Introduction
Maize
(Zea mays L.) is one of the most
resourceful and multipurpose crops, having widest adaptability in distinct
ecologies (Khan et al. 2018; Ali et al. 2020). Universally, it is also
known as queen of cereals due to its highest genetic potential and provides
food, feed, fodder for human and animal consumption (Kumar et al. 2013). Maize provides raw material to the industry for the
preparation of corn oil and starch, corn syrup and flakes, dextrose, cosmetics,
wax, alcohol and tanning material for leather industry (Arain 2013). During 2018-2019, the maize was grown on
an area of 1.374 million hectares which produced 6.826 million tons of grains
with average grain yield of 4968 kg ha-1 in Pakistan (Anonymous 2018–2019).
Though maize exceeds other cereals in productivity, however, in Pakistan the
farming community is still getting low yield as compared to other growing
countries (Sajjad et al. 2016). Maize
low yield is attributable to many factors including, genotypes low potential,
soil variation, fertility gradient, water shortage and temperature fluctuations
(Sajjad 2018; Hussain et al. 2019;
Minhas et al. 2020).
Development of high yielding and well-adapted genotypes with desirable
traits usually remains the main objective of plant breeding (Ali 2015; Ali et al. 2017). Large number of breeding
methods has been developed to enhance the economic yield of the various crops
(Ali et al. 2018, 2019). However, recurrent selection is a commendable breeding method
used to improve the populations particularly those of cross-pollinated species.
Being an important breeding strategy, restoration of genetic variability and
subsequent improvement within the population can be made through recurrent
selection (). Recurrent selection can accrue desirable genes and create new gene
complexes for genetic variation and improvement within population.
Recurrent
selection is a reselection made generation after
generation, with reunion of selected plants to produce a population for the
next cycle of the selection (Darrah et al. 2019; Sheikh et al. 2019). Germplasm subjected to
recurrent selection with the idea to ensure isolation of superior inbreds from the original population. However, isolation of
an outstanding inbred line depends on two
factors, a) the ratio of promising genotypes found in the original population,
and b) the efficiency of selection during the inbreeding of desirable genes (Pixley et al. 2006; Ahmad et al. 2010).
Based on visual observations for yield related traits in improved populations C1
and C2, the simple recurrent selection was found more suitable for
improvement in waxy corn (Khamkoh et al. 2019).
Selfed
progeny recurrent selection is considered more efficient than full-sib and
half-sib family’s selection in maize (Hallauer and Carena 2012; Sheikh et
al. 2019). The S1 selfed progeny
recurrent selection was found is an excellent option for attaining improvement
within maize populations and enhancement of grain yield in maize (Chen et al. 2019). The S1
selection was conducted for grain yield under different environments and
concluded that S1 family selection has been found efficient for
improving grain yield in maize (Badu-Apraku et al. 2013). Selfed
progeny recurrent selection using either S1 or S2 lines
is considered more useful for population improvement compared to other methods
of recurrent selection (Hallauer et al. 2010).
In selfed progeny recurrent selection, the
desirable alleles are fixed rapidly and deleterious alleles are exposed and
eliminated early in selection (Abdulmalik et al. 2017; Guimaraes et al. 2018). However, several studies
suggested that genetic diversity in the populations reduced after the initial
cycles, limiting opportunities for selection (Noor et al. 2013; Udo et al. 2017).
Significant decrease was recorded for
morphological traits while increase in yield traits was achieved with S1
recurrent selection (Horne et al.
2016; Kolawole et al. 2017, 2019). Selection in cycle-2 produced maximum grain yield with
significant genetic gain and hence, S1 recurrent selection was
recommended for significant improvement in maize (Bedada
and Jifar 2010).
Genotype performance depends on population, environment and genotype by
environment interaction (GEI) (Gomez and Gomez 1984). Genotype is an
individual’s genetic make-up and its phenotypic expression depends on the
environments surrounding it (Andorf et al. 2019). Genotypes may perform well
in one environment but not so well in other. Genotypes exhibits different
behaviour in different environments (years and locations) due to their varied
genetic makeup (Annor et al. 2019). Genotypes, environments, and genotype by environment
interaction determine the individual’s phenotype and that is why GEI is an important
aspect of plant breeding.
Similarly, selection differential and genetic gain are also very
important breeding tools which guides the breeder about the genetic potential
of selected populations in maize (Ullah et
al. 2013; Cobb et al. 2019). Likewise, expected and observed responses, and
genetic gain substantiate that how much improvement is expected and realized
during selection in maize (Sajjad et al.
2016; Udo et al. 2017). Maize S1
and S2 selfed families revealed
significant enhancement in grain yield with desirable genetic gain (Ali et al. 2012; Chen et al. 2019). Past studies revealed that S1 and S2
based selections were found effective in improving maize populations for
earliness and yield related traits (Bedada and Jifar 2010; Ayiga-Aluba et al. 2015).
Therefore, after development of maize
improved cyclical populations (PSEV3-C1 and PSEV3-C2)
from base population (PSEV3-C0) through recurrent selection, the
present study was designed with the objectives a) to assess the performance of
C1 and C2 populations in comparison to original
population C0 in different environments for earliness and yield
traits, b) to ascertain the expected and observed responses in selection
cycle-1 and cycle-2, and c) to quantify the cycle-wise and average genetic gain
in the maize improved populations.
Materials and Methods
Development
of breeding material, sites and procedure
Maize
original population PSEV3-C0 was derived from a cross between maize
cultivar Azam and hybrid CHSW (Single cross hybrid, white kernels with late
maturity from CIMMYT). For improving maize base population 'PSEV3-C0'
for earliness and yield traits through selfed progeny
recurrent selection, the breeding material was developed in five consecutive crop seasons for three years
(during spring and summer - 2014 and 2015, and spring - 2016) at Cereal
Crops Research Institute (CCRI), Pirsabak - Nowshera, Pakistan (Table 1).
