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Research article
Phenotypic diversity of sorghum [Sorghum bicolor (L.) Moench]
genotypes based on qualitative and quantitative traits
Wedajo Gebre
a,b,*
, Firew Mekbib
b
, Alemu Tirfessa
c
, Agdew Bekele
d
a
Department of Plant Science, Jinka University, Jinka, Ethiopia
b
School of Plant Sciences, Haramaya University, Harar, Ethiopia
c
Department of Sorghum Breeding, College of Agriculture, Kansas State University, Manhattan, United States
d
Department of Plant Breeding, RAISE-FS, Stichting Wageningen Research (SWR) Ethiopia, Hawassa Liaison Ofce, Ethiopia
ARTICLE INFO
Keywords:
Clustering
Divergence
Genetic distance
Qualitative traits
Quantitative traits
Sorghum
ABSTRACT
It is essential to comprehend genetic diversity for the conservation and effective utilization of
crop genetic resources. There is limited information on genetic resource collection, phenotypic
characterization, and conservation of sorghum genotypes in Ethiopia, particularly in the southern
region. The study was conducted at the Jinka Agricultural Research Centre during the 2021
cropping season, to quantify and qualitatively assess the morphological diversity of sorghum
genotypes. 225 genotypes were evaluated using a simple lattice design replicated twice. Data on 8
qualitative traits were collected and subjected to the Shannon-Weaver diversity index (H
′
) and
multivariate analyses and data on 17 quantitative traits were subjected to multivariate analyses.
The estimate of H
′
ranged from 0.46 for grain color to 0.95 for panicle compactness and shape.
Panicle exsertion, midrib color, pericarp color, awns, grain covering, and panicle compactness
and shape were the most effective traits for distinguishing among sorghum genotypes. The rst
three and six principal components explained (59.0 %) and (76.5 %) of the total variation for
qualitative and quantitative traits, respectively. A cluster analysis based on qualitative and
quantitative traits identied ve and four distinct groups, respectively. The highest inter-cluster
distance was found between cluster I and V (D
2
=98.72) for qualitative, while the highest inter-
cluster distance was found between cluster II and IV (D
2
=55.40) for quantitative traits. Within
and between clusters, the intra and inter-cluster distances ranged from 1.60 to 7.61 and
14.64–55.40 units, respectively. The observed genetic distance exhibited within and among
clusters has to be exploited for the selection of the most divergent parents in future breeding
programs.
1. Introduction
Sorghum [Sorghum bicolor (L.) Moench] is a C4 crop that is ranked fth in the world after wheat, maize, rice, and barley in terms of
production [1,2]. Large populations living in semi-arid regions of Asia and Africa rely on it as a major food source [3]. As a cereal crop,
it is well suited to drought-prone regions with poor soil quality. Sorghum is a staple food crop of Ethiopia. Sorghum ranked third in area
coverage, next to teff and maize, and fourth in production. The production of sorghum is an important food and feed source in the
lowland areas of Ethiopia, where mixed crop-livestock farming is practiced [4].
* Corresponding author. Department of Plant Science, Jinka University, Jinka, Ethiopia.
E-mail address: wedajo2009@yahoo.com (W. Gebre).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2025.e42378
Received 16 May 2024; Received in revised form 29 January 2025; Accepted 29 January 2025
Heliyon 11 (2025) e42378
Available online 30 January 2025
2405-8440/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license
( http://creativecommons.org/licenses/by-nc/4.0/ ).
Genetic variability is a precondition for varietal improvement in existing populations. Greater emphasis should be placed on
germplasm collection and characterization for present and future plant breeding programs to prevent genetic erosion [5]. Under-
standing the genetic diversity of a crop usually helps the breeder choose desirable parents for the breeding program and gene
introgression from distantly related germplasm. Hybridizing of more diverse genotypes or accessions would produce superior hybrids
with resistant to abiotic and biotic stresses [6]. It is a natural gift that certain crop species have genetic diversity due to geographical
separation or genetic barriers that prevent them from crossing. Knowledge of the genetic diversity among sorghum genotypes is
essential for their efcient utilization in plant breeding programs. Morphological traits are commonly used to analyze genetic diversity
[5]. Generally, morphological assays do not require sophisticated equipment or preparatory procedures. As a preliminary evaluation
tool, quantitative morphological traits provide an accurate and fast method for assessing diversity. The use of morphological char-
acteristics is the most common approach used to estimate the relationships between genotypes.
Landraces are locally well-adapted and preferred varieties nurtured and cultivated by farmers through the conventional method of
selection [7]. The need for further critical and systematic evaluation of these landraces and their utilization in grain sorghum
improvement is keenly felt. Over the years, several studies have attempted to estimate the genetic diversity of cultivated sorghum using
morphological traits [8–13]. The availability of diverse and resilient genetic resources is a potential and continued source for future
plant breeding and sustainability of agricultural productivity.
Characterization of genetic diversity requires comprehensive statistical procedures. The selection of promising genotypes for future
breeding programs was based on statistical methods, such as D
2
statistics and multivariate analysis. Chateld and Collins [14] stated
that principal components with eigenvalues less than one should not be considered signicant for principal component analysis.
Patroti et al. [15] suggested that multivariate techniques can be effective tools for estimating genetic variance to develop more
effective strategies for selecting germplasms and designing breeding programs that incorporate useful diversity. Mathematically, these
techniques involve the calculation of genetic distance metrics, such as Euclidean distance or Mahalanobis distance, clustering algo-
rithms, such as k-means or hierarchical clustering, and principal component analysis to identify the most signicant traits contributing
to genetic diversity and genotype selection [16,17]. Moreover, only limited studies have been conducted on sorghum in Ethiopia,
especially in the southern part of the country [18]. The genetic diversity of lowland sorghum accessions in southern Ethiopia has not
been characterized in terms of qualitative and quantitative traits. Additionally, multivariate analyses are needed to provide more
information regarding the genetic diversity of sorghum genotypes. Therefore, the present study aimed to assess the extent of genetic
diversity among 225 sorghum genotypes using qualitative and quantitative traits, to cluster the genotypes into homogeneous groups,
to estimate the genetic distance and relationships between genotypes, and to identify the major traits contributing to diversity.
Fig. 1. Description of accessions along with area of collection and accession number.
W. Gebre et al.
Heliyon 11 (2025) e42378
2
2. Materials and methods
2.1. Study site description
The eld experiment was conducted from May to September 2021 at the Jinka Agricultural Research Center (JARC) during the
main cropping season. Jinka Agricultural Research Center is located 729 km southwest of Addis Ababa at 360 ◦33
′
02.7
″
E, 050
◦46
′
52.0
″
N, and at an altitude 1420 m above sea level. The maximum, minimum, and average temperatures of the center for ten years
(2012–2022) were 27.88
0
, 17.610, and 22.74
0
◦C, respectively, while the mean annual rainfall was 1381 mm. The center soil type was
Cambisols [19]. The selected accessions represented all lowland areas of the sorghum-growing districts in Ethiopia (Fig. 1).
2.2. Experimental materials
The experiment was performed using 225 sorghum genotypes. A total of 210 sorghum accessions obtained from the Ethiopian
Biodiversity Institute (EBI) and 15 released sorghum varieties were considered (Table 1). Sorghum accessions with full passport data
Table 1
List of genotypes used for diversity study.
