J. Agric. Environ. Sci. Vol. 7 No. 1 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 43

Genotype by Environment Interaction and Yield Stability of Drought Tolerant Mung

Bean [Vigna radiata (L.) Wilczek] Genotypes in Ethiopia

Tekle Yoseph*

1

, Firew Mekbib

2

,

Southern Agricultural Research Institute, Jinka Agricultural Research Centre, Jinka, Ethiopia

*Corresponding author: tekleyoseph486@gmail.com

Received: May 4, 2022 Accepted: June 14, 2022

Abstract: A multi-environment evaluation of mung bean genotypes was conducted in six environments across

Ethiopia to select promising genotypes. This study was conducted to estimate the magnitude of genotypes by

environment interaction (GEI) and seed yield stability of the selected drought-tolerant mung bean genotypes

across different environments. A total of fifteen mung bean genotypes were used. Out of these, two released

varieties were used as standard checks. The field experiments were conducted during the 2019 main cropping

season at six locations namely Humbo, Gofa, Melkassa, Konso, Jinka, and Kako using a randomized complete

block design with three replications. Data were subjected to analysis of variance, Additive Main Effects and

Multiplicative Interaction (AMMI), and GGE bi-plot analysis. A combined analysis of variance revealed

two IPCAs for cross-validation of the seed yield variation. The magnitude of the GEI sum squares was 4.4 times

that of the genotypes sum squares for seed yield, indicating the presence of substantial differences in genotypic

responses across the environments. The results for the AMMI, Yield stability index (YSI), AMMI Stability Value

(ASV), and GGE biplot, analyses depicted that the genotypes G6 (NLLP-MGC-24), G13 (Acc006), and G3

(NLLP-MGC-15) were identified as stable and high yielders across the environments and should be considered

for variety release. AMMI1 biplot showed Kako was the potential and favorable environment for mung bean

production, while Humbo was an unfavorable for mung bean production.

Keywords: AMMI Stability Value, GGE biplot, Kako, Gofa, Yield Stability Index

This work is licensed under a Creative Commons Attribution 4.0 International License

1. Introduction

performance of genotypes under a variety of

locations and environmental conditions (Zu, 2010).

Genotypes tested in different locations and over

years have significant fluctuations in yield due to

variations in soil fertility, unpredicted rainfall, and

the presence of other biotic and abiotic stresses

(Kang, 1993). Differential response of genotypes to

different environmental conditions is termed

responses; or complex when the classification of

the genotypes is different from one environment to

another, which is quite common and has greater

importance in plant breeding (Mohammadi and

Amri, 2013). The magnitude of an environment,

genetics, and their interaction effects are a serious

problem for the yield and stability of genotype

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across environments because it reduces the

efficiency of the genetic gain. Comestock and Moll

selecting a widely adapted and stable genotype

across a wide range of environments (Ceccarelli,

1996).

Genotypes x environment interactions exist, when

the responses of the genotypes to different levels of

environmental factors fail to respond similarly

(Allard and Bradshaw, 1964). Major constraints in

breeding pulses such as mung beans are the high

genotype x environment (GxE) interactions and the

low genetic diversity in the primary gene pool

(2000) and Mehla et al. (2000) suggested testing

varieties over a large number of environments is

necessary to observe GEI effects

Grain yield performance is not the only parameter

for selection as a genotype with the highest grain

yield and would not be necessarily stable and

adaptable across locations and years. The plant

breeders need to identify adaptable and stable high-

yielding genotypes with other desirable traits under

varying environmental conditions as a desirable

et al., 2009), faba bean (Gemechu et al., 2002;

Gemechu and Musa, 2002; Musa and Gemechu,

2004; Gemechu et al., 2006; Mulusew et al., 2008;

Tamene et al., 2015; Asnakech et al., 2017; Tadele

et al., 2017; Tekalign et al., 2019), field pea

(Mulusew et al., 2009; Mulusew et al., 2014), and

mung bean (Asrat et al., 2012). However,

information on the effect of genotype,

environment, and GEI on mung bean yield with

drought-tolerant traits is limited in Ethiopia. There

have been only limited studies on the use of the

GGE biplot study for mung bean genotypes

drought-tolerant mung bean genotypes for wider

and /or specific recommendations for cultivation

under farmers’ conditions in Ethiopia.

temperatures of the study area over several years

(2009 to 2019) are presented in Table 1. The

weather data were collected from the nearby

stations, respective woreda, and zonal Bureau of

Agriculture and research centers (Personal

Communication).

