The objective of this activity is to
evaluate, select and recommend the high yielding, adaptable, stable and
disease resistant varieties. This activity has been done during the 2023 and
2024 main cropping seasons. A total of six groundnut varieties including
local were evaluated using RCBD with three replications. The data on days to
50% flowering, days to 90% maturity, plant height, number of primary branch
per plant, number of seed per pod and grain yield were collected and
subjected to analysis of variances using R- software. Combined analysis of
variance revealed that there was significant difference for all studied
traits except of primary branch per plant and number of seeds per pod across
the locations. The highest grain yield (1932.9 qt ha-1) was recorded by
Babile-1 followed by BaHa gudo (1799.8 qt ha-1) while the lowest yield
(1206.4 qt ha-1) was recorded by local check. Also the combined ANOVA showed
that environments, varieties and their interaction effects were
significantly different. The stability and high yielding ability of the
varieties has been graphically depicted by the AMMI bi-plot. Environment
Dabo Hana-2023 relatively showed high IPCA scores, contributed largely to
GEI. This environment was favorable for high yielding varieties based on
mean yield as they had more than the grand mean. The variation for seed
yield among the varieties for each variety was significant at different
environments. Varieties Babile-2 and BaHa gudo were specifically adapted to
high yielding environment. Local check variety was the most unstable
variety, while Babile-1 was more stable in comparison to other varieties. In
GGE bi-plot; IPCA1 and IPCA2 explained 69.99% and 19.69%, respectively, of
groundnut variety by environment interaction and made a total of 89.68% of
variation. Therefore, Babile-1 (1932.9 kg ha-1) and BaHa gudo (1799.8 kg
ha-1) were most stable recommended for the study area and similar
agro-ecologies and Dabo Hana was the ideal environment for groundnut
production.
Accepted:23/12/2024
Published: 31/12/2024
*Corresponding Author
Garoma Firdisa
E-mail: garomafirdisa21@
gmail.com
Keywords: AMMI,
G x E interaction, Groundnut, IPCA, grain yield
INTRODUCTION
Groundnut (Arachis hypogaea L.) plays an important
role as a food as well as a cash crop in Ethiopia (Dereje A. et al., 2012). In
Ethiopia, groundnut is grown in the lowlands and is the second important lowland
oilseed of warm climate next to sesame. It is playing an increasingly important
role as an alternative oil crop to an increasing number of small holder
farmers.Currently the crop is becoming
one of the high value crops that are growing in the lowlands areas of the
eastern Oromia. Groundnut, or peanut, is commonly called the poor man's nut.
Today it is an important oilseed and food crop (FAO, 2002). Groundnuts are
produced in the tropical and subtropical regions of the world, on sandy soils.
In Ethiopia, groundnut is cultivated predominantly by the traditional and
undeveloped farming community under rain-fed conditions. It occupies about
77,283.21 hectares of land with a corresponding gross annual production of
about 1,392,784.61 qt. (CSA, 2022). The yields of groundnut in Ethiopia
compared to other countries are very low i.e. below 18.02 qtha-1 as
compared to average yields on a global scale i.e. 15.2 qtha-1 but
with good management practices, yields can be increased to 30 qtha-1
(CSA, 2022). Groundnut production in Ethiopia is found to be constrained by
several biotic and abiotic factors i.e. critical moisture stress especially
during flowering and then after, lack of improved varieties and appropriate
production and post-harvest practices, and diseases affecting both above- and
underground parts of the plant (Alemeyehu et al., 2014).
Environmental
conditions play a significant role in the variation of agricultural traits in
groundnuts (Bonchev et al. 2018), and the performance of groundnuts is strongly
influenced by environmental factors (Gulluoglu et al. 2016). Therefore,
studying the stability of groundnuts genotypes to identify a suitable variety
is essential for maximizing yield potential for the regions (Kasno and
Trustinah 2015). Additionally, research on peanut stability can contribute to
the development of resilient and adaptable peanut varieties that can ultimately
lead to a more secure and sustainable peanut production system in the region.
Significant G×E interactions (GEI) reduce the association between genotype and
phenotype, making it hard to identify superior genotypes, thus affecting
breeding progress (Delacy et al. 1996).
Understanding the GEI
and stability analysis can help plant breeders’ select stable genotypes.
Several stability procedures have been developed to explain the GE interaction.