Genotype Code Genotype Code Genotype Code Genotype Code Genotype Code Genotype
26892 G1 69334-B G22 70845-B G43 74701 G64 204611 G85 74660
26899 G2 69335-A G23 71010 G44 74684-B G65 204615 G86 74661
26901 G3 69335-B G24 74641 G45 74686 G66 204621 G87 74663
26905 G4 69337 G25 71076 G46 74688 G67 204622 G88 74664
27907 G5 69338 G26 69331 G47 74689-A G68 204623 G89 74665
27909 G6 69339 G27 69332 G48 74689-B G69 204624 G90 74666
27915 G7 69341 G28 69333 G49 74693 G70 204625 G91 74667
27916 G8 70046 G29 69334 G50 200613 G71 74642 G92 74669
27918 G9 70050 G30 69335 G51 200615-A G72 74643 G93 74670
27915 G10 70052 G31 69336 G52 200615-B G73 74645 G94 74671
27916 G11 70055 G32 69337 G53 200615-C G74 74646 G95 74672
27918 G12 70056-A G33 69338 G54 200617 G75 74647 G96 74674
27919 G13 70056-B G34 69339 G55 201343 G76 74649 G97 74675
27922 G14 70063-A G35 69340 G56 201348 G77 74651 G98 74676
27924 G15 70084 G36 69341 G57 201453 G78 74652 G99 74677
69319 G16 70161 G37 74666 G58 204601 G79 74653 G100 74678
69321 G17 70229 G38 74667 G59 204602 G80 74654 G101 74679
69323 G18 70568 G39 74669 G60 204604 G81 74655 G102 74680
69327 G19 70678 G40 74670 G61 204605-A G82 74656 G103 74681
69332 G20 70229 G41 74675 G62 204605-B G83 74657 G104 74682
69334-A G21 70845-A G42 74684-A G63 204607 G84 74658 G105 74683
Code Genotype Code Genotype Code Genotype Code Genotype Code Genotype
106 74684 131 201344 156 201350 181 204616 206 211192
107 74685 132 201348 157 213019 182 204617 207 211486
108 74686 133 201349 158 213021 183 204618 208 213004
109 74687 134 201350 159 213026 184 204619 209 213008
110 74688 135 74685 160 214010 185 204611 210 213017
111 74689 136 74686 161 214043 186 204612 211 Melkam
112 74690 137 74687 162 214044 187 204613 212 Dekeba
113 74691 138 74688 163 214046 188 204614 213 Meko
114 74693 139 74689 164 214056 189 204615 214 Macia
115 74695 140 74690 165 214063 190 204616 215 Gambella 1107
116 74698 141 74691 166 214079 191 204617 216 Red Swazi
117 74699 142 74693 167 214109 192 204618 217 Seredo
118 74700 143 74695 168 215025 193 204619 218 Gobiye
119 74701 144 74698 169 215026 194 204620 219 ESH-1
120 74702 145 74699 170 216906 195 204621 220 ESH-2
121 74703 146 74700 171 216907 196 204622 221 ESH-4
122 74704 147 74701 172 204610 197 204623 222 ESH-5
123 74705 148 74702 173 204608 198 204633 223 Teshale
124 74706 149 74703 174 204609 199 204634 224 Argity
125 200613 150 74704 175 204610 200 204635 225 Tilahu
126 200614 151 200617 176 204611 201 204636
127 200615 152 201343 177 204612 202 206094
128 200616 153 201344 178 204613 203 206127
129 200617 154 201348 179 204614 204 206285
130 201343 155 201349 180 204615 205 206286
Note: In the table, the codes (1–2010) refer to genotypes obtained from the Ethiopian Biodiversity Institute (EBI), while codes (2011-225) refer to the
15 Melkassa Agricultural Research Center-released varieties.
W. Gebre et al.
Heliyon 11 (2025) e42378
3
were obtained from the Ethiopian Biodiversity Institute (EBI) and homogenized at Jinka University.
2.3. Experimental design
The experimental design was a 15 ×15 simple lattice with two replicates. Each genotype was planted in a plot size of 3.75 m
2
(each
row 5 m long, 0.75 cm between rows, 0.15 cm between plants and also spacing between blocks 2 m and 0.5 m between plots). There
were two rows in each plot, which accommodates thirty-three plants/seeds per row.
The seeds were sown by hand drilling, and seedlings were thinned with care after seven days of emergence to maintain a spacing of
15 cm between plants. All recommended rates of nitrogen, phosphorus, and sulfur (NPS)-containing fertilizer were applied during
sowing at a rate of 100 kg/ha, while urea was applied on a split base at a rate of 50 kg/ha at sowing, and the remaining was applied at
the tillering stage (when the plant reached knee height). All agronomic practices including pest control were applied according to
requirements.
2.4. Data collection
2.4.1. Qualitative data
Eight qualitative characters obtained from the standard sorghum descriptor lists for characterization were used for sorghum
phenotypic characterization [20] (Table 2). The qualitative traits used and their phenotypic classes are described in the table below.
2.4.2. Quantitative data
The procedure described in the IBGR/ICRISAT sorghum descriptor was used to measure 16 quantitative traits [20] based on ten
randomly selected plant per plot. The following data were collected during the study: days to owering (DF), days to maturity (DM),
plant height (PH), grain ll duration (GFD), and leaf characteristics including leaf length (LL), leaf number (LN), leaf width (LW), and
leaf area (LA). At maturity, researchers recorded grain yield and major yield components, such as panicle length (PL), panicle weight
(PW), panicle yield (PY), and thousand kernel weight (TKW). The data collected on a plot basis included the following: days to
owering (in days), days to maturity (in days from planting up to the time when 95 % of plants matured, or when seed texture becomes
hard), straw weight (in kilograms), biomass yield (in kilograms), grain yield (in kilograms), harvest index (in percentage), and 1000
seed weight (in grams). The data collected for ten plants included their height (cm), panicle length (cm), leaf length (cm), leaf width
(cm), leaf count, threshing percentage (%), and panicle yield (g). The leaf area (cm
2
) calculation per plant was based on the product of
the length and width of the third top leaf multiplied by the total number of leaves and a coefcient of 0.71 [21].
2.5. Data analyses
Phenotypic frequency distributions for the traits were calculated for all accessions. The Shannon-Weaver index of diversity (H
′
) was
computed for each character from the phenotypic frequencies, as described by Hennink and Zeven [22], using the following formula:
Hʹ=∑
n
k=0
PilnPi /ln(n)
where Pi is the proportion of individuals (genotypes) in the ith class and n-class trait, n is the number of phenotypic classes for a given
trait, and ln(n) is the natural logarithm of ln(n). H
′
was estimated for each trait and standardized by dividing it by log10 and normalized
to keep the H
′
values between 0.00 and 1.00. The diversity index was classied as high (H’≥0.06), intermediate (0.40≤H’≤0.60), or
low (0.10≤H ≤0.40), as described by Firdissa et al. [23]. All diversity index analyses were performed using Microsoft Excel. The
non-normalized ‘H’ values have been used to evaluate variance. A hierarchical [24] cluster was performed using the frequency dis-
tribution of phenotypic classes. The standard genetic distances from the phenotypic courses were used to construct a dendrogram by
the Ward method using Minitab Statistical Software version 17 [25]. The common intra and inter-cluster distances were calculated
using generalized Mahalanobis D2 statistics [26]. The test of signicance was conducted by using the chi-square test at probability
Table 2
Sorghum morphological descriptors and their keys used in the study.
Descriptor Key Descriptors and code
Panicle exsertion PE Slightly exserted <2 cm, exserted 2–10 cm (2), well exserted >10 cm (3), peduncle re-curved (4) but panicle is below the
ligule and clearly exposed splitting the leaf sheath, panicle covered by leaf sheath (5)
Panicle compactness and
shape
PCS (1 =loose erect, 2 =loose drop, 3 =compact elliptic (erect) and 4 =compact oval or recurved)
Grain color GLC 1 =white, 2 =yellow, 3 =red, 4 =brown, 5 =Buff, 6 =others
Pericarp color PC 1 =white, 2 =yellow, 3 =red, 4 =brown and 5 =buff
Glume color Gc 1. White; 2. Senna (yellow group); 3. Mahogany (greyed-orange); 4. Red; 5. Purple; 6. Black; 7. Grey
Grain covering GC 1 =grain uncovered, 2 =25 % of grain covered, 3 =50 % of grain covered, 4 =75 % of grain covered, 5 =grain fully
covered, 6 =glumes longer than grain
Awns AW 1. Present; 2. Absent
Midrib color MDC White (1), dull green (2), yellow (3), brown (4), purple (5)
W. Gebre et al.
Heliyon 11 (2025) e42378
4
levels of 5 % and 1 % using the n-2 degrees of freedom for the studied traits, where the signicance test of chi-square was based on the
tabular value and the calculated or obtained values from the generalized square distance values of the SAS output. Pseudo-F (PSF) and
pseudo-T2 statistics were used to dene the optimum cluster numbers [27]. Sharma [28] examined the contribution of each trait to
divergence using the formula [CTIC =SD/x * 100], where SD and x represent the standard deviations and mean performance of each
trait, respectively. Principal components analysis is expected to reduce the observed variables into a small number of components and
is computed using Minitab Software Version 17 [25]. In principal component analysis, the values were standardized to unit variance
and zero mean.
