ORIGINAL ARTICLE

Genetic Resources (2022), 3 (5), 24–35
DOI: 10.46265/genresj.UPPQ3994

https://www.genresj.org
ISSN: 2708-3764

Stylosanthes scabra: genetic variability of forage
quality traits

Fabiana Karla de Araújo Américo a, Marcelo Ayres Carvalho b, Juaci Vitória Malaquias b, Allan 
Kardec Braga Ramos b, Gustavo José Braga b, Claudio Takao Karia b and Carlos Eduardo 
Lazarini da Fonseca *,b

a Universidade Estadual de Feira de Santana-UEFS, Bahia, Brazil
b Embrapa Cerrados, Planaltina, DF, Brazil

Abstract: Stylosanthes scabra Vogel is a tropical legume grown in dry tropical and subtropical environments. The objective 
of this research was to evaluate the genetic diversity of forage quality traits for 80 accessions of S. scabra. Seven plants 
from each accession were planted in a single-line plot with no replicates at Embrapa Cerrados, Brazil. All plants were 
harvested 90 days after planting. Crude protein (CP), in vitro dry matter digestibility (IVDMD), neutral detergent fibre 
(NDF), acid detergent fibre (ADF), lignin (LIG), hemicellulose (HEMIC) and cellulose (CELLU) were estimated. Data were 
submitted to principal component analysis (PCA) and a cluster analysis was performed to identify groups of similarity. 
Simpson and Shannon–Weaver diversity indices estimated the genetic diversity. The average values of CP, IVDMD, NDF, ADF, 
LIG, HEMIC and CELLU were 220g/kg, 560g/kg, 516.8g/kg, 368g/kg, 69.4g/kg, 148.8g/kg and 298.6g/kg, respectively. 
There was a significant difference among collection sites for IVDMD, ADF and CELLU. The first two principal components 
accounted for 73% of the total variation. The 80 accessions resulted in four clusters, among which significant differences 
were observed for CP, IVDMD and ADF. Group IV, with 24 accessions, had the highest CP and IVDMD concentrations and the 
lowest ADF concentration, being the highest-quality forage group. Diversity indices were 0.78 and 0.81 for Simpson’s and 
Shannon–Weaver’s, respectively. In conclusion, there is genetic diversity for forage quality traits among S. scabra.

Keywords: Tropical legume, Genetic resources, Nutritional value, Diversity index, Germplasm characterization

Citation: de Araújo Américo, F. K., Carvalho, M. A., Vitória Malaquias, J., Braga Ramos, A. K., Braga, G. J., Karia, C. T., 
Lazarini da Fonseca, C. E. (2022). Stylosanthes scabra: genetic variability of forage quality traits. Genetic Resources 3 (5), 24–
35. doi: 10.46265/genresj.UPPQ3994.

© Copyright 2022 the Authors.

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are

credited.

Introduction

Brazil is a major beef producer and exporter, with
an estimated herd of 171.8 million heads and about
158 million hectares of pastures (18.7% of Brazilian
territory), of which 70% are cultivated and 30% are
natural (IBGE, 2017). Most of the livestock farming in
Brazil is still in extensive form, with the use of forage
crops for livestock direct feeding into pasture (Hoffmann
et al, 2014). Most production systems are based on the
monoculture of tropical grasses of African origin. The
use of high-quality forages improves the profitability

∗Corresponding author: Carlos Eduardo Lazarini da Fonseca
(carlos.lazarini@embrapa.br)

of production systems; an option to obtaining pastures
with a high nutritional value is the introduction of
legumes associated with grasses.

Legumes play an important role in animal production
due to their high protein concentration compared
to grasses, in addition to soil biological nitrogen
fixation (Neres et al, 2012). Several studies showed
the associated use of legumes and grasses enables a
significant increase in the nutritional value of ruminants’
diet (Carvalho and Pires, 2008).

When working with genetic resources it is very
important to evaluate the genetic diversity of a set of
germplasm, which will allow genetic improvement and
the efficient use of a species’ available germplasm.

Received: 29.11.2021 Accepted: 22.02.2022 Published online: 23.03.2022

https://www.genresj.org
https://www.dx.doi.org/10.46265/genresj.UPPQ3994
https://www.genresj.org
https://www.dx.doi.org/10.46265/genresj.UPPQ3994
mailto:carlos.lazarini@embrapa.br


Genetic Resources (2022), 3 (5), 24–35 Genetic variability in Stylosanthes scabra 25

Germplasm characterization include studies of eco-
geographic and demographic adaptation, plant genetics,
physiology and pathology, parameters of an organism’s
vital cycle and yield evaluation, among other studies.
Breeding programmes should begin only after appropri-
ate germplasm characterization (Cameron, 1983).

Characterization is the best way to understand the
variability in a germplasm collection and, consequently,
increase its use by plant breeders. It is also important
in monitoring the genetic stability of germplasm
storage processes. Characterization of germplasm can
be based on molecular, biochemical, morphological, and
agronomic features.

Despite the importance of forage legumes in various
production systems, the adoption of tropical legume
germplasm has been poor in several Latin American
countries (Shelton et al, 2005; Boddey et al, 2020).
However, according to Kretschmer (1988), there are
about 18,000 species of forage legumes and at least
1,000–2,000 species with potential for cultivation.

The genus Stylosanthes includes nitrogen-fixing and
drought-tolerant species economically important for
perennial pasture, green manure and land recov-
ery (Marques et al, 2018). Its importance as a forage
crop has increased in recent years due to its potential
to improve the nutritional quality of mixed pastures in
tropical and subtropical regions (Cameron et al, 2004;
Costa, 2006; Rocha, 2014; Epifanio et al, 2020; Carvalho
et al, 2020). Due to its easy adaptation to acid and low-
fertility soils, it has been used in pastures to minimize
nitrogen deficiency in the soil, increase protein content
in the diet, as well as improve pasture carrying capacity,
longevity and productivity (Resende et al, 2008; Rocha,
2014).