Multivariate methods include additive main effects and multiplicative
interaction models (AMMI) (Gauch and Zobel 1988). In addition, the GGE biplot
methodology as a superior approach for the graphical analysis of multi environmental
data (Yan and Kang 2003), that provides the possible identification of
high-yield and stable genotypes (Karimizadeh et al. 2013). Therefore, this
activity was conducted with objective of evaluating adaptability of improved
ground nut varieties and selects the best performing adapted variety for the
target areas.
MATERIALS
AND METHODS
Description
of the study Area
The field experiment was conducted during the 2023-2024 G.C main
cropping seasons for two years at three districts (Bedele, Dabo Hana and Gechi)
in Buno Bedele Zone, South Western Oromia where agro ecology assumed to be
conducive for Linseed production (Fgure 1).
Bedele
District
Bedele district is
located in Buno Bedele zone of Oromia Regional National State, Southwestern
Ethiopia. The district is located between 8°14'30''N to 8°37'53''N and
36°13'17''E to 36°35'05''E is about 483km road distance south-west of Finfine.
It is covers 74497.425 hectares of which 47,986, 9477, and 10,120 hectares are
cultivated, forest and grazing land, respectively. The area is covered with
variety of crops and species of natural vegetation. The dominant crops in the
area are maize, tef, sorghum, finger millet and haricot bean. The major land
use types are cultivated land/cropland, forestland and grazing land (Bedele
development agricultural office, 2019).
Dabo
Hana District
Dabo Hana is one of
the districts in Buno Bedele Zone, Oromia Regional State Southwest part of
Ethiopia. The district is bordered on the south by Bedele, on the west by Dega
and Mako, on the north by Chewaka and Leka dulecha, on south west by Chora, on
the east and north east by Jima Arjo. The administrative center of this
district is Dabo Hana. The district is located 521 km away from the capital
city of the country and 38 km away from Bedele Town. The district is located at
an average elevation of 1190-2323 masl and located at 8°30' 21" to 8°43'
29" N latitude and 36°5'27" to 36°26' 19”E longitude. It is generally
characterized by warm climate with mean annual maximum temperature of 28°C and
minimum temperature of 11°C. The annual rainfall ranges from 900-2200mm
Gechi
District
Gechi is one of the
districts in Buno Bedele Zone, Oromia Regional State Southwest part of
Ethiopia. The district is bordered on the south by Didessa, on the west by
Didessa River, on the north by Bedele, and on the east by Jimma Zone. The
administrative center of this district is Gechi. The district is located 465 km
away from the capital city of the country and 18 km away from Bedele Town. The
district is located at an average elevation 1277-2467m.a.s.l and located at
8°16'60’’N latitude and 36°34'00’’E longitude. The annual rainfall ranges from
1500-2100mm. The economy of the area is based on coffee production system in
which the dominant crops are maize, tef, sorghum and wheat and also
horticultural crops.
Figure
1:
Map of the study areas
Experimental
Materials
A total of six
groundnut varieties (Babile-1, Babile-2, Baha gudo, BaHa jidu and Werer-961)
including one local check were used as planting materials.
Table 1: Description of
Groundnut varieties used in the experiment
Varieties
Altitude ranges
(m.a.s.l)
Year of Release
Source
Babile-1
650-1400
2016
HU
Babile-2
650-1400
2016
HU
BaHa-jidu
650-1400
2012
HU
Werer-961
650-1400
2004
WARC/EIAR
BaHa gudo
650-1400
2012
HU
Local
650-1400
-
Farmer
Note: HU=Haramaya University, WARC=
Werer Agricultural Research Center, EIAR=Ethiopia Institute of Agricultural
Research
Experimental
Design and Field Work
The treatments were
laid with a randomized completed block design (RCBD) with three replications
was used in the study. The experimental plots were 1.5 m × 3 m (4.5m2)
in size, with rows separated by 0.3m. The distance between plots and blocks was
0.5 m and 1m, respectively. Each plot had five rows, with the middle three used
for the collection of the data and the two outermost rows were used as border
rows. The experimental sites were plowed three times with draught animals
called oxen before planting, and 100 kg ha−1 seed rate and 100
kg ha−1 of NPS fertilizer rate were used according to crop
recommendations.
Data
Collection
Days to
flowering -
were calculated from the date of planting when 75% of the crop stand produced
the first flower.
Days to
maturity -
The number of days from planting to physiological maturity of the plants was
used to compute the days to maturity.
Plant
height -
the average height of five randomly selected plants, measured from the base to
the tip of the plant.
