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African Crop Science Journal, Vol. 24, No. 3, pp. 247 - 257 ISSN 1021-9730/2016 $4.00
Printed in Uganda. All rights reserved © 2016, African Crop Science Society
African Crop Science Journal by African Crop Science Society is licensed under
a Creative Commons Attribution 3.0 Uganda License. Based on a work
at www.ajol.info/ and www.bioline.org.br/cs
DOI: http://dx.doi.org/10.4314/acsj.v24i3.3
RESPONSE OF LOCALLY ADAPTED PEARL MILLET POPULATIONS TO S1 PROGENY
RECURRENT SELECTION FOR GRAIN YIELD AND RESISTANCE TO RUST
G. LUBADDE, P. TONGOONA1, J. DERERA1 and J. SIBIYA1
National Semi-Arid Resources Research Institute, P. O. Box 56, Soroti, Uganda
1University of KwaZulu Natal, Pietermaritzburg Campus, Private Bag X01, Scottsville, 3209, South Africa
Corresponding author: glubadde@gmail.com
(Received 6 March, 2016; accepted 18 July, 2016)
ABSTRACT
In the semi-arid zones of Uganda, pearl millet (Pennisetum glaucum (L.) R. Br.) is mainly grown for food and
income; but rust (Puccinia substriata var indica (L.) R. Br.) is the main foliar constraint lowering yield. The
objective of the study was to genetically improve grain yield and rust resistance of two locally adapted populations
(Lam and Omoda), through two cycles of modified phenotypic S1 progeny recurrent selection. Treatments
included three cycles of two locally adapted pearl millet populations, evaluated at three locations. Significant net
genetic gain for grain yield (72 and 36%) were achieved in Lam and Omoda populations, respectively. This led to
grain yield of 1,047 from 611 kg ha-1 in Lam population and 943 from 693 kg ha-1 in Omoda population.
Significant improvement in rust resistance was achieved in the two populations, with a net genetic gain of -55 and
-71% in Lam and Omoda populations, respectively. Rust severity reduced from 30 to 14% in Lam population
and from 57 to 17% in Omoda population. Net positive genetic gains of 68 and 8% were also achieved for 1000-
grain weight in Lam and Omoda, respectively. Traits with a net negative genetic gain in both populations were
days to 50% flowering, days to 50% anthesis, days to 50% physiological maturity, flower-anthesis interval,
plant height and leaf area.
Key Words: Pennisetum glaucum, Puccinia substriata
RÉSUMÉ
Dans la zones semi-arides en Ouganda, le milet perlé (Pennisetum glaucum (L.) R. Br.) est principalement cultivé
comme culture vivrière et de rente, mais la maladie de rouille (Puccinia substriata var indica (L.) R. Br.) est la
contrainte majeure affectant le rendement. L’ojectif de l’étude était l’amélioration du rendement en grains et la
résistance à la maladie de rouille chez deux populations localement adaptées milet perlé (Lam and Omoda), ceci
à travers deux cycles de selection reccurente. Les traitements consistaient à trois cycles de deux populations
localement adaptées de milet perlé, évaluées dans trois milieu différents. Un gain genetique significatif de 72 et
36% de rendements en grain a été observe respectivement chez les populations de Lam et de Omoda. Ceci a
occasionné des rendements en grains de 1,047 kg ha-1 au lieu de 611 kg ha-1 chez la population de Lam et 943 kg
ha-1 au lieu de 693 kg ha-1 chez la population de Omoda. Une amélioration significative de la résistance à la maladie
de rouille a été obtenue au sein des deux populations, avec des gains génétiques nets de -55 et -71% respectivement
chez les populations de Lam et Omoda. La sévérité de la maladie de rouille a été de 30% à 14% au sein de la
population de Lam population et de 57% à 17% au sein de la population de Omoda. Un gain génétique positif net
de 68 et 8% ont été également obtenu respectivement pour le poid de 1000 grains de Lam et 1000 de Omoda. Les
caractères comme le nombre jours à 50% de floraison, le nombre de jours à 50% anthèse, le nombre de jours à 50%
G. LUBADDE et al.
248
de maturité physiologique, l’intervalle de temps entre la floraison et l’anthèse, la hauteur des plants et la surface
des feuilles.
