Scientia Horticulturae 145 (2012) 17–22
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Scientia Horticulturae
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Definition of morpho-agronomic descriptors for the characterization of yellow
passion fruit
Jacqueline Araújo Castro a , Cláudia Garcia Neves a , Onildo Nunes de Jesus b , Eder Jorge de Oliveira b,∗
a
b
Federal University of Recôncavo da Bahia, Cruz das Almas Campus, 44380-000 Cruz das Almas, BA, Brazil
Embrapa Cassava and Fruits, P.O. Box 007, 44380-000 Cruz das Almas, BA, Brazil
a r t i c l e
i n f o
Article history:
Received 15 May 2012
Received in revised form 17 July 2012
Accepted 25 July 2012
Keywords:
Passiflora edulis
Multivariate analysis
Principal component
Minimum descriptors
a b s t r a c t
A varietal description should help to resolve the conflicts of identification that may arise in the registration
and protection of cultivars. The objective of this study was to evaluate the discriminatory capacity and
to select minimum descriptors for the characterization and evaluation of yellow passion fruit varieties
(Passiflora edulis Sims f. flavicarpa Deg.). Twenty quantitative and eight qualitative descriptors related to
plant, leaves, flowers and fruits traits were analyzed in 24 genotypes of yellow passion fruit. The quantitative descriptors were subjected to a principal components analysis, and the selection of descriptors
was based on direct selection and Singh method, whereas the qualitative descriptors were analyzed by
correlation. Eight quantitative descriptors were discarded by direct selection, and seven were discarded
using Singh method. However, only four descriptors common to both methods were considered to be
redundant. Of the eight qualitative descriptors evaluated, two were discarded because they were correlated with the others. Therefore, the list of minimum descriptors to describe yellow passion fruit varieties
can be composed of 22 descriptors, 16 quantitative and six qualitative, with a high contribution to the
total variance and low correlation with the others. There was no loss of information with the elimination
of those descriptors. In addition to the smaller number of descriptors providing valuable information, it
will also allow a reduction in the labor, time and resources spent in the characterization of new varieties
of yellow passion fruit.
© 2012 Elsevier B.V. All rights reserved.
1. Introduction
Brazil is considered the center of diversity for the genus Passiflora, and the yellow passion fruit (Passiflora edulis Sims f. flavicarpa
Deg.) is the species most grown in Brazil (Oliveira et al., 2012). Brazil
is the largest producer and consumer of passion fruit in the world
(Janick and Paull, 2008), and this leading position is the result of the
development of this crop during the last three decades (Gonçalves
and Souza, 2006). The evolution of passion fruit production shows
that the research related to the development of genetically superior varieties and hybrids and the improvement of the production
system have been of great importance (Faleiro et al., 2008).
The morphological and agronomic characterization of
germplasm and new passion fruit varieties using descriptors
is a key aspect that must be considered in breeding programs. In
addition, molecular markers are valuable tools for characterization
of germplasm because they are not influenced by environmental
∗ Corresponding author. Tel.: +55 75 33128041; fax: +55 75 33128097.
E-mail addresses: [email protected] (J.A. Castro),
[email protected] (C.G. Neves), [email protected]
(O.N. de Jesus), [email protected] (E.J. de Oliveira).
0304-4238/$ – see front matter © 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.scienta.2012.07.022
conditions or plant phenology, as in the previous descriptors.
Although, some molecular markers are available for Passiflora
(Ganga et al., 2004; Oliveira et al., 2005; Bellon et al., 2007; Santos
et al., 2011), the intraspecific polymorphism is low, which has
been limited its use as a complementary variable for protection of
passion fruit varieties.
The term “descriptor” refers to an attribute or character that
allows the discrimination of genotypes, the presence of redundant descriptors is common when many traits are evaluated (Daher
et al., 1997). Accordingly, many descriptors are judged unnecessary
because their contribution to the total variability is small while they
increase the resources used in field measurements (Oliveira et al.,
2012).
