length. In case of paternal testers, for spike length, the general combine
ability (GCA) effect ranged between -0.47 and 0.69. (P ≤ 0.01) significant positive GCA influences were showed by
male tester (IBWSN-205(1718)) (0.69**), while the (P ≤ 0.01) significant and supreme negative GCA influences
were recorded by paternal tester (IBWSN-192(1718)) (-0.47**). In total female
lines (Benazir-13) and male tester (IBWSN-205(1718)) was rated as the finest
general combiner for spike length (Table 4).
The SCA
influences for spike length in the F2 generations, starting from
-0.81 to 1.10. The 53.33% F2 populations displayed positive and
useful SCA influences ranged between 0.05 and 1.10, whereas remaining 46.67%
revealed negative SCA effects (-0.81 and -0.04). Therefore, for spike length
revealed that highly significant and maximum positive SCA influences were
recorded in F2 population (SKD-1) × (IBWSN-205(1718)) (1.10**) and
were discovered to be the most effective specific cross combinations. However,
the F2 population (Shafaq-06) × (IBWSN-205(1718)) (-0.81**) had the
most highly significant (P ≤ 0.01)
and largest negative SCA impacts for spike length (Table 5).
The general
combine ability (GCA) influences on grain production in parental lines ranged
from -190.07 to 506.27. Highly significant (P
≤ 0.01) and positive favorable GCA influences were detected in female
line (Khyber-87) (506.27**) and (Benazir-130
(216.82**) while, the highly significant (P
≤ 0.01) and maximum negative undesirable GCA influences were detected
in maternal line (SKD-1) (-371.51**) for seed yield (kg ha-1). In
state of paternal testers, for grain yield (kg ha-1) the GCA
influences ranged between -163.44 and 122.02. The required favorable positive
GCA influences were found in paternal tester (IBWSN-192(1718)) (122.02**) that
were highly significant (P ≤ 0.01)
while, (P ≤ 0.01) significant
and maximum undesired negative GCA influences were recorded in paternal tester (IBWSN-24(1718)) (-163.44**). In total maternal (Khyber-87), (Benazir-13)
and male tester (IBWSN-192(1718)) were regarded as the most effective general
combiners in terms of grain yield (Table 4).
The specific combine ability (SCA)
impacts on grain yield in F2 populations ranged from -178.36 to
318.53. In counting of hundred 53.33% F2 generations were displayed
positive and desired SCA impacts ranged between 33.11 and 318.53.
Highly significant (P ≤ 0.01)
and favorable positive SCA impacts were noted in F2 generation
(TD-1) × (IBWSN-192(1718)) (318.53**), which was more suitable as the best
specific cross combination for the development of grain yield. Likewise, maximum
undesired negative SCA effects were detected in F2 population (TD-1)
× (IBWSN-24(1718)) (-176.33*) (Table 5).
In parental
lines, for harvest index the general combine ability (GCA) effects ranged
between -2.65 and 2.65. Highly significant (P
≤ 0.01) positive and favorable GCA influences were noted in female
line (Khyber-87) (2.65**) while the significant (P ≤ 0.01) and maximum negative
undesirable GCA influences were observed in female line (Shafaq-06) (-2.65**). In state of paternal testers, the influences
of GCA ranged from -0.38 to 0.57. Therefore, in paternal testers there is no
significant positive GCA influences. In total the female line (Khyber-87) for
harvest index, was regarded as the best and top general connecters (Table 4).
For harvest index,
the specific combine ability (SCA) impacts in F2 generations ranged
from -1.76 to 1.68. Therefore, SCA impacts were positive and desired in 46.67
percent of F2 generations (0.03 to 1.68). On the other hand, the
remaining 53.33 percent, had negative SCA consequences (-1.76 to -0.12).
Similarly, the F2 populations (TD-1) × (IBWSN-24(1718)) (1.68*)
displayed significant (P ≤ 0.05)
positive favorable SCA impacts and were acknowledged as the good specific cross
combination for harvest index (Table 5).
Gene action and degree of
dominance
Overall, due to the fact that variance of
general combining ability (σ2GCA), was lesser than the variance
of specific combining ability (σ2SCA), indicated of
non-additive type gene action was controlled for all the examined traits. The
values of additive genetic variance were lower than the dominance genetic
variance for all the studied traits. The ratios of variances of GCA also supported
these analyses to SCA (σ2GCA/σ2SCA) which were
less than unity, which is confirmed by the ratio of additive genetics variance
to dominant genetics variance (σ2A/σ2D) for all
the tested traits. While the ratio of the degree of dominant (σ2D/σ2A)½
is greater than unity for all the investigated traits in this experiment. As a
result, it was discovered that non-additive gene action governed the
inheritance for all the evaluated traits i.e., days to heading, plant
height, flag leaf area, tiller plant-1, spike length, grain yield
and harvest index. Based on the frequencies of alleles or genes identified in
parental genotypes, the varying ratios of GCA to SCA variances (σ2GCA/σ2SCA).
