agronomy
Article
Assessment of Yield Stability of Bambara Groundnut
(Vigna subterranea (L.) Verdc.) Using Genotype and
Genotype–Environment Interaction Biplot Analysis
Rita Adaeze Linus 1,2, Oluwaseyi Samuel Olanrewaju 1,3 , Olaniyi Oyatomi 1, Emmanuel Ohiosinmuan Idehen 2
and Michael Abberton 1,*
1 Genetic Resources Center, International Institute of Tropical Agriculture (IITA),
Ibadan 200001, Oyo State, Nigeria; linusrita07@gmail.com (R.A.L.); olusam777@gmail.com (O.S.O.);
o.oyatomi@cgiar.com (O.O.)
2 Department of Plant Breeding and Seed Technology, Federal University of Agriculture Abeokuta,
Abeokuta 110001, Ogun State, Nigeria; emmaidehen@yahoo.com
3 Unit for Environmental Sciences and Management, Potchefstroom Campus, North-West University,
Potchefstroom 2520, South Africa
* Correspondence: m.abberton@cgiar.org
Abstract: Biplot analysis has emerged as a crucial statistical method in plant breeding and agricul-
tural research. The objective of this research was to identify the best-performing genotype(s) for
the environments in three distinct regions of Nigeria while also examining the characteristics and
magnitude of genotype–environment interaction (GEI) effects on the yield of Bambara groundnut
(BGN). The study was conducted in Ibadan, Ikenne, and Mokwa, utilizing a sample of 30 acces-
sions. The yield of BGN was found to be significantly affected by accessions, environment, and
their interaction through a combined analysis of variance, with a p-value < 0.001. Biplots were
utilized to demonstrate the pattern of interaction components, specifically the genotype’s main effect
and genotype–environment interaction (GEI). The initial two principal components elucidated the
complete variance of the GGE model, encompassing both genetic and genotype-by-environment
Citation: Linus, R.A.; Olanrewaju, interaction effects (PC1 = 87.81%, PC2 = 12.19%). The accessions that exhibited superior performance
O.S.; Oyatomi, O.; Idehen, E.O.; in each respective environment, as determined by the “which-won-where” polygon, were identified
Abberton, M. Assessment of Yield as TVSu-2223, TVSu-2236, TVSu-2240, and TVSu-2249 in Mokwa; TVSu-2214 in Ikenne; and TVSu-
Stability of Bambara Groundnut (Vigna 2188 in Ibadan. The accessions TVSu-2207 and TVSu-2199 exhibited stability in all environments,
subterranea (L.) Verdc.) Using Genotype whereas the accessions TVSu-2226, TVSu-2249, TVSu-2209, TVSu-2184, TVSu-2204, and TVSu-2236
and Genotype–Environment demonstrated adaptability. In addition, the accessions TVSu-2240 and TVSu-2283 were stable and
Interaction Biplot Analysis. Agronomy adaptable in all environments. The accessions that were chosen have been suggested as suitable
2023, 13, 2558. https://doi.org/ parental lines for breeding programs aimed at enhancing grain yield in the agro-ecological zones
10.3390/agronomy13102558 that were evaluated. This study’s findings identify BGN accessions with adaptability and stability
Academic Editor: Chuyu Ye across selected environments in Nigeria, suggesting specific accessions that can serve as suitable
parental lines in breeding programs to enhance grain yield, thereby holding promise for improving
Received: 14 July 2023
food security.
Revised: 4 September 2023
Accepted: 7 September 2023
Published: 4 October 2023 Keywords: Bambara groundnut; biplot analysis; genotype–environment interaction; multi-locational
trials; yield
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland. 1. Introduction
This article is an open access article Vigna subterranea (L.) Verdc., commonly known as Bambara groundnut (BGN), is
distributed under the terms and
a leguminous crop mainly cultivated in semi-arid regions of Africa to ensure food and
conditions of the Creative Commons
nutritional security. According to the FAO, 58,900 metric tons (Mt) of BGN are currently
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/ being grown, and it was expected to be over 100,000 Mt in 2008 [1]. The crop contains
4.0/). various nutrient and anti-nutrient contents [2–5]. These crops provide an essential source
Agronomy 2023, 13, 2558. https://doi.org/10.3390/agronomy13102558 https://www.mdpi.com/journal/agronomy
Agronomy 2023, 13, 2558 2 of 15
of food for humans and cattle [6], and recent investigations have suggested that they may
be used to cure illnesses like diarrhea [3]. The seeds of these crops are also rich in protein,
carbohydrates, fat, minerals, and fiber [2,4,5]. The crop is recognized for its capacity to
withstand drought conditions and generate satisfactory crop yields even in the presence
of drought-induced stress [4,6]. According to various reported studies, BGN exhibits
significant genetic diversity [3,5,7–9]. In addition, indigenous rhizobia populations are
responsible for nodulating the crop, and their molecular diversity and phylogeny have
been evaluated in soils from Ghana and South Africa [10]. As a legume, it has the capacity
to fix nitrogen, hence its importance in mixed cropping systems [11]. The studies on
BGN have concentrated on enhancing agricultural practices and post-harvest techniques,
creating contemporary genotypes that exhibit increased productivity and nutritional value,
augmenting value through processing, and facilitating entry into the marketplace [12]. In
one of such studies, a comparison was made between the nutritional, physicochemical,
and functional properties of protein concentrate and isolate derived from newly developed
BGN genotypes and those of market samples [13]. Another study investigated the impact
of soaking and boiling on the levels of the anti-nutritional factors, oligosaccharide contents,
and protein digestibility of recently developed BGN cultivars [14]. The evaluation of
consumer awareness and acceptance of BGN as a protein source for incorporation into
complementary foods in rural regions has also been reported [15]. BGN is a significant crop
that is crucial in ensuring food and nutrition security in semi-arid regions of Africa.
BGN is still widely cultivated as a landrace because research on developing improved
varieties of the crop is still very much limited when compared with major crops. The
cultivation of Bambara groundnut is not limited to its center of origin in West Africa but
has also been expanded to other regions such as South America, Asia, and Oceania [16].
However, there is still a lack of proper seed systems and widely shared best agronomic
practices for this crop [17]. Research and development efforts are needed to establish
modern crop management techniques and value chains to maximize the economic gains
from Bambara groundnut production [12]. The crop is typically grown in regions with a
dry or semi-arid climate [18,19]. It grows in a wide range of soil types, including sandy,
loamy, and clay soils. However, well-drained sandy or loamy soils are generally preferred,
allowing for better root penetration and water infiltration [18]. It can be planted directly
in the field or in containers for later transplanting. The seeds are sown at a depth of
about 3–5 cm, with 30–40 cm spacing between rows and 10–15 cm between plants [20].
The recommended planting density may vary depending on the specific variety and
local conditions.
A crop variety must have a high yield and adapt to the environment to be success-
fully grown there. However, stability and adaptability are influenced by environmental
conditions, which can lead to the genotype–environment interaction (GEI) phenomenon.
This makes crop trait variability in various situations higher. Plant breeders are becoming
increasingly interested in GEI studies to find long-term answers to factors affecting plant
growth and development and to produce stable and adaptable varieties. In contrast, phe-
notypic analysis does not provide as strong a case for the genotype as does the influence of
the environment on traits, either alone or in conjunction with genotype [21]. Consequently,
assessing the crop’s stability is essential, which is why a plant breeder must conduct GEI
research to validate stable and superior varieties.