The
number of crop stand - was recorded as the average number of plant’s population
taken from the four middle rows of the plots
The
number of seeds per plant - It was calculated as the mean number of seeds collected
from five randomly selected from the four middle rows of the plots.
Seed
yield (kg plot−1) - It was calculated as the entire seed yield
produced from the plants harvested and threshed and converted into seed yield
per hectare.
Data
Analysis
The analysis of
variance (ANOVA) for each location and combined analysis of variance over
locations were performed using the R program and the mean separation was done
using Least Significant Difference (LSD) at the 5% probability level.
RESULT
AND DISCUSSION
Pooled
Analysis of Variance
Combined Analysis of
Variance Analysis of variance was carried out to determine the effects of
varieties, location and their interaction on seed yield of groundnut varieties.
Accordingly, the Environment and Genotypes on their own displayed significant
level of variability in their yield responses at p < 0.001, while GEI effect
was significant at p < 0.05. This indicates that this indicates the big
influence of environment and varieties on yield performance of groundnut
varieties and the varieties do not show consistent performance across the studied
environments. The significant effect of environments indicated that the testing
environments were significantly different from each other for expressing their
yield potential. The mean yield potential of the varieties varying across
environments and among varieties indicating the varieties were expressing their
potentials even though they were affected by environments and genetic
variations.
Table
2:
Combined ANOVA for GY of six (6) Groundnut varieties across locations
SOV
Df
SS
MS
F value
Pr(>F)
Rep
2
93108
46554 ns
0.2888
0.75000
Years
1
22102
22102ns
0.1156
0.7346542
Locations
2
6576279
3288140***
20.3967
8.110e-08
Treatment
5
6499475
1299895***
8.0634
3.841e-06
Trt*years
5
7437121
1487424***
3.8888
0.0001972
Loc*years
2
66413788
33206894***
11.5757
1.625e-06
Trt*locations
22
6465531
293888*
1.8230
0.02893
Error
76
12251930
161210
Note:
SOV=Source of Variations, SS=Sum of Squares, MS=Mean Sum of Squares,
Df=Degree of freedom, ***=Highly significant (0.001), *=significant at (0.05)
& ns= non-significant
Mean
Seed Yield of Groundnut Varieties Evaluated at Six Environments
The average
environmental seed yield across varieties ranged from the lowest of 1393.2 kgha-1
at Bedele -2023 to the highest of 2035.5 kgha-1 at Dabo Hana-2023),
with a grand mean of 1651.7 kgha-1
(Table 3). The varieties average seed yield across environments ranged from the
lowest of 1206.4kgha-1
for local check to the highest of 1932.9 kgha-1 for Babile-1 (Table
3). This difference could be due to their genetic potential of the varieties,
and also environment explained large variation indicated the existence of
diverse mega environments.
Table
3:
Mean seed yield (kgha-1) of groundnut varieties evaluated at six
environments
Varieties
Mean of seed yield across environment
Mean
D/Hana-2022
D/Hana-2023
Bedele-2022
Bedele-2023
Gechi-2023
Babile-1
1961.4abc
2787.0a
1932.0a
1735.2a
1774.1a
1932.9a
Babile-2
2100.0ab
2081.5b
1396.9b
1511.1ab
1685.2a
1743.3ab
BaHa jidu
2576.9a
1927.8b
1546.3ab
1238.9bc
1673.2a
1772.7ab
Werer-961
1548.3bc
1703.7b
1400.0b
1688.9a
1011.1b
1454.9bc
BaHa gudo
2144.6ab
2562.9a
1302.5bc
1242.6bc
1773.1a
1799.8a
Local
1296.06c
1150.0c
857.6c
942.6c
1495.9ab
1206.4c
Mean
1937.9
2035.5
1405.9
1393.2
1568.8
1651.7
LSD 0.05
727.9
395.8
466.8
440.1
31.4
332.6
CV %
20.7
10.7
18.3
17.4
581.8
30.5
P-value
*
***
*
*
*
**
Note: GM= grand mean, LSD=least significant difference, CV= coefficient of
variation, *= significant, **= highly significant
Agronomic
performance
Differences among the
genotypes were significant for a number of characters (Table 3). The tested
groundnut varieties shows statistically significant variation that ranged from
37.00 (BaHa jidu) to 54.78 (local check) for days to flowering and 110.94
(Babile-1) to 142.1667 (local check) for days to maturity. From this result
Babile-1 was the earliest to maturity whereas local check was the late maturing
variety. On the other hand, in terms of plant height Babile-1, Babile-2, and
BaHa gudo were the shorter whereas BaHa jidu and Werer-961 were comparatively
the taller varieties.
