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Global Advanced Research Journal of Agricultural Science (ISSN: 2315-5094) Vol. 3(8) pp. 271-277, August, 2014.
Available online http://garj.org/garjas/index.htm
Copyright © 2014 Global Advanced Research Journals
Full Length Research Paper
Agronomic performance and genetic dissimilarity among
cowpea (Vigna unguiculata (L.) Walp.) genotypes
Jeferson Antonio da Silva Santos1, Carla Medianeira Giroletta Soares1, Agenor Martinho
Corrêa1, Paulo Edurdo Teodoro1, Larissa Pereira Ribeiro1*, Hadassa Kathyuci Antunes de Abreu
1
Department of Plant Science, State University of Mato Grosso do Sul, University Unit of Aquidauana - Rodovia
Aquidauana/UEMS, Km 12, Zip Code 79200000, Aquidauana, Mato Grosso do Sul, Brazil.
Accepted 13 August, 2014
The aim of this study was to evaluate the agronomic performance and genetic divergence among cowpea
(Vigna unguiculata (L.) Walp.) genotypes in Savanna/Pantanal Brazilian ecotone region. The experiment
was conducted in 2009 in the experimental area at State University of Mato Grosso do Sul, University Unit
of Aquidauana (UEMS/UUA). Treatments consisted of 17 cowpea genotypes arranged in a randomized
complete block design with four replications. The following characters were evaluated: days to flowering,
days to maturity, mass of five pods, grain mass of five pods, grain index, mass of hundred grains and
grain yield. The genetic dissimilarity between treatments was determined by multivariate techniques that
are based on clustering analysis and principal components. The genotypes did not differ for the character
grain yield. The multivariate analysis used brought together the genotypes into distinct groups. There
was high correlation between Ward’s and Tocher’s clustering methods based on the Euclidean distance
mean square. Hybrids with high heterotic effect will be obtained from the crossing between the genotypes
MNC99-510F-16-3 x Pingo-de-ouro-1-2 and BRS-Paraguassu x Inhuma.
Keywords: divergence, genetic variability, clustering methods, principal components, dissimilarity mensures.
INTRODUCTION
Cowpea (Vigna unguiculata (L.) Walp.) is one of the most
important and strategic food sources for tropical and
subtropical regions of the world. Currently represents about
15% of the bean production in Brazil, and constitutes the
staple dish of lower income classes (Teófilo et al., 2008).
According to Leite et al. (2009), although it is considered
a tropical crop with wide adaptation to different
environments, cowpea still have low productivity levels
-1
(300 kg ha ), being the use of low technological level in the
*Corresponding Author's Email: [email protected]
activity, associated to use of cultivars with low productive
potential, the main causes that contribute for this scenario
(Cardoso & Ribeiro, 2006).
It is noted a wide genetic variability in the species, which
features lines and cultivars that differ regarding phenology,
plant health, productive potential and commercial quality.
This justifies the conduct of trials located in different
climate and soil conditions in order to indicate the most
productive and best commercial acceptance cultivars for
each place.
In Brazil there are cultivars with high commercial
acceptance. However, there is no breeding program aiming
at evaluation
and recommendation in specific
272. Glo. Adv. Res. J. Agric. Sci.
Table 1. Chemical analysis of the soil at experimental area in layer 0-0.20 m
pH
6.1
P
-3
(mg dm )
44.4
O.M.
(%)
1.4
K
Ca
Mg
Al
-3
--------------------.(cmolc dm )------------------0.25
2.0
0.3
0.1
H+Al
CEC
2.7
5.25
V
%
49
P: Mehlich. O.M.: organic matter; CEC: cation exchange capacity.
environments and areas of the Brazilian Midwest region,
where the crop is in full expansion (Oliveira et al., 2002).
Teixeira et al. (2010), evaluating the agronomic
performance of cowpea cultivars in the State of Goiás,
-1
obtained yield higher than 2,000 kg ha , which emphasizes
the adaptability and yield potential of the crop in this
Brazilian region.
Considering the importance that the crop show in the
country, it is necessary that studies be conducted to
assess the genetic dissimilarity, aimed at the selection the
most divergent and higher productive potential genotypes
for future breeding program. The genetic diversity
evaluation of populations allows the knowledge of the best
hybrid combinations, enabling the achievement of superior
genotypes in segregating generations (Passos et al., 2007)
and, consequently, aiding the breeder in selection of the
most promising and favorable combinations to crossings
(Falconer, 1989).