The study of genotype–environment interaction is commonly conducted using two
primary techniques: additive main effects and multiplicative interaction (AMMI) and
a genotype plus genotype–environment interaction (GGE) biplot. Two techniques are
employed to produce a basic graphical depiction of a complicated genotype, involving a
two-way table of the environment and principal component analysis [22]. The disparity
between the two techniques can be attributed to handling average values preceding the
application of Singular Value Decomposition (SVD). The Singular Value Decomposition
(SVD) technique is utilized on the data pertaining to additive main effects and multiplicative
Agronomy 2023, 13, 2558 3 of 15
interaction (AMMI), with the exclusion of both genotype and environmental means, as
stated in reference [23]. In this study, we will be using the GGE biplot method.
The GGE biplot methodology employs a graphical representation known as a biplot to
exhibit the two prominent factors, namely genotype and genotype–environment interaction,
responsible for generating variation. The GGE biplot is the most suitable method for assess-
ing genotype performance regarding mean versus stability, discriminating power versus
representativeness of the test environment, and multi-environment analysis, including
the “which-won-where” pattern [24,25]. Since its introduction, several multi-environment
analysis applications utilizing the GGE biplot approach have been documented. The
yield stability of 95 accessions of BGN in four environments in Nigeria was analyzed by
Olanrewaju, Oyatomi, Babalola, and Abberton [21] using the GGE biplot, while Mndolwa
et al. [26] reported on the GGE biplot analysis of yield stability for Andean dry bean acces-
sions grown in Tanzania under various abiotic stress regimes. Dalló et al. [27] also reported
on soybean performance and stability in a multi-environment trial utilizing GGE biplot
analysis. Furthermore, GGE biplot analysis has been implemented to study the stability of
various crops, including wheat [28–30], soybean [27,31], maize [32], sorghum [33], sweet
potato [34], lemon grass [35], barley [36], cowpea [37], and rice [38].
The aim of this study is to assess the stability of yield among thirty distinct BGN
accessions. The results from this study will enable breeders to advise farmers appropriately
on which accession to use where, provided that the various accessions meet the end-user
quality preferences.
2. Materials and Methods
2.1. Plant Materials
The study involved 30 BGN accessions, as indicated in Table 1. The study locations
comprised the varied geographic regions of Ibadan, Ikenne, and Mokwa, which are within
the confines of the International Institute of Tropical Agriculture (IITA) Ibadan in Nigeria.
The experiment included 30 BGN accessions meticulously chosen from a recent assemblage
of BGN accessions that originated in Cameroon. The accessions were obtained and sub-
sequently conserved at the IITA GenBank, a specialized facility specifically designed to
contribute to the preservation and management of genetic resources.
Table 1. The accessions used and their corresponding serial number.
S/N Accessions S/N Accessions
1 TVSu-2188 16 TVSu-2209
2 TVSu-2190 17 TVSu-2207
3 TVSu-2193 18 TVSu-2204
4 TVSu-2194 19 TVSu-2206
5 TVSu-2199 20 TVSu-2223
6 TVSu-2200 21 TVSu-2226
7 TVSu-2201 22 TVSu-2235
8 TVSu-2184 23 TVSu-2236
9 TVSu-2202 24 TVSu-2240
10 TVSu-2181 25 TVSu-2241
11 TVSu-2285 26 TVSu-2244
12 TVSu-2284 27 TVSu-2249
13 TVSu-2256 28 TVSu-2254
14 TVSu-2221 29 TVSu-2283
15 TVSu-2218 30 TVSu-2214
2.2. Study Site Description
The study was carried out in three distinct agro-ecological zones, namely Ibadan,
Mokwa, and Ikenne (Figure 1). The geographical coordinates 7◦40′19.62 N, 3◦91′73.13 E
correspond to Ibadan, which has been categorized as a derived savannah. Similarly, Mokwa,
located at 9◦12′16.98 N, 5◦20′61.09 E, has been classified as a Guinea savannah, while Ikenne,
Agronomy 2023, 13, x FOR PEER REVIEW  4  of  15 
2.2. Study Site Description 
The study was carried out  in  three distinct agro-ecological zones, namely  Ibadan, 
Mokwa, and Ikenne (Figure 1). The geographical coordinates 7°40′19.62 N, 3°91′73.13 E 
Agronomy 2023, 13, 2558 correspond  to  Ibadan, which  has  been  categorized  as  a  derived  savannah.  Sim4 oilfa1r5ly, 
Mokwa, located at 9°12′16.98 N, 5°20′61.09 E, has been classified as a Guinea savannah, 
while  Ikenne, situated at 6°51′00.873 N, 3°41′48.528 E,  falls under  the rainforest region. 
Tshiteu astteuddyat u6t◦i5li1z′e0d0. 8t7h3e Nfi,e3ld◦4 s1t′a4t8i.o5n2s8 Ees, tfaablllsisuhnedde rbtyh ethrea iInnfoterrenstarteiognioanl .ITnhsteitsututed yofu Ttirliozpeidcal 
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oabssepravretdo f tthherostuugdhyo. uTth etphree cisppiteactiifionedl evteilms ew erfreameet,i cualnodu slythoeb searvveerdagther opugrehcoipuitttahtieon 
mspeaescuifiredmteimntes wfraemree d, oancudmtheentaevde raasg 7e5p.2r6e cmipmita, t2i6o.n4 mema,s uanredm 5e.7n4ts mwmer iend Iobcaudmane,n Mtedokaws a, 
a7n5d.2 I6kmenmn,e2, 6re.4spmemct,ivaenldy.5 I.n74 amddmitinonIb, athdea nre, sMeaorkcwha p, raensdenIktse ndnaeta,  rreesgpaercdtiivnegly t.hIen laodwdeitsito ann, d 
htihgeherests earerchorpdreeds entetsmdpaetraatruegreasr daint gtthhee  ltohwreees tsainteds. hiIgbhaedsatnr eceoxrpdeerdientecmedp eara  truarnegsea tof 
tethmeptehrraeteusreitse,s  .wIibthad  tahne elxopweerisetn  rcecdoardreadn gtemofpteermatpuerrea tbueriensg,  w2i2t.h61t h°eCl oawneds ttrheec ohridgehdest 
tetemmppeerraattuurree rbeeaicnhgin22g. 631 .◦7C3 a°Cnd. Tthe  hriegchoersdtetdem tepmerpaetruarteurreea rcahningge 3in1. 7M3o◦kCw. Ta hwearse cboertdweeden 
2t3e.m01p °eCra taunrde r3a0n.0g7e °inC.M Foinkawllayw, iat swbaest wobeesner2v3e.d01 ◦ thCat ainnd Ik3e0n.0n7 ◦e, Cth. eF imnailnlyim, ituwma steombpseerrvaetdure 
rtehgaist tienreIkde nwnaes, t2h4e.0m1 i°nCim, wumhetreemasp ethraet umreaxreimgiustmer etdemwpaesr2a4t.u0r1
◦
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ctheomicpee rtaot ucroenadtutacitn  tehde wreasse2a8r.c9h1
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  aCcr.oTshs emcuhlotiicpelet oloccoantdiouncst, thneamreesleya rIcbhadaacrno, sIskmenunltei,p  laend 
Mloockawtioan, sf,ancialmitaetleydI bthade aenv,aIlkueantnioen,  aonfd  tMheo ksewleac,tfeadc ilBitGatNed  atchceesesvioalnusa  tuionndeorf  tvhaerisoeulesc ategdro-
cBliGmNatiacc cceosnsdiointisonusn.d  er various agro-climatic conditions.