The elimination of redundant descriptors is an important strategy to reduce the work required in collecting data without causing
any significant loss in genotype discrimination (Oliveira et al.,
2006, 2012). Techniques such as principal components (Cruz et al.,
2004), regression (Beale et al., 1967) and discriminant analysis
(Mardia et al., 1979) have also been utilized as methods to identify
descriptors having the highest information content for germplasm
characterization and genetic improvement.
These strategies have been applied to some fruit species, including cocoa (Dias et al., 1997), cupuaçu (Alves et al., 2003), euterpe
18
J.A. Castro et al. / Scientia Horticulturae 145 (2012) 17–22
Table 1
List of passion fruit accessions used for morphological and agronomic evaluations.
Accessions
Origin
State
Type
BGM007
BGM017
BGM022
BGM023
BGM034
BGM043
BGM048
BGM049
BGM051
BGM078
BGM079
BGM092
BGM158
BGM168
BGM181
BGM185
BGM188
BGM205
BGM208
BGM222
BGM277
BGM292
BGM311
BGM325
Brasil
Brasil
Brasil
Brasil
Brasil
Portugal
Venezuela
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
Brasil
São Paulo
São Paulo
Bahia
São Paulo
PR
Funchal
–
Mato Grosso
Minas Gerais
Bahia
Bahia
Bahia
São Paulo
São Paulo
São Paulo
São Paulo
São Paulo
São Paulo
São Paulo
Bahia
Bahia
Bahia
Bahia
Distrito Federal
Landrace
Landrace
Landrace
Landrace
Hybrid
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Landrace
Variety
Landrace
Landrace
Landrace
Landrace
Landrace
Hybrid
palm (Oliveira et al., 2006) and papaya (Oliveira et al., 2012). However, studies about the importance and sufficiency of minimum
descriptors for yellow passion fruit have not been reported. The
objective of this work was to generate information of the discriminatory capacity of the currently used descriptors and to select the
minimum number that would allow the suitable characterization
of genotypes.
2. Materials and methods
Twenty-four genotypes of yellow passion fruit belonging to
the Active Passion Fruit Germplasm Bank (AGB-Passion fruit) at
Embrapa Cassava and Fruits and currently used as the parents for
hybrid production and validation for recommendation as new varieties were evaluated (Table 1). The test was conducted in Cruz das
Almas, Bahia, Brazil (12◦ 48 38 S and 39◦ 6 26 W), at the Embrapa
experimental area during 2009–2011 planting seasons. The average annual temperature is 24.5 ◦ C, with 1240 mm of average annual
rainfall and 82% average relative humidity. The experiments were
performed using an increased augmented block design, with three
common checks, ten replications and plots with 10 plants. The
spacing used was 2.6 m between rows and 3.7 m between plants.
We used a vertical system for the canopy, with single strand of
galvanized wire (14 gauge) at 2 m above the ground.
The evaluation was performed using 20 quantitative and 8 qualitative descriptors related to the characteristics of the plant, leaves,
flowers and fruits (Table 2). A total of 22 of the descriptors belong
to the list of descriptors proposed by the Brazilian Ministry of
Agriculture, Livestock and Food Supply (Brasil, 2008) based on
International Union for the Protection of New Varieties of Plants
(UPOV, 2009), and the remainder are used in the characterization
of passion fruit germplasm by Embrapa Cassava and Fruits. The
descriptors were evaluated in one growing cycle (February–June
2010), covering the two main seasons (summer and winter). The
sampling consisted of ten fruits collected randomly from different
plants.
