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PUBLISHED DATE: - 02-11-2024
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STABILITY ANALYSIS OF YIELD AND
NUTRITIONAL TRAITS IN COWPEA (VIGNA
UNGUICULATA) ACROSS ENVIRONMENTS
Anippe Salah
Department of Crop Sciences, Faculty of Agriculture, Ain Shams University,
Cairo, Egypt
INTRODUCTION
Cowpea (Vigna unguiculata), a drought-tolerant
legume, plays a critical role in the food security and
nutrition of millions of people in tropical and
subtropical regions. It is a valuable source of
protein, vitamins, and minerals, and is grown as a
major crop for both human consumption and
livestock feed. However, cowpea productivity and
nutritional quality are highly influenced by
environmental conditions, including variations in
temperature,
rainfall,
soil
fertility,
and
management practices. These environmental
factors can cause significant genotype ×
environment (G × E) interactions, which may result
in inconsistent performance across different
growing seasons and regions.
For sustainable cowpea production, it is essential
to identify genotypes that exhibit stable
performance in terms of both yield and nutritional
traits across varying environmental conditions.
Stable varieties can ensure consistent harvests and
nutritional quality, which is especially important in
regions prone to climatic variability. While several
studies have focused on enhancing the yield of
cowpea through genetic improvement, there is less
emphasis on the stability of its nutritional traits,
such as protein content and micronutrient levels,
which are critical for human health.
This study aims to evaluate the stability of key yield
and nutritional traits of cowpea across different
RESEARCH ARTICLE
Open Access
Abstract
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environments. By assessing the interaction
between genotypes and diverse agro-climatic
conditions, the research seeks to identify high-
yielding and nutritionally stable cowpea varieties.
The findings of this study are expected to
contribute to the development of improved cowpea
varieties that are resilient to environmental
stresses and offer consistent nutritional benefits,
thereby supporting the long-term sustainability of
cowpea as a staple crop in vulnerable regions.
METHOD
Experimental Design and Location
The study was conducted over two consecutive
growing seasons across multiple environments
with distinct agro-climatic conditions to evaluate
the stability of yield and nutritional traits in
cowpea (Vigna unguiculata). Field trials were set
up in different regions known for their varying
climatic conditions, including differences in
temperature, rainfall, and soil fertility, which can
significantly affect the performance of cowpea. The
trials were conducted in three locations: a semi-
arid region, a tropical zone, and a subtropical
region, chosen to represent the diverse
environments where cowpea is cultivated. The
environmental conditions in these locations were
selected based on their differences in temperature,
precipitation, and soil type, which are key factors
influencing the growth and productivity of cowpea.
The experimental design followed a Randomized
Complete Block Design (RCBD), with three
replications per genotype per environment. Each
trial site was prepared with standardized
agronomic practices to reduce variation due to
management differences. A total of 12 genetically
diverse cowpea genotypes, representing a wide
range of yield potential and nutritional qualities,
were selected for this study. These genotypes were
chosen based on their diverse genetic
backgrounds, including their variations in
agronomic traits like yield and nutritional
composition.
Field Management and Data Collection
The field management practices across all sites
were kept consistent to avoid introducing
extraneous variability in the data. All genotypes
were sown at a uniform seed rate and row spacing,
ensuring that plant density remained constant
across locations. Irrigation, fertilization, pest and
disease management, and other cultural practices
were standardized to ensure that all genotypes had
the same growing conditions. The plots were
managed according to local agricultural best
practices, adapted for each site, to ensure that the
cowpea plants were grown under optimal
conditions for each environment.
The key data points collected in this study included
both agronomic and nutritional traits. Agronomic
traits such as pod yield (kg/ha) and seed weight (g)
were measured at maturity. Yield was recorded
from the central rows of each plot to avoid border
effects, and seed weight was determined by
weighing 100 seeds per genotype. Nutritional traits
focused on protein content, and mineral content,
including iron, zinc, and calcium, were assessed at
harvest. Protein content was measured using the
Kjeldahl method, and mineral content was
analyzed using atomic absorption spectroscopy
(AAS), a standard technique for determining the
concentration of micronutrients in plant samples.