The first selection cycle (C1) was based on S1-lines
while second cycle (C2) was on S2-lines. During Spring 2014, for developing S1 (selfed) lines in cycle-1, 500 plants were selfed. At harvest time, 255 selfed
ears were selected and stored for evaluation in the next summer season. In the
second stage during Summer - 2014, a part of seed from 255 selected selfed progenies along with base population (PSEV3-C0)
were sown in ear-to-row method and evaluated in a 16 × 16 partially balanced
lattice design with two replications. While the remnant seed of S1-selected
progenies was saved for use in the recombination of selected families in the
next spring season. A plot size of four rows, 10 meters long with 0.25 m and
0.75 m distance between plants and rows, respectively was maintained. In the
third stage during Spring 2015, 25 selected S1 families were
recombined and their seeds were bulked to form PSEV3 (S1)-C1
population (C1). During the same growing season (Spring 2015), a
part of seed from the selected S1 families was grown and selfed to produce S2 plants. During Summer 2015,
the seeds from 169 selected S2 families along with base population
were tested in partially balanced lattice design (13 × 13), replicated twice.
Same plot size was maintained as described earlier for S1. At
maturity, seventeen best S2 families were selected and stored to
study in the next season. During spring - 2016, the selected S2
families were recombined and their seeds were bulked to develop PSEV3 (S2)-C2
population (C2).
Cyclical populations PSEV3(S1)-C1, PSEV3(S2)-C2
and base population PSEV3-C0 were evaluated during summer season
over four environments i.e., two consecutive years (2016 and 2017) and
two locations i.e., a) Cereal Crops Research Institute (CCRI), Pirsabak - Nowshera, Pakistan,
(situated between 32° N latitude and 72°E longitude with an altitude of 288 m),
and b) the University of Agriculture (UAP), Peshawar, Pakistan (situated
between 34° N latitude and 71° E longitude with an altitude of 350 m). At CCRI, the soil
was sandy loam and moderately calcareous having pH (7.7), organic matter
(0.34%), nitrogen (0.034%), P2O5
(0.0029%), K2O (0.051%). At
UAP, the soil was silt clay loam with alkaline
nature having pH (7.8), organic matter (0.81%), nitrogen (0.063%), P2O5
(7.18 mg kg-1) and K2O (112 ppm). At each
location, the experiment was carried out in a randomized complete block design
(RCBD) with four replications. Four-row sub-plots were maintained, 10 m long
with 0.25 m and 0.75 m distance between plants and rows, respectively.
Crop husbandry
Maize is a
shallow-rooted crop, and it requires fine good tilth and well-prepared soil for
successful germination and growth of crop. To get this, the field was ploughed
with deep plough then harrowed with planking each time to make the soil loose,
fine, levelled and pulverized. The stubbles of the previous crop left in the
field were also removed. A recommended fertilizer dose at the rate of 200:90:90
NPK kg ha-1 was applied. Half dose of nitrogen (N), whole doses of
phosphorus (P2O5) and Potash (K2SO4)
were applied during land preparation and just before planting in the form of
Urea, Single Super Phosphate (SSP) and Sulphate of Potash (SOP), respectively.
The remaining half N was applied in the form of Urea as side dressing about 4-5
weeks after germination. Weeds were controlled with Primextra
Gold @ 1.5 L ha-1 as a pre-emergence application. The leftover weeds
were manually controlled carrying out weeding and earthing-up operations. Maize
borer was eradicated with Confidor-WP60 @ 50 g per 10 kg of maize seed through
seed treatment before sowing. After one month, Furadon-3% granules @ 20 kg ha-1
were applied in the whirls. The crop was irrigated at the proper interval as
and when required, until one week before harvesting. All the entries were
equally treated during the cropping seasons. Maximum and minimum temperatures
data for maize spring and summer crop seasons during 2014 to 2017 at CCRI and
UAP is provided in Fig. 1.
Data
recorded
Data
were recorded on days to silking (days counted from
planting to silk emergence in 50% plants of each plot), plant height (plant
height was measured as an average distance from soil surface to the node of
flag leaf on 10 competitive plants in each plot and then averaged), ear height
(ear height was recorded as an average distance from soil surface to the node
bearing the primary ear shoot on 10 competitive plants per plot and then
averaged), ears per plant (ears per plant were estimated as ratio of total
number of ears to number of plants). Prolificacy (%), and grain yield (kg ha-1)
(Carangal et al. 1971) were estimated
with the help of the following relationships.
Where,
MC
= Moisture content (%) in grains at harvest
FEW
= Fresh ear weight (kg) at harvest
Shelling
coefficient = 0.80
Statistical analyses
Analysis of variance: The recorded data for each trait was subjected to analysis of variance
techniques using Statistix 8.1 software (Statistix, Analytical Software, Tallahassee, FL, USA, 1985–2003)
appropriate for genotype by environment interaction (Gomez and Gomez
1984). After getting the significant mean squares, the means were further
compared and separated by using LSD0.05 test.
Response
to selection
Response
to selection and genetic gain are tools for measuring the improvement in a
parental population with the selection.
Response to selection was formulated as suggested by Lush (1940).
R1 = C1– C0
R2 = C2– C1
Where,
C0: Mean of the parent population
C1:
Mean of cycle one
C2: Mean of cycle two
Genetic
gain
Genetic
gain (%) was estimated using the relationship as suggested by Keeling (1982).
Genetic gain (%) = [(Cn – Cn-1)/Cn]
× 100
Where,
Cn : advanced cycle population after selection
Cn-1:
cycle population before selection
Results
Three
maize cyclical populations (PSEV3-C0, PSEV3-C1 and
PSEV3-C2) were evaluated under four
different environments i.e., two each years (2016 and 2017) and
locations (CCRI and UAP). Combined analysis of variance revealed that years
were significant for ear per plant (P≤0.05)
and grain yield (P≤0.01) (Table
2). Locations revealed significant (P≤0.01)
mean differences for the majority of the traits while merely significant (P≤0.05) for ears per plant and
prolificacy. In year by location (Y × L), the mean squares were significant (P≤0.01) for almost all traits.
Significant (P≤0.01)
differences were recorded among cyclical populations (C) for days to silking, plant height and grain yield. However, in
interactions, varied trends were observed for different traits. The cyclical
population by year (C × Y) interactions were highly significant for all the
traits except prolificacy and ears per plant. Similarly, population by location
(C × L) interactions were significant (P≤0.01)
for days to silking and plant height. Likewise, for
population-year-location (C × Y × L) interactions, significant (P≤0.01) variations were recorded
for plant height, and non-significant for all other traits. The trait-wise
results are discussed herein.
Days to silking
Overall,
the maize populations grown during 2017 showed less days to silking
than 2016 (Table 3). In locations, populations grown at CCRI revealed fewer
days to silking than UAP. For year’s × location
means, minimum and at par days to silking were
recorded for all the populations grown at CCRI during 2016 and 2017. Minimum
days to silking at CCRI confirmed that almost all the
populations comparatively matured earlier due to high temperature (Fig. 1).