Embrapa Cerrados has a germplasm bank (AGB) with
thousands of accessions of the genus Stylosanthes, of
which 80 are from Stylosanthes scabra Vogel species.
Genetic resources conservation and characterization are
of utmost importance to support breeding programmes
for incorporating novel agronomic traits and developing
new cultivars (Wetzel et al, 2012; Moreira et al, 2015).

Genetic resources characterization has been used
to quantify genetic diversity, its magnitude, nature
and distribution among and within populations (Boldt,
2011; Cruz et al, 2011). The evaluation of the nutritional
quality diversity of S. scabra accessions can contribute
to expanding the use of these genetic resources, and
provides important information to high-quality forage
breeding programmes (Pereira et al, 2011). Thus, the
objective of this study was to access the genetic diversity
of forage quality traits among 80 accessions of S. scabra
from the Embrapa Cerrados active germplasm bank.

Materials and methods

Experimental setup

An experiment was carried out at the Embrapa Cerrados
Research Centre, in Planaltina, Federal District, Brazil
(15◦ 35’ S, 47◦ 42’ W; 993m a.s.l.), from December 2017

to April 2018, under irrigated conditions, to evaluate the
genetic variability of accessions of S. scabra. The climate
at the experimental site is tropical savannah according to
the Köppen–Geiger classification (Peel et al, 2007). The
experiment was planted in clay soil (Rhodic Haplustox
Oxisol) with, on average: pH 5.1, organic matter
concentration of 29g/kg, K concentration of 46mg/kg,
Al concentration of 25mg/kg and P concentration of
2.2mg/kg (Mehlich-I) at 0–0.2m soil depth.

Seeds from all samples were treated by mechan-
ical scraping with 100-grit sandpaper and sown in
polyethene trays with 60 cells of 230cm3 filled with
a commercial substrate. Fifty-day old seedlings were
planted in single-line plots spaced 0.5m within and 1.0m
between plots. Each plot consisted of seven plants from
a specific accession.

Plant material

Eighty accessions of S. scabra collected in 9 different
States from 4 distinct geographical regions in Brazil
were evaluated: 26 from the States of Goiás (GO),
Mato Grosso (MT), and Mato Grosso do Sul (MS)
in the Central-West Region; 10 from Pará (PA) and
Tocantins (TO) in the North Region; 36 from Bahia
(BA) and Maranhão (MA) in the Northeast Region; and
8 from Minas Gerais (MG) and Rio de Janeiro (RJ)
in the Southeast Region. Detailed information on each
accession is presented in Table 1.

Data collection and analysis

All plants were harvested 90 days after transplanting
to the field at 10cm stubble height. After an individual
morphological plant characterization, all seven plants
from each accession were pooled together, dried for
72 hours in a forced-air oven at 55◦C, ground through
a 1mm-screen Wiley mill (A. H. Thomas Co., Boulder,
CO), stored and sealed in plastic containers, and sent to
the Analytical Plant Chemistry Laboratory at Embrapa
Cerrados.

The samples were then analyzed to estimate the
content of neutral detergent fibre (NDF), acid detergent
fibre (ADF), lignin (LIG), crude protein (CP) and in vitro
dry matter digestibility (IVDMD) on dry matter (DM)
basis. DM content of the samples was determined by
drying approximately 2g of each sample in a forced-
air oven at 105◦C ± 2◦C for at least 2 hours (AOAC,
1996). Sequential NDF, ADF and LIG analysis from
0.5g initial sample weight placed in ANKOM F57 filter
bags (Van Soest et al, 1991; Komarek and Sirois, 1993;
Komarek et al, 1994; Vogel et al, 1999; Mertens, 2002)
was performed for each accession sample. For NDF and
ADF analysis, samples were digested in an Automated
Fiber Analyzer model ANKOM 2000 (ANKOM Tech.
Corp., Fairport, NY, USA) using a neutral detergent
solution pH6.9–7.1, without α-amylase, and 1M H2SO4

acid detergent solution, sequentially after drying and
weighing procedures. LIG determination was performed
by placing the ADF dry residue in a 12M H2SO4 solution
and incubated in a Tecnal in vitro incubator system



26 de Araújo Américo et al Genetic Resources (2022), 3 (5), 24–35

Table 1. Passport data of Stylosanthes scabra Vogel accessions from the Embrapa Cerrados germplasm bank. Embrapa Cerrados,
Planaltina, DF, 2020. CPAC Number, Germplasm bank accession number. Brazilian States: BA, Bahia; GO, Goiás; MA, Maranhão;
MG, Minas Gerais; MT, Mato Grosso; MTS, Mato Grosso do Sul; PA, Pará; RJ, Rio de Janeiro; TO, Tocantins. Brazilian Geographic
Region: CO, Central-West; N, North; NE, Northeast; SE, Southeast.