Genotype × Environment Interaction Analysis
To assess the stability of the cowpea genotypes
across environments, a thorough statistical
analysis was conducted. The genotype ×
environment (G × E) interactions were evaluated to
determine how different genotypes responded to
the various environmental conditions. Analysis of
variance (ANOVA) was performed for each trait
(yield, seed weight, protein, and micronutrients) to
assess the significance of genotype, environment,
and G × E interactions.
The combined analysis of variance was performed
across all three locations and two growing seasons
to determine the overall effect of genotype,
environment, and G × E interaction. The model for
the combined analysis accounted for the variation
among genotypes, environments, and their
interactions, providing insight into how each factor
influences the expression of the traits under study.
Significant differences between genotypes,
environments, and G × E interactions were tested
at a 5% significance level (p < 0.05).
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Stability Analysis using Eberhart and Russell Model
To evaluate the stability of genotypes, the Eberhart
and Russell (1966) stability model was used. This
model is a widely recognized method for assessing
the stability of genotypes in multi-environment
trials, providing a quantitative measure of
genotype
performance
across
varying
environmental
conditions.
The
stability
parameters used in the model include:
Mean Performance (
𝑌𝑖
): The average performance
of each genotype across environments.
Regression Coefficient (
𝑏𝑖
): This measures the
sensitivity of the genotype to changes in the
environment. A genotype with a regression
coefficient (
𝑏𝑖
) close to 1 indicates that it responds
to environmental changes similarly to the average
genotype. A genotype with a regression coefficient
significantly different from 1 indicates a genotype
that is more responsive (bi > 1) or less responsive
(bi < 1) to environmental changes.
Deviation from Regression (
𝑆
²
𝑑
): This measures
the stability of the genotype in response to
environmental changes. A low
𝑆
²
𝑑
value indicates
that the genotype performs consistently across all
environments, whereas a high value suggests more
variability in performance across different
environmental conditions.
Genotypes with a bi value close to 1.0 and a low S²d
indicate stable performance across environments.
A genotype with a high yield, a bi value close to 1.0,
and a low S²d would be considered stable and ideal
for broader cultivation. Conversely, genotypes with
a high S²d or a bi significantly different from 1 are
considered unstable, with performance varying
widely across environments.
Biplot Analysis
To provide a visual representation of the genotype
× environment interactions and further assess the
stability of genotypes, biplot analysis was
performed. The biplot allows the graphical display
of both genotypes and environments in a two-
dimensional space, making it easier to interpret the
interactions. In a biplot, genotypes are represented
by vectors, and the environmental factors are
represented as points. The angle between vectors
and the distance from the origin provide insights
into the relationship between genotypes and
environments.
A genotype that performs consistently across
environments is represented by a vector that
points in the same direction in the biplot,
regardless of the environment. Genotypes with
more variable performance across environments
show vectors that change direction significantly.
Environments that significantly influence the
performance of genotypes are positioned closer to
the respective genotypes in the biplot, while those
with less influence are placed farther away.
Statistical Analysis and Software
Statistical analysis was carried out using SAS 9.4
and R software. For the ANOVA, the data were
analyzed using the PROC GLM procedure in SAS,
which provided estimates for the variance
components of genotype, environment, and G × E
interactions. The Eberhart and Russell stability
model was implemented using the R package
"agricolae," which allows for the calculation of
stability parameters. Biplot analysis was
performed using the "FactoMineR" package in R,
which provided a graphical representation of the G
× E interactions.
Data Interpretation and Selection of Stable
Genotypes
The results from the ANOVA, stability analysis, and
biplot were used to identify the most stable
genotypes in terms of both yield and nutritional
traits. Genotypes that exhibited consistent high
yield, stable protein content, and stable
micronutrient levels across all environments were
considered ideal candidates for further breeding
and selection. These stable genotypes were
prioritized for their potential to improve both the
productivity and nutritional quality of cowpea in
various agro-climatic conditions.