The standardization was performed as Xj (j = 1, 2, . . ., p) because
the variables were measured in different units. The difference in
the scales of the quantitative variables was eliminated by the use
of reduced variables, as follows: Zij = Xij − X̄j /S(Xj ), where Zij = the
standardized variable in individual i for the characteristic j; Xij = the
value observed in individual i for characteristic j; X̄j = the estimated
average of characteristic j; and S(Xj ) = the standard deviation of the
data with characteristic j. Therefore, the correlation matrix of the
standardized variables was the following:
⎡
1
⎢r
⎢ 21
⎣M
R=⎢
rp1
⎤
r12
r1p
1
r2p ⎥
M
O
⎥
⎥
M ⎦
rp2
1
where
rjj = r(Xj , Xj ) = C ôv(Zj , Zj ) =
C ôv(Xj , Xj )
V âr(Xj ) V âr(Xj )
The selection of descriptors was based on principal component
analysis using the generalized Mahalanobis distance (D2 ) and executed based on the average of the measurements taken from the
correlation matrix using the Genes program (Cruz, 2008).
The principal components were obtained from the correlation
matrix based on the following expressions: |R − I|=0, which gives
the eigenvalues 1 , 2 , K, p and |R − i I|ai = , and the eigenvectors
a1 , a2 , K, ap , wherein R = the correlation matrix between the evaluated characteristics; i = the eigenvalues of the matrix R; ai = the
eigenvector associated with eigenvalue i ; I = the identity matrix
of order p (p = number of characteristics); and = the null vector of
dimension p × 1. The eigenvectors ai were standardized to obtain
a∗i such that a ∗i a∗i = 1 for i = 1, 2, K, and p, and a ∗i a∗j = 0 for i =
/ j.
The relative importance of the principal component was evaluated by the percentage of the total variance explained, i.e., the
percentage of one individual eigenvalue in relation to the total
eigenvalues of the other components or the percentage of one
eigenvalue in relation to the trait of the matrix R, which is given
by the following:
CPj =
V âr(CPj )
p
j=1
V âr(CPj )
× 100 =
j
p
j=1 j
× 100 =
j
trace(R)
× 100
To discard the less-informative quantitative descriptors, the
Singh method (1981) based on the relative importance of characteristics and a second method based on direct selection (Jolliffe,
1973) were used. Any descriptor that had a higher absolute weight
coefficient (eigenvector) in the principal component of the lower
eigenvalue was discarded, starting from the last component and
ending with the component possessing an eigenvalue of no higher
than 0.70.
The final elimination of a descriptor was conducted taking into
account the information from both procedures. After discarding
the quantitative descriptors consistent in both methods, the Pearson correlation coefficient was used to evaluate the efficiency of
elimination, as the discarded characteristics should be correlated
to other selected characteristics.
The elimination of qualitative descriptors was conducted based
on Pearson’s correlation coefficient among all of the descriptors.
The significance of the correlation coefficient was analyzed using
the t test. All of the statistical analyses were conducted using the
GENES program (Cruz, 2008).
3. Results
Estimates of the eigenvalues associated with the principal components and their relative and cumulative variances explained
57.13% of the total variation in the first two components. The most
variation was distributed up to the 10th principal component, corresponding to 95.31% of the relative variation that was observed
(Table 2). The first component, which explained 36.82% of the total
J.A. Castro et al. / Scientia Horticulturae 145 (2012) 17–22
19
Table 2
Quantitative and qualitative descriptors used in the characterization of 24 yellow passion fruit genotypes.