However, maximum days to silking were observed in
populations grown during 2016 at UAP. Population (C) means over environments
revealed that on average, minimum days to silking
were observed in population C2 (50.31 days), followed by C1
(52.25 days) and C0 (54.88 days). On average, days to silking were decreased from 54.88 (base population - C0)
to 52.25 and 50.31 days in improved populations C1 and C2,
respectively. In interactions of population by year by location (C × Y × L)
means, minimum and same days to silking were recorded
in improved population C2 grown during 2016 and 2017 at CCRI and
UAP, respectively. However, base population C0 grown at UAP revealed
maximum days to silking during both years. For days
to silking, the values of expected responses were
lesser than observed in cycle-1 and cycle-2 populations (Table 5). In cycle-1
and cycle-2, the genetic gain values were -2.63 and -1.94 days, respectively
with an average reduction of -2.29 days (-4.16%) (Table 6). Days to silking were reduced from 54.88 (C0) to 50.31
days (C2) with overall genetic gain of -4.57 days (-8.33%).
Plant height
For years, the least plant height was observed
in maize populations grown during 2017 compared to 2016 (Table 3). On average
the populations showed minimum plant height at UAP than CCRI. In year ×
location interaction, the populations grown during 2017 at UAP revealed minimum
plant height compared to CCRI. Maximum
and same plant height was recorded in populations grown during 2016 and 2017 at
CCRI (175.92 cm). Cyclical population means over environments revealed that minimum
and alike plant height was observed in C0 (164.81 cm) and C1
(168.00 cm), while C2 showed maximum plant height (175.69 cm). The
cyclical population-year-location interactions revealed that minimum and
similar plant height was obtained in original population-C0 grown
during 2017 and improved population-C1 grown during 2016 at UAP.
Maximum and same plant height was obtained in improved populations C2
and C1 grown during 2016 and 2017 at CCRI. The improved populations
C1 and C2 showed significant increase in plant height
compared to original population. In cyclical populations for plant height, the observed responses were larger
than the corresponding expected responses in cycle-1 and cycle-2 (Table 5).
Plant height increased with succeeding cycles with overall genetic gain of
10.88 cm (6.60%) (Table 6). Cycle wise gains were 3.19 and 7.69 cm in cycle-1
and cycle-2, respectively while average genetic gain was 5.44 cm (3.30%) for
plant height.
Ear height
For
years, the minimum ear height was recorded for populations grown during 2017,
followed by 2016 (Table 3). In locations, the populations grown at UAP revealed
minimum ear height while maximum at CCRI. Cyclical population means over
environments ranged from 75.38 (C0) to 79.13 cm (C2).
Similarly, interaction means for population × year × location ranged from 67.75
to 89.25 cm. However, minimum ear height was observed in base population-C0
grown during 2017 at UAP, followed by improved populations C2 and C1 during 2016 and 2017, respectively at UAP. The increased ear height was observed in population C2 and C1 grown during 2017 and 2016, respectively
at CCRI. Ear height was significantly increased in
improved populations C1 and C2 compared to base
population. Among populations for ear height, the observed responses were
greater than expected responses (Table 5). Ear height was increased with
succeeding selection cycles with overall genetic gain of 3.75 cm (4.97%) (Table
6). Cycle-wise gain values were 3.12 and 0.63 cm in cycle-1 and cycle-2,
respectively, with average genetic gain of 1.88 cm (2.49%) for ear height.
Table 1: Maize base
and improved populations with various characteristics used in the study
Genotypes |
Source |
Type |
Kernel shape/color |
Plant height |
Maturity |
Pedigree |
Base population PSEV3-C0 |
CCRI |
OPP |
Flint white |
Medium |
Medium |
Cross between maize cultivar Azam and CHSW
(Single cross hybrid, white kernels with late maturity from CIMMYT) |
PSEV3 (S1)-C1 |
CCRI |
OPP |
Flint white |
Medium |
Medium |
Derived from recombination ofS1 selected maize families |
PSEV3 (S1)-C2 |
CCRI |
OPP |
Flint white |
Medium |
Medium |
Derived from recombination ofS2 selected maize families |
CCRI — Cereal Crops Research
Institute, Pirsabak - Nowshera,
Pakistan; OPP — Open pollinated
population
Table 2: Mean squares of maize cyclical populations (PSEV3-C0,C1,C2) for
varioustraitsevaluatedduring2016and 2017 at CCRI and UAP
Source of variation |
df |
Days to silking |
Plant height |
Ear height |
Ears plant-1 |
Prolificacy |
Grain yield |
Years (Y) |
1 |
3.52 |
36.75 |
0.08 |
0.005* |
55.32 |
6411366.89** |
Locations (L) |
1 |
54.19** |
3201.33** |
1200.00** |
0.031* |
299.75* |
14920507.57** |
Y × L |
1 |
13.02** |
330.75** |
0.75 |
0.030* |
291.026* |
38889863.71** |
R (LY) |
12 |
1.60 |
62.10 |
33.26 |
0.005 |
47.44 |
989818.87* |
Cycles (C) |
2 |
83.90** |
500.06** |
64.58 |
0.001 |
13.04 |
10638602.17** |
C × Y |
2 |
0.52 |
286.19** |
236.33* |
0.001 |
8.24 |
2244654.65* |
C × L |
2 |
19.56** |
282.02** |
7.75 |
0.001 |
7.97 |
25336.99 |
C × Y × L |
2 |
0.77 |
244.31** |
25.00 |
0.001 |
11.97 |
39442.18 |
Error |
24 |
1.27 |
38.37 |
46.22 |
0.006 |
59.67 |
438449.48 |
CV (%) |
- |
2.15 |
3.65 |
8.75 |
8.26 |