Figure 1. Map showing the geographical locations of the experimental sites. 
Figure 1. Map showing the geographical locations of the experimental sites.
22.3.3. .SSooili lSSaammpplliinngg aand Analysis 
TToopssoiill ssampplleessw wereereo botbatinaiendefdro fmro0mt o01  t5oc m15 ocvmer othveere ntthiree epnlotitrue spinlogtt huesisnogil athueg esroil 
aaungderp uant dto gpeutth teorgtoetohbetra tino aobcotaminp oas citoemsapmospiltee bseafmorpelees btaebfloirseh iensgtathbelisehxipnegr itmheen etx. pTehreimsoeilnt. 
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cahneamlyicsaels  (asnanaldy,scelsa y,(ssailnt,dp, Hc,laoyr,g asnilitc, caprHbo, no(rOgaCn),ict otcaalrbNon(n it(rOoCge),n  )t,oetxaclh  aNn ge(naibtlreogCean), 
e(xccahlcainugme)a,bMleg C(ma a(cganlecisuiumm),) ,MKg( p(omtaasgsniuesmiu),ma)v,a Kila (bploetPas(spihuoms)p, haovrauisla),bNlea P(s (opdhiuosmp)h,oMruns), 
N(ma a(snogdainuemse)),, MCun ((cmopapnegra)n, eFsee)(i, rConu) ,(caonpdpZenr),( zFine c()i)roannd), panardt icZlne  s(izziencd)i)s tarnibdu tpioarntiactlet hseize 
onset of the experiment.
distribution at the onset of the experiment. 
2.4. Field Trials and Yield Data
A randomized complete block design was used in the trials to analyze the various
accessions. Thirty mancozeb-treated seeds were included in each accession and were
planted in the 2020 growing season. The accessions were cultivated in triplicate to guarantee
accuracy, leading to a cumulative count of three replicates. Ten plants were chosen from
Agronomy 2023, 13, 2558 5 of 15
every accession in each replicate and assigned to a specific plot. The dimensions of the plots
were made uniform, with a length of 22 m. Each plant within a plot was separated by 2.2 m,
and there was a gap of 2 m between each adjacent plot. A solitary block was established
within each replicate to improve experimental precision and account for potential variability.
The planting activities were initiated on predetermined dates for each research site. The
commencement of planting in Mokwa was initiated on 15 July 2020. The planting dates
for Ikenne and Ibadan were recorded as 29 August and 13 August, respectively. Before
implementing a weekly irrigation schedule, the plants depended solely on precipitation
to fulfill their water requirements. The crops depended on precipitation until rainfall
cessation, at which time an irrigation regimen was instituted. This methodology aimed
to replicate authentic environmental circumstances and facilitate the proliferation and
maturation of the accessions in conventional rainfed and irrigated settings. The formula
following formula was used to convert the weight of the seeds into a yield of the grain:
Yield (kg/ha plot yield × 10,000
) = plot area . This was then converted to hectares. An electronic
balance was used to attain a triplicate of each accession’s total seed weight. Weeding was
carried out manually as required using hoes and uprooting.
2.5. Statistical Analysis
Field data collection was performed using a field book [39], and data entry was
carried out using Microsoft Excel 2016. For stability analysis in this study, the well-known
regression model jointly developed by Eberhart and Russell [40] was applied. Eberhart
and Russell’s model utilizes a joint linear regression approach, where the environmental
indices are regressed against the yield of each genotype. The model characterized the
genotype’s behavior using the equation Yij = µi + βiIj + δij. Here, Yij represents the average
performance of genotype I in environment j, µi denotes the overall average performance of
genotype i across all environments, βi represents the regression coefficient capturing the
response of genotype i to the environmental index, Ij is the environmental index calculated
as the difference between the average of each environment and the overall average, and δij
represents the deviation of genotype i from its regression in environment j.
The GGE biplot approach was employed for analyzing genotype–environment inter-
actions and determining yield stability in cases of significant differences. The biplot was
generated from the first two principal components (PC1 and PC2) using the environment-
focused yield [41]. GEA-R version 4.1 [42] was used for the analysis. The fitting of the model
involved applying singular value decomposition (SVD) to PC1 and PC2 [43], resulting in
the following equation:
Yij = µ + βj + λ1ξi1η1j + λ2ξi2η2j + εij. (1)
Yij represents the average trait value for genotype i in environment j, µ represents
the overall mean, βj represents the main effect of environment j, and µ + βj represents the
average yield across all genotypes in that environment. The singular values for PC1 and
PC2 are denoted as λ1 and λ2, respectively. The eigenvectors of genotype i for PC1 and
PC2 are ξi1 and ξi2, while the eigenvectors of environment j for PC1 and PC2 are η1j and
η2j, respectively. The notation εij represents the residual associated with genotype i in that
environment j.
The GGE-biplot analysis was employed to generate graphs for various purposes,
including (i) analyzing mean performance and stability, (ii) identifying the “which-won-
where” pattern, (iii) exploring the relationship among test environments, (iv) assessing
ranking discrimination, and (v) evaluating the representativeness of test environments.
The correlation between the two environments was evaluated by measuring the angles
between the position vectors within the GGE biplot [44].
Agronomy 2023, 13, 2558 6 of 15
3. Results and Discussion
The assessment of variances on GEI has the capacity to function as a stability estimator
within breeding programs. The occurrence of GEI has been recorded in various crops,
including wheat, cotton, and sugarcane [45–47]. Furthermore, applying precise statistical
methodologies, such as the GGE biplot, is a widely used practice in assessing GEI in plant
breeding initiatives [21,25,26,48,49]. In addition, cultivating crops with high productivity
that are well suited to a given environment requires adherence to specific expectations,
including but not limited to efficient nutrient absorption, resistance to weed infestation,
and increased yield.
In contrast to the AMMI model, the GGE biplot method represents the genotypic main
effect as a multiplicative effect with respect to the genotype–environment interaction. The
uniformity of signs observed in the PC1 scores across all locations indicates the presence of
a non-crossover genotype–environment interaction in PC1. The genotypic PC1 scores are
often significantly correlated with the genotype’s main effects, rendering them a practical
substitute for the latter. However, it is essential to note that the two concepts are fundamen-
tally distinct. The genotypic main effect is characterized as a consistent genotypic impact
across all environments. However, the anticipated yield projections from PC1 in the GGE
biplot for a specific genotype exhibit variability. The degree of variation of the observed
phenomenon is directly proportional to the PC1 scores of the corresponding environment.
The notion that the yield response of genotypes is proportional is deemed more reasonable
and biologically tenable compared to the concept of additive main effects.