Therefore, due to their significant GCA effects, the various parental genotypes
exhibited a desirable ratio of GCA and SCA variations (Table 6).
Percent contribution of
populations to total variance
The proportional contribution of
populations to total variance, showed that the maternal lines obtained highly
contributed to the total variance compare to tester and lines × testers
interactions for majority of the traits i.e., days to heading (70.36),
plant height (69.94), grain yield (76.03), and harvest index (74.02),
followed by lines ×
testers interactions. And for flag leaf area (40.49), tillers plant-1
(49.17) and spike length (37.76) showed that highest shares to total variance
was due to lines × testers interactions followed by lines. The results clearly
showed that the paternal lines and lines × testers interactions/F2
populations had highly contributed to the total variance that played essential
role in dealing with the variation in the existing studies (Table 7).
Discussion
Days to heading are an important marker of earliness in crop output.
Early heading is desirable and plant growers are keen to create new varieties
of wheat genotypes with early maturity. As a result of delayed heading, the
short time for grain filling, eventually led to a reduction in grain weight
(Ullah et al. 2018). In wheat days to
heading, is an important trait that donate towards high yield and early heading
is desirable for wheat breeders because grain filling duration increases which
eventually outcomes in high yield Iqbal et al. (2017). Overall,
there were significant (P ≤ 0.01)
discrepancies between parental genotypes and the F2 population,
indicating that genotypes had greater genetic diversity and greater
opportunities for improvement through intense selection in subsequent
segregating generations. In past combining ability analysis discovered
significant variation among F2 populations for various agronomical
traits in wheat (Adhikari et al. 2020). The capacity of parental
genotypes and their F2 populations to combine their desirable
alleles in F2 generation after hybridization was investigated Table 6: Estimation of
genetic components among lines, testers and line × tester populations for the
studied traits in bread wheat during the year 201920 at CCRI, Pirsabak
Nowshera
Genetic components |
DH |
PH |
FLA |
Tp-1 |
SL |
GYLD |
HI |
σ2GCA |
0.93 |
1.70 |
0.03 |
0.00 |
0.03 |
11612.57 |
0.29 |
σ2SCA |
2.22 |
3.61 |
0.49 |
0.13 |
0.47 |
26126.28 |
1.26 |
σ2A |
1.87 |
3.41 |
0.06 |
0.01 |
0.06 |
23225.13 |
0.59 |
σ2D |
2.22 |
3.61 |
0.49 |
0.13 |
0.47 |
26126.28 |
1.26 |
σ2GCA/σ2SCA |
0.42 |
0.47 |
0.06 |
0.02 |
0.06 |
0.44 |
0.23 |
σ2A/σ2D |
0.84 |
0.94 |
0.13 |
0.04 |
0.12 |
0.89 |
0.47 |
(σ2D/σ2A)½ |
1.09 |
1.03 |
2.79 |
4.77 |
2.83 |
1.06 |
1.46 |
Table 7: Proportional contribution of (female) lines, (males) testers and their derived line × tester/F2
population to the total variability for various traits were estimated in lines
5 × 3 testers mating design of bread wheat during the year 201920 at CCRI,
Pirsabak Nowshera
Studied traits |
Lines (%) |
Testers (%) |
Line × Tester interactions (%) |
Total Variability |
Days to heading |
70.36 |
10.00 |
19.64 |
100 |
Plant height |
69.94 |
3.03 |
27.04 |
100 |
Flag leaf area |
31.58 |
27.93 |
40.49 |
100 |
Tiller plant-1 |
29.85 |
20.98 |
49.17 |
100 |
Spike length |
29.58 |
32.65 |
37.76 |
100 |
Grain yield |
76.03 |
10.91 |
13.06 |
100 |
Harvest
index |
74.02 |
4.11 |
21.87 |
100 |
using line × tester analysis to
assess the capacity of parental genotypes to combine their beneficial genes in
F2 generation after hybridization. Combining abilities have two
categories i.e., GCA and SCA and both types were calculated in the
current study. Therefore, GCA effects are often caused by additive gene
effects, whereas SCA impacts are caused by dominant or epistatic gene effects
(Griffing 1956; Kempthorne 1957). The general combining ability of a parental
genotype in a cross series supports the breeder in identifying potential
genotypes for crossing programs based on GCA findings and mean performance
(Singh and Chaudhary 1985). Specific combining ability refers to the capacity
of one parent genotype to perform well with another parent in a specific cross
combination, which might aid in the development of promising hybrids (Mandal
and Madhuri 2016). In wheat breeding programs for the said trait, parental
genotypes with desirable GCA effects were regarded as the best parental
genotypes and good general combiners (Afridi et al. 2017, 2018).