Mots Clés: Pennisetum glaucum, Puccinia substriata
INTRODUCTION
Pearl millet (Pennisetum glaucum (L.) R. Br.) is an
important multipurpose cereal, which performs
well under low input environments of Asia and
Africa (Izge et al., 2006). It is grown for food, and
stover used as cooking fuel, building material or
fed to livestock (Vetriventhan et al., 2008;
Basavaraj et al., 2010). It is adapted to drought-
prone environments, where other cereals hardly
survive; thus making it the world’s hardiest crop
(Reddy et al., 2012). Despite the importance and
resilience to adverse conditions, on-farm
productivity in Uganda is still low (400-600 kg
ha-1) compared with the potential of over 4000 kg
ha-1 achieved under research environments
(Kountche et al., 2013). This is partly due to biotic
constraints such as downy mildew (Sclerospora
graminicola Sacc.), Striga spp., blast
(Pyricularia grisea (L.) R. Br.), ergot (Claviceps
fusiformis Loveless.), smut (Tolyposporium
penicillariae Bref.), rust (Puccinia substriata var
indica (L.) R. Br.,) and birds (Lakshmana et al.,
2010).
However, in Uganda rust is one of the major
diseases reducing grain yield (Lubadde et al.,
2014). The most economical option to control rust
is to breed for resistance. However, breeding for
monogenic resistance has not been effective
because of the multiple races of the pathogen
that exist; but developing varieties with many
small-effect genes has proved to be the most
effective approach to control the disease. This
may be achieved through recurrent selection
since it leads to accumulation of many small-effect
gene loci (Menkir and Kling, 2007). The result is
an improved population with maintained genetic
variability, which enables response to further
improvement (Baskaran et al., 2009).
Recurrent selection has the advantage of
increasing the frequency of favourable alleles
through additive, partial dominance, dominance
or over dominance gene actions. Several recurrent
selection schemes have been adopted to improve
pearl millet populations. Through full-sib
recurrent selection, Bidinger et al. (2006)
improved grain yield and stover quality; while
significant increases in grain yield and striga
resistance were achieved through five cycles of
both full-sib phenotypic recurrent selection and
S1 progeny recurrent selection (Kountche et al.,
2013). Although full-sib recurrent selection has
the advantage of the improved populations being
in their natural highly heterozygous state, there
is less probability to identify and move forward
desirable recessive alleles compared to S1
progeny recurrent selection (Kountche et al.,
2013). The S1 progeny recurrent selection is
superior to either half- or full-sib schemes for
improving grain yield, because it leads to
increased panicle length and surface area (Dutt
and Bainiwal, 2005). Basing on this superiority,
the phenotypic S1 progeny recurrent selection
scheme was adopted to improve two locally
adapted populations in Uganda. The objective
of this study was to improve grain yield and
resistance to rust of two locally adapted and
commonly grown pearl millet populations,
through two cycles of phenotypic S1 progeny
recurrent selection.
MATERIALS AND METHODS
Experimental materials. Two experimental
populations (Lam and Omoda) were selected from
the predominantly pearl millet growing regions
in northern and eastern Uganda. The Lam
population was collected from northern; while
the Omoda population was collected from eastern
Uganda. The populations were described by
farmers as being low grain yielding and rust
susceptible, but drought tolerant, with good taste.
Farmers described Omoda population as short in
terms of duration and plant height; while Lam
population was tall and late maturing. These traits
were the basis for improvement through
phenotypic S1 progeny recurrent selection.