2.2. Experimental materials

A total of fifteen selected genotypes were used. Out

of these, two released varieties were used as

standard checks and thirteen genotypes were

The experiments were laid out using a randomized

complete block design with three replications.

During planting, blended NPSB fertilizer at the rate

of 100 kg ha

management practice namely, weeding was carried

out uniformly for all experimental units.

Experiments were planted from early June to early

July of the 2019 cropping season at each location.

The plot size was 4 m long, 0.3 m between rows,

middle three rows, which have a 3.6 m

area.

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analyzed using this model because AMMI

partitions the sum of squares into the interaction

= error

2.5.3. GGE biplot analysis

The GGE biplot has many visual interpretations

that additive main effects and multiplicative

interaction do not have; particularly it allows

visualization of any crossover G x E interaction.

GGE biplot is close to the best additive main

biplot is built on the first two major components of

a principal component analysis (PCA) using the

Site Regression (SREG) model. When the first

component is highly correlated with the main effect

of the genotype, the proportion of the yield is

considered to be due only to the characteristics of

the genotype. The second component represents the

part of the yield due to the G×E (Yan, 2011). The

model for a GGE biplot (Yan, 2002) is based on

singular value decomposition of the first two

Where

and second principal components,

respectively

the first and second principal components,

respectively

j for the first and second principal

components, respectively

th

computed for grain yield and the GEI analyses of

variance. Accordingly, regression coefficient (bi)

and deviation from linear regression (S

interaction principal component axes (IPCA) scores

of genotype and environment and AMMI Stability

Value from the AMMI model were computed as

per the established standard procedures for each

model. The pooled deviations mean square was

genotypes mean square and regression mean square

were tested against the pooled error mean square

using the F-test.

2.5.5. AMMI stability value (ASV)

Since the AMMI model does not make provision

for a quantitative stability measure that guides us to

rank genotypes in terms of their yield stability. The

AMMI stability values (ASV) were calculated to

study the stability of genotypes across the

environments following the formula of Purchase

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 47

to their yield stability. Therefore, AMMI stability

value (ASV) was computed to quantify and rank

al., 2011).

Where

The ASV is the distance from zero in a two-

between IPCA1 and IPCA2 scores to compensate

for the relative contribution of IPCA1 and IPCA2

to the total GEI sum of squares.

2.5.6. Yield stability index (YSI)

YSI incorporates both mean yield and stability in a

single criterion. Low values of both parameters

show desirable genotypes with high mean yield and

stability (Bose et al., 2014; Tumuhimbise et al.,

2014). The yield stability index was calculated

using the following formula below [4].

3. Results and Discussion

3.1. Combined analysis of variance across

The combined analysis of variance showed

significant differences in the environment,

genotype, and genotype-by-environment

interactions (Table 3). The result revealed that there

were significant variations among genotype,

As presented in Table 3, days to maturity, five

plant pods, seeds per pod, and a hundred seed

weights were significantly (P ≤0.01) influenced due

to genotype, environments, and genotype x

affected due to genotype. The environment had

exerted a significant (P≤0.01) effect on days to

flowering. The results also depicted that GEI for

days to flowering, days to maturity, plant height,

five plants pod number, number of pods per plant,

hundred seed weight and seed yield per hectare

of mung bean genotypes were influenced by

environmental factors and it also indicated the

presence of genetic variability among the tested

significant, which coincides with the reports of

several researchers (Dhillion et al., 2009; Tyagi

and Khan, 2010) on soybean, Kan et al. (2010) on

chickpea, Nigussie et al. (2015) on common bean,

Yeyis et al. (2014) on field pea, Akande (2009),

and Tariku et al. (2018) on cowpea genotypes.

Moreover, this study revealed that the magnitudes

of the GEI sum square were about 4.4 times that of

the genotypes sum squares for seed yield,

indicating that there were considerable differences

genotypes, indicating that there was a substantial

difference in genotypic response across

environments. The larger sum of squares of GEI

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compared to the genotype indicated larger

differences in genotypic response across

and phenotypic values (Crossa, 1990). The GEI in

the current analysis was a cross-over type whereby

a change in the ranking of genotypes for a target

environment because of the difficulty to interpret

seed yield based on genotype and environment

means alone. This finding is in line with the

previous report of Asrat et al. (2009) on soybean.