Given the above, the aim of this study was to evaluate
the agronomic performance and genetic dissimilarity
among 17 cowpea genotypes in Savanna/Pantanal
Brazilian ecotone region.
MATERIAL AND METHODS
The experiment was conducted during April to July 2009 in
the experimental area of the State University of Mato
Grosso do Sul, University Unit of Aquidauna (UEMS/UUA),
city of Aquidauna-MS, region belonging to transition zone
between the Savanna and Pantanal, comprising the
geographic coordinates 20º 20'S and 55° 48'W, at an
altitude of 207 meters.
The region climate, according to the classification
described by Köppen is Aw (Tropical Savanna) with an
annual average rainfall of 1200 mm and maximum and
minimum temperatures of 33 and 19 C, respectively. The
soil of the area was classified by Schiavo et al. (2010) as
Ultisol dystrophic sandy loam texture, whose chemical
characteristics are described in Table 1.
The experimental design was randomized blocks with
four replicates. Experimental unit consisted of four plants
rows with 5.0 m long, spaced 0.80 m, taking as useful area
only the two central rows of each plot, where assessments
were processed.
Treatments consisted of 17 cowpea genotypes
constituted by lineages MNC99-510F-16-3, MNC99-537F14-2, MNCO1-611F-11, MNCO1-614F-15, MNCO1-631F11, MNCO1-631F-15 and MNCO1-649E-2, and by
commercial cultivars Canapuzinho, Canapuzinho-2,
Inhuma, Pingo-de-ouro-1-2, Pingo-de-ouro-2, Paulistinha,
Patativa, BRS-Paraguassu, BRS 17-Gurguéia and BRSMarataoã. All genotypes are prostrate or semi-prostrate
sized, coming from Embrapa Meio-Norte, Teresina-PI.
The area preparation consisted of two successive heavy
disking and two leveling disking. Sowing was done
manually on 18.4.2009 in open grooves mechanically, with
5 to 10 cm deep and at a seeding rate of 16 seeds per
meter. The chemical fertilizer of formula 4-20-20 was
distributed manually and incorporated into the soil of the
-1
grooves corresponding to a dose 300 kg ha .
At seven days after emergence (DAE) thinning of the
seedlings was performed, leaving eight seedlings per
meter. Hand weeding was made from 15 DAE, which
minimized the competitive effect of Ipomoea sp. and
Beldroega sp, main weeds of experimental area.
Cultural treatments were restricted to nitrogen
topdressing, performed 25 days after sowing, using the
-1
equivalent to 60 kg ha , distributed in a continuous bead,
using urea as nitrogen source.
At 30 DAE was carried out control of Diabrotica
speciosa, Cerotoma spp, Aphis craccivora and Empoasca
kraemeri with the use of Metamidófós insecticide at dose
-1
900 mL ha of commercial product with 600 grams of
active ingredient.
The following characters were evaluated: days to
flowering (DF), days to maturity (DM), mass of five pods
(MFP), grain mass of five pods (GMP), grain index (GI),
mass of hundred grains (MHG) and grain yield (YIE).
The MHG was calculated using the formula:
MHG = {(GMP/5)/(NGP/5)*100}
- wherein NGP refers to number of grains of five pods.
The GI was conceived with the formula:
GI = {(GMP/5)/(MFP/5)*100}
Initially, to verify the existence of variability among the
genotypes, the data obtained were subjected to analysis of
Santos et al. 273
Table 2. Mean square (MS), coefficient of variation (CV) and average values for days to flowering and days to maturity (DF and DM, respectively), mass of
five pods, grain mass of five pods and mass of hundred grains (MFP, GMP and MHG, respectively), grain index (GI) and grain yield (YIE) of 17 cowpea
genotypes.