8.28 |
10.22 |
*, ** = Significant at P≤0.05
and P≤0.01, respectively
Table 3: Performance of maize cyclical populations (PSEV3-C0, C1,
C2) over environments for various traits
Cyclical populations |
2016 |
2017 |
Means |
||
CCRI |
UAP |
CCRI |
UAP |
||
|
Days to silking (days) |
|
|||
PSEV3-C0 |
52.50 |
58.00 |
52.75 |
56.25 |
54.88 |
PSEV3 (S1)-C1 |
51.00 |
54.25 |
51.75 |
52.00 |
52.25 |
PSEV3 (S2)-C2 |
50.00 |
50.75 |
50.50 |
50.00 |
50.31 |
Means |
51.17 |
54.33 |
51.67 |
52.75 |
- |
Means (years) |
52.75 |
|
52.21 |
|
- |
Means (locations) |
51.42 |
- |
- |
53.54 |
- |
LSD0.05 Cycles: 0.80, Locations:
0.69, Years: NS, Y × L: 0.95, C × Y × L: NS |
|||||
|
Plant height (cm) |
|
|||
PSEV3-C0 |
169.25 |
171.75 |
170.00 |
148.25 |
164.81 |
PSEV3 (S1)-C1 |
180.25 |
151.25 |
181.50 |
159.00 |
168.00 |
PSEV3 (S2)-C2 |
178.25 |
171.50 |
186.75 |
166.25 |
175.69 |
Means (cm) |
175.92 |
164.83 |
179.42 |
157.83 |
- |
Means (years) |
170.38 |
- |
168.63 |
- |
- |
Means (locations) |
177.67 |
- |
- |
161.33 |
- |
LSD0.05 Cycles: 4.52, Locations:
3.69, Years: NS, Y × L: 5.22, C × Y × L: 9.04 |
|||||
|
Ear height (cm) |
|
|||
PSEV3-C0 |
82.75 |
73.25 |
77.75 |
67.75 |
75.38 |
PSEV3 (S1)-C1 |
87.25 |
73.50 |
81.25 |
72.00 |
78.50 |
PSEV3 (S2)-C2 |
77.75 |
71.75 |
89.25 |
77.75 |
79.13 |
Means (cm) |
82.58 |
72.83 |
82.75 |
72.50 |
- |
Means (years) |
77.71 |
- |
77.63 |
- |
- |
Means (locations) |
82.67 |
- |
- |
72.67 |
- |
LSD0.05Cycles:
NS, Locations: 4.10, Years: NS, Y × L: NS, C × Y × L: NS
Fig. 1: Maximum and minimum
temperatures for spring and summer maize crop seasons during 2014 to 2017 at
CCRI and UAP
Source:
Pakistan Forest Institute (PFI), Peshawar, Pakistan; Cereal Crops Research
Institute (CCRI), Pirsabak - Nowshera,
Pakistan
Table 4: Performance of maize cyclical populations (PSEV3-C0, C1,
C2) over environments for yield related
traits
Cyclical populations |
2016 |
2017 |
Means |
||
CCRI |
UAP |
CCRI |
UAP |
||
|
Ears per plant (#) |
|
|||
PSEV3-C0 |
0.98 |
0.89 |
0.95 |
0.93 |
0.93 |
PSEV3 (S1)-C1 |
0.96 |
0.86 |
0.93 |
0.96 |
0.92 |
PSEV3 (S2)-C2 |
0.99 |
0.88 |
0.95 |
0.95 |
0.94 |
Means (#) |
0.97 |
0.87 |
0.94 |
0.94 |
- |
Means (years) |
0.92 |
- |
0.94 |
- |
- |
Means (locations) |
0.96 |
- |
- |
0.91 |
- |
LSD0.05 Cycles: 0.06, Locations 0.05, Years: 0.05, Y × L: 0.07, C × Y × L: NS |
|||||
|
Prolificacy (%) |
|
|||
PSEV3-C0 |
97.33 |
88.57 |
95.14 |
92.42 |
93.36 |
PSEV3 (S1)-C1 |
95.40 |
85.67 |
92.72 |
95.71 |
92.38 |
PSEV3 (S2)-C2 |
98.85 |
87.58 |
95.39 |
94.90 |
94.18 |
Means (%) |
97.19 |
87.27 |
94.42 |
94.34 |
- |
Means (years) |
92.23 |
- |
94.38 |
- |
- |
Means (locations) |
95.81 |
- |
- |
90.81 |
- |
LSD0.05 Cycles:
NS, Locations: 4.60, Years: NS, Y × L:
6.51, C × Y × L: NS |
|||||
|
Grain yield (kg ha-1) |
|
|||
PSEV3-C0 |
4081 |
6921 |
5964 |
5256 |
5556 |
PSEV3 (S1)-C1 |
4956 |
8073 |
7387 |
6684 |
6775 |
PSEV3 (S2)-C2 |
4927 |
7716 |
8207 |
7562 |
7103 |
Means (kg ha-1) |
4655 |
7570 |
7186 |
6501 |
6478 |
Means (years) |
6113 |
- |
6844 |
- |
- |
Means (locations) |
5921 |
- |
- |
7036 |
- |
LSD0.05 Cycles: 483.20, Locations:
394.50, Years: 394.50, Y × L: 557.90, C × Y × L: NS
Table 5: Expected and observed
responses in maize cyclical populations over two cycles of selection for
various traits
Traits |
Selection cycle-1 |
Selection cycle-2 |
||
Expected response (Re) |
Observed response (Ro) |
Expected response (Re) |
Observed response (Ro) |
|
Days to silking (days) |
-1.30 |
-4.76 |
-1.56 |
-5.43 |
Plant height (cm) |
8.95 |
19.27 |
20.14 |
50.05 |
Ear height (cm) |
5.24 |
7.79 |
7.59 |
20.47 |
Ears per plant (#) |
0.02 |
-0.01 |
0.03 |
-0.01 |
Prolificacy (%) |
1.44 |
-0.63 |
2.25 |
-0.48 |
Grain yield (kg ha-1) |
1899.11 |
2673.44 |
2228.88 |
3560.96 |
Re = Expected response (h2 *S); Ro
=Observed response (µp - µ)
Table 6: Genetic gains (cycle-wise, overall and average) in maize cyclical
populations over two cycles of selection for various traits
Traits |
Cyclical populations |
Cycle-wise gain |
Overall gain |
Average gain |
|||||||
Cycle-1 |
Cycle-2 |
||||||||||
C0 |
C1 |
C2 |
(unit) |
(%) |
(unit) |
(%) |
(unit) |
(%) |
(unit) |
(%) |
|
Days to silking
(days) |
54.88 |
52.25 |
50.31 |
-2.63 |
-4.79** |
-1.94 |
-3.71** |
-4.57 |
-8.33** |
-2.29 |
-4.16** |
Plant height (cm) |
164.81 |
168.00 |
175.69 |
3.19 |
1.94** |
7.69 |
4.58* |
10.88 |
6.60** |
5.44 |
3.30* |
Ear height (cm) |
75.38 |
78.50 |
79.13 |
3.12 |
4.14NS |
0.63 |
0.80NS |
3.75 |
4.97NS |
1.88 |
2.49NS |
Ears per plant (#) |
0.934 |
0.924 |
0.942 |
-0.010 |
-1.071NS |
0.018 |
1.948NS |
0.008 |
0.857NS |
0.004 |
0.428NS |
Prolificacy (%) |
93.36 |
92.38 |
94.18 |
-0.98 |
-1.05 NS |
1.80 |
1.95NS |
0.82 |
0.88NS |
0.41 |
0.44NS |
Grain yield (kg ha-1) |
5555.54 |
6775.31 |
7102.90 |
1219.77 |
21.96** |
327.59 |
4.84NS |
1547.36 |
27.85** |
773.68 |
13.93** |
*, ** = Significant at P≤0.05 and P≤0.01, respectively; NS = Non-significant
Ears per plant
For
years, the ears per plant produced by the maize populations during 2016 and
2017 were comparable with least differences (Table 4). For locations, the
highest number of ears per plant was produced by populations at CCRI, followed
by UAP. In year × location means, the populations revealed maximum ears during
2016 at CCRI, followed by the populations grown during 2017 at CCRI and UAP.