Locally adapted pearl millet populations to S1 progeny 249
Developing cycles. Selection and recombination
trials were done at the National Semi-Arid
Resources Research Institute (NaSARRI),
located in Serere district in eastern Uganda.
Approximately 2000 C0 plants of each population
were grown and 500 plants (20% selection
pressure) with rust severity of less than 20%,
selected from each population and selfed. The
selfed seed was bulked and 0.10 ha (20 m x 50 m)
plots planted with each population for
recombination to form the first cycle (C1) seed.
The modification was to rogue before flowering
leaving plants with less than 20% rust severity
for recombination. The same procedure was
adopted to form cycle two (C2). A summary of
the recurrent selection scheme indicating the time
frame is shown in Table 1.
Field evaluation of the cycles. Evaluation of
cycles C0, C1 and C2 of the two populations was
done in 2014, at three sites; namely Serere
(1°32’N, 33°27’E, 1140 metres above sea level),
Kitgum (03°132 N, 032°472 E, 969 m.a.s.l) and
Katakwi (01°542 N, 034°002 E, 1107 m.a.s.l). The
three sites were rust hot-spots and located in
areas where pearl millet is predominantly grown.
Materials were planted in 20 m x 50 m plots, in a
completely randomised block design, with three
replicates and 60 cm x 30 cm spacing. Hand
weeding was done twice and NPK fertiliser was
applied at 40:30:35 kg ha-1, respectively, as
recommended by Khairwal et al. (2007). The
fertiliser was applied in two splits, the first one at
sowing and the other 4 weeks after sowing.
Data collection and analysis. Data were collected
at 50% physiological maturity on at least 36 plants
per plot. The data included; rust severity at 50%
physiological maturity, using the modified
Cobb’s disease severity scale (0-100%); panicle
length (Lp cm) and panicle girth (Wp cm) for
calculating panicle area (cm2) using formula (3.14
x Lp x Wp), 1000-grain weight, plant height, days
to 50% flowering, days to 50% anthesis, flower-
anthesis interval, days to 50% physiological
maturity, number of productive tillers, grain yield,
harvest index, leaf length and leaf breadth of third
leaf from plant top for calculating leaf area . Data
analyses were conducted using SAS software,
Version 9.2 (SAS Institute. Inc., 2012), where
TABLE 1. Modified S1 recurrent selection scheme for pearl millet rust resistance study in Uganda
Season Activity
First season (February - June 2012) Growing 2000 plants for each population (C0 populations) and keeping remnant seedInoculation with rust urediniospores Selecting (S0) and selfing
500 plants (S1 progeny) showing low severity (10-20%) from each population and bulking the seed
Second season (August - November 2012) Growing 2000 plants of each population and inoculating with rust urediniospores Rogueing was done before flowering to leave 500 plants with
less than 20% rust severity for recombination. Bulking of selected C0 plants was done to form C1 seed and remnant seed kept
First season (February - June 2013) Growing 2000 plants from each of the two C1 populations Inoculation with rust urediniospores Selecting and selfing 500 plants with less than 20%
rust severity to form S1 progeny
Second season (August - November 2013) Growing 2000 of S1 progeny from the C1 populations and inoculating with rust urediniospores Rogueing before flowering to leave 500 plants with
less than 20% rust severity for recombinationBulking seed to form C2 seed
First season (February - June 2014) Evaluating the C0, C1, and C2 of each population in three rust hot spot environments (Serere, Kitgum and Katakwi)
G. LUBADDE et al.
250
analysis of variance for the traits was determined
based on Proc GLM using the model:
Y= µ + rep (sites) + sites + cycles + varieties +
sites x varieties + varieties x cycles + sites x cycles
+ random error
Where:
Y = observed value; µ = grand mean; rep/blocking
= replication effect with 3 levels; sites = site effect
with 3 levels; varieties = effect of varieties; cycle
= cycle effect and interaction of cycles, sites and
varieties
Response to selection was determined using the
means of the cycles C0, C1 and C2 for populations;
Broad sense heritability was calculated using the
formula; H2=Vg/Vp*100:
Where:
H2 = Broad sense heritability, Vg= genetic
variance; Vp= phenotypic variance = (Vg +
interaction variances + estimated error mean
square); µC0 = mean for C0; µC1= mean for C1; µC2=
mean for C2.