3. Results and discussion
3.1. Morphological diversity analysis based on qualitative traits
3.1.1. Frequency distribution
The morphological characterization of genotypes based on qualitative variables revealed a high level of variance among genotypes
(Table 3), indicating that the morphological traits evaluated were polymorphic with varying levels of variability. Martiwi et al. [29] in
29 sorghum genotypes and Sawadogo et al. [30] in 42 sorghum genotypes supported this nding.
In this study, 55.1 % of the genotypes exhibited slight panicle exertion, followed by 36 % that were exserted, 5.8 % that were well
exserted, 1.3 % with peduncle re-curved, and 1.8 % with the panicle covered by leaf sheath. These ndings are consistent with the
previous results reported by Derese et al. [31], in sorghum, where 26.32 % of the genotypes showed exserted panicles, while 73.68 %
had well-exserted panicles, and Sawadogo et al. [32] in sorghum found that most of the genotypes had panicle exsertion of 86.21 %,
while a small proportion of 13.79 %) had no panicle exsertion. Panicle compactness (inorescence type) varied greatly among sor-
ghum accessions, with 39.6 % of the genotypes having a compact elliptic panicle shape, followed by 26.2 % having a loose erect
panicle, 18.2 % having a loose drop, and 16 % having a compact oval or curved shape (Table 3). This result is in line with Desmae et al.
[33], who reported semi-compact, to compact head types (68 %) of sorghum landraces from North Eastern Ethiopia. This nding
contradicts the results of Youssef et al. [34], who reported that among 10 sorghum ecotypes, 30 % were semi-loose panicle types,
Table 3
Frequency distribution and Shannon-Weaver Diversity Indices (H
′
) of eight qualitative characters sorghum genotypes at Jinka, in 2021.
No. Qualitative Character Index and description adopted Frequency (%) H’
1 Panicle exsertion Slightly exserted <2 cm 55.1 0.62
Exserted 2–10 cm 36
Well exserted >10 cm 5.8
Peduncle re-curved 1.3
Panicle covered by leaf sheath 1.8
2 Panicle compactness and shape Loose erect 26.2 0.95
Loose drop 18.2
Compact elliptic (erect) 39.6
Compact oval or re-curved 16
3 Grain color White 0.4 0.46
Yellow 76
Red 15.1
Brown 8
Buff 0.4
4 Pericarp color White 39.1 0.75
Yellow 2.2
Red 38.7
Brown 17.8
Buff 2.2
5 Glume color White 39.1 0.59
Senna (yellow group) 2.7
Mahogany (greyed-orange) 38.2
Red 17.8
Purple 0.4
Black 1.8
6 Grain covering (%) Grain covering 25 24.4 0.85
Grain covering 50 20.9
Grain covering 75 22.2
Grain fully covered (100) 32.4
7 Awns Awned 21.4 0.75
Awnless 78.6
8 Midrib color White (colorless) 42.2 0.73
Dull green 26.2
Yellow 11.1
Purple 0.4
others 20
Overall mean 0.71
W. Gebre et al.
Heliyon 11 (2025) e42378
5
whereas the other characteristics (loose, semi-compact, and compact) showed a decreased frequency (20 %). In addition, according to
the results of Chantereau et al. [35] and Naoura et al. [36], dry season/lowland sorghum cultivars and accessions have compact
panicles and glume hairiness. Similarly, Apunyo et al. [37] reported that among the 100 accessions of sorghum collected from the
northern and eastern regions of Uganda, 40.8 % showed pronounced, very loose drooping, 23 % showed compact elliptic, 7. % had half
broom, 4. 1 % displayed loose erect, 2.0 4 % compact oval, 1 % loose droop (1 %), semi-loose erect, and 1 % broom corn (1 %).).
Similar results were reported by Sangwan et al. [38] regarding panicle compactness and shape. Elangovan et al. [39] studied ear head
compactness, shape, and glume color. Missihoun et al. [40] used the color of the glume, that is, panicle type. The results could be
explained in two different ways; rst, the distribution of the different panicle types seems to be inuenced by temperature, humidity,
and rainfall patterns. Second, the distribution of different races of sorghum in the region appears to follow similar temperature, hu-
midity, and rainfall patterns. Therefore, the results indicate that the compact and semi-compact panicle types were more frequent, as
was the compact oval panicle with a goose peduncle, characteristic of the race durra Furthermore, guinea (often with an open panicle)
and caudatum (often with a moderately compact panicle) were detected in this study as semi-compact and loose to very loose panicles,
respectively [41–43].
For grain color, the yellow phenotypic class was the most phenotypic class with a frequency of 76 %, while red, brown, white, and
buff were the least observed phenotypic classes on sorghum genotypes under study. This indicated yellow grain color was the most
frequent, with a frequency of 76 % among 225 sorghum genotypes (Table 3). In contrast, Apunyo et al. [37] reported that the pro-
portion of genotypes with brown was found to be 51.7 % among the 100 sorghum genotypes. The varied grain color may be due to
differences in pericarp color. Regarding pericarp color, the white pheno-typic class was the most phenotypic class with a frequency of
39.1 % followed by red (38.7 %), brown (17.8 %), yellow (2.2 %), and buff (2.2 %). The maximum number of pheno-typic classes (six)
was observed for glume color. The white phenotypic class was the most phenotypic class with a frequency of 39.1 %, followed by
mahogany with a frequency of 38.2 %, while red, senna, black, and purple were the least observed phenotypic classes on sorghum
genotypes under study. This indicates that white and red glume colors were the most frequent, with frequencies of 39.1 % and 38.7 %
among 225 sorghum genotypes respectively. This shows that white and mahogany glume colors were widely distributed among all
landraces in the regions, while red, senna, purple, and black glume colors had limited distribution. Similarly, Adugna et al. [44] re-
ported that among 34 sorghum landraces, white glumes took the leading proportion (40 %) and si-enna (yellow group), mahogany
(grey orange group), and red (orange group) glumes were known to have 2 %, 0 %, and 3 %, respectively. In contrast, Alade et al. [45]
found that 46 % of the 50 sorghum genotypes had black glume color. Similarly, Akatwijuka et al. [46] reported that among 184
sorghum accessions, black glume color accounted for 70.2 %. Also, Sulisty et al. [47] reported a higher occurrence of 44.44 % black
glume color among local sorghum genotypes in East Java. The extent of phenotypic classes for grain covering varied from 25 % to 100
% (descriptor states). Of the 225entire accessions, out of 225 accessions 32.40 % were covered by 100 % glume, which was in
agreement with the ndings of Naveen et al. [48] who reported that among the 96 sorghum germplasm accessions, 54.16 % of ge-
notypes showed 100 % glume coverage. The extent of the seeds covered by the glumes varied from 25 % to 100 %. For all the ac-
cessions, the number of accessions in which the glumes had covered 50, 75, 25, and 100 % of the grain increased in the order given,
implying the predominant occurrence of race bicolor in this region because enclosing glumes and loose panicle types are often
characteristic of this race [42,49].