However, F2 populations with acceptable SCA effects, on the other
hand, were deemed the greatest particular combiners for days to heading and
plant height in consideration. Moreover, our results are agreement with Patel et
al. (2020) who had (P ≤ 0.01)
significant negative and desirable GCA effects in lines and non-additive gene action was
primarily involved for days to heading. These assertions imply that doubling
the number of days before heading will increase plant height and the quantity of spikelets spike-1,
both of which lead to ultimate production (El-Gammaal and Morad 2018).
On the other
hand, plant height is the key morpho-physiological character. Plant breeders
are attracted to low height homogeneous plants because shorter plants are more
sensitive to manures and are unaffected by lodging in wheat breeding programs
during storms. Plant breeders select short stature cultivars for the region
with high chances of lodging and tall varieties for regions with drought
conditions (Khan et al. 2010). Therefore, in the present research except
for testers and lines × testers interactions for plant height, extremely
significant differences were seen across the genotypes studied. Similarly,
Kumar et al. (2018) found highly significant variations in plant height
across the investigated crosses and lines. In wheat, lines × testers design
worked as a rapid measure of screening of genetic stocks based on GCA/SCA
effects, rather than their variances and revealed that dominant gene activity
was discovered for plant height (Nazeer et al. 2013). Previous studies
revealed and our results are agreed to El-Gammaal and Morad (2018) had the
highly significant negative and desirable GCA effects for the plant height and
the negative are useful for breeder who are interested for short stature plant in bread
wheat. Moreover, non-additive type of gene action is observed for the said
trait and supported by (Shah et al. 2019). Furthermore, our results are
agreement with Ali and Abdulkhaleq (2019) had the highest percent shares to
total variance was due to the fact of lines followed by lines × testers interactions for plant height.
Similarly, due
to the facts of its vital function in
photosynthesis, flag leaf area is regarded as the most important yield
contributing characteristic (Al-Tahir 2014). Breeders want broad flag leaf area
because broad leaves provide a wider surface area for sunlight to catch,
resulting in more photosynthates being produced, which enhances grain output
(Iqbal et al. 2017). Overall, our findings are similar to (Khan et
al. 2020) who reported significant differences among lines × testers
interactions for flag leaf area. Combining
ability demonstrated that significant positive GCA and SCA impacts are
desirable for wheat in flag leaf area, tillers plant-1, spike length, grain yield and harvest index. Therefore, our finding is supported by (Saeed
and Khalil 2017) who estimated significance positive GCA and significance
positive SCA were reported for flag leaf area.
Similarly, Ishaq et al. (2018) was validated to our finding that
non-additive gene action is involved for flag leaf area in wheat and also found
that lines × testers interactions contributed the most percent shares to
overall variation, which is vital point for the development of wheat varieties
and hybrid.
To begin with
tillers plant-1 is a significant yield boosting characteristic that
contributes to increased grain output in wheat (Tilley et al. 2019). Tillering determines the extent of the
plant canopy, photosynthetic area, and, most critically, the number of spikes
carrying grains at maturity (fertile shoots), all of which are important
factors in production (Xie et al.
2016). Tillers plant-1 is frequently linked to the number of
productive spikes as well as the resulting grain production. The genetic makeup
of bread wheat frequently differs in terms of tillering potential. A higher
number of tillers plant-1 confirms optimal plant populations and as
a result higher grain yield. Our findings are consistent with those of (Khan et
al. 2020) who stated significant (P ≤
0.01) differences in tillers plant-1 across bread wheat
genotypes. Therefore, the past studied for
tillers plant-1 (Rashmi et al. 2020) had significantly
extensive favorable GCA and SCA impacts in maternal lines, paternal testers,
and combinations. Likewise, our findings
align with those of Saira et al. (2019) had that σ2GCA
is less than σ2SCA, indicating
that non-additive gene action was implicated for tillers plant-1
and had the highest shares to total variance was due to lines × testers
interactions, this was good impact in F2 populations.