Gain per cycle was determined using differences
between cycle means as:
(µC2 – µC0), (µC2 – µC1), (µC1 – µC0)
RESULTS AND DISCUSSION
Analysis of variance of the cycles. Sites had no
significant effect on grain yield (GY) and four
other traits (flower-anthesis interval (FAI), days
to 50% physiological maturity (PSM50), plant
height (PLH) and number of productive tillers
(PRT0) but were important for rust severity
(RUST), flowering days (FLO50), anthesis days
(ANT50), panicle area (PAR), leaf area (LAR),
harvest index (HI) and 1000-grain weight (TGW)
(Table 2). The main effects of cycles and
populations were significantly important for all
the traits. In addition, the interactions involving
the cycles, populations and sites were
significantly (P<0.05) important for GY and RUST
and five other traits. The variation of cycles and
populations in response to GY, RUST and other
traits across sites may be attributed to genetic
differences in the base populations used in this
study. Such variation was also reported by Dutt
and Nirania (2005), who worked at Chaudhary
Charan Singh, Haryana Agricultural University,
Hisar-India. Unlike in this study, they reported
significant differences in cycle for GY, PRT and
PLH when they compared various schemes of
recurrent selection. Likewise, Bidinger et al.
(2006) reported no significant effects of cycles
for PAR and HI, thus contrasting with our study
findings.
The variation of cycles and populations in
trait response may be due to the genetic
differences in base populations (Table 3), as seen
for Omoda population. The variation in response
to selection indicates a possibility to improve the
traits through phenotypic S1 progeny recurrent
selection (Bidinger et al., 2006). This observation
was also reported by Kannan et al. (2014) in their
study to quantify response to recurrent selection
for grain yield and related traits using SSR
markers. They noted that the possibility was due
to pearl millet being a highly cross-pollinating
crop, with a high level of genetic variability. The
significant effect of cycles and populations on
GY and RUST also shows the suitability of the
phenotypic S1 progeny recurrent selection to
improve the quantitative traits (Hallauer and
Darrah, 2008). The improvement can be achieved
in diverse environments, with minimal
antagonistic interaction (Bidinger and Raju, 2000).
Individual populations had varying response
to selection (Table 4). In Lam population, the effect
of cycles led to increase in grain yield of 436.50
kg ha-1. The observed increase in grain yield was
due to cumulative improvement in TGW, PRT, HI
and improved rust resistance. The rust resistance
increased through the increase of favourable
genes as a result of selection, which largely
depends on the quality of the population being
improved. The response to selection also had
negative significant effects on many traits in the
study populations (Table 5).
Broad sense heritability estimates (Table 3)
were relatively high for GY, RUST, FLO50, ANT50,
TGW for the populations; an indicator that the
phenotypic S1 progeny recurrent selection was
effective in improving these populations.