Genotypes
BRS 17-Gurguéia
BRS-Marataoã
BRS-Paraguassu
Canapuzinho
Canapuzinho-2
Inhuma
MNC99-510F-16-3
MNC99-537F-14-2
MNCO1-611F-11
MNCO1-614F-15
MNCO1-631F-11
MNCO1-631F-15
MNCO1-649E-2
Patativa
Paulistinha
Pingo-de-ouro-1-2
Pingo-de-ouro-2
Average
CV %
MS
DF
--- (days) --53 a
52 a
47 ab
45 ab
45 ab
43 b
49 ab
44 ab
50 ab
48 ab
50 ab
51 ab
50 ab
46 ab
48 ab
43 b
44 ab
47
7.35
43.89*
DM
75 a
75 a
72 abc
73 abc
74 abc
70 bc
74 abc
73 abc
74 ab
74 ab
73 abc
74 ab
76 a
71 abc
73 abc
69 c
72 abc
73
2.62
61.03*
MFP
------ (g) -----32.1
35.2
33.6
39.4
40.0
37.7
43.1
33.0
40.7
39.4
42.0
40.1
39.7
38.6
44.1
30.6
36.9
38.0
16.89
ns
61.37
GMP
21.0
21.8
22.1
25.6
24.4
26.0
27.1
17.5
26.7
25.0
26.7
25.7
25.0
24.2
27.7
22.8
23.5
24.3
17.74
ns
27.24
GI
(%)
65.7 ab
62.3 b
67.6 ab
66.8 ab
60.9 b
70.0 ab
63.2 b
62.9 b
65.9 ab
63.6 b
63.6 b
64.1 b
63.8 b
62.9 b
62.74 b
75.9 a
65.1 b
65.1
6.14
96.14*
MHG
(g)
32.3 b
40.5 ab
32.3 ab
41.6 ab
41.4 ab
39.2 ab
45.8 ab
34.1 ab
44.6 ab
43.4 ab
48.2 a
45.3 ab
41.0 ab
40.1 ab
47.6 ab
36.1 ab
39.6 ab
40.8
15.09
83.64*
YIE
-1
(kg ha )
1054.7
957.2
1148.5
910.5
837.0
689.0
1042.6
1047.7
968.4
937.6
910.5
785.0
861.1
822.7
882.5
790.5
766.9
906.6
27.81
ns
58984.5
ns
and *: not significant and significant at 1% by F-test, respectively. Means followed by same letter in the column do not differ by Tukey’s test at 5%
probability.
variance by F-test and the means were compared by
Tukey’s test at 5% probability.
The genetic divergence among the treatments was
determined by multivariate techniques, which are based on
clustering analysis and principal components. In clustering
analysis was used the mean Euclidean distance (Cruz and
Carneiro, 2003) as dissimilarity measure, and in the group
delimitation, the nearest-neighbor hierarchical method
(Johnson and Wichern, 1992) and Tocher’s optimization
method (Cruz and Carneiro, 2003).
The single linkage method by nearest-neighbor has the
purpose of gather individuals more similar to each other;
then, again identify the most similar pair to form another
individual’s pair, and thus successively, forming groups
according to their similarities (Cruz and Carneiro, 2003). The
Tocher’s optimization method described by Cruz and Carneiro
(2003) constitutes in a simultaneously clustering, in which is
carried the separation of individuals at one time. This method
has the characteristic that the dissimilarity measures mean
within each group should be smaller than the mean distances
between any groups. As to principal component analysis,
genetic dissimilarity was evidenced by scores dispersion in
bidimensional graph with axes represented by first principal
components that explained at least 80% of the global
variation among genotypes. All statistical analyzes were
performed with the application GENES (Cruz, 2013), and
followed the procedures recommended by Cruz and
Carneiro (2003).
RESULTS AND DISCUSSION
There are significant differences (p<0.01) among the
genotypes for characters DF, DM, GI and MHG, allowing to
infer about the existence of genetic variability in the
population (Table 2).
The average flowering period is 47 days, being BRS
Marataoã and BRS-17-Gurguéia the cultivars that
flourished more lately, with average 52 and 53 days,
respectively. Pingo-de-ouro-1-2 and Inhuma are the more
precocious cultivars, the two starting the flowering at 43
days. These results allow the conclusion that the cultivars
used prove to be belated to the character when compared
to values found by Freire Filho et al. (1981) and Lopes et
al. (2001) that found days to flowering between 32 to 43
days.
For character days to maturity the genotypes differ in
amplitude from 69 to 75 days with an average of 73 days,
274. Glo. Adv. Res. J. Agric. Sci.
Table 3. Variables (eigenvalues – τ), percentage variances [τ (%)] and accumulated variances of the principal components (PC) for estimation of genetic
dissimilarity among 17 cowpea genotypes.
PC
PC 1
PC 2
PC 3
τ
14,749.91
46.35
10.09
τ (%)
99.56
0.31
0.13
which according to Embrapa (2003) classifies the
genotypes as medium-early cycle. The cultivars BRSMarataoã and BRS 17-Gurguéia show the more lately
maturations (75 days), while the cultivar Pingo-de-ouro-1-2
(69 days), show the more early-cycle. For Ehlers & Hall
(1997) earliness is an important feature due to specific
climate of each region, because early cultivars and
lineages can escape of dry periods that occur frequently in
semi-arid areas. This has contributed to increase, or at
least stabilize, the cowpea production in long dry spells
(Cisse et al., 1995).