Contrary to previous ndings, Nadjiam [50] and Elangovan et al. [7] Chadian sorghum accessions and Indian indigenous sorghum
collections show a higher occurrence of 25 % glume coverage. Awnlessness is a trait used by farmers to select accessions against bird
invasions. Approximately 78.6 % of the accessions possessed an awn, whereas 21.4 % were awnless. There were more accessions with
awns than without awns. This nding is in agreement with Alade et al. [39], who reported that 58 % of the genotypes had awns, while
42 % were awnless among the 50 sorghum genotypes. This nding contradicts the results of Nadjiam [50], where most of the ge-
notypes did not have awns (96.69 %).
The midrib color of the sorghum genotypes showed ample variation. White midrib color was the most frequently observed
phenotypic class with a frequency of (42.2 %), followed by dull green (26.2 %), yellow (11.1 %), and others (20 %) respectively for
sorghum genotypes under study. Similar ndings have been reported by several researchers on sorghum based on midrib color [51,37,
and30]].
3.1.2. Shannon-Weaver diversity indices
Assessment of genetic diversity is crucial in any crop improvement program to identify high-yield genotypes [52]. Shannon-Weaver
diversity indices (H
′
) were used to compare phenotypic diversity among the qualitative characters. The estimates of the
Shannon-Weaver diversity index (H
′
) showed the observed phenotypic characters, the (H
′
) values ranged from 0.46 for grain color to
0.95 for panicle compactness and shape (Table 3). Characters such as panicle exsertion, midrib color, pericarp color, awns, grain
covering, and panicle compactness and shape showed Shannon diversity values of 0.62, 0.73, 0.75, 0.75, 0.85, and 0.95, respectively,
exhibiting a high percentage contribution to the total variation compared to others. The overall high diversity index (H’ =0.71)
conrmed the existence of a high level of diversity among the sorghum genotypes, which is consistent with Doggett’s long-standing
hypothesis that Ethiopia is not only one of the centers of diversity but also the center of domestication of sorghum [43,49]. The lowest
Shannon diversity index was observed for grain color (H’ =0.46) and glume color (H’ =0.59). Most of the studied phenotypic classes
showed moderate to high diversity indices, indicating high genetic diversity for the studied traits, which provides a diverse range of
genotype choices based on the desired traits and breeding program objectives. Previous sorghum research has shown similar results,
with a high Shannon-Weaver diversity index for qualitative traits [53]. In contrast, Sawadogo et al. [32] found the lowest Shannon
diversity index for panicle extraction and the highest Shannon-Weaver diversity for grain color.
The overall mean diversity index (H
′
) was high for all the studied traits, except for some (Table 3). The highest overall mean
W. Gebre et al.
Heliyon 11 (2025) e42378
6
diversity index (H
′
) was recorded for panicle compactness and shape (H’ =0.95), followed by grain covering (H’ =0.85), pericarp
color (H’ =0.75), and presence of awn (H’ =0.75). The high level of diversity index (H
′
) indicated the availability of the genetic
potential of sorghum genotypes and the presence of many important desirable genes for sorghum improvement for the intended traits
and further genetic studies. The low level of diversity for some of the phenotypic classes, such as grain color, might indicate that the
overall mean diversity index (H
′
) was high for all the studied traits, except for some (Table 3). The highest overall mean diversity index
(H
′
) was recorded for panicle compactness and shape (H’ =0.95), followed by grain covering (H’ =0.85), pericarp color (H’ =0.75),
and presence of awn (H’ =0.75). The high diversity index (H
′
) indicates the genetic potential of sorghum genotypes and the presence of
desirable genes for sorghum improvement and genetic studies. The low level of diversity for some phenotypic classes, such as grain
color, might indicate the existence of a narrow genetic base, and a small sample size greatly contributed to a low diversity index. Traits
with two to four phenotypic classes (i.e., descriptor states) generally have a higher diversity index than those with more than four
phenotypic classes [43]. As Negassa [54] remarked, this result signied the caution that should be exercised while interpreting the
estimates of the diversity index of different traits with different phenotypic classes as measured by the Shannon-Weaver diversity
index. Thus, it might be misleading to compare the values of H
′
from traits with different classes.
The high variability of qualitative traits is probably related to the high racial diversity encountered within this type of sorghum
genotype. Indeed, the predominance of three races (durra, guinea, and caudatum) and several intermediate races of sorghum were
observed in this study. The strong differentiation of genotypes resulting from several cycles of self-pollination may also be responsible
for the signicant morphological variability of the accessions.
3.1.3. Principal component analysis
Principal component analysis (PCA) was used to determine the characteristics that accounted for most of the total variation. The
data were standardized to mean zero and variance before computing the principal components based on the correlation matrix, which
was calculated using the SAS version 9.1 software packages [55]. The rst three principal components, with eigenvalues greater than
unity, explained 59.0 % of the total variation (Table 4). Principal component one (PC1) alone accounted for 28 % of the total variation.
Pericarp color, glume color, grain covering, and the presence of awns had the highest loadings on PC1. Accordingly, pericarp color,
glume color, grain covering, and awns had relatively better values for the coefcient of variation on the PC1 axis. These were the most
distinctive traits that signicantly contributed to the variations among the genotypes. The second principal component (PC2)
explained 19 % of the total variation and was highly positively correlated with grain color, panicle exsertion, presence of awn, and
midrib color. It was also highly negatively correlated with panicle compactness and shape, grain covering, glume color, and pericarp
color. Panicle exsertion, grain color, presence of awns, and midrib color were the most distinctive characteristics on the PC2 axis,
whereas PC3 explained 13 % of the total variation and was highly correlated with midrib color, grain covering, and presence of awn.
Consequently, on the PC3 axis, midrib color (0.926), grain covering (0.156), and presence of awn (0.118) were the most distinctive
characteristics causing variation among the genotypes. Martiwi et al. [29] indicated that the rst two principal components explained
52.58 % of the total variation, and reported that the rst glume color, grain shape, and grain sub-coat of sorghum were considered the
most distinctive characteristics. Based on the rst two principal components (PCs), midrib color, grain covering, and presence of awns
were prominent characteristics in the screening of sorghum genotypes. Similarly, Elangovan and Kiran [56] suggested that glume
covering, head compactness, grain luster, grain vigor, and the presence of awns were prominent characteristics in the screening of
sorghum genotypes. The remaining PCs explained 9 % of the total variation, which was mainly associated with midrib color. Therefore,
of all the characteristics, midrib color was found to be the most discriminative trait differentiating genotypes collected from southern
Ethiopia.
Principal component analysis is a multivariate technique that observes relationships between several variables. A mathematical
procedure transforms several possibly correlated variables into a small number of uncorrelated variables and examines patterns of
variation and the relative importance of each trait in explaining the observed variability. In the present study, principal component
analysis based on eight qualitative traits was performed. Hair et al. [57] suggested that principal components (PCs) with eigenvalues
greater than unity and component loadings greater than ±0.3 were considered meaningful and valuable.
Table 4
Principal component analysis (PCA) on qualitative traits of 225 sorghum genotypes.
PC1 PC2 PC3 PC4 PC5 PC6 PC7
PCs −0.240 −0.548 −0.137 0.550 0.039 0.447 0.346
PE −0.014 0.530 0.005 0.416 0.726 −0.133 −0.025
GLC 0.067 0.703 −0.314 −0.298 −0.070 0.233 0.505
PCL 0.967 −0.029 −0.028 0.030 0.041 0.227 −0.090
Gc 0.968 −0.032 −0.027 0.030 0.045 0.223 −0.091
GC 0.439 −0.484 0.156 −0.170 0.225 −0.508 0.459
AW 0.294 0.384 0.118 0.577 −0.534 −0.350 0.108
MDC −0.067 0.185 0.926 −0.065 0.011 0.289 0.126
Eigen value 2.22 1.49 1.01 0.93 0.87 0.84 0.63
Proportion 0.28 0.19 0.13 0.12 0.11 0.10 0.08
Cumulative 0.28 0.46 0.59 0.71 0.82 0.92 1.00
PCs =panicle compactness and shape, PE =panicle exsertion, PCL =pericarp color, GLC =grain color, Gc =glume color, GC =grain covering, AW =
presence of awn, MDC =midrib color, PC =principal component.