Plant parts
Nature of descriptor
Descriptor
Abbreviation
Flower
Flower
Flower
Flower
Flower
Flower
Flower
Flower
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Leaf
Leaf
Leaf
Leaf
Leaf
Plant
Plant
Plant
Qualitative
Qualitative
Qualitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Qualitative
Qualitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Qualitative
Qualitative
Quantitative
Quantitative
Quantitative
Qualitative
Quantitative
Quantitative
Banding of corona filaments
Color of rings of corona filaments (except white)
Corona filaments
Bract length (cm)
Sepal length (cm)
Sepal width (cm)
Corona width (cm)
Width of colored rings of corona filaments (cm)
Pulp color
Skin color
Fruit weight (g)
Fruit length (cm)
Fruit width (cm)
Thickness of fruit skin (cm)
Fruit skin weight (g)
Pulp weight (g)
Total soluble solids, measured (o Brix)
Total titratable acidity, expressed in grams of citric acid/100 g
Ratio between the total soluble solids and titratable acidity
Number of seeds per fruit
Leaf blade: sinus depth
Petiole: nectary position
Leaf blade length (cm)
Maximum width of leaf blade (cm)
Petiole length (cm)
Stem color
Number of fruit
Yield (tons per ha)
FL-BCF
FL-CCF
FL-CF
FL-BL
FL-SL
FL-SW
FL-CW
FL-WCR
FR-PCOL
FR-SC
FR-WE
FR-LE
FR-WI
FR-TFS
FR-SW
FR-PW
FR-TSS
FR-TTA
FR-RTTATSS
FR-FR-NS
LF-SD
LF-NP
LF-LBL
LF-MWLB
LF-PL
PL-SC
PL-PL-NF
PL-YD
Table 3
Estimates of the eigenvalues associated with the principal components and their accumulated relative variances obtained from 20 quantitative descriptors that were evaluated
in 24 yellow passion fruit genotypes.
Component
(%)
(%) accumulated
Component
(%)
(%) accumulated
1
2
3
4
5
6
7
8
9
10
7.36
4.06
2.01
1.41
1.01
0.88
0.79
0.64
0.47
0.44
36.82
20.30
10.04
7.05
5.03
4.42
3.93
3.20
2.35
2.18
36.82
57.13
67.17
74.21
79.24
83.66
87.59
90.79
93.13
95.31
11
12
13
14
15
16
17
18
19
20
0.39
0.21
0.13
0.10
0.05
0.03
0.02
0.01
0.00
0.00
1.96
1.03
0.66
0.49
0.25
0.13
0.09
0.06
0.02
0.00
97.28
98.30
98.96
99.45
99.69
99.83
99.92
99.97
100.00
100.00
Table 4
Estimates of the weighting coefficients associated with the principal components with eigenvalues less 0.70 and identification of the descriptors to be discarded in each
component (in bold) for direct selection in 24 yellow passion fruit genotypes.
Descriptora
PL-NF
PL-YD
FR-WE
FR-LE
FR-WI
FR-TFS
FR-SW
FR-PW
FR-TSS
FR-TTA
FR-RTTATSS
LF-LBL
LF-MWLB
LF-PL
FL-BL
FL-SL
FL-SW
FL-CW
FL-WCR
FR-NS
a
Principal components
20
19
18
17
0.0000
0.0005
−0.0007
−0.0006
−0.0017
0.0019
0.0008
0.0017
0.0003
0.0004
−0.0026
0.0043
0.0009
0.0044
−0.0002
0.0019
−0.0014
0.0030
−0.0010
−0.0005
0.1823
0.9215
25.346
31.036
18.747
−0.6275
0.0717
−0.1792
14.836
−0.8009
0.8353
15.564
-0.8126
−0.0912
0.0505
−13.008
−0.3596
−0.4846
−20.186
11.401
10.469
−15.283
0.6734
20.690
10.300
−0.8214
−0.1627
0.4100
24.204
33.252
−0.8823
−0.2235
10.597
−0.1958
0.6180
−0.4676
0.1315
−0.4462
−0.2972
12.021
0.1692
−11.551
0.6754
0.3736
−0.1697
Descriptor: see abbreviations in Table 2.