The genotypes do not show differences regarding mass
of five pods, obtaining an average of 38 g. The same
occurred for grain mass of five pods, that obtained average
of 24.3 g. Already the character grain index oscillate
regarding the tested genotypes, being the general average
of 65.1%. The greatest GI is verified for Pingo-de-ouro-1-2,
with average of 75.9%, which, however, do not differ of
BRS 17-Gurguéia, BRS-Paraguassu, Canapuzinho,
Inhuma and MNCO1-611F-11.
According to Freire Filho et al. (2005) GI is an important
character in the cultivars intended for green beans
production because cultivar efficiency measures in the
photosynthates allocation to grains, besides serving as a
reference for selection works. The values obtained for the
trait indicate that the genotypes have potential for be
cultivated for green beans production because the
consumer preference is for cultivars, whose GI is above
than 60% (Embrapa, 2003).
The general average for mass of hundred grains is 40.8
g. The lineage MNCO1-631F-11 obtained the highest
average (48.2g), differing, however, only the cultivar BRSParaguassu, whose average was the lowest observed
(32.3 g). Barriga and Oliveira (1982) found average values
for this character of 18.83 and 14.62 g, respectively. Lopes
et al. (2001) and Oliveira et al. (2003) also had lower
averages (12.47 and 16.49 g, respectively).
There are no differences among genotypes for grain
yield. The genotypes MNC99-510F-16-3, MNC99-537F-142, BRS-Paraguassu and BRS 17-Gurguéia have yield
-1
higher than 1,000 kg ha . Although not statistically differ
among themselves, the cultivars shown a relatively high
variation between the highest and the lowest YIE, which is
-1
-1
1,148.5 kg ha for BRS-Paraguasssu, and 689.0 kg ha
for Inhuma, respectively. The experiment general average
% accumulated
99.56
99.87
100.00
-1
is 906.6 kg ha , considered above the national average for
-1
the same period, which is approximately 370 kg ha .
However, the average yield obtained in this work was lower
than that obtained by Matos Filho et al. (2009), Lopes et al.
(2001), Teixeira et al. (2010) and Bezerra et al. (2008) that
-1
were 1,007, 1,049, 1,307 and 1,705 kg ha , respectively.
It can be observed in Table 3 that the first two principal
components explain more than 80% of the total variance
contained in the original data set (99.87% of the total
accumulated variance). The use viability of the principal
components technique is restricted to the concentration of
available variability between the first variables, which is
recommended by Cruz et al. (2004) as above 80%.
Thus, the first two principal components satisfactorily
explain the manifested variability among genotypes,
allowing interpret the analysis with considerable
simplification through of a bidimensional dispersion graphic
of the scores obtained, according to Figure 1.
Scores visual inspection in bidimensional graphic (Figure
1) permitted to divide the 17 genotypes into eight groups,
so constituted: group 1 (Inhuma); group 2 (MNCO1-631F15, Pingo-de-ouro-1-2 and Pingo-de-ouro-2); group 3
(Canapuzinho-5 and Patativa); group 4 (MNCO1-649E-2
and Paulistinha); group 5 (MNCO1-631F-11 and
Canapuzinho); group 6 (BRS-Marataoã, MNCO1-611F-11
and MNCO1-614F-15); group 7 (BRS 17-Gurguéia,
MNC99-510F-16-3 and MNC99-537F-14-2) and group 8
(BRS-Paraguassu).
The genetic dissimilarity among 17 cowpea genotypes
based on the mean Euclidean distance square and in
nearest-neighbor clustering method allocates the
genotypes in six distinct groups at 50% similarity (Figure
2). The formed groups are constituted in the following way:
group 1 (MNCO1-631F-11, Paulistinha, MNC99-510F-163, MNCO1-611F-11, MNCO1-614F-15, MNCO1-631F-15,
MNCO1-649E-2, Canapuzinho, Canapuzinho-2, Patativa,
Pingo-de-ouro-2 and Inhuma); group 2 (BRS-Marataoã);
group 3 (BRS 17-Gurguéia); group 4 (BRS-Paraguassu);
group 5 (MNC99-537F-14-2); and group 6 (Pingo-de-ouro1-2).
Based on the mean Euclidean distance, the pair formed
among the genotypes MNCO1-631F-11 and Paulistinha is
the nearest, differing from that obtained by the technique of
principal components, where Canapuzinho and MNCO1631F-11 are the most similar pair. Such pairs, for having
Santos et al. 275
Figure 1. Scores graphic dispersion of 17 cowpea genotypes using the first two principal components.