The cyclical population means enunciated non-significant variations over years
and locations i.e., 0.93 (C0),
0.92 (C1) and 0.94 (C2). Interaction means of cyclical
population × year × location ranged from 0.86 to 0.99. The interaction means
were also nonsignificant for the said trait; however, comparatively larger
value was recorded in C2 (0.99) grown during 2016 at CCRI while
smaller value stood for C1 (0.86) grown during 2016 at UAP. Among
cyclical populations for ears per plant, the expected responses were higher
than observed responses in both selection cycles (Table 5). The ears per plant
were non-significantly enhanced and hence, the genetic gain was not formulated
for the said trait.
Prolificacy
Maize
improved and base populations grown during 2017 revealed the highest
prolificacy than 2016 (Table 4). However, for locations the higher prolificacy
was observed in populations grown at CCRI, followed by UAP. According to year
by location interactions, maize populations revealed higher prolificacy at CCRI
while least at UAP during 2016. Cyclical populations over years and locations
revealed that prolificacy was greater in C2 (94.18%), followed by C0
(93.36%) while the least value was recorded in C1 (92.38%) for the
said trait. The cyclical population × year × location interactions enunciated
that the highest prolificacy was achieved in improved population-C2
grown during 2016 at CCRI, followed by base population-C0 during
2016 at CCRI and C1 during 2017 at UAP. However, the minimum
prolificacy was observed in improved population-C1 grown during 2016
at UAP. Among cyclical
populations, the expected responses were greater than corresponding observed
responses for prolificacy in cycle-1 and cycle-2 (Table 5). In selection
cycles, the prolificacy was increased in improved population C2
compared to original population.
Grain
yield (kg ha-1)
For
maize populations, the highest grain yield was obtained during 2017, followed
by 2016 (Table 4). In locations, populations grown at UAP revealed maximum
grain yield succeeded by CCRI. Population means for year by location revealed
that maximum grain yield was recorded during 2016 at UAP, followed by
populations grown during 2017 at CCRI. However, the populations grown during
2016 at CCRI exhibited minimum grain yield. Cyclical population means over
years and locations ranged from 5555.54 (C0) to 7102.90 kg ha-1
(C2). However, improved populations C2 (7102.90 kg
ha-1) and C1 (6775.31 kg ha-1) revealed the
highest grain yield compared to original population C0 (5555.54 kg
ha-1) over environments. In cyclical population × year × location
interactions, the highest grain yield was delivered by improved cyclical
populations C2 grown during 2017 at CCRI, followed by C1
grown during 2016 at UAP. Minimum grain yield was manifested by base population
C0 grown during 2016 at CCRI. The observed responses were higher in
magnitude than expected in both cycles of selection (Table 5). The yield
increased with proceeding cycles with overall genetic gain of 1547.36 kg ha-1
(27.85%), with average genetic gain of 773.68 kg ha-1 (13.93%)
in both cycles (Table 6). In grain yield, the highest increase in form of genetic
gain was recorded in cycle-1, followed by cycle-2.
Discussion
Selection of suitable populations is crucial
and very important in maize breeding for getting higher yields and to
periodically check the agronomic performance of recommended populations for
specific growing locations (Santos et al.
2002). Plant breeding is a vital tool allowing recombination of genes between
diversified and superior genotypes, aiming at exploiting the genetic divergence
for obtaining genotypes with greater potential and adaptability to different
environmental conditions (Allard 1971). Hence, S1 progeny selection
in terms of units of selection and recombination of S1 progenies is
of immense interest for maize breeders (Badu-Apraku et al.
2013).
Maize populations and various interactions
showed significant differences for majority of the earliness, morphological and
yield traits under different environments which authenticated that genotypes
have greater genetic variability and scope for further improvement. Significant differences were observed among various
maize populations for female and male
flowering, plant and ear height, and other yield traits in recurrent
selection (Shah et al. 2006,
2007; Khalil et al. 2010). The S1 families and genotype ×
location interactions revealed significant impact on earliness, morphological
and yield traits in maize (Ali et al. 2011). Environments as well as
genotypes were significant for days to flowering, plant and ear height, and
100-grain weight; genotype x environment interactions (GEI) were significant
for number of plants and ears, ear weight, and grain weight; while GEI means
were nonsignificant for days to flowering,
plant height, ear height, and 100-grain weight in maize (Tardin
et al. 2007). Significant differences
revealed by testcrosses of maize S1 lines and genotype by location
interactions for maturity, plant and ear height, yield traits, while nonsignificant for anthesis, silking interval (Rahman et al. 2015). Significant effect of genotype by environment interactions was recorded
on yield contributing traits while evaluating maize testcrosses for yield and its associated traits (Sajjad et
al. 2016, 2020b).
Flowering is an important stage of growth in
maize because it symbolizes maturity and transition from vegetative phase to
reproductive phase (Bolanos and Edmeades 1996). The improved population C2
took fewer days to silking and attained increased
plant and ear height compared to C1 and C0 populations.