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Heliyon 11 (2025) e42378
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3.1.4. Cluster analysis
Cluster analysis was performed based on the similarity among the genotypes, which provided the relative position of genotypes in
the group. The genotypes were grouped into ve distinct clusters based on the eight qualitative traits studied (Table 5 and Fig. 2),
indicating the existence of genetic divergence among the genotypes, which might provide a better chance to select genotypes with
different weights for sorghum improvement. Based on the cluster data, several researchers placed more emphasis on the involvement
of diverse parents in crossing programs for the high heterotic response, as well as transgressive segregants in early generations for high
grain yield and other important traits that occur due to the reshufing of alleles. The results indicated signicant variations in
qualitative traits among the sorghum genotypes. Desmae et al. [33] suggested that emphasis should be placed on the genetic diversity
of sorghum genotypes. Therefore, genotypes can be chosen from a dendrogram based on genetic diversity, and organizing genotypes
according to their similarity is extremely important. Using a multivariate analysis of qualitative traits, Singh et al. [58] clearly
identied different genotypes into different clusters based on their similarity indices, the type of cluster that would be selected further,
and which parents would be selected for hybridization. The utilization of diverse parents to increase the likelihood of obtaining su-
perior varieties has been documented by Seetharam and Ganesamurthy [59], Adugna [60], and Derese et al. [31]. Similarly, Behl et al.
[61] suggested that an increase in heterosis occurs within a limited range of diversity.
The cluster analysis by the UPGMA method, from the ascending hierarchical classication carried out based on the qualitative
morphological characters, shows a distribution of genotypes into one major, two medium, and one minor group. A dendrogram
condensed the genetic similarity among sorghum genotypes based on eight qualitative traits (Fig. 2). The number of genotypes varied
from 6 (Cluster V) to 154 (Cluster I) (Table 5). Cluster analysis revealed that sorghum genotypes were divided into different clusters.
The rst cluster was the largest, consisting of one hundred fty genotypes and accounting for 68.44 % of the total genotypes. The
second and third clusters consisted of only thirty-three and twenty-two genotypes, each accounting for 14.66 % and 10.22 %,
respectively, while the fourth and fth clusters consisted of ve and one genotypes, which accounted for 2.22 % and 9.77 %,
respectively. More focus should be on cluster I for selecting genotypes as parents to cross with those in clusters IV and V.
The greatest distance between clusters was observed between clusters I and V. Additionally, high inter-cluster distances were
observed between cluster II and every other remaining cluster (Table 6). This indicated that the genotype grouped under cluster IV was
signicantly different from the other genotypes. The inter-cluster distance was the lowest between cluster II and cluster III, indicating
genetic similarity among the genotypes in these clusters for qualitative traits. The largest distance within a cluster was found in Cluster
V. This indicated the presence of a high degree of variability among the genotypes in Cluster V.
As per the contribution of traits for inter-cluster analysis, the traits were classied as high contributors (CTIC ≥74 %), intermediate
contributors (70 %≤CTIC≤74 %), and low contributors (CTIC <70 %). Characteristics such as midrib color, grain covering, glume
color, pericarp color, and panicle compactness and shape contributed the most to genetic divergence in the entire genotype, whereas
awns and grain colors contributed little to genetic divergence (Table 7). In addition, Sellakumar et al. [62] reported that color, panicle
compactness, and pigmentation at the leaf sheath contributed signicantly to the inter-cluster difference in seventeen little millet
varieties.
3.2. Multivariate diversity analysis based on quantitative traits
3.2.1. Principal component analysis
Multivariate statistical tools provide valuable information for identifying morphological diversity within and between germplasm
[63]. Principal component analysis was used to interpret the variations and identify the driving traits among the 17 quantitative traits.
The analysis aimed to gain a deeper understanding of the complex relationships between the various traits and their impact on overall
variability. The principal component analysis revealed six components with eigenvalues greater than unity, accounting for approxi-
mately 76.5 % of the total variance among the genotypes (Table 8). Consequently, the six principal component scores could be utilized
to review the original 17 variables for further analysis. Chaudhary et al. [64] proposed a method for determining principal components
using a correlation matrix and genotype scores. Specically, the method uses the rst component to capture the most variance, with
the subsequent components showing eigenvalues greater than one. This approach ensures that principal components are based on
Table 5
Clustering of the 225 sorghum genotypes using qualitative traits.
Cluster Number of
genotypes
Genotypes
I 154 1, 2, 3, 4, 9, 10, 11, 12,14, 15,16, 17,18, 19,20, 22, 23, 24, 25, 26, 28, 32, 33, 34, 35, 36, 38, 39, 41,42, 45, 47, 48, 49, 50, 51, 54,
55, 56, 58, 59, 60, 65, 66, 68, 70, 71, 73, 74, 76, 78, 79, 80, 81,82, 83, 86, 87, 90, 91, 92, 95, 96, 100, 103, 104, 105, 106, 107,
108, 109, 110, 112, 114, 115, 116,118, 120,122, 123, 124, 125,126, 130, 131, 133, 134,135, 137, 138, 139, 140, 141, 142, 143,
145,146, 147, 148, 153, 154, 155, 156, 157,159, 162, 163, 164, 165, 167, 168, 169, 170, 171, 172,173,174,177,178, 179, 181,
183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 195, 197, 200, 202, 205, 207, 208, 210, 211, 212, 213, 214, 215, 217, 218, 219,
220, 221, 222, 223, 224, 225
II 33 5, 7, 8, 29, 37, 40, 43, 44, 46, 53, 57, 64, 77, 88, 93, 97, 102, 111, 121, 127, 136, 149, 150, 151, 152, 158, 161, 182, 199, 201, 204,
209, 216
III 22 6,13,21, 27, 52, 61, 62, 63, 85, 89, 98, 117, 128, 129, 132, 160, 166, 180, 193, 194, 198, 206
IV 6 30, 69, 72, 75, 113, 176
V 10 31, 67, 84, 94, 99, 101, 144, 175, 196, 203
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Heliyon 11 (2025) e42378
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reliable and valid measurements. By adhering to these guidelines, researchers can increase the accuracy and robustness of their
analysis.
The principal components (PCs) that have eigenvalues greater than one were considered to make a signicant contribution of at
least 10 % to the variation explained in the specic traits being studied. This implies that these PCs have played an additional role in
the specic trait by at least one unit. According to Premkumar et al. [65], the rst principal component contains a signicant portion of
the variability, whereas the second and subsequent components exhibit reduced variability. Correspondingly, Pugahendhi et al. [66]
reported that 71.38 % of the difference was explained by the rst seven PCs for 20 agro-morphological traits in six sorghum genotypes.
Ali et al. [67] found that the rst three principal components explained 79.10 % of the variation in 11 quantitative characteristics of 20
sorghum genotypes. Desmae et al. [33] used the rst ve principal components with eigenvalues greater than 1 to account for 70.99 %
of the total variance among 974 sorghum genotypes. Using the rst three principal components with eigenvalues greater than 1,
Fig. 2. Hierarchical clustering of the pattern of 225 sorghum genotypes (UPGMA) based on 8 qualitative characters.
Table 6
Average intra (Bolded Diagonal) and inter-cluster (off-Diagonal) generalized squared distance (D
2
) values for qualitative traits.
Cluster I II III IV V
I 1.72 38.15** 14.64* 32.14** 98.72**
II 1.60 45.34** 55.40** 67.70**
III 4.56 40.73** 85.26**
IV 7.61 88.15**
V 10.83
**,* indicates signicant at 1 % and 5 % level of signicant;
χ
2
5
=11.07 and 15.086 at 5 % and 1 %, probability level, respectively.
Table 7
Levels of trait contribution for inter-cluster analysis of 225 sorghum genotypes.