Access
ID

CPAC
Number

Brazilian City/State Brazilian Geographic
Region

Latitude Longitude

1 512 Rondonópolis-MT CO 16◦ 18’ 00” S 54◦ 45’ 00” W

2 961 Rio de Janeiro-RJ SE 22◦ 54’ 06” S 43◦ 10’ 27” W

3 963 Camaçari-BA NE 12◦ 40’ 60” S 38◦ 19’ 00” W

4 964 Nazaré-BA NE 13◦ 00’ 00” S 39◦ 01’ 60” W

5 965 Feira de Santana-BA NE 12◦ 22’ 00” S 39◦ 06’ 00” W

6 966 Cruz das Almas-BA NE 12◦ 40’ 00” S 39◦ 06’ 00” W

7 967 Mucuri-BA NE 18◦ 04’ 60” S 39◦ 34’ 00” W

8 968 Pé de Serra-BA NE 11◦ 49’ 57” S 39◦ 36’ 06” W

9 969 Feira de Santana-BA NE 12◦ 16’ 00” S 39◦ 01’ 60” W

10 970 Santo Amaro-BA NE 12◦ 31’ 88” S 38◦ 49’ 60” W

11 971 Feira de Santana-BA NE 12◦ 22’ 00” S 39◦ 06’ 00” W

12 972 Catu-BA NE 12◦ 20’ 60” S 38◦ 23’ 60” W

13 973 Conceição do Almeida-BA NE 12◦ 55’ 00” S 39◦ 16’ 00” W

14 974 Catu-BA NE 12◦ 20’ 60” S 38◦ 23’ 60” W

15 976 Monte Recôncavo-BA NE 12◦ 37’ 88” S 38◦ 37’ 00” W

16 977 Valença-BA NE 13◦ 19’ 60” S 39◦ 15’ 00” W

17 978 Nazaré -BA NE 12◦ 51’ 45” S 38◦ 58’ 55” W

18 979 Ladeira Grande-BA NE 13◦ 00’ 00” S 39◦ 01’ 60” W

19 980 Barreiro-BA NE 12◦ 10’ 60” S 38◦ 23’ 60” W

20 983 Chiador-MG SE 22◦ 00’ 00” S 43◦ 00’ 00” W

21 985 Cuiabá-MT CO 15◦ 40’ 00” S 55◦ 32’ 60” W

22 986 Nortelândia-MT CO 14◦ 28’ 60” S 56◦ 45’ 00” W

23 988 Campo Verde-MT CO 15◦ 39’ 00” S 55◦ 17’ 60” W

24 990 Anastácio-MS CO 20◦ 34’ 00” S 55◦ 36’ 00” W

25 993 Coxim-MS CO 18◦ 15’ 00” S 54◦ 40’ 00” W

26 997 Senador Canedo-GO CO 16◦ 37’ 60” S 49◦ 07’ 60” W

27 999 Capinzal do Norte-MA NE 04◦ 37’ 00” S 44◦ 22’ 00” W

28 1000 Porto Franco-MA NE 06◦ 19’ 60” S 47◦ 19’ 60” W

29 1001 Porto Franco-MA NE 06◦ 25’ 60” S 47◦ 21’ 00” W

30 1003 Porto Franco-MA NE 06◦ 19’ 60” S 47◦ 19’ 60” W

31 1007 Alexânia-GO CO 16◦ 10’ 00” S 48◦ 30’ 00” W

32 1009 Rio Verde-MS CO 18◦ 43’ 88” S 54◦ 47’ 60” W

33 1010 Coxim-MS CO 18◦ 30’ 00” S 54◦ 45’ 00” W

34 1013 Itiquira-MT CO 17◦ 08’ 88” S 54◦ 51’ 00” W

35 1016 São José da Serra-MT CO 15◦ 40’ 00” S 55◦ 21’ 00” W

36 1017 Cuiabá-MT CO 15◦ 43’ 00” S 55◦ 43’ 60” W

37 1018 Campo Verde-MT CO 15◦ 43’ 00” S 55◦ 17’ 60” W

38 1019 Primavera do Leste-MT CO 15◦ 35’ 60” S 54◦ 07’ 60” W

39 1020 General Carneiro-MT CO 15◦ 40’ 00” S 52◦ 46’ 60” W

40 1025 Lagoa da Confusão-TO N 11◦ 34’ 00” S 50◦ 40’ 00” W

41 1026 Campo Verde-MT CO 15◦ 16’ 00” S 54◦ 55’ 60” W

Continued on next page



Genetic Resources (2022), 3 (5), 24–35 Genetic variability in Stylosanthes scabra 27