In recurrent selection, the selected maize
populations were reported with significant improvement in early flowering (Okporie
et al. 2013; Reis et al. 2013). Based upon negative
expected responses, decrease in days to
tasseling, silking and pollen shedding was
observed in the progenies of selected maize S1 lines (Khalil et al. 2010). Though late flowering
genotypes were high yielding due to accumulation of comparatively larger
quantity of photosynthate during vegetative growth; however, early flowering is
still desirable to protect maize crop from various biotic and abiotic stresses.
Present results further revealed that C2 population was
simultaneously improved for flowering traits and showed early maturity. Delay
in flowering with increase in plant and ear height were reported in S1
recurrent selection in two maize populations, however, that delay was
manageable (Ruiz-de-Galarreta and Alvarez 2017; Udo et al.
2017).
Moderate plant height and central position
of the top ear on the maize plant is favourable due to its least vulnerability
to lodging which ultimately contribute to good yield (Noor et al. 2013;
Rahman et al. 2015). Very dwarf
cultivars have thick canopy, less air and sun light penetration
to the lower parts of the plants, causing substantial reduction in grain yield.
On the other hand, taller plants are more vulnerable to lodging, so moderate
plant stature is desirable. Under recurrent selection, the sub-tropical maize
populations revealed nonsignificant variation for plant and ear height (Sohail et al.
2018). Maize breeders therefore, seriously consider
these two traits to improve the lodging resistance and to reduce yield losses
in maize.
Among genotypes and genotype-year-environment interactions, the
improved population C2 showed best performance with enhanced mean
values for yield traits, followed by population C1. Results further
authenticated that C2 genotypes performed better during 2016 at
CCRI, followed by 2017 at UAP. However, base population C0
demonstrated weaker performance during 2016 and 2017 at both locations.
Phenotypic superiority of the selected breeding material over the original
population revealed increased ear length, kernel rows per ear, kernel index,
and grain yield in maize (Sajjad et al. 2016,
2020a). Selection for grain yield provided remarkable genetic gains for ears
per plot, ear weight, 100-grain weight and grain yield in full-sib families of
maize (Cunha et al. 2012). Positive
selection differential was observed for ear length, kernel rows per ear,
1000-kernel weight and grain yield (Okporie et al. 2013). Past studies authenticated
that increase in yield components was based on the evaluation of maize S1
and S2 selfed families (Berilli et al.
2011, 2013; Chen et al. 2019).
Selected maize S1 and S2 populations effectively enhanced
the accretion of desirable genes for quantitative traits with significant
enhancement in yield attributing traits (Kolawole
et al. 2017, 2019).
Based on the populations C1 and C2 performance, simple
recurrent selection was found more effective in improving maize populations for
grain yield (Horne et al. 2016; Khamkoh et al. 2019). Results further revealed greater proportion of
genetic variation among selected and original maize populations indicated
enhanced genetic disparity and efficiency of recurrent selection for improving
grain yield simultaneously.
In present studies, the observed responses
were far greater than expected for earliness, morphological and yield traits.
For earliness, maximum negative responses were reported to be desirable for
flowering which confirmed by improvement in selected populations for earliness
traits. The cycle-wise and average
genetic gain values were significantly negative for days to silking.
In comparison, the C2 populations were observed with greater
negative responses than C1 families for early flowering. For plant
and ear height and grain yield, the observed responses were positive and
greater than expected responses in selected populations of cycle-1 and cylce-2.
However, for ears per plant and prolificacy,
the values of observed responses were negative and less than expected responses
in both cycles. For plant height and yield related traits the values for all
the genetic gain were significantly positive. Maize full-sib families
with direct selection for earliness and yield attributes revealed negative
values of genetic gain for flowering while positive for yield related traits
(Cunha et al. 2012). Based on
selection differential, the highest observed and expected responses were
recorded for earliness and yield related traits in improved maize populations,
however, the responses were negative for earliness traits in maize (Ishaq et al.
2014). Badu-Apraku et al. (2013) reported varied and encouraging genetic gain per
cycle for yield related traits in maize under recurrent selection. However, Carangal et al.
(1971) reported close resemblance between observed and expected responses with
two cycles of S1 recurrent selection in maize. Present studies
authenticated that two cycles of phenotypic selection helped up to some extent
in accumulating favourable alleles in the improved maize populations.
Conclusion
Maize improved population C2 showed
improvement in earliness and grain yield over the environments, followed by C1
population. Base population C0 recorded with late flowering
and minimum grain yield over environments. Overall, the observed responses were
greater than expected for majority of the traits in cylce-1 and cycle-2. Greater values of cycle-wise and average
genetic gain were recorded in C2 populations compared to C1
families for various traits. The selfed progeny
recurrent selection was found more effective in improving maize base population
for flowering and yield traits.
Acknowledgements
Authors are thankful to the University of
Agriculture, Peshawar (UAP), Pakistan for
administrative support, and also to the Department of Plant Breeding and
Genetics, UAP for various assistances throughout the research project.
Author Contributions
MS, NUK and
SG visualized the idea, designed and executed the study; SUK and IHK recorded
and analyzed the data; SAK, SA and NA made Tables and illustrations; IT and ZB
collected the review and drafted the manuscript; SMK and IH interpreted the
results and reviewed the contents. All authors improved the write-up by reading
and approved the final version of the manuscript.
References
Abdulmalik RO, A Menkir, SK Meseka, N Unachukwu, SG Ado, JD Olarewaju, DA Aba, S Hearne, J Crossa,
M Gedil (2017). Genetic gains in grain yield of a
maize population improved through marker assisted recurrent selection under
stress and non-stress conditions in West Africa. Front Plant Sci 8; Article 841
Ahmad M, S Khan, F Ahmad, NH Shah, N Akhtar (2010).
Evaluation of 99 S1 lines of maize for inbreeding depression. Pak J Agric Sci 47:209‒213
Ali F, D Shahwar, M Muneer, W Hassan, H
Rahman, M Noor, T Shah, I Ullah, M Iqbal, K Afridi, H Ullah (2012).
Heritability estimates for maturity and morphological traits based on testcross
progeny performance of maize. J Agric Biol Sci 7:317‒324
Ali F, M Muneer, H Rahman, M Noor,
D Shahwar, S Shaukat, J Yan
(2011). Heritability
estimates for yield and related traits based on testcross progeny performance
of resistant maize inbred lines. J Food Agric Environ 9:438‒443
Ali S (2015). Genetic
analysis and genotype by environment studies in maize. Ph.D. Dissertation.