Locally adapted pearl millet populations to S1 progeny 251
TABLE 2. Mean squares for analysis of variance for Lam and Omoda pearl millet populations in Uganda
Source DF GY RUST FLO50 ANT50 FAI PSM50 PLH PRT PAR LAR TGW HI
Sites 234516.48ns 169.72* 29.97** 24.34** 0.41ns 6.35ns 58.32ns 0.36ns 2454.46* 32392.93* 1.35* 73.85*
Rep(site) 625601.89ns 34.94* 1.07ns 0.62ns 1.11* 5.89ns 59.41ns 4.33ns 767.13* 6440.34ns 0.34ns 21.36ns
Cycle 2539185.32** 4049.58** 1218.29** 1346.35** 3.21* 1261.91** 19162.01** 14.75* 1181.70* 311315.33** 23.86** 53.68*
Populations 17921.15* 2092.66** 10660.58** 11545.71** 17.66** 48631.81** 198852.00** 17.77* 54075.93** 2708272.14** 179.02** 343.12**
Variety*cycle 241223.95* 719.19** 259.12** 307.68** 2.25* 515.18** 10164.46** 100.62** 136.619ns 80857.41** 14.91** 102.47*
Sites*cycle 431076.57* 28.29ns 40.54** 46.24** 0.22ns 82.55* 44.31ns 0.87ns 899.92* 4116.23ns 0.65ns 14.76ns
Sites* populations 244506.15ns 58.14* 0.15ns 0.01ns 0.18ns 57.83ns 409.02* 10.77* 1647.40* 11363.98ns 0.39ns 6.68ns
Sites* populations *cycle 45355.92* 46.16* 3.47* 4.73* 0.87ns 177.26** 80.83ns 7.28ns 1057.23* 17267.05* 0.29ns
Error 30 41580.04 15.69 0.99 1.12 0.49 5.62 81.36 4.03 342.97 5558.34 0.56 20.41
Rsquare 56.59 96.40 99.79 99.78 73.51 99.69 99.07 73.16 88.26 95.69 94.08 63.78
CV (%) 25.14 15.07 1.21 1.23 19.47 1.88 4.02 23.53 13.34 10.96 8.62 38.62
SD 203.91 3.96 0.99 1.06 0.7 2.37 9.02 2.01 18.52 74.55 0.73 4.52
Grand mean 811.05 26.28 82.43 86.03 3.59 126.42 224.48 8.53 138.78 680.35 8.66 11.70
Testing done at α = 0.05, * = significant P<0.05, * = significant P<0.0001, ns = non-significant P>0.05. GY = grain yield (kg plant-1), RUST = rust severity at 50% physiological maturity, FLO50
= days to 50% flowering, ANT50 = days to 50% anthesis, FAI = flower-anthesis interval (days), PSM50 = days to 50% physiological maturity, PLH = plant height (cm), PRT = number of productive
tillers, PAR = panicle area (cm2), LAR = leaf area (cm2), TGW =t housand grain weight (g), HI = %harvest index
G. LUBADDE et al.
252
TABLE 3. Estimates for genetic variance, phenotypic variance and broad sense heritability for Lam and Omoda pearl millet populations in Uganda
Variance Traits
GY RUST FLO50 ANT50 FAI PSM50 PLH PRT PAR LAR TGW HI
Lam
Vg 429526.4 678.33 1297.21 1468.22 5.39 1694.16 28549.41 72.15 1055.5 352848.1 36.13 121.87
VP 578630.1 751.66 1327.67 1497.85 8.74 1948.18 28898.92 91.37 3207.84 387091.4 38.34 193.4
(%H2)74.23 90.24 97.71 98.02 61.62 86.96 98.79 78.96 32.9 91.15 94.26 63.02
Omoda
Vg 150882.9 4090.45 180.21 185.8 0.07 82.93 777.05 43.22 262.81 39324.63 2.64 34.28
VP 267236.2 4394.79 227.63 235.81 1.28 177.84 1322.49 60.06 5942.46 93271.01 5.04 165.44
(%H2)56.46 93.08 79.17 78.79 5.49 46.63 58.76 71.96 4.42 42.16 52.31 20.72
GY = grain yield (kg plant-1), RUST = rust severity at 50% physiological maturity, FLO50 = days to 50% flowering, ANT50 = days to 50% anthesis, FAI = flower-anthesis interval (days), PSM50
= days to 50% physiological maturity, PLH = plant height (cm), PRT = number of productive tillers, PAR = panicle area (cm2), LAR = leaf area (cm2), TGW = thousand grain weight (g), H I= %harvest
index
However, the low heritability estimates achieved
for FAI, PAR and HI in Omoda population shows
that these traits needed more than two selection
cycles for improvement; the low heritability being
an indicator for a possibility for genetic
improvement through recurrent selection. The
high heritability estimates imply that for most of
the traits, the phenotypic variation observed was
mainly due to genetic effects; an indicator that
these traits may be improved in diverse
environments as also noted by Abuali et al. (2012)
and Ezeaku and Mohammed (2006).