In addition, the major yield component is spike length, which adds to
the final yield of more grains. Spikes with longer spikes are more willing to
enable more spikelets to form Iqbal et al. (2017). Spike length and grain yield
unit-1 area have a direct relationship. Using lines × testers
examinations (Hama-Amin and Towfiq 2019) displayed (P ≤ 0.01) significant variation for spike length amongst
wheat genotypes. Similarly, our
finding is supported by Sharma et al. (2019) observed positively significant (P ≤ 0.01) SCA effects on spike
length in F2 populations. Consequently, our findings are consistent
with those of (Soni et al. 2018), who discovered non-additive kind of gene
action and (Hakeem et al. 2020)
had the highest percent shares to total variance was due to the fact of lines × testers interactions
(70%) for spike length.
Likewise, the
grain yield is a multipart character. Plant breeders are generally interested
in developing high yielding cultivars to meet the country's food requirement by
directly or indirectly enhancing this characteristic. The final grain yield is
the significant wheat production trait and the contribution of many components
makes the nature of grain yield more complex. In the present study, our results
are strongly in agreements with Farooq et al. (2019) who concluded
highly significant discrepancies in grain yield among wheat cultivars, maternal
lines, paternal testers, and lines × testers interactions. In wheat the earlier studies of Farooq et
al. (2018) and Din et al. (2021) shown (P ≤ 0.01) significant positive
GCA and SCA influences these investigations are agreements with our results for
grain yield. Therefore, our findings are
in accordance with Sarfraz et al. (2020) revealed that non-additive gene
acts were primarily governed for grain yield.
To begin with
harvest index is an important wheat crop feature directly related to biological
yield and grain production. For harvest index (Khan et al. 2020)
revealed that lines × testers analysis observed significant (P ≤ 0.01) variations across wheat
treatments and lines × testers interactions and it means that the selections
are desirable. The earlier premeditated of Dhoot et al. (2020)
discovered highly positively significant GCA and SCA influences in wheat F2
populations; this research agrees with our harvest index findings. Therefore, our finding is in line with Baranda
(2020) revealed that SCA variance was more than GCA variance in macaroni wheat,
the variance of GCA to SCA variance was smaller than unity, indicating that non-additive
gene activity was primarily involved in the genetic development of the
characteristic. Furthermore, our discoveries are contract with Din et al.
(2021) who observed that the maximum percent share to total genetic variance
was due to lines, which indicates that a strong maternal influence for harvest
index in studied wheat crop.
Conclusion
Parental lines i.e., TD-1, SKD-1, Benazir-13,
Khyber-87, testers IBWSN-192(1718) and IBWSN-205(1718) and their F2
populations Shafaq-06 × IBWSN-192(1718), TD-1 × IBWSN-192(1718), Benazir-13 ×
IBWSN-192(1718), SKD-1 × IBWSN-205(1718) and TD-1 × IBWSN-24(1718) was
observed as the best general and specific combiners, respectively and showed
outstanding performance for yield productive traits and found better mean
performance. Therefore, for all of the parameters the ratio of variances of GCA
to SCA were less than unity and additive variance was smaller than dominance
variance it means that non-additive type of gene actions. In contrast, degree
of dominance was greater than unity which confirmed that all the characters
were controlled by non-additive type of gene actions. Therefore, non-additive
gene action implies that populations selection in yield productive traits
should be postponed to the later segregating generations for further
improvement in these tested selective traits of bread wheat. Therefore, these F2
populations could be used in future wheat breeding programs to develop
outstanding production traits and high yielding wheat genotypes.
Acknowledgements
Thanks to all
authors, specially to Hidayat Ullah, Khilwat Afridi and Md. Nashir Uddin.
Moreover, thanks to the Director, Wheat Breeding Section, Cereal Crops Research
Institute (CCRI), Pirsabak-Nowshera, Khyber Pakhtunkhwa, Pakistan, for providing
the breeding material and cultivated land to carry out the existing research.
Author Contributions
Contributed materials and
planned the experiments HU and KA. In addition, HR, SA, MA, GS, JK and HK
contributed in the data collections of this research, however the analysis/
tools: Wrote the paper MB.
Conflicts of Interest
All authors declare no conflicts of interest.
Data Availability
Data presented in this study will be available on a fair
request to the corresponding author.
Ethics Approval
Not applicable in this paper.
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