PC1: principal component 1; PC2: principal component 2; 1: BRS 17-Gurguéia; 2: BRS-Marataoã; 3: BRS-Paraguassu 4: Canapuzinho;
5: Canapuzinho-2; 6: Inhuma; 7: MNC99-510F-16-3; 8: MNC99-537F-14-2; 9: MNCO1-611F-11; 10: MNCO1-614F-15; 11: MNCO1631F-11; 12: MNCO1-631F-15; 13: MNCO1-649E-2; 14: Patativa; 15: Paulistinha; 16: Pingo-de-ouro-1-2; 17: Pingo-de-ouro-2.
Figure 2. Dendogram of genetic dissimilarity among 17 genotypes of cowpea, obtained by nearest-neighbor clustering method, using the mean Euclidean
distance square.
1: BRS 17-Gurguéia; 2: BRS-Marataoã; 3: BRS-Paraguassu 4: Canapuzinho; 5: Canapuzinho-2; 6: Inhuma; 7: MNC99-510F-16-3; 8: MNC99-537F-14-2;
9: MNCO1-611F-11; 10: MNCO1-614F-15; 11: MNCO1-631F-11; 12: MNCO1-631F-15; 13: MNCO1-649E-2; 14: Patativa; 15: Paulistinha; 16: Pingo-deouro-1-2; 17: Pingo-de-ouro-2.
the same standards of similarity, are not recommended for
use in breeding programs by hybridization, to the genetic
variability, essential in any breeding program, not be
restricted in order to derail the gains to be obtained by
selection.
By mean Euclidean distance, the most divergent pair is
composed by MNC99-510F-16-3 and Pingo-de-ouro-1-2,
while the canonical variables identified BRS-Paraguassu
and Inhuma as more divergent. This high divergence, in
principle, allows recommend the crossing between these
pairs in order to maximize the heterosis in progenies and
increase the occurring possibility of segregants in
advanced generations (Cruz et al., 2004).
276. Glo. Adv. Res. J. Agric. Sci.
Table 4. Groups formed by Tocher’s optimization method, based on the mean Euclidean distance square matrix.
Group
1
2
3
4
5
6
Genotypes
11, 15, 7, 9, 10, 12, 13, 4 and 5
2 and 8
14, 17 and 6
16
3
1
1: BRS 17-Gurguéia; 2: BRS-Marataoã; 3: BRS-Paraguassu; 4: Canapuzinho; 5: Canapuzinho-2; 6: Inhuma; 7: MNC99-510F-16-3; 8: MNC99-537F-14-2;
9: MNCO1-611F-11; 10: MNCO1-614F-15; 11: MNCO1-631F-11; 12: MNCO1-631F-15; 13: MNCO1-649E-2; 14: Patativa; 15: Paulistinha; 16: Pingo-deouro-1-2; 17: Pingo-de-ouro-2.
The application of the Tocher’s optimization method,
based on the mean Euclidean distance square matrix,
separated the genotypes into six groups (Table 4). Group 1
is constituted by genotypes MNCO1-631F-11, Paulistinha,
MNC99-510F-16-3, MNCO1-611F-11, MNCO1-614F-15,
MNCO1-631F-15, MNCO1-649E-2, Canapuzinho and
Canapuzinho-2; group 2 is formed by BRS-Marataoã and
MNC99-537F-14-2; group 3 is constitued by Patativa,
Pingo-de-ouro-2 and Inhuma; and the groups 4, 5 and 6
are formed by Pingo-de-ouro-1-2, BRS-Paraguassu and
BRS 17-Gurguéia, respectively.
Overall, there is good agreement in distribution of
individuals in the face of clustering expressed by nearestneighbor and Tocher’s methods, based on mean Euclidean
distance square. These results corroborate the obtained by
Cargnelutti Filho et al. (2010), which evaluating the
consistency of the common bean cultivars clustering
pattern according to dissimilarity measures and clustering
methods, verified a good correlation among nearestneighbor and Tocher’s methods, based on the mean
Euclidean distance.
CONCLUSIONS
The genotypes did not differ for the character grain yield.
The multivariate analysis used brought together the
genotypes into distinct groups. There was high correlation
between Ward’s and Tocher’s clustering methods based
on the Euclidean distance mean square. Hybrids with high
heterotic effect will be obtained from the crossing between
the genotypes MNC99-510F-16-3 x Pingo-de-ouro-1-2 and
BRS-Paraguassu x Inhuma.
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