Furthermore, a distinctive characteristic of this concept is that it identifies locations that
enable the identification of genotypes with a higher main effect. The GGE biplot exhibits a
notable advantage in distinguishing between genotype responses that are proportionate and
disproportionate. This differentiation holds significant implications for both crossover and
non-crossover genotype–environment interactions. The comprehension of the interactions
can be attained by correlating PC1 and PC2 scores with genotypic and/or environmental
covariates.
Therefore, this study aimed to evaluate the stability and productivity of 30 accessions
of BGN across three distinct locations, thus creating three unique environments in Nigeria.
This was achieved through a multi-environmental trial. The study commenced with a
combined analysis of variance (ANOVA) on aggregated data to assess the extent to which
genotype and environment contribute to variance and evaluate GEI’s statistical significance.
Subsequently, a stability analysis was performed using GGE biplot analysis. This was
achieved by visually examining the connections between the assessed genotypes and
environments, identifying potential mega-environments within the investigated region,
and assigning a ranking to both the genotypes and environments based on their yield.
The substantial differences and high coefficient of variation (94%) in the study’s
findings show significant variability in yield within the chosen population. This variability
can be harnessed to enhance breeding programs. According to earlier studies [50,51],
the availability of traits facilitates choosing the best lines for enhancement through trait-
assisted selection. Yan and Kang [44] have posited that the extent of environmental variation
depends on the number of genotypes and environments. Furthermore, Aremu et al. [52]
asserted that the environment constitutes the primary source of diversity in plants. As
such, it is imperative to consider it in the context of plant breeding.
3.1. Soil Analysis
The soil nutrient composition and properties were lowest pH in Ikenne, followed
by Mokwa and Ibadan, while phosphorous was lowest in Mokwa and highest in Ikenne
(Table 2). Carbon and nitrogen were highest in Ikenne. For the soil properties, Mokwa
contained the highest amount of sand and the lowest amount of clay compared to the other
two locations. At the same time, for the mineral contents, Ibadan had more calcium, mag-
nesium, potassium, sodium, zinc, and manganese than the other two locations. Meanwhile,
Ikenne had the highest amount of copper, and Mokwa had the highest iron content.
Agronomy 2023, 13, 2558 7 of 15
Extensive research highlights the crucial role of soil and climate in influencing crop
yield [53–57]. Various soil types yield different responses from crops. Sandy soil supports
the successful production of BGN despite hindering crop emergence [18]. Its permeable
composition and abundant pores facilitate plant growth. When dry, sandy soil develops
narrow cracks that benefit crops in regions with erratic rainfall [58,59]. Conversely, clay soil
retains water but expands and contracts, stressing plant roots. Understanding these soil–
climate interactions aids in optimizing agricultural productivity, particularly in semi-arid
areas with unpredictable precipitation patterns.
Table 2. Soil analysis result of the three experimental locations (Mokwa, Ibadan, and Ikenne).
Locations pH bray P % % % % % Ca Mg K Na ppm ppm ppm ppm
(1:1) (mg/kg) OC N SAND CLAY SILT (cmol/kg) (cmol/kg) (cmol/kg) (cmol/kg) Zn Cu Mn Fe
Mokwa 5.51 1.73 0.05 0.01 83.00 10.67 6.33 0.91 0.27 0.02 0.02 2.49 0.43 41.24 1303.16
Ibadan 6.30 13.90 0.20 0.10 80.67 13.67 5.67 2.69 0.80 0.54 0.10 4.35 0.34 128.29 83.34
Ikenne 4.91 22.46 0.30 0.12 76.33 20.00 3.67 1.51 0.40 0.24 0.08 1.20 2.05 116.71 88.29
3.2. Pooled Analysis of Variance
A pooled analysis of variance was conducted to determine the significance of the GEI
(Table 2). However, the large impact of GEI on yield poses a great challenge to identifying
the best genotypes in relation to yield. Hence, breeders should consider quantifying GEI
when developing strategies for complex traits like yield [21]. In addition, to produce
reliable results, genotype and environmental factors should be prioritized in selection
processes. In this study, accessions, environments, and interaction effects were all significant
sources of variation at p < 0.001 (Table 3). This implies that the yield of the accessions
in each environment varies significantly, which agrees with the reports of Olanrewaju,
Oyatomi, Babalola, and Abberton [21] and Chibarabada, Modi, and Mabhaudhi [60], who
found significant interactions between accessions and environments in response to yield.
Specifically, in this study, the reported climate in each location was different, as reported
earlier. With respect to this, the most significant variation observed in this study can
be attributed to the environment (Table 3) which is influenced by the various climatic
conditions in each environment. The climatic condition is an important factor in crop
production and yield. For instance, BGN thrives well in moderate rain and temperature
because high amounts of rainfall affect the pods, which are under the ground [6]. A similar
result was obtained on the grain yield of corn in the studies of Hudson et al. [61] and Azrai
et al. [62]. Their findings attributed the majority of the variations observed to the effect of
the environment. On the contrary, Esan et al. [63] ascribed a larger portion of observed
variation in their study to the BGN genotypes.
Table 3. Variance analysis of yield data collected from BGN trials conducted in Ibadan, Ikenne, and
Mokwa.
Source of Variations Df Sum Sq. Mean Sq. F value P r (>F)
Rep 2 56,702,056 28,351,028 10.1142 6.912 × 10−5 ***
Env 2 13,354,504 506,677,252 180.757 <2.2 × 10−16 ***
Accns 29 427,984,896 14,758,100 5.2649 1.043 × 10−12 ***
Env:Accns 58 590,078,489 10,173,767 3.6295 2.547 × 10−11 ***
Residuals 178 498,949,269 2,803,086
Coefficient of variation = 0.94. DF = degree of freedom. Sum sq. = sum of squares. Mean sq. = mean square.
*** Significant at p < 0.001.
This study reports a significant contribution of GEI (p < 0.001) to the yield of BGN
accessions, hence justifying the need for further analysis.
Agronomy 2023, 13, x FOR PEER REVIEW  8  of  15 
 
Table 3. Variance analysis of yield data collected from BGN trials conducted in Ibadan, Ikenne, and 
Mokwa. 
Source of Variations  Df  Sum Sq.  Mean Sq.  F value  P r (>F) 
Rep  2  56,702,056  28,351,028  10.1142  6.912 × 10−05 *** 
Env  2  13,354,504  506,677,252  180.757  <2.2 × 10−16 *** 
Accns  29  427,984,896  14,758,100  5.2649  1.043 × 10−12 *** 
Env:Accns  58  590,078,489  10,173,767  3.6295  2.547 × 10−11 *** 
Residuals  178  498,949,269  2,803,086     
Coefficient of variation = 0.94. DF = degree of freedom. Sum sq. = sum of squares. Mean sq. = mean 
Agronomy 2023, 13, 2558 square. *** Significant at p < 0.001.  8 of 15
This study reports a significant contribution of GEI (p < 0.001) to the yield of BGN 
accessions, hence justifying the need for further analysis. 