The high heritability estimates have been
reported for many traits. Dutt and Bainiwal (2005)
reported high heritability estimates of 80 and 53%
for GY and PAR, respectively. In addition, high
broad sense heritability estimates have been
reported for panicle dimensions (Varu et al., 2005);
while Kountche et al. (2013) reported a 71%
heritability estimate for days to 50% flowering
after five cycles of recurrent selection; although
in this study more than 79% was achieved after
only two cycles of recurrent selection. However,
for GY, Bidinger and Raju (2000) reported low
heritability estimates of 16%. The high broad
sense heritability for TGW was also reported by
Borkhataria et al. (2005) and Solanki et al. (2002),
although Sachan and Singh (2001) reported
moderate broad sense heritability for the same
trait. Therefore, findings from the current study
are consistent with some previous investigations.
Performance of the cycles. Grain yield for the
cycles for the two populations were significantly
different across locations; where C2 performed
better than C1 and C0 (Table 4). This indicates a
positive response to phenotypic S1 progeny
recurrent selection. Lam population had grain
yield improved from 611 to 1,047 kg ha-1, compared
with Omoda, which had a mean grain yield
improved from 693 to 943 kg ha-1. Bidinger and
Raju (2000) reported that if high genetic variation
exists in selected progeny, increase in grain yield
may be due to recombination effects of the cycles.
However, in our study the highest grain yield
attained was still low compared with the potential
of over 4000 kg ha-1 recorded by Kountche et al.
(2013) after five cycles of recurrent selection. This
implies that further improvement in grain yield
may be possible through more selection cycles.
Locally adapted pearl millet populations to S1 progeny 253
TABLE 4. Means for selected traits of cycles for Lam and Omoda pearl millet populations
Traits Lam Omoda
C2 C1 C0 Standard error LSD(0.05) C2 C1 C0 Standard error LSD(0.05)
GY 1047.10a 811.70b 610.60b 280.13 287.72 943.10a 761.18b 692.54b 70.91 72.83
RUST 13.45a 16.83a 29.89b 5.00 5.14 16.76a 23.99b 56.77c 3.60 3.70
FLO50 85.22a 95.11b 109.12c 0.97 1.00 64.61a 67.22b 73.32c 1.15 1.19
ANT50 88.74a 99.07b 114.14c 0.92 0.95 67.52a 70.29b 76.40c 1.02 1.04
FAI 3.52a 3.96a 5.02b 0.68 0.70 2.92a 3.07a 3.08a 0.46 0.47
PSM50 141.10a 160.64b 167.55c 2.19 2.25 92.95a 97.63b 98.64b 2.07 2.12
PLH 222.52a 301.33b 331.63c 11.18 11.48 154.78a 163.27b 173.34c 6.23 6.39
TOT 13.82a 12.53a 13.06a 2.24 2.3 7.87a 8.16a 12.27b 1.69 1.74
PRT 12.37a 7.49b 7.45b 2.08 2.14 6.02a 7.51a 10.34b 1.83 1.88
PRO 89.11a 59.68b 57.51b 7.32 7.52 77.35a 85.35ab 91.65b 12.51 12.85
PAR 158.67a 179.99ab 172.63b 16.04 16.48 112.37a 107.46a 101.58a 21.25 21.83
LAR 675.81a 1025.73b 1011.34b 80.23 82.41 381.21a 482.63b 505.36b 68.89 70.76
1000 GWT 9.14a 5.95b 5.44b 0.58 0.6 10.70ab 10.89a 9.87b 0.93 0.95
BY 0.42a 0.52b 0.59b 0.08 0.09 0.21a 0.20a 0.28b 0.04 0.04
HI13.04a 8.77b 5.72b 3.3 3.39 14.31a 12.22a 16.12a 4.88 5.01
Means with the same letter are not significantly different at P = 0.05; C0, C1 and C2 are cycles for the base populations, cycle 1 and cycle 2. GY = grain yield (kg plant-1), RUST = rust severity
at 50% physiological maturity, FLO50 = days to 50% flowering, ANT50 = days to 50% anthesis, FAI = flower-anthesis interval (days), PSM50 = days to 50% physiological maturity, PLH = plant
height (cm), TOT = total number of tillers, PRT = number of productive tillers, PRO = %productive tillers, PAR = panicle area (cm2), LAR = leaf area (cm2), TGW = thousand grain weight (g), BY