Department of Plant Breeding and Genetics, The
University of Agriculture, Peshawar, Pakistan
Ali S, NU Khan, IH Khalil, M Iqbal,
S Gul, S Ahmed, N Ali, M Sajjad, K Afridi, I Ali, SM Khan (2017). Environment
effects for earliness and grain yield traits in F1 diallel
populations of maize (Zea mays L.). J Sci Food Agric 97:4408‒4418
Ali S, NU Khan, R Gul, I Naz, R Goher, N Ali, SA Khan, I Hussain, M
Saeed, M Saeed (2018). Genetic analysis for earliness and yield traits in
maize. Pak J Bot 50:1395‒1405
Ali S, NU Khan, S Gul, R Goher, I Naz, SA Khan, N Ali, M Saeed, I
Hussain, SM Khan, I Ali (2019). Heterotic effects for yield related attributes in F1
populations of maize. Pak J Bot
51:1675‒1686
Ali S, NU Khan, S Gul, SU Khan, I
Tahir, Z Bibi, IH Khalil, N Ali, SA Khan, I Hussain, I Ali, SM Khan (2020). Genotype by environment interactions affecting heterotic effects in
maize for earliness traits and grain yield. Intl J Agric Biol 23:983‒993
Allard RW(1971).
Principles of Genetic
Improvement of Plants. Edgard Blucher, São Paulo, Brazil
Andorf C, WD Beavis, M Huford, S Smith, WP Suza,
K Wang, M Woodhouse, J Yu, T Lübberstedt (2019).
Technological advances in maize breeding: Past, present and future. Theor Appl Genet 132:817‒849
Annor B, B Badu-Apraku, D Nyadanu, R Akromah, MAB Fakorede (2019). Testcross
performance and combining ability of early maturing maize inbreds
under multiple-stress environments. Sci Rep 9:13809-13819
Anonymous (2018–2019).
Pakistan Economic Survey 2018–2019. Pakistan Bureau of Statistics (PBS),
Ministry of Finance, Revenue and Economic
Affairs, Govt. of Pakistan, Islamabad, Pakistan
Arain GN (2013). Center Pivot Irrigation System Valley Irrigation Pakistan
(Private), Limited, Pakistan (http://www.valleyirrigation Pakistan.
com/wp-content/uploads/2012/09/Maize-Cultivation-in-Pakistan1.pdf)
Ayiga-Aluba J, R Edema, G Tusiime,
G Asea, P Gibson (2015). Response
to two cycles of S1 recurrent selection for Turcicum
leave blight in an open pollinated maize variety population (Longe 5). Adv Appl
Sci Res 6:4‒12
Badu-Apraku B, M Oyekunle,
MAB Fakorede, M Aderounmu
(2013). Effects of
three cycles of S1 selection on genetic variances and correlations
of an early maize population under drought and well-watered environments.
ASA, CSSA, and SSSA International Annual Meetings, Nov. 3‒6, Tampa, Florida, USA
Bedada LT, H Jifar (2010). Maize (Zea
mays L.) genetic advances through S1 recurrent selection in
Ethiopia. J Environ
Issues Agric Dev Count 2:154‒169
Berilli APCG, MG Pereira, LSA Gonçalves,
KS Cunha, HC Ramos, GAS Filho, ATJ do-Amaral (2011). Use of
molecular markers in reciprocal recurrent selection of maize increases
heterosis effects. Genet Mol Res 10:2589‒2596
Berilli APCG, MG Pereira, RS Tindade, FR da-Costa, KS da-Cunha (2013). Response to the selection in the 11th cycle
of reciprocal recurrent selection among full-sib families of maize. Acta Sci 35:435‒441
Bolanos J, GO Edmeades (1996). The importance of anthesis-silking interval in breeding for drought tolerance
in tropical maize. Field Crop Res 48:65‒80
Carangal VR, SM Ali, AF Koble, EH Rinke, JC Sentz (1971). Comparison of S1
with testcross evaluation for recurrent selection in maize. Crop
Sci 11:658‒661
Chen ZH, YF Zhu,
AG Wang, XY Guo, X Wu, PF Liu (2019). Effects of reciprocal recurrent selection on grain yield
in two tropical-temperate maize synthetic populations Tuxpeño-Reid
and Suwan-Lancaster. Amer
J Plant Sci 10:298‒308
Cobb JN, RU Juma,
PS Biswas, JD Arbelaez, J Rutkoski, G Atlin, T Hagen, M
Quinn, EH Ng (2019). Enhancing the rate of genetic gain in
public-sector plant breeding programs: Lessons from the breeder’s equation.
Theor Appl Genet
132:627‒645
Cunha KSD, MG Pereira, LSA Gonçalves, APCG Berilli, EC
de-Oliveira, HCC Ramos, ATA Júnior (2012). Full-sib reciprocal
recurrent selection in the maize populations CIMMYT and Piranão. Genet Mol Res 11:3398‒3408
Darrah LL, MD McMullen, MS Zuber (2019). Breeding, Genetics
and Seed Corn Production. In: Corn, 3rd edn, pp:19–41. Sergio O, Serna-Saldivar (Eds.). AACC International
Press, Washington DC, USA
Gomez KA, AA Gomez
(1984). Statistical
Procedures for Agricultural Research. John Wiley and Sons, New York,
USA
Guimaraes AG, ATA Junior, JEA Filho, GF Pena, C Vittorazzi,
MG Pereira (2018). Population structure and impact of recurrent selection
on popcorn using EST-SSR markers. Acta
Sci Agron 40:1‒10
Hallauer
AR, MJ Carena (2012). Recurrent
selection methods to improve germplasm in maize. Maydica
57:226‒283
Hallauer AR,
JB Miranda-Filho, MJ Carena (2010). Quantitative Genetics in Maize
Breeding, 3rd edn.
Springer, New York, USA
Horne DW,
MS Eller, JB Holland (2016). Responses to
recurrent index selection for reduced Fusarium ear rot and lodging and for
increased yield in maize. Crop Sci 56:85‒94
Hussain M, A Latif, W Hassan, S
Farooq, S Hussain, S Ahmad, A Nawaz (2019). Maize
hybrids with well-developed root system perform better under deficit
supplemental irrigation. Soil Environ
38:203‒213
Ishaq M, G Hassan, H Rahman, M Iqbal, IA Khalil, SA Khan, SA Khan, R Ullah, J
Hussain (2014). Estimates of heritability and expected response for maturity
and grain yield related traits in half-sib recurrent families of maize. Pak
J Biotechnol 11:141‒151
Keeling BI (1982). Effect
of soybean mosaic virus on root volume and dry weight of soybean plants.