16
15
14
−0.0729
0.0863
−0.6582
0.0832
0.2452
−0.1410
0.0080
0.1118
0.0679
0.0961
−0.8419
−0.1606
0.9725
0.4791
0.4605
0.0080
0.6107
−0.0495
−0.0444
−0.1043
14.319
26.263
0.8915
24.763
−0.3320
−0.6395
−0.2609
-21.138
−20.591
−0.6444
−0.5016
11.037
−0.1692
−0.1299
−0.6363
−0.9447
0.7077
−0.9874
−19.483
−0.1581
−0.3596
−0.0960
12.106
0.2916
0.6232
0.4604
−0.0679
0.8094
−0.8804
−0.5406
−0.0855
−0.5434
0.3017
−0.1785
−0.4527
0.2619
−0.1960
0.0441
−0.2316
0.0841
0.1644
0.1904
−0.1507
0.0552
−0.1904
−0.1294
−13.514
13
20
J.A. Castro et al. / Scientia Horticulturae 145 (2012) 17–22
Table 5
Relative contribution of 20 descriptors evaluated in 24 yellow passion fruit genotypes belonging to Embrapa Cassava and Fruit using Singh method (1981).
Descriptora
S.j
%
Descriptor
S.j
%
PL-NF
PL-YD
FR-WE
FR-LE
FR-WI
FR-TFS
FR-SW
FR-PW
FR-TSS
FR-TTA
36231.07
2932.79
866.59
1578.47
1268.63
346.47
866.64
635.05
332.13
769.45
71.08
5.75
1.70
3.10
2.49
0.68
1.70
1.25
0.65
1.51
FR-RTTATSS
LF-LBL
LF-MWLB
LF-PL
FL-BL
FL-SL
FL-SW
FL-CW
FL-WCR
FR-NS
699.21
560.46
498.05
516.41
272.02
492.55
544.64
372.26
1007.79
182.15
1.37
1.10
0.98
1.01
0.53
0.97
1.07
0.73
1.98
0.36
a
Descriptor: see abbreviations in Table 2.
variation, exhibited the descriptors PL-NF, FR-PW, FR-WI, FL-BL
and FL-BL as the highest weights. The second component, which
explained 20.30%, is associated with the descriptors PL-YD, FR-PW,
FL-BL, FR-SW and FR-WE. In component 3, which explained 10.4%
of the total variation, the descriptors with the highest weights were
FR-NS, PL-YD, LF-MWLB, FL-WCR and FR-PW (Table 3).
The preliminary discarding performed using direct selection
showed that eight of 20 descriptors (40%) with the highest weights
in absolute value from the last principal component could be
discarded because their presented components had eigenvalues
smaller than 0.7 (Table 3).
Using direct selection, the first character that was suggested to
be discarded was LF-PL, which had the highest weighted coefficient
in the module with the last principal component (0.004), followed
by the characteristics FR-LE, FR-TSS, FR-TFS, LF-MWLB, FR-TTA, FRRTTATSS and FL-BL (Table 4).
According to the Singh method (1981), the observed values of
relative contribution ranged from 0.36 to 71.08%. The characteristics with the highest relative contributions to the passion fruit
diversity (ranging from 2.48 to 71.08%) were PL-NF, PL-YD, FR-LE
and FR-WI. The characteristics FL-WCR, FR-SW, FR-WE, FR-TTA, FRRTTATSS, FR-PW, LF-LBL, FL-SW and LF-PL showed intermediate
contributions, with estimates between 1.01 to 1.98% of the total
variation. According to these criteria, the descriptors LF-MWLB,
FL-SL, FL-CW, FR-TFS, FR-TSS, FL-BL and FR-NS would initially be
identified for discarding because they present lowindividual contributions, with less than 1.0% of the total variation (Table 5).
Based on the simultaneous analysis, four descriptors were indicated for discarding (FL-SL, FR-TSS, LF-MWLB and FL-BL) (Table 6).
Thus, the combination of both methods has allowed a reduction of
20% of the quantitative descriptors to be evaluated for the complete
description of the passion fruit genotypes analyzed.
Pearson’s correlation coefficients among the quantitative
descriptors selected and discarded showed a high efficiency for the
Table 7
Estimates of Pearson correlation coefficients between selected and discarded quantitative descriptors evaluated in 24 yellow passion fruit genotypes.