= biological yield (kg plant-1), HI = %harvest index
G. LUBADDE et al.
254
TABLE 5. Genetic gain for Lam and Omoda pearl millet populations in Uganda
Trait Lam population Omoda population
Response to selection Genetic gain Response to selection Genetic gain
C2-C1 C1-C0 Net response (C2-C1)/ (C1-C0)/ Net gain C2-C1 C1-C0 Net response (C2-C1)/ (C1-C0)/ Net gain
C2-C0 C0*100 C0*100 (C2-C0)/ C2-C0 C0*100 C0*100 (C2-C0)/
C0*100 C0*100
GY 235.40 201.10 436.50 29.00 32.94 71.49 181.92 68.64 250.56 23.90 9.91 36.18
RUST -3.38 -13.06 -16.44 -20.06 -43.70 -55.00 -7.23 -32.78 -40.01 -30.13 -57.74 -70.47
FLO50 -9.89 -14.01 -23.89 -10.40 -12.84 -21.90 -2.62 -6.10 -8.72 -3.90 -8.32 -11.89
ANT50 -10.33 -15.07 -25.39 -10.43 -13.20 -22.25 -2.77 -6.11-8.88 -3.94 -8.00 -11.62
FAI -0.44 -1.07 -1.50 -11.10 -21.22 -29.96 -0.15 -0.01 -0.16 -4.79 -0.40 -5.16
PSM50 -19.54 -6.91 -26.45 -12.17 -4.13 -15.79 -4.68 -1.01 -5.69 -4.80 -1.02 -5.77
PLH -78.81 -30.30 -109.11-26.15 -9.14 -32.90 -8.48 -10.08 -18.56 -5.20 -5.81 -10.71
PRT 4.88 0.04 4.92 65.22 0.52 66.09 -1.49 -2.83 -4.32 -19.82 -27.34 -41.74
PAR -21.32 7.36 -13.97 -11.85 4.26 -8.09 4.91 5.88 10.80 4.57 5.79 10.62
LAR -349.92 14.39 -335.53 -34.111.42 -33.18 -101.42 -22.73 -124.15 -21.01 -4.50 -24.57
TGW 3.19 0.51 3.70 53.55 9.40 67.97 -0.19 1.02 0.83 -1.74 10.31 8.40
HI 4.28 3.05 7.33 48.77 53.31 128.28 2.09 -3.90 -1.81 17.07 -24.19 -11.25
GY = grain yield (kg ha-1), RUST = rust severity at 50% physiological maturity, FLO50 = days to 50% flowering, ANT50 = days to 50% anthesis, FAI = flower-anthesis interval (days), PSM50= days
to 50% physiological maturity, PLH = plant height (cm), PR T= number of productive tillers, PAR = panicle area (cm2), LAR = leaf area (cm2), TGW = thousand grain weight (g), HI = %harvest
index
Locally adapted pearl millet populations to S1 progeny 255
But further selection for grain yield should be
done concurrently with selection for rust
resistance as the mean rust severity attained after
two cycles was still above the resistance severity
level of less than 10% in both populations. In
the present study, rust severity was reduced to
14% from about 30% recorded in the base
population for Lam; while a reduction to 16.8%
rust severity from 57% was observed in the
Omoda population. This shows an improvement
in rust resistance attained through the two cycles
of phenotypic S1 progeny recurrent selection.