3.3. GGE Biplot Analysis
3.3. GTGhEe Banipalolyt sAinsaolyfsMis ET’s adaptation and stability using the GGE biplot is effective [64].
It enaTbhlee santahleyseisv aolfu MatEioT’ns aodfatphteateionnv iarnodn mstaebnitlitcye nutseinregd thoen GtGhEe bGipGloEt visi ewffe’csticvaep [a6c4i]t.y  for
dIti secnraimbliensa  tthioe nevaanlduarteiopnre osfe nthtaet ievnevnireosnsm[4e8n]t.  cTehnitsergeidv eosni tthaen GedGgEe voievwer’st hceapAacMitMy  fIobr iplot
adnisaclryismisin[a6t5i]o.nF aunrdth reerpmreosreen, ttahteivbeinpelsost [d4i8s]p. lTahyiss agipvoesly igt oann tehdagtee onvceorm thpea sAseMsMalIl bgiepnloott ypes
wanitahliynsiist s[6a5r]e. aF,uwrtihtherimtsovree,r ttihcee sbirpeplorte dseisnptlianygs tah epogleyngootny ptheast seintucaotmedpaaststehs eagllr geeanteosttydpeisst ance
fwroitmhinth eitso raigreina,. Twhitehb  iitpsl ovteirstipceasr tirteiporneesdenitnintog stehceto  grsenboytyppeersp esnitduiactuedla ralti nthese tghraetaitnetset rsect
edaicshtansicdee froofmth tehep oolryiggionn. T. hTeh ebivpelortte ixs pgaerntoittiyonpeeds einxthoi bseitctsourps ebryio preprpeerfnodrimcualnarc elininest htheamt  ega-
einntveirrsoenctm eeancht, swidhei olef tthhee ypoalryegocno.n Tsihdee vreerdteixn fgeerniootrygpeens oetxyhpibeist sfourpearlilotre psteerdforemnvanircoen imn ents
wthhee nmpelgaac-eednviinroansmecetnotr,  lwachkilien gthseuyit aabrlee  ceonnvsiirdoenrmede nitnsfe[r4i7o]r.  Ignenthoitsypsteusd  fyo,rt haellr etleasttieodn ship
aemnvoinrogntmheenttess twehnevni rpolnacmeden itns aw saesctmoro ldaeclkeidngb sauseitdabolen eennvviirroonnmmeenntst -[c4e7n].t eIrne tdhi(sc esntutedryin, g, 2)
athned  reenlavtiiroonnsmhipen  at-mmoentgri ct-hper etseestr veinnvgir(oSnVmPe,n2t)s wwiaths omuotdtheleeds cbaalisnegd oopnt ieonnviirnonthmeenGt-EA-R
centered (centering, 2) and environment-metric-preserving (SVP, 2) without the scaling 
software. The results suggested that the environments could be classified into three mega-
option  in  the GEA-R  software. The  results  suggested  that  the  environments  could  be 
environments. The biplot described all the observed variations, with PC1 (axis 1) explaining
classified into three mega-environments. The biplot described all the observed variations, 
87.81% and PC2 (axis 2) explaining 12.19% (Figure 2). From the biplot, all the accessions
with PC1 (axis 1) explaining 87.81% and PC2 (axis 2) explaining 12.19% (Figure 2). From 
athned  ebnipvloirto, namll enthtes wacecreessmioonsst lyanldo aedninvigroonnmtehnetsfi  rwsterceo mmpoostnlye nlto.aSdiimngi laornfi  tnhdei nfigrsstw  ere
rceopmoprtoendebnyt. OSliamnirleawr  afijnud, iOnygsa towmeri,e Braebpaolrotlead,  abnyd AOblabnerretwonaju[2, 1O];yEastaonm, iO, kBea,bOalgoulan,b  oandde,  and
OAbbibseerstaonn [[6231]];; Eansadnm, Ookset,b Oipgluontbaondaely, asnesd. Obisesan [63]; and most biplot analyses. 
 
FFiigguurree 22.. GGGGEE bbiipplolot tggrarapphhiciacla rlerperperseesnetnattiaotnio onf othfet h3e0 B30amBbamarab agrraougnrodunnudt ancuctesascicoensss iino nthsei nthtrheee three
locations. 
locations.
3.4. Stability Analysis
  Plant breeders select genotypes based on yield stability and adaptation. Yield stability
is a cultivar’s ability to provide steady yields in different agro-climatic conditions, while
adaptability is its ability to thrive in specific environmental conditions. A stable, high-
yielding cultivar maintains yields in harsh situations, ensuring food security and reducing
the farmer’s risk. Adaptability is essential for a genotype/accession to function well in
a specific environment or soil type. Farmers need adaptable crop varieties to maximize
their harvests. Therefore, breeders must consider adaptation and production stability while
choosing genotypes/accessions. Yield stability and adaptation can be contrasting, making
it difficult to achieve both. An adaptable genotype/accession in one environment may not
be stable in other environments. Hence, stability and adaptability affect plant varieties’
production efficiency. An accession can be beneficial if it has a high grain output and can
improve productivity in various environments [66].
Agronomy 2023, 13, x FOR PEER REVIEW  9  of  15 
 
3.4. Stability Analysis 
Plant  breeders  select  genotypes  based  on  yield  stability  and  adaptation.  Yield 
stability is a cultivar’s ability to provide steady yields in different agro-climatic conditions, 
while adaptability  is  its ability  to  thrive  in specific environmental conditions. A stable, 
high-yielding cultivar maintains yields  in harsh  situations, ensuring  food  security and 
reducing the farmer’s risk. Adaptability is essential for a genotype/accession to function 
well  in  a  specific  environment  or  soil  type.  Farmers  need  adaptable  crop  varieties  to 
maximize  their harvests. Therefore, breeders must consider adaptation and production 
stability  while  choosing  genotypes/accessions.  Yield  stability  and  adaptation  can  be 
contrasting, making it difficult to achieve both. An adaptable genotype/accession in one 
environment may not be stable in other environments. Hence, stability and adaptability 
Agronomy 2023, 13, 2558 affect plant varieties’ production efficiency. An accession can be beneficial if it has9 ao fh1i5gh 
grain output and can improve productivity in various environments [66]. 
Consequently, evaluating adaptability and stability  is essential  for enhancing crop 
produCcotinosne.q  Iune natdlyd, ietivoanlu, astianbgilaitdya pantaabliylsitiys anlldowstsa bbilrietyediesress steon  etivaal lfuoarten  ahnadn cainsgsecsrso pthe 
gpernoodtyupctei’osn/a. ccInesasidodni’tsi opnr,osdtaubcitliivtye apnoatelynstisalasl laonwds  lbimreietdateirosntso  inev eaaluchat enavnidroansmseesnst th[6e7]. 
Sgtaebniolittyyp ae’nsa/laycscise shsieolpns’s ipdreondtiufyct igveenpootytepnetsi atlhsaat ncdonlismisitteantitolyn spienrfeoarcmh wenevlli raocnrmosesn at  [r6a7n]g. e 
oSf teanbviliirtyonamnaelnytssi,s ehneslupsriindge nthtiafyt tgheen goetynpoetysptheas/taccocnessissitoenst layrpe errefloiarmblew aenllda acrdoaspstaabralen g[6e7]. 