Descriptors selecteda
Discarded
PL-NF
PL-YD
FR-WE
FR-LE
FR-WI
FR-SW
FR-PW
FR-TTA
FR-RTTATSS
LF-LBL
LF-PL
FL-SL
FL-SW
FL-CW
FL-WCR
FR-NS
*
**
a
FR-TFS
FR-TSS
LF-MWLB
FL-BL
0.06
0.51*
0.57**
0.40*
0.69**
0.64**
0.34
0.28
−0.38
0.45*
0.35
−0.39
−0.15
−0.01
0.32
0.30
0.34
0.55**
0.30
0.27
0.37
0.34
0.21
0.02
−0.38
0.66**
0.22
−0.41*
−0.17
−0.25
0.53**
0.11
0.11
0.53**
0.54**
0.53**
0.61**
0.53**
0.46*
−0.37
−0.37
0.89**
0.55**
−0.30
−0.17
−0.06
0.53**
0.33
−0.29
−0.07
0.34
0.26
0.21
0.24
0.41*
−0.05
0.11
0.00
0.08
0.61**
0.38
0.21
−0.07
0.13
Significatively at 5%, respectively by t test.
Significatively at 1%, respectively by t test.
Descriptor: see abbreviations in Table 2.
elimination of the redundant characteristics, as the four quantitative descriptors listed for final discarding have nine (LF-MWLB),
six (FR-TFS), four (FR-TSS) and two (FL-BL) positive correlations
with the selected descriptors (Table 7). Consequently, these sixteen quantitative characteristics (PL-NF, PL-YD, FR-WE, FR-LE,
FR-WI, FR-SW, FR-PW, FR-TTA, FR-RTTATSS, LF-LBL, LF-PL, FL-SL,
FL-SW, FL-CW, FL-WCR and FR-NS) were selected as non-redundant
descriptors for the analysis of passion fruit genotypes.
Regarding the eight qualitative descriptors (morphological)
evaluated, the genotypes showed a variable number of classes,
being divided into two (PL-SC, LF-SD, LF-NP, FL-BCF and FL-CF),
three (FR-PCOL and FR-SC) and four classes (FL-CCF) (Table 8).
The correlation analysis performed between all of the qualitative
descriptors (Table 9) indicated that FR-PCOL and LF-NP showed two
and three positive correlations, respectively, with other descriptors indicated for discarding. The descriptors PL-SC, LF-SD, FL-BCF,
FL-CCF, FR-SC and FL-CF were selected to compose the final list of
minimum descriptors for yellow passion fruit.
4. Discussion
The establishment of minimum descriptors for yellow passion fruit arises from the need to optimize the characterization
and description of new genotypes produced in breeding programs
because a large number of descriptors leads to increased costs and
inefficient use of time. In addition, the determination of genetic
Table 6
Pre-selected descriptors for direct selection and selection using Singh method (1981) from the analysis of 20 quantitative descriptors in 24 yellow passion fruit genotypes.
Descriptora
PL-NF
PL-YD
FR-WE
FR-LE
FR-WI
FR-TFS
FR-SW
FR-PW
FR-TSS
FR-TTA
a
b
Pre-selected2
Selected
Singh method
Direct selection
S
S
S
S
S
D(4)
S
S
D(3)
S
S
S
S
D(2)
S
D(4)
S
S
D(3)
D(6)
S
S
S
S
S
D
S
S
D
S
Descriptor: see abbreviations in Table 2.
Number in parentheses indicates the discharge order, S = selected, D = discarded.
Descriptora
FR-RTTATSS
LF-LBL
LF-MWLB
LF-PL
FL-BL
FL-SL
FL-SW
FL-CW
FL-WCR
FR-NS
Pre-selectedb
Selected
Singh method
Direct selection
S
S
D(7)
S
D(2)
D(6)
S
D(5)
S
D(1)
D(7)
S
D(5)
D(1)
D(8)
S
S
S
S
S
S
S
D
S
D
S
S
S
S
S
J.A. Castro et al. / Scientia Horticulturae 145 (2012) 17–22
21
Table 8
Classification of 24 yellow passion fruit genotypes based on the analysis of qualitative descriptors.