Table 4:
Combined mean yield related traits of six (6) Groundnut varieties over two years at Dabo Hana, Gechi and Bedele
districts, Buno Bedele Zone
Varieties
DTF
(days)
DTM
(days)
PLH
(cm)
NPB/sta
NS/P
HSW(g)
Dis(ELS)
Babile-1
40.83b
110.94b
40.79b
28.59
1.94
74.17a
2mr
Babile-2
37.11c
117.28b
40.72b
29.44
2.22
76.67a
2mr
BaHa jidu
37.00c
118.17b
45.29ab
30.10
2.11
61.39b
2mr
Werer-961
37.06c
116.00b
47.28a
27.17
2.00
56.39b
3ms
BaHa gudo
38.22c
117.39b
39.43b
27.74
2.28
75.44a
2mr
Local
54.78a
142.1667a
44.8ab
33.97
2.00
58.89b
2mr
GM
40.83
120.32
43.06
29.50
2.10
67.16
LSD (0.05)
1.99
8.26
6.13
6.34
0.48
6.64
CV %
7.37
10.38
21.51
5.92
34.62
14.95
P-value
***
***
*
NS
NS
**
Note: DTF= Days to Flowering, DTF= Days to Maturity, PLH= Plant height (cm), NB/PL= Number of primary stand, NS/P= Number of seed per Pod, ELS=early leaf
spot, GM= Grand mean, LSD=
Least significant different, CV=
Coefficient of variation, NS=
Non-significant, *=significant,
**= highly significant
Additive
Main Effects and Multiple Interaction (AMMI) model
AMMI analysis of
variance for Seed yield of eight groundnut varieties tested in six environments
showed that environments, varieties and their interaction effects were
significantly different (P< 0.001 & 0.05). (Table 5) indicating the
importance of applying AMMI analysis to investigate the main effects of
varieties and environment and the complex patterns of their interaction. The
environment modeled significant effect on the seed yield of groundnut, which
explained 17.81% of the total variation indicating the existence of a
considerable amount of deferential response among the varieties to changes in
growing environments and the differential discriminating ability of the test
environments. GEI contribute 17.06% of total variation while the varieties
contribute 17.38% of the total variation.
Table
5:
Partitioning of the Explained Sum of square (Ex.SS) and Mean of square (MS)
from AMMI analysis for seed yield of six groundnut varieties evaluated at five
environments
SOV
DF
SS
MS
%Variance Explained
% Cumulative
Environment
4
6663466.4
1665866.59**
17.81
Genotype
5
6499475.4
1299895.08**
17.38
Interaction.
20
6378344.0
318917.20*
17.05
PCA1
8
2467351.2
308418.90*
44.8
44.8
PCA2
6
2065963.5
344327.24*
37.5
82.3
Error
68
11198488.2
164683.65
Total
127
37395259.5
294450.86
Note: ** Significant difference at (P≤0.01),
SOV=source of variation, DF=degree of freedom, SS=sum of square, MS=mean sum
of square, PCA=principal component of axis
AMMI
Biplot Analysis for Seed Yield
The AMMI 1 biplot was
generated using the first IPCA and mean grain yield of the genotypes and
environments whereas AMMI 2 biplots was used genotypic and environmental scores
of the first two IPCAs. Furthermore, the relative magnitude and direction of
genotypes along the abscissa and ordinate axis in biplot is also important to
understand the response pattern of genotypes across environments and to
differentiate high yielding and adaptable genotypes (Samonte et al., 2005). In
the present study, the genotypes and environment which found at the right side
of the perpendicular line are those which gave mean grain yield above the grand
mean. Accordingly Babile-1, Babile-2, BaHa gudo and BaHa jidu were found in
this category. Furthermore, environment Dabao Hana-2022 and Dabo Hana-2023
which found at the right side of the perpendicular line, were also gave mean
grain yield above the grand mean. Thus, they are better environments for
commercial production of groundnut lines found specifically or widely adapted
to them. The rest of the genotypes and environments gave mean grain yield lower
than the grand mean (Figure 2).
Figure 2: AMMI
bi plot of IPCA 1 against grain yield
GGE Biplot Analysis
for Yield
GGE bi plot is
important to visualize the genotype by environment interaction. The GGE bi plot
graphic analyses of the thirty soybean genotypes tested across the six
locations are presented in the figures below.