Triats CI CII CIII CIV V Mean Std CTIC (%)
AW 1.65 1.77 1.83 1.82 1.80 1.79 0.41 22.98
GC 7.70 7.24 2.37 1.53 6.49 4.80 2.81 58.50
GLC 2.13 2.21 2.33 2.59 2.26 2.32 0.64 27.77
Gc 1.39 2.47 2.35 2.22 3.71 2.47 1.41 56.98
MDC 1.78 2.06 1.54 3.82 3.40 2.50 1.88 75.16
PCL 1.44 2.47 2.35 2.22 3.71 2.48 1.41 56.88
PCs 2.83 2.33 2.67 2.35 2.43 2.48 1.09 43.79
PE 1.74 1.38 1.62 1.67 1.71 1.59 0.81 50.98
CTIC=Contribution Inter-cluster divergence, STD =standard deviation, PCs =panicle compactness and shape, PE =panicle exsertion, PCL =
pericarp color, GLC =grain color, Gc =glume color, GC =grain covering, AW =presence of awn, MDC =midrib color.
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Heliyon 11 (2025) e42378
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Premkumar et al. [65] accounted for 74.15 % of the total variance among 28 sorghum genotypes.
The individual contributions of the principal components (PCs) are explained separately. However, the rst principal component
explained the highest share of the total variation (24.8 %). The remaining ve PCs explained 51.7 % of the total variance. The rst
principal component, with an eigenvalue of 4.223, represented a substantial portion of the variance at 24.8 %). The remaining three
principal components, namely PC2, PC3, and PC4) had eigenvalues of 2.783 and 2.047 and they recorded 16.4 % and 12.0 % of the
total variability, respectively. In contrast, the PCs from PC4 to PC6 had weak or no discriminatory powers, as shown in Table 7.
According to Fraino et al. [68], the results indicate that the rst PC, which had an eigenvalue of 7.42, explained 25.07 % of the total
variation. The three remaining principal components had eigenvalues of 4.26, 2.14, and 1.68, accounting for 12.64 %, 9.63 %, and
8.72 % of the total variance, respectively. These three PCs had the highest discriminatory power, contributing to 47.34 % of the total
variance. In contrast, the rest of the PCs showed weak discriminatory power for 60 fonio accessions, which is consistent with Karadi
and Kajjidoni’s [69] ndings on 208 sorghum accessions.
The study found that all examined traits had a signicant impact on the variations in the principal components (PCs) due to their
loading effects. The rst six principal components were the most signicant. According to Raji’s [70] method, characters with coef-
cient values greater than 0.3 were considered important and had a large effect on the overall variation observed in this study. On the
other hand, traits with coefcient values less than 0.3 were not considered to have signicant effects. This is consistent with the results
of Hair et al. [71], who similarly noted that a loading effect greater than 0.3 for any trait was deemed signicant.
In this study, the traits with the most substantial loading effects on the rst principal component were grain yield per hectare
(0.976), yield per panicle (0.971), biomass yield per hectare (0.911), and threshing percentage (Table 8). Therefore, the rst principal
component was notably inuenced by the yield and yield-related traits. This result contradicts that of Derese et al. [31], who found that
reproductive traits were signicantly different among 196 sorghum accessions. The second component had a distinct inuence on the
vegetative traits, such as the time taken for physiological maturity (0.763), duration of grain lling (0.760), number of leaves (0.605),
and straw yield per hectare (0.557). The third principal component was primarily inuenced by leaf area (0.929), leaf width (0.918),
leaf length (0.281), and thousand-kernel weight (0.261). Premkumar et al. [65] obtained similar result when 28 sorghum genotypes
were studied. The second principal component associated with days to owering and maturity indicates a close relationship with
yield-related characteristics. However, the third principal component positively inuenced several traits, including leaf area, leaf
width, leaf length, thousand kernel weight, grain yield, harvest index, and threshing percentage.
The fourth component (0.636) was highly inuenced by straw yield (0.599) and days to owering (0.599), whereas the grain-lling
period (−0.590) had a negative impact (Table 8). The fth component was positively inuenced by panicle weight (0.781), thousand
kernel weight (0.470), and panicle length, whereas it was negatively affected by days to owering (−0.263), days to maturity
(−0.126), and threshing percentage (−0.464). The sixth component was strongly inuenced by panicle length (0.594), whereas straw
yield per hectare negatively affected it (−0.372). Therefore, these characteristics contribute to productivity. Sorghum yield
improvement programs can be used effectively to select genotypes. Overall, the rst six principal components, each with an eigenvalue
exceeding 1.0, accounted for 76.5 % of the total variation. Elangovan et al. [7] found that the rst two principal components accounted
for 77.40 % of genetic variability. Desmae et al. [33] found that the rst ve principal components (PCs) accounted for approximately
71 % of the variability in 974 sorghum landraces. Similarly, Tesfaye [71] reported that the rst four PCs explained 71.9 % of the
variance in sorghum genotypes.
Table 8
Principal component analysis (PCA) on quantitative traits of 225 sorghum genotypes.
Traits PC1 PC2 PC3 PC4 PC5 PC6
DF 0.029 −0.210 −0.119 0.599 −0.263 0.524
DM −0.135 0.763 −0.173 −0.302 −0.126 0.197
GfP −0.130 0.760 −0.079 −0.590 0.041 −0.127
PH 0.032 0.521 −0.055 0.196 −0.108 0.384
NOL −0.019 0.605 −0.132 0.140 0.099 0.182
LL −0.130 0.421 0.281 0.008 −0.164 0.119
LW −0.262 0.138 0.918 0.050 0.075 0.033
LA −0.263 0.159 0.929 0.043 0.062 0.041
Pl −0.287 0.133 −0.084 −0.065 0.148 0.594
Yp 0.971 0.123 0.108 −0.016 0.052 0.036
PW 0.375 0.076 −0.165 0.002 0.781 0.119
SY 0.100 0.577 −0.080 0.636 0.128 −0.372
BM 0.911 0.303 0.071 0.192 0.066 −0.083
TKW 0.096 −0.271 0.261 0.013 0.470 0.174
GY 0.976 0.124 0.108 −0.021 0.027 0.044
HI 0.664 −0.403 0.103 −0.480 −0.095 0.303
TP 0.790 0.082 0.211 −0.021 −0.464 −0.046
Eigen value 4.223 2.783 2.047 1.537 1.247 1.167
Proportion 0.248 0.164 0.120 0.090 0.073 0.069
Cumulative 0.248 0.412 0.533 0.623 0.696 0.765
PC =principal component, DF =days to owering, DM =days to maturity, GFP =grain lling period, PH =plant height (cm), LL =leaf length, LN =
leaf number, LW =leaf width, LA =leaf area (cm
2
), PL =panicle length (cm), YP =yield per panicle(g), PW =panicle weight (cm), SY =straw yield
(kg), BM =biomass yield (kg), TKW =thousand kernel weight (g), GY =grain yield(kg), HI =harvest index(%), TP =threshing percentage (%).
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Heliyon 11 (2025) e42378
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The loading plot provided insights into the similarities and differences between the traits studied. The biplot shows that traits such
as plant height, straw yield, grain-lling period, panicle weight, and yield per panicle, which are located near the origin, have smaller
loading effects and less inuence. Characteristics such as grain yield, leaf area, leaf width, and panicle length were located far from the
origin and had a greater inuence on this classication. Among the traits studied, biomass yield, panicle weight, panicle length, and
grain yield had the greatest loading effect, indicating that these characteristics had a signicant impact. The loading plot shows the
contribution of each characteristic to PC1 and PC2. Additionally, the genotypes classied using quantitative traits were explained by
PC1 and PC2 (Fig. 3). In the loading plot, genotypes closer to each other and overlapping share similar characteristics, whereas those
farther away and far from the origin are genetically diverse.
Almost all genotypes were concentrated in the rst, second, and fourth quadrants, and relatively few were found in the second and
third quadrants. In comparison with the other genotypes, G140, G25, G224, G33, G20, and G172 diverged greatly.
3.2.2. Genetic divergence analysis
The genetic distance between genotypes was estimated using D
2
based on the 17 quantitative traits studied. The results showed that
the D
2
between the clusters was highly signicant (P =0.01), indicating a high degree of genotype diversity. According to the D
2
results, 225 genotypes were grouped into ve clusters with variable numbers of genotypes, indicating considerable genetic diversity.