Descriptora
FR-PCOL
PL-SC
LF-SD
LF-NP
FL-BCF
FL-CCF
FL-CF
FR-SC
a
Class
1
2
3
4
5
6
7
4.17
–
–
91.67
95.83
–
95.83
8.33
62.50
37.50
–
8.33
4.17
–
4.17
75.00
33.33
62.50
–
–
–
4.17
–
1.67
–
–
–
–
–
4.17
–
–
–
–
4.17
–
–
4.17
–
–
–
–
–
–
–
87.50
–
–
–
–
95.83
–
–
–
–
–
Descriptor: see abbreviations in Table 2.
and phenotypic differences is important to characterize materials, with the goal of the registration or protection of new varieties.
Among the methodologies for establishing minimum descriptors,
we can highlight the use of principal component analysis and Singh
method.
In yellow passion fruit, the estimates of the eigenvalues associated with the principal components and their relative and
cumulative variance explained 57.13% of the total variation for
the first two components (CP1 and CP2). Estimates close to those
observed in this study were observed by Martel et al. (2003) in
races of Amazonian peach palm (Bactris gasipaes Kunth) (59.20%)
and by Oliveira et al. (2012) in cultivars of papaya (Carica papaya L.)
(52.09%). Conversely, when performing the selection of descriptors
for the germplasm characterization of euterpe palm (Euterpe oleracea Mart.), Oliveira et al. (2006) reported lower values, explaining
35.8% of the total variation in the first two principal components.
According to Pereira et al. (1992), the distribution of variation is
associated with the nature and number of the characters used in the
analysis, and it is concentrated in the first components only when
few agronomically important traits or for certain groups (plant,
flower, fruit and agronomic) are evaluated.
The discarding of eight quantitative descriptors by direct selection was possible because the variables that were highly correlated
with the principal components of the smaller variance showed variations that were almost negligible. According to Daher et al. (1997),
the principal components technique is advantageous because it
allows the evaluation of the importance of each character studied
over the total variation among the accessions evaluated, allowing
the elimination of less-discriminating characters because they are
already correlated with the other variables or by their invariance.
Using the same criteria, Oliveira et al. (2006) showed similar
results: they first made the preliminary evaluation to discard 18
(64%) morphological descriptors used to characterize euterpe palm
germplasm. In the present study, of the eight primary characteristics suggested to be discarded by direct selection, five are fruit traits
(FR-LE, FR-TSS, FR-TFS, FR-TTA and FR-RTTATSS), two are related to
the leaves (LF-PL and LF-MWLB), and one is related to the flowers
(FL-BL). However, as the fruits possess more commercial interest,
the discarding of fruit traits seems to be drastic with regard to yellow passion fruit. In contrast, using Singh method, we observed
that the characters that had the most contribution to the diversity
of the accessions were the fruit number, yield, length and diameter,
whereas seven characteristics, three related to the flowers (FL-SL,
FL-CW and FL-BL), three related to the fruits (FL-SL, FR-TSS and FRNS) and one related to the leaves (LF-MWLB), have been suggested
to be discarded due to their low contribution (4.90%) to the total
diversity. Therefore, in this second method, the characteristics recommended to be discarded have a better distribution in different
parts of the plants, which could alleviate the discarding of the five
descriptors related to the fruit indicated by the direct selection.
Considering that the component analysis pointed out eight characters to be discarded versus seven using Singh method, there was
an overall divergence of information among the two methods. Thus,
to reduce inconsistencies in the elimination of descriptors, it is common to adopt two or more procedures to indicate the most relevant
descriptors. For instance, Oliveira et al. (2006) used principal component analysis and the selection with reanalysis to define the most
important morphological descriptors for euterpe palm. Santos et al.
(1995) have also employed techniques of principal component and
multiple regression analysis for the selection of discriminant or
explanatory descriptors for the grain yield of pigeon pea (Cajanus
cajan (L.) Millsp.).