Tohfee nGvGirEo nbmipelnotts  i,se nhseulprifnugl tinh aatnthaleygzeinngo ttyhpee ss/taabcicleitsysi oannsda aredarepltiaabbilleitayn dofa MdaEpTta  [b6l4e][.6 T7]h.is 
mTehtehoGdGoElobgiyp lohtaiss  htheelp  fcualpianbailnitayl yztoin  gastcheertsatianb iloitpytiamnadl adgeanpotatybiplietsy/aocfcMesEsiTon[6s 4]t.haTth  iasre 
smuietathboled ofloorg ya hpaasrttihceuclaarp aebnivliitryotnomasecnetr.t aAinccooprtdiminagl gtoe ntohtey pEebse/rahcacerts sciooneffis tchiaetnatr eansualityasbisle  in 
Ffiogruarep 2a,r aticccuelsasrioennsv iTrVonSmu-e2n2t0.7A acncdo rTdVinSgu-t2o1t9h9e wEebreer hstaarbt lceo aecffirocsiesn atlla ennavlyirsoisnimneFnigtsu, rweh2i,le 
accessions TVSu-2207 and TVSu-2199 were stable across all environments, while accessions
accessions TVSu-2226, TVSu-2249, TVSu-2209, TVSu-2184, TVSu-2204,  and TVSu-2236 
TVSu-2226, TVSu-2249, TVSu-2209, TVSu-2184, TVSu-2204, and TVSu-2236 were adaptable,
were  adaptable,  and  accessions  TVSu-2240  and  TVSu-2283  were  both  stable  and 
and accessions TVSu-2240 and TVSu-2283 were both stable and adaptable. However,
aadcacpetsasibolne.s HTVowSue-v2e1r9,3 a,cTcVesSsuio-2n1s8 1T,VTSVuS-u2-129234, 1T,VanSdu-T2V18S1u,- 2T2V5S4uw-2e2re41s,t aabnlde  aTnVdSpue-2rf2o5r4m wedere 
swtaeblllea  cacnodrd  ipnegrftoortmheedC Vw-melel anacacnoarldyisnigs  (Ftoig uthree 3)C. VM-muletia-nen  vainroalnymsiesn  t(Ftriigaulsrea re3)c. ruMciuallti-
einnviprloannmt benrete tdriianlgs taorea csrsuescsiatlh ien ipmlapnatc tbroefeGdiEnIgo tno dasifsfeesrse ntht eg eimnoptaycpte os/f cGuEltIi voanr sd. iffIteriesnt 
geesnsoetnytpiaelst/ocuclotinvsairdse. rItt hise easvseernatgiael ptoe rcfoonrmsidanecr ethoef atrvaeirtsagaen dpetrhfoersmtaabnicliety ofo ftrgaeitnso atynpde tshe 
swtabhielnitys eolef cgtienngodtyepsierse dwgheenno  tsyepleecst.inTgh  idseissiriemdp  goertnaonttyptoesm.  iTnhimis izise  pimotpeonrttiaanl tc otom meinrcimiailze 
plootsesnetsiaflo rcofamrmeerrsc.iaAl mloosnsegs thfoera  cfacremsseiorsn. sAemvaolunagt etdhei natchciessssitoundsy ,eavcacleusastieodn sinT VthSius- 2s2tu40dy, 
aaccnedssTioVnSsu T-2V2S8u3-2e2x4h0ib aitned TadVaSput-a2b2i8l3it yexahnidbitsetdab ailditayptaacbriolistsy tahnedt hsrteaebilloitcya taicornoss,s mthaek itnhgree 
lothcaetmionths,e mmakoisntgd tehseirma bthlee ammoostn dgetshireabslteu admieodnagc tchees sitoundsi.edS aimccielsasriotonso. uSirmrielsaur ltto, ovuar iroeusus lt, 
vcarroiopuasc cerosspi oancsc/esgseinonotsy/gpeensostuyiptaebs lseufiotrabimlep froorv iemdpbrroeveeddin bgreperdoignrga mprsobgarsaemdso bnatsheedir oyni etlhdeir 
ysietaldb islittaybihliatvye hbaeveen biedeenn itdifieendtifbieydv baryi ovuarsiostusd siteusd[2ie1s, 2[2,12,262,2,286,2,298,3,29,3,352–,3385,–6328–,642,–664,6,686].,68]. 
 
FFigiguurere  33.. Yielld sstatabbiliiltiytya naanlyaslyissiosf  tohfe  tahcec eascsicoenssiionntsh eine ntvhier oennmveirnotsnumseingtst huesEinbge rhthaert EanbderRhuarsts eallnd 
R(ubsi,sSe2lld (ib) im, So2ddeil). m(ao) dFreal.n (cais) F(CraVn)cviss .(C(bV) )m vesa. n(bb)i pmloetafno rbigprlaoint fyoire lgdr.ain yield. 
3.5. Environment and Genotype Ranking Analysis and Relationship among Environments
  The distance between each environment and the center of the axis determines the
performance of the environment [21]. Hence, Mokwa is the best-ranked environment
among the three environments studied (Figure 4a). It is characterized by moderate rainfall
and temperature. However, the ideal environment is represented by a small circle with an
arrow pointing to it [21]. The best-ranked accessions are those closer to the inner circle,
which are TVSu-2188 (the most ideal) followed by TVSu-2254, TVSu-2199, and TVSu-2200
(Figure 4b). These accessions are the stable accessions. Among the environments, it was
observed that there was a significant variation in the yield of the accessions. Similar results
were reported by Esan, Oke, Ogunbode, and Obisesan [63] and Oladosu, Rafii, Abdullah,
Magaji, Miah, Hussin, and Ramli [22] on the yield of BGN and rice, respectively.
Agronomy 2023, 13, x FOR PEER REVIEW  10  of  15 
 
3.5. Environment and Genotype Ranking Analysis and Relationship among Environments 
The distance between each environment and  the center of  the axis determines  the 
performance  of  the  environment  [21]. Hence, Mokwa  is  the  best-ranked  environment 
among the three environments studied (Figure 4a). It is characterized by moderate rainfall 
and temperature. However, the ideal environment is represented by a small circle with an 
arrow pointing to it [21]. The best-ranked accessions are those closer to the inner circle, 
which are TVSu-2188 (the most ideal) followed by TVSu-2254, TVSu-2199, and TVSu-2200 
(Figure 4b). These accessions are the stable accessions. Among the environments, it was 
observed that there was a significant variation in the yield of the accessions. Similar results 
Agronomy 2023, 13, 2558 10 of 15
were reported by Esan, Oke, Ogunbode, and Obisesan [63] and Oladosu, Rafii, Abdullah, 
Magaji, Miah, Hussin, and Ramli [22] on the yield of BGN and rice, respectively. 
 
FFiigguurree 44.. RRaannkkiinngg ooff tthheeb beestst( a()ae) nevnivroirnomnmenetn(bt )(bac) caecscseiosnsioinne ianc heaecnhv iernovnimroennmt. Renetd. cRoeldor croelporre sents
reenpvriersoennmtse enntsvwirohnilme belnatcsk wcohlioler  rbelparceks ecnotlos ra crceepsrseisoennst.s accessions. 