Table 1 continued
42 1240 Belo Horizonte-MG SE 19◦ 55’ 17” S 43◦ 56’ 05” W

43 1243 Abadiânia-GO CO 16◦ 07’ 88” S 48◦ 37’ 60” W

44 1246 Campinorte-GO CO 14◦ 22’ 60” S 49◦ 10’ 00” W

45 1250 Paráıso do Tocantins-TO N 10◦ 04’ 00” S 48◦ 49’ 60” W

46 1252 Miranorte-TO N 09◦ 15’ 00” S 48◦ 34’ 60” W

47 1253 Wanderlândia-TO N 06◦ 52’ 00” S 47◦ 51’ 00” W

48 1254 Darcinópolis-TO N 06◦ 45’ 00” S 47◦ 49’ 60” W

49 1256 Dom Eliseu-PA N 04◦ 07’ 60” S 47◦ 28’ 00” W

50 1257 Nova Rosalândia-TO N 10◦ 31’ 00” S 49◦ 01’ 60” W

51 1259 Porto Nacional-TO N 10◦ 58’ 60” S 48◦ 17’ 60” W

52 1260 Sta Rosa do Tocantins-TO N 11◦ 19’ 00” S 47◦ 52’ 00” W

53 1262 Lavrado-TO N 12◦ 07’ 00” S 46◦ 28’ 60” W

54 1264 Monte Alegre-GO CO 13◦ 15’ 00” S 47◦ 08’ 60” W

55 1265 São João da Aliança-GO CO 14◦ 30’ 00” S 47◦ 31’ 00” W

56 1267 Planaltina-GO CO 15◦ 22’ 60” S 47◦ 31’ 60” W

57 1268 Alexânia-GO CO 16◦ 07’ 00” S 48◦ 22’ 00” W

58 1270 Linda Vista-GO CO 13◦ 18’ 00” S 49◦ 07’ 60” W

59 1271 Porangatu-GO CO 12◦ 52’ 00” S 49◦ 07’ 60” W

60 1274 Goiânia-GO CO 16◦ 34’ 03” S 49◦ 15’ 54” W

61 4944 Matias Cardoso-MG SE 15◦ 01’ 48” S 43◦ 51’ 04” W

62 4945 Monte azul-MG SE 15◦ 14’ 01” S 43◦ 03’ 32” W

63 4946 Monte azul-MG SE 15◦ 15’ 12” S 43◦ 02’ 60” W

64 4947 Pajeú-MG SE 15◦ 17’ 12” S 42◦ 54’ 39” W

65 4950 Andaráı-BA NE 12◦ 48’ 39” S 41◦ 19’ 30” W

66 4951 Andaráı-BA NE 12◦ 48’ 48” S 41◦ 19’ 26” W

67 4952 Palmeiras-BA NE 12◦ 27’ 33” S 41◦ 29’ 51” W

68 4953 Palmeiras-BA NE 12◦ 27’ 15” S 41◦ 29’ 14” W

69 4954 Palmeiras-BA NE 12◦ 27’ 15” S 41◦ 29’ 21” W

70 4955 Palmeiras-BA NE 12◦ 27’ 09” S 41◦ 29’ 13” W

71 4956 Palmeiras-BA NE 12◦ 27’ 10” S 41◦ 29’ 24” W

72 4957 Iraquara-BA NE 12◦ 22’ 07” S 41◦ 31’ 00” W

73 4959 Iraquara-BA NE 12◦ 22’ 08” S 41◦ 31’ 01” W

74 4960 Iraquara-BA NE 12◦ 22’ 09” S 41◦ 30’ 59” W

75 4962 Utinga-BA NE 12◦ 06’ 11” S 41◦ 06’ 48” W

76 4963 Morro do Chapéu-BA NE 11◦ 55’ 02” S 41◦ 08’ 02” W

77 4965 Cafarnaum-BA NE 11◦ 45’ 25” S 41◦ 32’ 01” W

78 4966 Oliveira dos Brejinhos-BA NE 12◦ 18’ 06” S 42◦ 37’ 55” W

79 4986 Aracatu-BA NE 14◦ 28’ 36” S 41◦ 26’ 41” W

80 5180 Francisco Sá-MG SE 16◦ 27’ 56” S 43◦ 25’ 54” W



28 de Araújo Américo et al Genetic Resources (2022), 3 (5), 24–35

(TECNAL Scientific Equipments, Piracicaba, SP,
Brazil) for 3 hours. Ashing was done by placing folded
filter bags in crucibles in a muffle furnace at 500◦C for
at least 5.5 hours. N concentration was determined by
Kjeldahl method (AOAC, 1996) with a TecnalTM 0365
digestion–distillation system and CP was calculated as N
x 6.25. True IVDMD was determined by 48-hour ruminal
fermentation at 39.5◦C in a TecnalTM in vitro incuba-
tor system using the procedure described by Tilley and
Terry (1963), with modification by Goering and Van
Soest (1970). Hemicellulose concentration (Hemic) was
estimated by subtracting ADF from NDF and Cellulose
(CELLU) by subtracting LIG from ADF.

Statistical Analysis

Univariate analysis was performed through the F test
to detect potential differences among sites of collection
as well as similarity clusters for each quality trait.
Multivariate analysis was used to examine the genetic
variability of the accessions. A hierarchical clustering
analysis was done to sort out groups of similarity based
on Ward’s minimum variance method and their genetic
dissimilarity were estimated by the square root of the
generalized Mahalanobis D2 distances (Alvares et al,
2012; Bapurao et al, 2018). Cluster analysis allows
classifying accessions into homogeneous groups based
on their quality-traits values. Accessions within the
same group are more similar and homogeneous while
accessions among different groups are more divergent
and heterogeneous.

A principal components analysis (PCA) was per-
formed to discriminate among accessions and group
them. The goal of PCA is to provide a reduced dimension
model that would indicate differences among groups of
similarity. It also contributes to a better understanding of
the variables by describing how much of the total vari-
ance was explained by each one of them. Also, PCA anal-
ysis allows graphical visualization of the variables, iden-
tification of similar and divergent accessions, and high-
lights the variables that contribute most to the differen-
tiation of accessions (Philippeau and Philippeau, 1986).
All statistical analyses were done with the R software
version 3.6.2 programme (R Core Team, 2019).

The phenotypic diversity among accessions was
estimated by the index of Shannon and Weaver (1949)
corrected by the logarithm of the number of classes
and Simpson (1949): Shannon and Weaver corrected:
J = H’/ ln S = -

∑
pi * ln pi / ln S; and Simpson: D

= 1 -
∑

pi
2, where J is the Shannon corrected index,

H’ is the richness and evenness Shannon index, pi is
the proportion of the class i, and ln S the natural
logarithm of the total number of classes. Both indices
are used to express the concepts of richness, meaning
the number of different accessions in a sampling effort,
as well as evenness, meaning the degree of equality in
the abundance of individuals, or the relative uniformity
of their distribution across groups of accessions (Meng
et al, 1999). These indices are used to estimate
diversity within populations (Kosman and Leonard,

2007) and range from zero to one, where one represents
high genetic diversity and zero no genetic diversity.
They correspond to the probability of two individuals
randomly selected from a group to exhibit similar
characteristics (Carvalho and Quesenberry, 2009).

Results

The average concentration of CP, IVDMD, NDF, ADF,
LIG, HEMIC and CELLU were 220.6 ± 20.2g/kg, 560
± 56.0g/kg, 516.8 ± 44.1g/kg, 368 ± 32.9g/kg, 69.4
± 9.4g/kg, 148.8 ± 31.5g/kg and 298.6 ± 32.9g/kg,
respectively (Table 2).