To ensure that the identified stable genotypes
would be suitable for cultivation in diverse regions,
the final selection was based on the genotype’s
adaptability
to
both
environmental
and
management conditions. Genotypes that exhibited
good overall performance, stable nutritional
content, and adaptability across different
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environments were selected for further evaluation
and potential release to farmers.
RESULTS
The analysis of variance (ANOVA) showed
significant differences (p < 0.05) between
genotypes, environments, and genotype ×
environment (G × E) interactions for both yield and
nutritional traits. For pod yield, certain genotypes
consistently outperformed others, achieving
higher yields across all environments. However,
substantial G × E interactions were observed,
indicating that the performance of some genotypes
varied significantly with environmental conditions.
The analysis revealed that genotypes G6, G8, and
G12 demonstrated stable yield performance across
environments, as indicated by their regression
coefficients (bi) close to 1 and low deviation from
regression (S²d).
In terms of nutritional traits, protein content
exhibited
significant
variability
across
environments, with some genotypes showing a
stable protein profile regardless of environmental
changes. Genotypes G3, G7, and G11 exhibited high
and stable protein content, while others showed
significant fluctuations in protein levels under
different environmental conditions. Micronutrient
levels (iron, zinc, and calcium) also varied
significantly across environments, with G6 and G9
maintaining relatively stable concentrations of iron
and zinc. Calcium content was more sensitive to
environmental factors, with notable fluctuations in
most genotypes, though G4 and G10 showed better
stability.
Biplot analysis further revealed that the genotypes
with high stability in yield and protein content (e.g.,
G6 and G8) were associated with environments
that exhibited moderate climatic conditions, while
genotypes with high variability were generally
associated with more extreme environmental
conditions. The coefficient of variation (CV)
analysis confirmed these findings, with lower CV
values indicating higher stability in traits such as
pod yield, protein content, and mineral levels in
certain genotypes.
DISCUSSION
The results highlight the significant genotype ×
environment interactions for both yield and
nutritional traits in cowpea, which is consistent
with previous studies that have shown that cowpea
is highly responsive to environmental factors. This
study suggests that environmental factors such as
temperature, rainfall, and soil fertility strongly
influence both agronomic and nutritional
performance. The identification of stable
genotypes such as G6, G8, and G12 is crucial for
developing cowpea varieties that can maintain
consistent productivity and nutritional quality
across a wide range of agro-climatic conditions.
The stability of protein content in some genotypes
(e.g., G3, G7, and G11) across environments
indicates that these genotypes could be valuable
for improving the nutritional quality of cowpea in
regions where environmental conditions fluctuate.
The
consistent
levels
of
micronutrients,
particularly iron and zinc, in genotypes such as G6
and G9, suggest that these varieties may be
beneficial for improving the micronutrient density
of cowpea in diverse regions. However, the
variability in calcium content suggests that further
breeding efforts may be needed to develop
genotypes with stable and high calcium levels.
The G × E interaction observed in this study
underscores the importance of considering both
environmental and genetic factors in cowpea
breeding programs. The presence of such
interactions means that selecting cowpea varieties
for specific regions and environmental conditions
is essential to ensure stable performance.
Furthermore, understanding the stability of both
agronomic and nutritional traits can help develop
more resilient and nutritious cowpea varieties,
especially in regions affected by climate change.
CONCLUSION
This study successfully identified cowpea
genotypes with stable performance for both yield
and
nutritional
traits
across
different
environments. Genotypes G6, G8, and G12 showed
consistent and high yields, while G3, G7, and G11
demonstrated stable protein content. These
findings suggest that breeding programs should
focus on these stable genotypes to improve both
the productivity and nutritional quality of cowpea.
The results also highlight the need for
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environment-specific breeding strategies to
optimize the performance of cowpea in varying
agro-climatic conditions. By selecting genotypes
with stable performance across environments, it is
possible to enhance the resilience of cowpea
production and improve its role in addressing food
and nutritional security, particularly in regions
vulnerable to climate variability.
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