3.2.3. Genotype clustering
The analysis of variance revealed signicant variance across the 17 traits under study among the divergent genotypes, indicating
that the genotypes were highly variable. These results align with the ndings of Arshad et al. [72], who noted signicant genotype
variations for all 13 studied traits, indicating substantial genetic diversity. The signicant variation among genotypes indicates a broad
range of genetic diversity within the sorghum population. This presents a promising opportunity for selecting and breeding superior
genotypes that can contribute to increasing the overall yield potential of sorghum. By focusing on traits that show the most variation,
researchers and breeders can target specic characteristics to develop more productive and resilient varieties. Dividing the 225 sor-
ghum genotypes into four groups based on the similarity of the studied traits suggested that there were distinct patterns or clusters
within the data. Each group of genotypes is likely to exhibit similar characteristics or traits, indicating underlying similarities or
differences in the traits studied. This grouping can help in better understanding the diversity within sorghum genotypes and may
provide insights into how different traits are correlated or vary across groups. Similarly, Tesfamichael et al. [73] identied three
clusters of sorghum landraces. There were seven accessions in cluster 1, which were further subdivided into two subclusters. Cluster 1
consisted of EG 469, EG 883, and EG 849, with a genetic similarity of 0.92. Cluster 2 contained 15 accessions, while Cluster 3 contained
three genotypes. In a study conducted by Naoura et al. [36], fty-six sorghum genotypes were grouped into four distinct categories on
the dendrogram. The rst group comprised two cultivars, the second group had 10 cultivars, the third group had 28 cultivars, and the
fourth group had 15 cultivars. Similarly, Gobezayohu et al. [74] grouped 56 sorghum genotypes into four clusters, while Mulugeta
et al. [75] grouped forty-nine Sorghum genotypes into six distinct clusters.
Understanding the level of genetic diversity in the germplasm is fundamental for plant breeders as it guides breeding decisions,
enhances trait improvement, and contributes to the sustainable development of improved crop varieties [53,76]. The pattern of ge-
notype distribution among the different clusters indicated considerable genetic variability among the genotypes studied. Genotypes
that belong to the same cluster are more closely related than those belonging to different clusters. Monogenotypic clusters suggest that
Fig. 3. Loading plot showing association of 17 quantitative traits of genotypes.
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Heliyon 11 (2025) e42378
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such genotypes may have a completely different genetic makeup from that of the remaining genotypes, leading to the formation of a
separate cluster. Cluster I had the highest number of genotypes (144), followed by Cluster II (61), and Cluster VI (16). Cluster V and VI
had the lowest number of genotypes (1) (Fig. 4). A dendrogram that summarizes the homogeneities between sorghum genotypes based
on the 17 traits is shown in Fig. 4. The tested genotypes were grouped into six clusters, each containing a different number of ge-
notypes. The number of genotypes per cluster varied from 1 to 144. Cluster I was the largest cluster, comprising 144 genotypes,
followed by Clusters II and III, which contained 61 and 16 genotypes, respectively. Cluster V and VI contained only 1 genotype.
3.2.4. Mean performance of the Cluster
Cluster means provide a summary measure of the average performance of genotypes within each cluster for specic traits of interest
such as grain yield, disease resistance, and nutritional quality. By comparing cluster means, breeders can identify clusters that contain
genotypes with a higher average performance for the target traits. Table 9 presents the mean values of the 17 quantitative traits per
cluster. In the present study, the mean values varied among clusters for all traits. Genotypes that owered and matured in a shorter
time were found in cluster I, whereas those that took longer were found in clusters II and III. The maximum grain-lling period was
observed in clusters II and III, while the minimum was observed in cluster IV. The tallest plants were found in clusters I and II, whereas
the shortest were found in clusters III and IV. The largest number of leaves per plant was observed in clusters III and IV, whereas the
smallest was observed in cluster I. The longest leaf length was recorded in clusters III and IV, whereas the shortest was recorded in
clusters I and II. The widest leaf was found in cluster III, whereas the narrowest was found in clusters II and IV. The largest leaf area was
observed in cluster III, whereas the smallest was noted in clusters I, II, and IV. The longest panicles were found in clusters I and II, and
the shortest panicles were found in cluster IV. The highest yield per panicle and panicle weight were observed in cluster II, while cluster
III had the lowest yield. The highest straw and biomass yields were exhibited in clusters II and IV, whereas the lowest yields were noted
in clusters I and III. The largest thousand kernel weights were recorded in clusters I, II, and III, whereas the smallest was recorded in
cluster IV. Cluster I exhibited the highest grain yield per hectare, whereas Cluster III exhibited the lowest grain yield. In general, cluster
II performed the best for most traits and showed the highest mean performance for grain lling period, plant height, yield per panicle,
panicle weight, straw yield, biomass yield, thousand kernel weight, grain yield, harvest index, and threshing percentage. Overall,
selecting parents from Cluster II for their high mean values could be a strategic approach to improve genotypes and achieve breeding
objectives related to grain yield and other important traits. In contrast, clusters I and III had the lowest values for yield and yield-
related traits, indicating poor performance.
There was signicant variation in the performance of sorghum genotypes for different traits. This presents an opportunity to select
potential parents across the cluster for specic traits to improve sorghum production in the future. This study showed that there is
inherent genetic diversity among the sorghum genotypes. This is similar to ndings of previous studies by various authors [33,69,
and77-81].
Furthermore, this study discovered that a few characteristics signicantly inuence the genetic divergence between genotypes of
sorghum. These characteristics included plant height, biomass yield, leaf width, leaf area, yield per panicle, straw yield, and grain
production per hectare. Days to ower and days to maturity, on the other hand, have a minor role in genetic differences. A CTIC (%) of
≥25 % was a strong contributor, ≥10 % <25 % was a medium contributor, and <10 % was a small contributor for inter-cluster
divergence, according to the degrees of trait contribution for sorghum genotypes’ inter-cluster divergence research. It is note-
worthy that different degrees of trait contribution to inter-cluster divergence in eastern Ethiopia were observed by Werkissa et al. [82]
A CTIC (%) of >15 % was determined to be a strong contributor to inter-cluster divergence, ≥8 % <15 % to be a medium contributor,
Fig. 4. Dendrogram showing the relationships of sorghum genotypes based on quantitative Traits.
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Heliyon 11 (2025) e42378
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and <8 % to be a moderate contributor. In conclusion, there appears to be diversity in agronomic and morphological attributes based
on the observed variation among the 225 sorghum genotypes. This result is in line with earlier studies on sorghum genotypes [69,83].
3.2.5. Intra and inter-cluster distance
The results showed that there were signicant differences in the squared distances (D
2
) between Cluster I and Clusters II and IV,
cluster II and Clusters III and IV, and Cluster III and Cluster IV (P ≤0.01). However, no signicant variation was observed between
clusters I and III. The intra-cluster distance ranged from 1.60 to 7.61 units, while the inter-cluster distance ranged from 14.64 to 55.40
units (Table 10). The average inter-cluster D
2
was highest between Cluster II and Cluster IV (D
2
=55.40 units), followed by Cluster II
and Cluster III (D
2
=45.34 units), indicating the genotypes of these clusters show a high level of genetic divergence. These clusters are
expected to produce a higher frequency of desirable combinations for developing useful genetic stocks or varieties. Therefore, they are
genetically divergent. Selecting parents for the hybridization program among genotypes from diverse clusters would achieve novel
recombinants that increase the efciency for improving grain yield in sorghum. The nearest inter-cluster distance was found between
clusters II and III (14.64 units), followed by clusters II and IV (32.14 units). Genotypes that showed non-signicant variations among
the clusters had a narrow genetic base and lacked a diverse gene pool. Therefore, crossing parents from these clusters might not provide
higher heterotic value in future hybrid breeding programs in subsequent generations. However, it does not produce a wide range of
variability in segregated populations.