33..66.. Diissccrriimiinnaattiinngg aanndd RReepprreesseennttaattiviveenneessssa anndd“ “WWhihcihc-hW-Wono-nW-Whehree”reA” nAanlyasliyssis 
AAsssseessssiinngg aann iiddeeaall eennvviirroonnmmeenntti sisc crurucicaial li ninid iednetniftyifiynigngsu spuepreiorirogre gneentiectitcy ptyepseths atthat 
aarree wweellll  ssuuiitteedd ffoorr aa ppaarrtitcicuulalarre  ennvvirioronnmmenent.t. GGGGEEb bipiploltostsa raereu tuiltiizliezdedto  teov  eavluaaluteatteh ethe 
ddiissccrriimmiinnaattoorryy eeffffeecctt ooff aann eennvviirroonnmmeenntti nint etermrmsso of fg genenotoytpyeped idffieffreernetniattiiaotnioann adnidts itasb ailbitiylity 
ttoo eennccoommppaassss aallllo oththerera sassessessesdeden evnirvoinromnemntesn[t2s1 ,[6281],.6T8]h.e Tthheeo trheetiocraeltfircaaml ferwamorekwoof rthke oafv t-he 
erage environment coordination perspective relies on applying singular-value partitioning
average  environment  coordination  perspective  relies  on  applying  singular-value 
with a focus on the interplay between genotype and environment. The magnitude of the
peanrvtiirtoionnminengt awl vitehc toar  fioscduirse cotlny  pthroep  ionrtteiornpalalyto  btheetwsteaennd agrdendoetvyipaeti oannodf tehnevaivroernamgeenget.n oT-he 
mtyapgenaitcurodses ovfa trhioeu esnevnivroirnomnmenentatla lvceocntodri tiiso dnsir,eacstlrye pporrotpedorbtiyoYnaanl ,toK athneg ,sMtana,dWarodo ddse,vainatdion 
oCf otrhnee laiuvser[2a4g]e. Tghenuos,tythpeed aisccrroisms invaatroioryusc aepnavciirtoynomf aensptaelc icfiocnednitviiornons,m aesn  rteips odretteedrm biyn eYdan, 
Kbayntgh,e Msizae, oWf iotsodcosr, raenspdo nCdoirnngevlieucst o[r2.4]. Thus,  the discriminatory  capacity  of  a  specific 
envirTohnemaebnilti tiys dtoetdeirsmtiningeudis hbyd utheet osigzeen oeft iictsv caorirarnecsepsoannddinthge vceacptoarc.i ty to represent target
envirTohnem aebniltistysh toou dlidstbinegcuonisshid deureed tow gheinleestiecl evcatriniagntceesst aenndvi trhoen mcaepnatcsi[t3y1 t,o4 9r]e.pIrbeasdeannt thaarsget 
etnhveilroonngmesetnvtse csthoorualndd bteh ecosnmsaidlleersetda nwghleilwe sitehleacntiindge atelsetn evnirvoirnomnemnetnintst [h3e1c,4u9r]r.e Inbtasdtuand yh,as 
tihdee nlotinfygiensgt viteacstoar paenrdfe tchtet essmt eanllveisrto annmgelen twinithte ramn sidoefadl iesncrvimiroinamtieont aind threp cruersreenntat tsitvued-y, 
indesnst.ifHyionwg eviet r, aasc ceas siopnerTfeVcSt u-t2e2s0t 9 eisnvthireomnmosetndt iscinri mtienramtisn goafm  odnigsctrhime iancacteisosnio  nasnd 
rsetpurdeiesedn(tFaitgiuvreen5eass).. THhoewaenvgeler,b aectwceesesnioanc cTeVssSiuon-2a2n0d9 tihs ethaev emraogset- dacisccersismioinnaxtinsgre apmreosenngt sthe 
atchceersespiornese snttuadtiiveedn e(Fssigoufreth 5eaa)c. cTehssei oann:gtlhe ebleatrwgerenth aecacnesgslieo,nth aenldes  tshree parveesreangtea-taivcecetshseion 
aaxcicse srseiponre.sHenentsc et,hTeV  rSeup-r2e2s0e9nitsatthiveelneeassst  roepf rtehsee natactcievses.ion:  the  larger  the  angle,  the  less 
repreTsehnetauttiivliez athtieo nacocefstshieonp. oHlyegnocnea, lTVreSpure-2s2en09ta itsi otnheo lfeathste r“ewprheiscehn-wtaotinv-ew. here” biplot
constitutes a fundamental element of the genotype and environment interaction (GGE)
methodology. This approach facilitates the visualization of the interaction patterns between
genotypes and environments, thereby enabling the identification of crossover GEI, mega-
environment differentiation, and specific adaptation [69]. Identifying mega-environments
for the yield enables us to pinpoint exceptional accessions that excel in specific environ-
  ments. Particularly, the accessions located at the corners of the polygons in the biplot
represent the best-performing accessions in that environment. These accessions exhibit
superior performance and adaptability within the environment. Their prominence suggests
that they are highly responsive and exceptional regarding their potential yield within their
respective environments [63]. In determining which accession performed best at which
location, accessions TVSu-2223, TVSu-2236, TVSu-2240, and TVSu-2249 performed very
well in Mokwa. In contrast, accession TVSu-2214 performed best in Ikenne (Figure 5b).
Accession TVSu-2193 performed best in Ibadan, while accession TVSu-2188 performed
Agronomy 2023, 13, 2558 11 of 15
similarly in Ibadan and Ikenne. Accessions at the corners of the polygons in a “which-
won-where” polygon are the outstanding accessions in that environment [69]. Similar
Agronomy 2023, 13, x FOR PEER REVIEW  11  of  15 
  results were reported in various studies, including those by Esan, Oke, Ogunbode, and
Obisesan [63] and Nehe et al. [70].
 
FigFuigreur5e. 5(.a ()a)D Disisccrriimmiinnaattiivveenneessss aanndd rerperperseesnetnattiavteivneesnse osfs tohfe tahceceasscicoensss iwonitsh wthiet hentvhieroennmveirnotsn. m(be) nts.
“Which-won-where” analysis to identify the most suitable accessions for each test environment. Red 
(b) “Which-won-where” analysis to identify the most suitable accessions for each test environment.
color represents environments while black color represents accessions. 
Red color represents environments while black color represents accessions.