Genetic gains per cycle. A net positive genetic
gain for GY (72 and 36%) and TGW (68 and 8%)
was achieved, while a net genetic loss was
attained for FLO50 (-10 and -12%) and PLH (-33
and -11%), respectively (Table 5), in Lam and
Omoda populations. The results differ from those
reported by Dutt and Bainiwal (2005) where a 20%
genetic gain was achieved for GY when they
compared cycles of various schemes of recurrent
selection at Chaudhary Charan Singh, Haryana
Agricultural University, Hisar-India. Dutt and
Bainiwal (2005) further reported a genetic gain of
21% for PAR; while in this study a net negative
genetic gain of 8% and net positive genetic gain
of 11% was recorded for Lam and Omoda
populations, respectively. For TGW, a high
genetic gain of 68% was reported in Lam
population; while a much lower gain (8%) was
achieved for Omoda population; showing
variation in response to selection in the two
populations. This further emphasises the
importance of phenotypic S1 progeny recurrent
selection as an effective scheme for improving
pearl millet populations. A negative net genetic
gain for rust resistance was achieved in both
populations; indicating a genetic improvement
for rust resistance through selection. A positive
net genetic gain was achieved in the Lam
population for PRT and HI (Table 5); while a net
genetic loss was realised in the Omoda
population for the same traits. In addition, the
two cycles of selection resulted in a net genetic
loss in both populations for FLO50, ANT50, FAI,
PSM50, PLH, and LAR; an indicator that, through
selection, some traits may be improved while a
loss may occur in others. The loss in genetic gain
may be attributed to the effect of inbreeding
depression due to selfing. In addition, the rapid
change in genetic gain for GY and RUST after
two cycles of selection indicates that the two
traits were controlled by a relatively large number
of small effect genes.
CONCLUSION
Significant increases in grain yield and rust
resistance are achieved through two cycles of
phenotypic S1 progeny recurrent selection.
Results show that genetic variability exists for
low grain yield in the rust susceptible populations
and phenotypic S1 progeny recurrent selection
may effectively be exploited to improve the yield
and resistance to rust in locally adapted pearl
millet populations. The improvement in the grain
yield, rust resistance and other yield-related traits
is reflected in the significant desirable genetic
gains observed. The improvement in grain yield
and rust resistance is further confirmed by the
higher grain yield and lower rust severity
achieved in the second cycle of selection. This is
an indicator that through the two cycles of
phenotypic S1 recurrent selection genetic
improvement for grain yield and rust resistance
is achievable. However, higher broad sense
heritability estimates are evident in the Lam
population relative to Omoda population. This
indicates that the two populations have differing
potential for genetic improvement. In addition,
low heritability is registered for traits like flower-
anthesis interval, panicle area and harvest index
in Omoda population; implying that these traits
needed more cycles of recurrent selection to
achieve better genetic improvement. A rapid
change in genetic gain for grain yield and rust
resistance and other traits, after two cycles of
selection, in the two populations indicates that
the phenotypic S1 progeny recurrent selection is
effective in achieving genetic improvement of the
two traits and thus improving rust resistance and
grain yield of the two locally adapted populations
Lam and Omoda. Results from this study also
show that response to recurrent selection
depends on the genetic back ground of the
population and the target traits.
G. LUBADDE et al.
256
ACKNOWLEDGEMENT
African Centre for Crop Improvement for funding
the study, the National Agricultural Semi-Arid
Resources Research Institute and Ngetta Zonal
Agricultural Research and Development Institute
for providing the land for experimental set-up.
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