Means of IVDMD, ADF and CELLU were significantly
different for site of collection (Brazilian states, Table 3).
Accessions from BA, GO, PA and TO had higher IVDMD
concentrations. PA (only one sample) and TO states,
had the highest IVDMD concentration, 653.4g/kg and
590.6 ± 18.5g/kg, respectively, and were significantly
different from those for MS and MT states. Accessions
from GO, MA, PA, and TO had the lowest concentrations
of ADF and CELLU, with 368 ± 32.9g/kg and 298.6g ±
32.9g/kg, respectively. Lower fibre concentrations were
also found for PA (only one accession) and TO, with
341.4g/kg and 339.8 ± 28.3g/kg of ADF and 264.5g/kg
and 272.1 ± 23.2g/kg of CELLU, respectively (Table 3).

The PCA of the 80 accessions resulted in five principal
components explaining all variations (Table 4). The first
two principal components accounted for 73% of the
total variance among all accessions. The first PC was
responsible for 55% and the second for 18% of the
variation. Variables that are correlated with PC1 and
PC2 are the most important in explaining the variability
in the data set. The correlation between each variable
and the PC was used as the coordinate in a circle of
radius equals to one, and represents the intensity in
which the variable contribute to PC1 and PC2 (Figure 1).
Traits ADF, NDF, IVDMD and CP contributed most to
PC1, with values ranging from 0.73 to 0.90, while only
LIG had an important contribution to PC2, with a value
of 0.76. Figure 1 displays the correlation circle of the
PCA. Positively correlated variables point to the same
side of the plot and negatively correlated variables point
to opposite sides of the graph.

The Kelley–Gardner–Sutcliffe penalty function for
hierarchical cluster trees was used to define the number
of groups (Kelley et al, 1996). The minimum of
this function defines the group size. In this study,
clustering of the 80 accessions resulted in four clusters,
which means four diversity classes of forage nutritive
values, considering CP, IVDMD, NDF, ADF, and LIG
concentrations (Figure 2). All traits had high and similar
diversity values from Simpson’s that ranged from 0.771
to 0.781 and Shannon–Weaver’s indices that ranged
from 0.806 to 0.817 (Table 5).

Discussion

In Australia, ’Seca’ and ’Fitzroy’ S. scabra cultivars
have been released with high dry matter yields, ade-



Genetic Resources (2022), 3 (5), 24–35 Genetic variability in Stylosanthes scabra 29

Table 2. Mean ± standard deviation (SD), minimum and maximum values (Range) in g/kg, site of collection (state) mean square
(MSs), and p-value of five quality traits from 80 Stylosanthes scabra Vogel germplasm accessions. *, ** significant differences among
states at 0.05 and 0.01 α levels, respectively.

Trait
Statistics

Mean ± SD Range MSs p-value (Pr>F)
Crude Protein 220.6 ± 20.2 185.1–279.2 6.748 0.2003
In Vitro Dry Matter Digestibility 560.0 ± 56.0 427.9–718.8 71.550* 0.0127
Neutral Detergent Fibre 516.8 ± 44.1 419.3–630.1 21.640 0.3550
Acid Detergent Fibre 368.0 ± 32.9 288.2–456.4 31.045** 0.0014
Lignin 69.4 ± 9.4 45.7–91.6 1.243 0.186
Hemicellulose 148.8 ± 31.5 103.7–264.3 13.031 0.229
Cellulose 298.6 ± 29.9 229.9–395.3 30.034** 0.00016

Table 3. Seven quality traits, estimated from 80 Stylosanthes scabra Vogel germplasm accessions by Brazil source states. Given are
the number of accessions per state and values in g/kg for means ± standard deviation (Mean ± SD), minimum and maximum
values (Min, Max). Brazilian states: BA, Bahia; GO, Goiás; MA, Maranhão; MG, Minas Gerais; MT, Mato Grosso; MTS, Mato Grosso
do Sul; PA, Pará; RJ, Rio de Janeiro; TO, Tocantins.CP, Crude Protein; IVDMD, In Vitro Dry Matter Digestibility; NDF, Neutral
Detergent Fibre; ADF, Acid Detergent Fibre; LIG, Lignin; HEMIC, Hemicellulose; CELLU, Cellulose. Mean values for traits followed
by the same superscript letter are not significantly different, based on LSD - t student at 0.05 α level.

State BA GO MA MG MS MT PA RJ TO
Trait Nº 32 11 4 7 4 11 1 1 9

CP Mean 221.8 233.6 226.7 218.5 241.2 226.1 209.3 195.9 239.8
± SD 27.7 12.9 10.5 17.0 6.5 19.9 - - 15.6
Min 185.1 218.3 213.6 196.8 235.2 185.3 209.3 195.9 215.6
Max 279.2 257.0 238.7 240.8 249.6 250.2 209.3 195.9 265.5

IVDMD Mean 572.1ab 563.0ab 549.5abc 558.7abc 520.0bc 509.8c 653.4a 533.0abc 590.6a

± SD 55.4 55.1 23.0 60.3 73.9 47.3 - - 18.5
Min 476.0 467.7 517.9 479.4 427.9 431.4 653.4 533.7 563.9
Max 718.8 658.9 567.4 651.1 587.9 587.7 653.4 533.7 611.4

NDF Mean 511.1 515.3 518.6 539.1 502.6 541.3 470.6 541.7 499.8
± SD 39.3 52.5 46.0 58.0 21.6 38.6 - - 47.0
Min 419.3 442.6 459.9 475.8 488.2 485.7 470.6 541.7 445.8
Max 627.2 624.2 572.3 630.1 534.7 594.9 470.6 541.7 597.7