The utilization  of  the polygonal  representation  of  the  “which-won-where”  biplot 
Building on these findings, we now discuss suggestions for further enhancing the
constitutes a fundamental element of  the genotype and environment  interaction (GGE) 
utimlizetahtioodnoloofgbyi.p  Tlohtisa naaplypsroisaicnh pflaacniltitbarteese dtihneg  vainsduaalgizraitciuolnt uoraf ltrhees eianrtcehra, cmtioainn  lpyafttoecrunssi ng
onbBeGtwNeeann gderneolatytepdesc raonpds  .environments,  thereby enabling  the  identification of crossover 
GETI,o mstergean-getnhveirnotnhmeegnetn deirffaelirzeanbtiialtitioyno, faonudr sfipnecdiifincg asd, faupttuatrieonre s[6e9a]r.c  Ihdeenntdifeyainvgo rms sehgao-uld
enecnovmirpoansms eanbtsr ofoard ethres pyeiecltdr uemnaobfleasg uros -teoc opliongpiocianlt zeoxnceeps.tiWonealc aanccleesasironnms  tohraet aebxoceult  ihno  w
gesnpoetcyipfiec eannvdireonnvmieronntsm. Peanrtticwuolarrklyt,o tgheet hacecrebssyioinnsc lluocdaitnegd raet gthioen csorwnietrhs odfi ftfheer epnotlyegnovnirso  n-
meinn tathl ec obnidpiltoito nrse.pTrehsiesnwt  itlhleh eblepstu-psefirnfodrmgeinngo tyacpceessstihoants cainn  athdaatp  tentoviarobnrmoaednet.r  Trahnegsee  of
deavcecleospsmionens teaxlhciobnitd siutipoenrsio. r performance and adaptability within the environment. Their 
proFmurinthenercme osureg,geexsptsa nthdaitn gthtehye  asrceo pheigohflyo urerssptuondsyivteo  aincdo repxocerpatteioontahl erresgiagrndiifincga nthteairg ro-
nopmoitcentrtiaailt syibeeldy ownitdhignr athineiry riesldpeicstirveec oenmvmiroenmdednt.sD [6is3e].a Isne dretseirsmtaincineg,  dwrhoiucghh atcctoeslesiroan ce,
anpdenrfuotrrmiteiodn  baelsqt uaat liwtyhiacrhe  ljoucsattisoonm,  aectcreasistisonths aTtVshSuo-u2l2d23b, eTcVoSnus-i2d2e3r6e,d  T.VBSyu-e2v2a4l0u, aatnindg  a
broTaVdSeur-2r2a4n9g peeorfotrmaietsd, vberreye dweerlsl icna nMdoekvweal.o Ipn ecnohntarnacste,d acgcenssoitoynp TeVs Sthua-2t2e1x4h piberitfoerlmeveadt ed
yiebledstp iont Ieknetnianlea (nFdigudrees i5rba)b. Alecchesasriaocnt eTrVisStuic-s21fo93r penerdf-ourmseerds  abnesdt icno Inbsaudmane,r ws.hile accession 
TVTSou-a2c1c8o8u npterfoforremnevdir osinmmileanrltya liflnu  Icbtaudaatnio nansda nIdkeennnseu. rAe cthcescsoionnssi satet nthcye ocfoornuerrgs eonfo  tthyep es,
it ipsonleycgeosnssa ryint oa co“wndhuiccht-mwuonlt-iw-ehnevrier”o npmoelyngtotrni aalsres ptahnen  ionugtssteavnedrianlgs eaacscoensssi.oBnys eivna  ltuhaatti ng
geennovtyirpoensmuenndt e[r69v]a. rSyiminiglaern  rveisruolntsm wenertea lrceopnodrtietdio nins vaanrdioouvse  srtumduieltsi,p  ilnecyluedairnsg,  wtheocsaen bgya  in
a mEsoarne, cOokme,p Oreghuennbsoidvee, aunndd Oerbsitsaensadnin [g63o] fanthde Nireehfefi ecta acly. [a7n0]d.  consistency. This approach
will enBhuanildceintgh eond ethpeesned fianbdiliintgyso, fwoeu nrogwe ndoistycupses ssuuggggeessttiioonnss  faonr dfufrutrhtehre ernshtraenncigntgh ethnet he
reluiatibliizliattyioonf oofu brirpelsoet aarncahlyasnisd inb rpeleadnitn bgreineditiinagti vanesd. agricultural research, mainly focusing 
on FBiGnaNll ayn, dw reeclaatnedf ucrlloypsu.s  e the power of biplot analysis to speed up the development
and useToo fsbtreetntegrthgeenn tohtey pgeesnebryaliinzcaobrilpitoyr aotfi nougrt hfiensdeinidgse,a fsuitnutroe fruestuearercrhe seenadrecahvaonrsd sbhroeuelddi ng
proenjeccotms.pTashsi sa wbriolladcoern tsrpiebcutrtuemto otfh aegsrou-setcaoilnoagbicleal pzroongerse. sWs eo fcacnr oleparpnr omdourcet iaobnouatn hdowfo od
secguerniotyty, pneo t aonndly  einnvNiriognemrieanbt utwaolrsko  intogoeththeerr rebgyio  nins.clTudhienbg enreefigitosnosf  bwipitlho t dainffaelryesnist  in
environmental conditions. This will help us find genotypes that can adapt to a broader 
plant breeding and agricultural research will be maximized by widening the scope of
range of developmental conditions. 
research, including more agronomic traits, and adding multi-environment trials. This will
Furthermore,  expanding  the  scope  of  our  study  to  incorporate  other  significant 
lead to better genotypes that can deal with the many problems that arise in agricultural
agronomic  traits  beyond  grain  yield  is  recommended.  Disease  resistance,  drought 
systotelemrasn. ce,  and  nutritional  quality  are  just  some  traits  that  should  be  considered.  By 
evaluating a broader range of traits, breeders can develop enhanced genotypes that exhibit 
elevated yield potential and desirable characteristics for end-users and consumers. 
 
Agronomy 2023, 13, 2558 12 of 15
4. Conclusions
This research utilized biplot analysis to evaluate the impact of genotype–environment
interaction (GEI) and recognize high-performing genotypes of Bambara groundnut (BGN)
in three regions of Nigeria. Ultimately, the findings of this study suggest the potential
for utilizing biplot analysis as a valuable tool in crop breeding programs. The findings
underscore the significance of utilizing biplot analysis in plant breeding and agricultural
research. The recommended parental lines for breeding programs to improve BGN grain
yield based on stability and adaptability in the evaluated agro-ecological zones are TVSu-
2240 and TVSu-2283. These accessions have been selected based on their performance and
characteristics. The results obtained from this investigation offer valuable perspectives
for the enhancement of cultivars and the maximization of agricultural output across the
varied agro-ecological zones in Nigeria. Subsequent research endeavors may utilize these
findings to improve the productivity and resilience of BGN through precise breeding and
cultivation techniques.
To establish delineated mega-environments, gathering data from multiple locations
over cropping seasons is imperative. It is imperative for studies to incorporate a substan-
tial amount of historical data spanning multiple years. To gain a more comprehensive
understanding of the factors influencing the GEI, obtaining soil and meteorological data is
imperative.
Author Contributions: Conceptualization, R.A.L., O.S.O., O.O., E.O.I. and M.A.; data curation,
R.A.L., O.S.O., O.O., E.O.I. and M.A.; formal analysis, R.A.L. and O.S.O.; funding acquisition,
O.O. and M.A.; investigation, R.A.L., O.S.O. and O.O.; methodology, R.A.L. and O.S.O.; project
administration, O.O. and M.A.; resources, O.O. and M.A.; supervision, O.O., E.O.I. and M.A.;
validation, R.A.L. and O.S.O.; visualization, R.A.L. and O.S.O.; writing—original draft, R.A.L.;
writing—review and editing, O.S.O., O.O., E.O.I. and M.A. All authors have read and agreed to the
published version of the manuscript.
Funding: The work of GRC was funded by the Global Crop Diversity Trust and CGIAR.
Data Availability Statement: This manuscript has no associated data.
Acknowledgments: The assistance rendered by the staff of the Genetic Resources Center, IITA, is
greatly appreciated.
Conflicts of Interest: The authors declare no conflict of interest.
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