Crop Sci 22:629‒639
Khalil IA, H Rahman, D Shahwar,
I Nawaz, H Ullah, F Ali (2010). Response to selection for grain yield under maydis leaf
blight stress environment in maize (Zea
mays L.). Biol Divers Conserv 3:121‒127
Khamkoh W, D Ketthaisong, K Lomthaisong, K Lertrat, B Suriharn (2019). Recurrent selection method for improvement of lutein and zeaxanthin
in orange waxy corn populations. Aust
J Crop Sci 13:566‒573
Khan K, NU Khan, M Iqbal, H Sher, S
Gul, N Ali (2018). Populations of exotic × locally adapted germplasm – A potential source of inbred lines for superior
indigenous maize hybrids. Tarim
Bilim Derg J Agric Sci24:413‒421
Kolawole AO, A Menkir, E Blay, K Ofori, JG Kling (2019). Changes
in heterosis of maize (Zea mays L.) varietal cross hybrids after
four cycles of reciprocal recurrent selection. Cer
Res Commun 47:145‒156
Kolawole AO, A Menkir, M Gedil, E Blay, K Ofori, JG Kling (2017). Genetic
divergence in two tropical maize composites after four cycles of reciprocal
recurrent selection. Plant Breed 136:41‒49
Kumar A, SL Jat, R Kumar, OP Yadav (2013). Maize production systems for
improving resource-use efficiency and livelihood security. Directorate
of Maize Research, Pusa Campus, New Delhi, India
Lush JL (1940) Inter-size correlation regression of
offspring on dairy as a method of estimating heritability of characters. Proc Amer Soc Anim
Prod 33:293‒301
Minhas WA, M Hussain, N
Mehboob, A Nawaz, S Ul-Allah, MS Rizwan, Z Hassan (2020). Synergetic use of
biochar and synthetic nitrogen and phosphorus fertilizers to improves maize
productivity and nutrient retention in loamy soil. J Plant Nutr 43:1356‒1368
Noor M, D Shahwar,
H Rahman, H Ullah, F Ali, M Iqbal, IA Shah, I Ullah (2013). Change in
heritability estimates due to half-sib family selection in the maize variety
Pahari. Genet Mol Res 12:1872‒1881
Okporie EO, SC Chukwu, GC Onyishi (2013). Phenotypic recurrent selection for increase yield and chemical
constituents of maize (Zea mays L.). ‒
Pixley KV, T Dhliwayo, P Tongoona
(2006). Improvement of maize population by full-sib selection
alone versus full-sib with selection during inbreeding. Crop Sci 46:1130‒1136
Rahman H, H Ullah, L Shah, A
Ali (2015). Estimates of heritability and genetic advance for
morphological traits improvement in maize (Zea
mays L.). Acad J Agric Res 3:9‒14
Reis MCD, FL Guedes,
GB Abreu, JC Souza (2013). Reciprocal recurrent selection in maize enhances
heterosis and ears yield. ‒
Ruiz-de-Galarreta JI, A
Alvarez (2007). Six cycles of S1 recurrent selection in two Spanish maize synthetics. Span J Agric Res 5:193‒198
Sajjad M (2018). Response of a maize composite to selfed progeny recurrent selection for grain yield and
yield components. Ph.D. Dissertation.
Department of Plant Breeding and Genetics, The
University of Agriculture, Peshawar, Pakistan
Sajjad
M, NU Khan, S Gul, SU Khan, I Tahir, Z Bibi,S Ali, N Ali, SA Khan, SM Khan, I Hussain (2020a). Maize (Zea mays L.) cyclical populations
response over environments – developed via recurrent selection. Maydica 65:1‒10
Sajjad M, NU Khan, S Gul, SU
Khan, Z Bibi, S Ali, N
Ali, SA Khan (2020b). Maize improvement through selfed progeny recurrent selection across different
environments. Pak J Bot 52:541‒549
Sajjad M, NU Khan, H Rahman, K Khan, G Hassan,
S Gul, S Ali, K Afridi, I Ali, SM Khan (2016). Response of a maize composite
to selfed progeny recurrent selection for earliness
and yield traits. Maydica 61:1‒8
Which recurrent
selection scheme to improve mixtures of crop species? Theoretical
expectations. ‒
Santos PG,
FC Juliatti, AL Buiatti, OT
Hamawaki (2002). Evaluation
of the agronomic performance of corn hybrids in Uberlândia,
MG, Brazil.Pesq Agropec Bras37:597‒602
Shah SS, H Rahman, IH Khalil, A
Rafi (2006). Reaction of two maize synthetics to maydis leaf blight following
recurrent selection for grain yield. Sarhad
J Agric 22:263‒269
Shah SS, H Rahman, IH Khalil, M Iqbal (2007). Recurrent selection for maydis leaf
blight resistance and grain yield improvement in maize. Pak J Biol Sci
10:3632‒3637
Sheikh F, A Sohail, T Burni, F Hadi, M Asad, A Aziz, A Haleem, M Maryam,
Z Rahman (2019). Impact of half-sib family recurrent
selection on grain yield in maize population ZM-309. Pure Appl Biol 8:2399‒2408
Sohail A, Q Hussain, S Ali, M Manzoor, F Hadi, S Uddin, F Bashir, M Asad, S Sami, Z Yousafzai (2018). Evidence of improving
yield and yield attributes via half-sib family recurrent selection in maize (Zea mays L.). Intl J Curr
Res Biosci Plant Biol
5:45‒56
Tardin FD, MG Pereira, APC
Gabriel, ATA Júnior, GAS Filho (2007). Selection index
and molecular markers in reciprocal recurrent selection in maize. Crop
Breed Appl Biotechnol
7:225‒233
Udo EF, SO Ajala, AB Olaniyan
(2017). Physiological and morphological changes associated with recurrent
selection for low nitrogen tolerance in maize. Euphytica
213:140–152
Ullah K, H Rahman, M Noor, M Rehman, M Iqbal, S
Ullah (2013). Heritability estimates and yield performance of half sib families
derived from maize variety Sarhad White. Sarhad J Agric
29:29‒32