In the study, the maximum distance within clusters (D
2
) was found to be the highest in Cluster I at 7.61 units, followed by Cluster II
at 4.56 units and Cluster IV at 1.72 units. The results indicated that genotypes within Clusters III and IV exhibited greater diversity than
the other clusters in this study. This suggests that the genotypes in Clusters III and IV have a wider range of genetic variations or
differences, making these clusters more diverse in terms of their genetic makeup. Therefore, these distant genotypes could be useful in
sorghum breeding programs for obtaining a wider range of variability. The genotypes of Clusters II and III could also be used as parents
in hybrid or recombinant breeding programs because of their wider within-group distance. Cluster II had the lowest D
2
value, 1.60
units, suggesting that there was less genetic diversity or variety in this cluster. Overall, the intra-cluster distance was much lower than
the inter-cluster distance, suggesting homogeneity within clusters and heterogeneity between clusters. These ndings are consistent
with those of previous studies on sorghum genotypes by Shivani and Sreelakashmi [84] and Ahalawat et al. [6].
The intra-cluster distance values indicated the closeness of the genotypes within the same cluster. A low intra-cluster distance of
0.00 suggests that the clusters are made up of a single genotype and are less diverse, while a high intra-cluster D
2
value indicates that
genotypes within the same cluster have a greater genetic divergence and are thus more diverse. According to Murthy and Arunachalam
[85], the success of hybridization followed by selection depends largely on the choice of parents that exhibit high genetic diversity. The
intra-cluster heterogeneity observed in the current experiment can serve as a valuable guideline for selecting parents in recombination
breeding programs. The variation and diversity within clusters, such as the differences seen within Clusters III and IV, provide breeders
with important insights into the genetic landscape and the potential for trait combinations within those clusters. This indicates that the
genotypes in these cluster pairs were the most divergent, which means that the genotypic constituents of these cluster pairs comprised
genes from the most distantly related parents in terms of the traits studied. Genotypes from distinct clusters separated by a considerable
estimated statistical distance can be valuable resources for hybridization programs aimed at crop improvement in sorghum. The
genetic distance between clusters indicates signicant genetic differentiation and diversity among genotypes, which can be benecial
Table 9
Mean performance of sorghum genotypes for quantitative traits in four clusters.
Traits Clusters Mean Std CTIC (%)
CI CII CIII CIV
DF 77.13 77.12 78.18 82.75 77.24 6.38 8.26
DM 133.45 134.53 135.71 123.50 133.83 9.58 7.16
GFP 56.31 57.41 57.54 40.75 56.58 11.37 20.10
PH 222.67 237.66 203.24 200.50 225.79 60.17 26.65
NOL 9.20 9.75 10.43 10.00 9.45 1.56 16.52
LL 63.89 65.13 67.50 69.00 64.53 10.82 16.77
LW 0.94 0.84 1.64 0.85 0.95 1.18 124.05
LA 46.72 40.88 84.02 43.00 47.25 68.88 145.80
Pl 42.65 41.64 45.36 36.71 42.46 5.86 13.79
YP 60.22 92.01 31.35 60.33 68.03 21.74 31.96
PW 191.84 223.95 189.89 192.84 201.43 19.92 19.92
SW 2094.99 2539.89 1883.22 5384.75 2245.51 29.98 29.98
BM 7396.42 10672.38 4728.17 10748.00 8250.26 24.67 24.67
TKW 29.32 29.44 28.75 26.25 29.30 12.40 12.40
GY 5301.31 8132.39 2844.79 5362.97 6004.62 30.51 30.51
HI 0.71 0.76 0.61 0.48 0.72 11.28 11.28
TP 32.26 42.01 18.31 34.00 34.35 27.92 27.92
DF =days to owering, DM =days to maturity, GFP =grain lling period, PH =plant height (cm), LL =leaf length, LN =leaf number, LW =leaf
width, LA =leaf area (cm
2
), PL =panicle length (cm), YP =yield per panicle(g), PW =panicle weight (cm), SY =straw yield(kg), BM =biomass yield
(kg), TKW =thousand kernel weight (g), GY =grain yield(kg), HI =harvest index(%), TP =threshing percentage (%), Std =standard deviation,
CTIC=Contribution Inter-cluster Divergence.
W. Gebre et al.
Heliyon 11 (2025) e42378
13
for creating hybrids with novel combinations of traits and potentially superior performance. The results also revealed that the ge-
notypes were more diverse when considering individual traits than when considering a combination of traits.
4. Conclusions
Qualitative and quantitative traits were used to morphologically characterize the sorghum genotypes. This helped to assess the
genotypes more accurately and identify the best genotypes with desirable traits for further breeding programs. Among all the geno-
types, panicle compactness and shape had the highest diversity index (H
′
), whereas grain color had the lowest. The rst three principal
components, which had eigenvalues greater than unity, explained 59.0 % of the total variation. Four distinct groups were identied by
clustering the qualitative traits. Therefore, future studies should consider the distribution of genetic diversity and use it as a benchmark
for the collection, characterization, and conservation of genotypes.
Plant height, straw weight, panicle weight, yield per panicle, and total yield per plant were found to be the most important factors
contributing to the overall yield of sorghum. Therefore, during the selection process, special attention should be paid to these factors in
the development of high-yielding sorghum genotypes. Biomass yield was identied as the most important trait that positively inu-
enced the principal component analysis to detect phenotypic diversity. The rst six principal analyses detected the major contributing
traits that accounted for approximately 76.5 % of the total variation and were informative enough to distinguish 225 sorghum ge-
notypes. The observation that the highest inter-cluster generalized squared distance (D
2
) was recorded between Cluster II and Cluster
IV, followed by Cluster II and Cluster III, indicates signicant genetic differentiation and divergence between these clusters in the
studied sorghum genotypes. The distances between the clusters indicate a higher level of genetic divergence from each other. The
range of intra and inter-cluster distances was 1.60–7.61 units and 14.64–55.40 units, respectively. Therefore, the observed genetic
distance exhibited within and among clusters can be exploited through the crossing and selection of the most diverse parents for future
sorghum breeding programs. The intra-cluster distance was much smaller than the inter-cluster distance, indicating a high genetic
divergence. In summary, the ndings of the present study suggest that the genetic distance observed within and among clusters
presents an opportunity for exploitation in breeding programs. By strategically crossing and selecting the most divergent parents from
the clusters, breeders can capitalize on genetic diversity and differentiation to develop improved sorghum cultivars with desirable
traits and enhanced performance. Overall, the incorporation of a biochemical approach in conjunction with genetic and phenotypic
analyses can provide a more nuanced and comprehensive assessment of the genetic diversity of sorghum genotypes. This integrated
approach holds promise for advancing variety development efforts, enabling breeders to exploit the full spectrum of genetic variations
present in sorghum populations and drive innovation in sorghum breeding programs.
CRediT authorship contribution statement
Wedajo Gebre: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptuali-
zation. Firew Mekbib: Visualization, Validation, Supervision, Conceptualization. Alemu Tirfessa: Writing – review & editing,
Visualization, Supervision, Conceptualization. Agdew Bekele: Writing – review & editing, Visualization, Methodology,
Conceptualization.
Data availability statement
Data will be made available on request.
Declaration of competing interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
Acknowledgments
The authors are indebted to Jinka University, Ofce of the Vice President of Academics, Research, Technology Transfer, and
Community Service for the nancial support. We would also like to thank the Jinka Agricultural Research Centre for providing
experimental plots.
Table 10
Average intra- (bolded diagonal) and inter-cluster (off-diagonal) generalized squared distance (D
2
) values for quantitative traits.
Cluster I II III IV
I 1.72 38.15** 14.64ns 32.14**
II 1.60 45.34** 55.40**
III 4.56 40.73**
IV 7.61
* **,* indicates signicant at 1 %, and 5 % probability level;
χ
2
15
=25.00 and 30.58 at 5 % and 1 % probability level values, respectively.
W. Gebre et al.
Heliyon 11 (2025) e42378
14
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