ADF Mean 370.3ab 357.9bc 336.0c 390.0a 366.6abc 390.3a 341.4bc 420.2a 339.8c

± SD 28.8 23.3 24.4 36.1 11.8 36.5 - - 28.3
Min 371.6 368.9 335.0 392.3 364.1 385.0 341.4 420.2 346.2
Max 382.3 371.7 345.4 412.4 372.9 411.7 341.4 420.2 364.8

LIG Mean 69.2 66.5 76.2 76.4 65.0 67.2 76.9 82.0 67.7
± SD 9.6 8.2 13.3 6.8 5.1 9.4 - - 9.5
Min 49.9 55.8 62.3 68.7 59.5 45.7 76.9 82.0 58.5
Max 91.6 85.9 91.4 87.5 70.1 81.1 76.9 82.0 86.4

HEMIC Mean 140.7 157.3 182.6 149.1 136.1 151.0 129.2 121.4 160.0
± SD 25.7 41.3 37.9 34.2 21.3 20.2 - - 41.4
Min 103.7 119.2 152.5 119.5 113.3 130.3 129.2 121.4 128.4
Max 221.1 253.6 234.1 214.9 164.8 189.9 129.2 121.4 264.3

CELLU Mean 301.2ab 291.4ab 259.9c 313.5a 301.5ab 323.1a 264.5bc 338.2a 272bc

± SD 22.9 23.2 16.1 30.7 11.9 37.3 - - 23.2
Min 234.7 250.0 239.2 273.6 286.1 284.3 264.5 338.2 230.0
Max 363.9 318.7 275.3 359.4 313.3 395.3 264.5 338.2 299.7

quate nutritive values, drought tolerance and per-
sistence (Edye et al, 1998). In Nigeria, Akinlade
et al (2008) found CP concentrations ranging from
147g/kg to 151g/kg for four S. scabra cultivars. In

Argentina, Ciotti et al (1999) evaluated 33 accessions of
6 different species of Stylosanthes in which 4 S. scabra
accessions had CP mean values ranging from 78g/kg
to 134g/kg. In Brazil, CP from Stylosanthes commercial



30 de Araújo Américo et al Genetic Resources (2022), 3 (5), 24–35

Table 4. Principal component analysis (PCA) of the 80
accessions of Stylosanthes scabra Vogel. CP, Crude Protein;
IVDMD, In Vitro Dry Matter Digestibility; NDF, Neutral
Detergent Fibre; ADF, Acid Detergent Fibre; LIG, Lignin.

PCA PC1 PC2 PC3 PC4 PC5
Eigen values 2,77 0,89 0,67 0,43 0,24
Proportion of
variance (%)

55,48 17,77 13,41 8,57 4,78

Cumulative
variance (%)

55,48 73,24 86,65 95,22 100,00

CP 0,73 -0,27 0,51 -0,37 -0,08
IVDMD 0,74 -0,47 0,05 0,47 -0,08
NDF -0,77 -0,09 0,55 0,18 0,24
ADF -0,90 -0,07 0,16 0,05 -0,40
LIG -0,54 -0,76 -0,29 -0,19 0,09

Table 5. Simpson and Shannon–Weaver diversity indices for
Stylosanthes scabra Vogel.

Trait Simpson Shannon–Weaver
Crude Protein (CP) 0.781 0.813
In Vitro Dry Matter
Digestibility (IVDMD)

0.771 0.806

Neutral Detergent Fibre
(NDF)

0.779 0.812

Acid Detergent Fibre (ADF) 0.774 0.808
Lignin (LIG) 0.779 0.817

cultivars from different species or a mixture of species
(S. guianensis cv. Mineirão, S. macrocephala cv. Pioneiro,
and a mixture of S. capitata and S. macrocephala cv.

Figure 1. Correlation circle illustrating the direction and
intensity of the five forage quality variables used in the
principal component analysis. CP, Crude Protein; IVDMD, In
Vitro Dry Matter Digestibility; NDF, Neutral Detergent Fibre;
ADF, Acid Detergent Fibre; LIG, Lignin.

Campo Grande) are reported with concentrations rang-
ing from 98 to 210g/kg (Braga et al, 2020). The mean
value obtained in this study for S. scabra was 220g/kg,
higher than those from Argentina and higher or similar
to other studies in Brazil. This CP high value reflects the
species’ potential to be used as a forage in animal pro-
duction systems in the tropics.

The S. scabra NDF and ADF values obtained in
this research are similar to the ones reported for 71
accessions from five Stylosanthes species collected in
Brazil (Santana, 2010): S. angustifolia with 540g/kg of
NDF and 378g/kg of ADF; S. capitata with 490g/kg of
NDF and 330g/kg of ADF; S. macrocephala 545g/kg of
NDF and 394g/kg of ADF; S. pilosa with 531g/kg of NDF
and 394g/kg of ADF; and S. viscosa with 553g/kg of
NDF and 390g/kg of ADF in average. The average fibre
concentration of S. scabra in this study was 517g/kg
for NDF and 368g/kg for ADF, which indicate that the
fibre content among different Stylosanthes species is
similar. This fibre characteristics together with the high
CP content indicate that S. scabra species may be used,
as other Stylosanthes species, as an important forage for
seasonally dry tropical and subtropical environments in
Brazil.

There were significant differences among sites of
collection for IVDMD, ADF, and CELLU, pointing to the
existence of phenotypic variability among accessions
(Table 2). The range for IVDMD, ADF and CELLU
were 291g/kg, 168g/kg and 165g/kg, respectively. This
indicates that there is potential genetic diversity among
genotypes for IVDMD, ADF and CELLU to be successfully
exploited in a breeding programme. Differences among
genotypes from different states were not significant for
CP, NDF, LIG, and HEMIC. Thus, genetic gains from
selection might be less effective when selecting for high
CP and HEMIC, as well as low NDF and LIG from this
collection.