3.2. Comparison of mean seed yield across the

The average environmental seed yield across

-1

and G8, respectively (Table 4). This indicates that

the tested genotypes had inconsistent performance

across the tested environments. In this study, most

also confirmed the high diversity of weather

conditions during growing seasons and also the

locations had different soil types, temperatures, and

rainfall as well as altitude, directly affecting the

performances of the genotypes. Hence, the

selection and development of mung bean varieties

The results of the present study are in agreement

with the work of Tariku et al. (2018) on the cowpea

genotypes were expected to have a high mean seed

yield with stable performance across all test

locations. However, such genotypes are very rare to

find in practice. Therefore, high-yielding and

relatively stable genotypes can be considered as a

reference for genotype evaluation (Yan and Tinker,

2006).

In this study, the mean values of seed yield and

yield-related traits are presented in table 5. The

highest mean seed yield (1580 kg ha

) was

66.72 to 98.98 days. Genotypes (G6, G13, G14,

and G15), respectively took 96.33, 98.98, 97.72,

and 98.39 days to attain their physiological

also recorded for the genotypes (G3, G2, and G8)

while the minimum number of pods per five plants

of 38.5 and 44 were recorded for G11 and G15,

respectively. Pods per plant varied from 14.03 for

G13 to 25.14 for G5. Seeds per pod ranged from

9.29 for G11 to 12.35 for G1. Hundred seed weight

genotype by environment interaction, DTF= days to flowering, DTM days to maturity, PH= plant height, FPP= five plants

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yield, while the environment significantly

reported by Kocaturk et al. (2019) on soybean

effects were observed due to environment,

genotype, and G×E interaction for the seed yield

and yield components. In this study, the

Regarding the multiplicative component, genotype

et al. (2019) on soybean, who reported that the GE

interaction explained (20.84%) of the total

variation. The highest share of the total sum

squares was contributed by environment and

genotype total sums of squares as compared to the

GEI, with large differences among environmental

means causing most of the variation in seed yield

of mung bean. This finding also coincides with the

previous works on cowpea (Akande, 2009;

The AMMI model extracted two significant

Interaction Principal Component Axis (IPCAs)

multiplicative component of the AMMI further

revealed that the mean squares were highly

principal component axis (IPCA1) and significant

component axis (IPCA2). Hence, these two IPCAs

(IPCA1 and IPCA2) captured 47.4% and 7.4% of

in agreement with the previous reports by Tamene

et al. (2013) for field pea, Hagos and Fetien (2013)

for bread wheat, and Ashraf et al. (2016) on flax.

In this study, the two IPCA's accounted for greater

than 50% of the interaction of sum square and were

for cross-validation of seed yield variation

explained by GEI that can easily be visualized with

the aid of the biplot whereas, the residual was

considered as noise. The results were in agreement

with the several authors who took the first two

IPCAs for GGE biplot analysis for different crops

GEI and indicated that AMMI with the first two

multiplicative terms was the best predictive model.

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) of 15 mung bean genotypes

E = Environments, G = Genotypes, SS = Sum of Squares, MS = Mean Squares, DF = Degree of Freedom, IPCA1 =

Interaction Principal Component Analysis Axis 1 scores, IPCA2 = Interaction Principal Components Analysis Axis 2 scores.

3.3.2. GGE biplot analysis

The GGE biplot displays the genotypic main effect

(G) and genotype by environment (G x E)

3.3.3. Mean performance and stability of

Desirable genotypes are those located close to the

ideal genotype. Genotypes G8, G6, and G15 can be

thus used as benchmarks for the evaluation of

mung bean genotypes since they are placed near the

ideal genotype and found near the first concentric

circle, and thus are desirable genotypes. This

finding is in line with the reports by Muez et al.

(2015), who found outstanding genotypes near to

were found far distant from the first concentric

circle (Figure 1).

The ideal genotype is the one presenting high

means and is identified based on the length of the

vector; thus, the longer the PC1 and PC2 without

projections and the closer to the concentric circle,

the better the genotype (Santos et al., 2017). Such

an ideal genotype is defined by having the greatest

vector length of the high-yielding genotypes and

with zero GE, as represented by the small circle

starting from the middle concentric circle pointed

with arrow concentric circles were drawn to help

Genotypes (G8, G6, and G15) are located on the

next consecutive concentric circles, and these

genotypes are considered the most desirable

genotypes. On the other hand, undesirable

genotypes were those very far distant from the first

concentric circle; namely, genotypes (G4, G10, and

G11) in Figure 1.

The ranking of fifteen mung bean genotypes based

on their mean yield and stability performance is

shown in Figure 2. The line passing through the bi-

interaction effect and reduced stability in either

direction away from the biplot origin and separates

genotypes with below-average means from those

with above-average means (Bhartiya et al., 2017).

For selection, the ideal genotypes are those with

both high mean yield and high stability. The