In the present study, four descriptors were consistent in both
analyses, two were related to the fruits (FR-TFS and FR-TSS), one
was related to the leaves (LF-MWLB), and one was related to the
flowers (FL-BL). Thus, the combined use of direct selection and
Singh method could reduce errors and relax the drastic discarding of descriptors. Similar discard percentages were also found by
Oliveira et al. (2006), with 21.43% of the characters discarded in
euterpe palm, and Martel et al. (2003), eliminating 33.33% of the
morphological descriptors used in peach palm. Employing the same
criteria of direct selection and Singh method, Oliveira et al. (2012)
showed that 40% of papaya descriptors could be eliminated. Additionally, studying the contribution of 18 descriptors in guariroba
Table 9
Estimates of Pearson correlation coefficients between all qualitative descriptors evaluated in 24 yellow passion fruit genotypes.
Descriptora
PL-SC
LF-SD
LF-NP
FL-BCF
FL-CCF
FL-CF
FR-SC
FR-PCOL
PL-SC
LF-SD
LF-NP
FL-BCF
FL-CCF
FL-CF
FR-SC
−0.06
–
–
–
–
–
–
–
−0.27
−0.16
–
–
–
–
–
–
−0.44*
0.23
0.63
–
–
–
–
–
−0.50*
0.16
0.43
0.69**
–
–
–
–
0.29
−0.27
−0.72
−0.73**
−0.22
–
–
–
−0.50*
0.16
0.43
0.69**
1.00
−0.22
–
–
0.38
0.13
0.35
−0.36
−0.46*
−0.59
−0.46*
–
*
**
a
Significatively at 5%, respectively by t test.
Significatively at 1%, respectively by t test.
Descriptor: see abbreviations in Table 2.
22
J.A. Castro et al. / Scientia Horticulturae 145 (2012) 17–22
(Syagrus oleracea (Mart.) Becc.), Pinto et al. (2010) discarded 55% of
the descriptors.
It is important to note that the descriptors of great importance in
breeding programs, such as skin thickness and total soluble solids,
were also discarded from the analyses but without a loss of information, as they are correlated with at least two other selected
characters. A similar phenomenon was observed in the study of
Oliveira et al. (2006) in which four relevant characters (fruit weight,
fruit width, fruit weight per bunch and fruit yield) were discarded
for the evaluation and selection of euterpe palm, with little loss of
information because the discarded characters were strongly associated with the other characters studied.
Moreover, the sixteen selected quantitative descriptors (PL-NF,
PL-YD, FR-WE, FR-LE, FR-WI, FR-SW, FR-PW, FR-TTA, FR-RTTATSS,
LF-LBL, LF-PL, FL-SL, FL-SW, FL-CW, FL-WCR and FR-NS) will be of
great importance for the characterization of the germplasm of yellow passion fruit and should comprise a list of minimum descriptors
of this species.
In addition to the quantitative descriptors, there are also qualitative descriptors that are simple in nature and are easily measurable
using a previously established scale. Of the eight characters evaluated, two (25%) were discarded because they are correlated
with other characters. Using the Pearson’s correlation coefficient,
Oliveira et al. (2012) have also discarded eight (25%) of 21 multicategorical descriptors used in the characterization of papaya.
A list of the 22 descriptors selected (16 quantitative and six
qualitative) in this report should represent an important tool for
the characterization of the germplasm of yellow passion fruit and
for measuring the genetic variability within germplasm collections,
providing information for conservation and breeding programs.
Furthermore, these minimum descriptors will allow a reduction in
field labor, decreasing both the time and resources spent in passion
fruit characterization.
Acknowledgements
The authors acknowledge CNPq (National Council for Research
and Development) and FAPESB (Foundation for Research Support
of the Bahia State) for the financial support. J.A.C acknowledge
CAPES (Coordination for Improvement of Higher Education Staff)
for their master degree fellowships. E.J.O. acknowledges CNPq for
the research fellowship.
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