The negative correlation between digestibility and
fibre concentrations in forage species is well known
because the carbohydrates in the NDF and ADF portions
of plant tissues are mostly cell walls indigestible
to ruminants. Thus, the lower ADF and CELLU
concentrations from PA and TO indicate that those
accessions may produce segregant populations when
used in breeding programmes for higher nutritive
quality forage. This is only an indication, additional
studies should be carried out since higher IVDMD and
lower fibre contents may not be due only to genetic
effects but also management practices, plant age and
other environmental effects.

The goal of PCA is to provide a reduced dimension
model that would indicate measured differences among
groups. It also can contribute to a better understanding
of the set of variables by describing how much of the
total variance is explained by each one. The circle of
correlations graphic illustrates the direction as well as
the intensity of the variable vectors in the first two
components (Figure 1). Note that CP, IVDMD, NDF, ADF
vectors have closer inclinations along PC1, with CP and



Genetic Resources (2022), 3 (5), 24–35 Genetic variability in Stylosanthes scabra 31

Figure 2. Dendrogram illustrating the distribution of 80 accessions of Stylosanthes scabra Vogel in four clusters based on
Mahalanobis distance. The accessions are numbered as in Table 1.

IVDMD vectors pointing to the same orientation, as are
NDF and ADF but in opposite directions. The IVDMD
and ADF concentrations are the closest to the circle of
correlations, indicating the better representation of the
traits on the definition of PC1. Yet, LIG was the only trait
with a better representation on PC2. Variables close to
the centre of the circle are less important for the first
component. In brief, there was a positive correlation
between CP and IVDMD and between NDF and ADF,
as well as negative correlations among the former traits
with the latter ones. ADF, IVDMD and LIG were the traits
with the best representations on the first two principal
components.

In this study, 80 accessions resulted in four clusters,
which means four diversity classes of forage nutritive
values, considering CP, IVDMD, NDF, ADF, and LIG
concentrations (Figure 2). Concentrations of HEMIC and
CELLU were not considered in the analysis since they
are highly correlated to NDF and ADF concentrations,
respectively, from which they are directly derived
(Table 4). The four Mahalanobis distance clusters were
plotted in the PCA graphic, each in a 0.95 probability
level ellipsis, to get a better view of their distribution
(Figure 3). Scatter plots of the principal component
scores were convergent with the clusters’ ellipsis,
allowing the visualization and identification of locations
with higher concentrations of a given trait in the forage.

Four out of the five original variables had high
contributions to the first principal component. This
first component is a measure of forage quality when
IVDMD and CP increase. Also, it is a measure of lack

of quality when the fibre components NDF and ADF
increase. Furthermore, the first principal component
correlates most strongly with the ADF (r = 0.90).
Thus, this first principal component may be considered
primarily a measure of the ADF concentration. This
implies that accessions with higher values of ADF
tend to have a lower quality of forage. The second
principal component increases with the decrease of
LIG concentration. This component can be viewed
as a measure of forage quality as LIG concentration
decreases. PCA analysis did separate the accessions into
two dimensions, even though the traits are quantitative
with a scatter distribution along PC1 and PC2. All five
quality characteristics had high loading values in the first
two principal components, ranging from 0.73 to 0.90,
indicating their importance as S. scabra descriptors.

Cluster II and IV with 28 and 24 accessions,
respectively, were the largest, representing together 65%
of the total accessions. Cluster I with 16 accessions and
Cluster III with 12 accessions represented the remaining
35% of the total number of accessions (Table 6). The
analyses of variance among clusters for each trait are
presented in Table 7. There were highly significant
differences among clusters for CP, IVDMD and ADF,
indicating substantial genetic variability. Cluster IV
included accessions with the highest CP and IVDMD
concentrations and the lowest ADF concentration,
representing the group of accessions with the higher
forage quality, and may be used for hybridization
in breeding programmes. Clusters I and II had the
lowest CP and IVDMD, as well as the highest ADF



32 de Araújo Américo et al Genetic Resources (2022), 3 (5), 24–35

Figure 3. Dispersion of 80 Stylosanthes scabra Vogel accessions in relation to the first two components and four Mahalanobis
similarity clusters. The accessions are numbered as in Table 1 and grouped in four clusters as in Table 6.

concentrations, representing the lowest nutritive value
cluster. Cluster III had intermediate values for all traits
and its CP concentration was significantly different from
all other clusters.

Formation of similarity groups and respective estima-
tion of their divergence is useful when choosing acces-
sions to be used as progenitors for superior hybrid
combinations in breeding programmes. Divergence esti-
mates by Mahalanobis D distances are presented in
Table 8. The intra-group distances were very similar for
all four clusters, ranging from 3.15 to 3.37, and were
consistently lower than the inter-cluster distances, which
ranged from 6.92 to 12.47, as expected. These distances
clearly suggest a great deal of genetic similarity within
and genetic divergence among clusters. The shortest
inter-cluster distance was between Cluster II, indicat-
ing lower divergence. The highest inter-group distance
was 12.46 between Clusters I and III, followed by 11.99
between Clusters III and IV, and 11.98 between Clusters
I and IV. High divergences among groups were consis-
tent with the ANOVA results where Cluster IV was signif-
icantly different from Clusters I and III, for CP, IVDMD
and ADF. In a breeding programme, crosses between
accessions with maximum divergence may give rise to
genetically diverse lines and the potential production of
heterotic transgressive segregants. A great deal of varia-
tion in subsequent generations is expected from crosses
among plants of maximum inter-class distances clusters.
This may not be true between low-divergent clusters as
Clusters I and II.

The pattern of distribution of accessions in different
clusters did not reflect the geographical diversity of the
site of collection (states). Accessions from each state

were consistently distributed among the four clusters of
forage quality (Figure 4). Cluster I presented accessions
from five states, Cluster II from eight, Cluster III from
seven, and Cluster IV from six, all out of a total of nine
states. Thus, the observed traits diversity was somewhat
similar among the different collection sites, leading to a
lack of effect of the source locations on the four quality
groups definition considering this germplasm collection.

Simpson’s and Shannon–Weaver’s indices expressed
the diversity among accessions within each population,

Figure 4. Geographic distribution of collection sites of the
80 Stylosanthes scabra Vogel accessions in Brazil, coloured
according to the clusters in which they were grouped.



Genetic Resources (2022), 3 (5), 24–35 Genetic variability in Stylosanthes scabra 33

Table 6. Distribution of 80 Stylosanthes scabra Vogel accessions in four clusters based on the forage quality variables. Embrapa
Cerrados, Planaltina, DF, 2020.

Cluster Nº of Accessions Accessions ID
I 16 1, 3, 4, 7, 9, 13, 18, 19, 25, 27, 31, 32, 35, 36, 41, 44
II 28 2, 5, 6, 8, 10, 11, 12, 15, 16, 17, 20, 21, 22, 23, 28, 38, 39, 48, 49, 51, 53, 56, 59, 60, 61, 66,

71, 80
III 12 14, 3033, 34, 37, 42, 43, 52, 57, 63, 68, 69
IV 24 24, 26, 29, 40, 45, 46, 47, 50, 54, 55, 58, 62, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79

Table 7. Mean concentrations ± standard deviations of CP, IVDMD, NDF, ADF, and LIG of Stylosanthes scabra Vogel accessions
distributed in four clusters. ** Significant differences among clusters at 0.01 α level. Means followed by the same subscript letter
within the same column are not different, LSD - t student at 0.05 α level. CP, Crude Protein; IVDMD, In Vitro Dry Matter Digestibility;
NDF, Neutral Detergent Fibre; ADF, Acid Detergent Fibre; LIG, Lignin.

CP (%) IVDMD (%) NDF (%) ADF (%) LIG (%)
Cluster I 21,58 ± 2.21c 50,35 ± 4.27c 50,98 ± 3.32 36,73 ± 2.64 a 6,13 ± 0.72
Cluster II 21,25 ± 1.35c 54,81 ± 4.40b 52,06 ± 2.99 38,36 ± 2.77 a 7,63 ± 0.63
Cluster III 22,97 ± 1.74b 55,96 ± 3.88b 58,36 ± 3.52 37,65 ± 3.66 a 7,19 ± 1.24
Cluster IV 24,82 ± 1.41a 61,18 ± 3.72a 48,37 ± 2.51 34,60 ± 2.98 b 6,55 ± 0.54
Mean 22.65 ± 1.72** 56,00 ± 4.12** 51.68 ± 4.37n.s 36.80 ± 3.14** 6,94 ± 0.95n.s.
Range 18.53–27.92 42.79–71.88 44.13–52.41 33.18–40.23 4.57–7.56

Table 8. Mean intra- and inter-cluster Mahalanobis (D)
distances of 80 accessions of Stylosanthes scabra Vogel.
Embrapa Cerrados, Planaltina, DF, 2020.

Cluster I II III IV
I 3.22
II 6.92 3.15
III 12.47 9.71 3.37
IV 11.98 9.79 11.99 3.18

in this case, each different Brazilian state where
collecting was done. High values for both indices suggest
a significant amount of genetic variation for forage
quality traits.

Conclusions

There is considerable genetic diversity for forage
nutritive values among the S. scabra accessions from
Embrapa Cerrado’s germplasm bank. This variability
was reflected across geographic areas in Brazil for in
vitro digestibility, acid detergent fibre, and cellulose
concentrations.

Independent of the site of collection (Brazilian
states), 24 high-quality S. scabra accessions are promis-
ing parents for breeding due to their higher digestibility
and protein content, as well as lower lignin concentra-
tions.

S. scabra accessions from Embrapa Cerrado’s
germplasm bank presented significant genetic diver-
sity that may be useful for developing new high-quality
improved forage populations in a breeding programme.

Author contributions

All authors contributed to the study conception and
design. Fabiana Karla de Araújo Américo, Marcelo Ayres

Carvalho, Allan Kardec Braga Ramos and Claudio Takao
Karia did all genetic material preparation, experiment
implementation and conduction, and data collection
and organization. Fabiana Karla de Araújo Américo
and Carlos Eduardo Lazarini da Fonseca did the wet
lab analysis. Juaci Vitória Malaquias, Marcelo Ayres
Carvalho and Carlos Eduardo Lazarini da Fonseca did
the data analysis. Fabiana Karla de Araújo Américo,
Marcelo Ayres Carvalho and Carlos Eduardo Lazarini
da Fonseca wrote the first draft of the manuscript,
and all authors commented on the different versions
of the manuscript. Marina de Fátima Vilela did the
geographical information plots. Gustavo José Braga was
responsible for reviewing the manuscript. All authors
read and approved the final manuscript.

Conflict of interest statement

All authors have no affiliations with or involvement in
any organization or entity with any financial interest or
non-financial interest in the subject matter or materials
discussed in this manuscript.

Funding

This work was partially funded by the Coordenação
de Aperfeiçoamento de Pessoal de Ńıvel Superior -
CAPES (Coordination for the Improvement of Higher
Education Personnel, a Federal Agency) which awarded
a scholarship to the first author. This work was part of
her dissertation in partial fulfilment of the requirements
of Doctor of Science (DSc) in Plant Genetic Resources
from the Universidade Federal de Feira de Santana.



34 de Araújo Américo et al Genetic Resources (2022), 3 (5), 24–35

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	Introduction
	Materials and methods
	Experimental setup
	Plant material
	Data collection and analysis
	Statistical Analysis

	Results
	Discussion
	Conclusions
	Author contributions
	Conflict of interest statement
	Funding