Ranking
of Genotype
Stability can be
identified using concentric circles and also ideal genotypes are at the center
of the concentric circle. The ideal genotype is the one that with the highest
mean performance and absolutely stable (Yan and Kang, 2003). The genotypes that
are closer to the ideal genotypes are the best performing genotypes. Hence, the
GGE bi plots shows that BaHa gudo is an ideal variety, with other varieties
like Babile-1 and Babile-2 are desirable varieties as they are closer to the
ideal variety on the bi plot. The varieties local and Werer-961 are the most
undesirable varieties as they are too far to the ideal variety on the bi plot.
Figure
3: Ranking of the
varieties
Ranking
of Environment
The most
representative of the locations (ability to represent the mega environment) and
the most powerful to discriminate varieties (Naroui et al., 2013) reported that the ideal environment is the one
located at the center of the concentric circles, and it is possible to identify
desirable environments based on their closeness to the ideal environment (Mahdieh et al., 2016) reported that a
testing location has less power to discriminate genotypes when located far away
from the center of the concentric circle or to an ideal location. Therefore,
Among the test locations, location Dabo Hana-2023 which fell into the center of
concentric circles was an ideal test location in terms of being the most
representative of the overall locations and the most powerful to discriminate
the performance of the tested genotypes Next to the first concentric circle
location, locations Bedele-2022 is close to the ideal location while,
Gechi-2023 is detected as the weakest locations to discriminate varieties
(Figure 4).
Figure 4: Ranking of the varieties of six groundnut variteis acroos
the locations
Which-Won-Where
Pattern
According to Yan et
al., (2002), the polygon view of GGE bi plot indicates the best genotypes in
each environment and group of environments. In this situation, the polygon is
formed by connecting the genotypes that are farthest away from the bi plot
origin, such that all the other genotypes are contained in the polygon. In this
case, the polygon connects all the farthest genotypes and perpendicular lines
divide the polygon into sectors. Sectors help to visualize the
mega-environments. This means that winning genotypes for each sector are placed
at the vertex. Polygon view of the groundnut varieties tested at three locations
presented in (figure 5). The genotypes found at the vertex of the polygon
perform best in the environments within the sector (Yan and Tinker, 2006).
Accordingly, Babile-1, BaHa jidu, Werer-961 and local varieties were the vertex
groundnut varieties. From this figure, Babile-1 variety best performer at
Beadele-2022, Bedele-2023 and Dabo Hana 2023 in the first mega environment. In
the second mega environment is Daba Hana-2022 and Gechi-2023, with winner
variety BaHa jidu. From the figure, Werer-961 and local had no environment on
the vertex. This indicates that varieties in the vertex without environment
performed poorly in all the locations (Alake et al., 2012).
Figure 5: Which Won Where pattern plot
CONCLUSION
AND RECOMMENDATION
The combined analysis
of variance of total seed yield (kgha-1) indicated that there was
highly significant (p < 0.01) difference among genotypes, locations and
genotype by location interaction. Most of the total sum of squares in total
seed yield was explained by genotype (17.38%) than location and the
interaction.
The presence of
significant genotype by location interaction effect showed that some genotypes
adapted to wider locations. The ANOVA from AMMI model showed that environment,
genotype and genotype x environment interaction contributed 17.81, 17.38 and
17.05%, to total sum square of grain yield, respectively.
The results indicated
the presence of genetic variability in the groundnuts varieties for most of
agro morphology traits The significant differences among locations, the
significant effects of G x L interactions on seed yield and other traits showed
the differential response of varieties over locations and managements and the
test locations were different each other.
Acknowledgments
The authors greatly acknowledged Oromia Agricultural Research
Institute (IQQO) through the Bedele Agricultural Research Center for the
financial support and Fdis Agricultural Research Center and Haramaya University
are also highly acknowledged for providing the groundnut improved varieties
also all research staffs for technical support.
REFERENCES
Bonchev B., Velcheva N. and Stamatov S. 2018.
Assessment of productive potential of peanut varieties (Arachis hypogaea L.)
from the Bulgarian breeding program and opportunities for genetical
improvement. Int. J. Environ. Agric. Biotech., 3(5): 1700-1703
Delacy I.H., Basford K.E., Cooper M., Bull
J.K. and Mclaren C.G. 1996. Analysis of multi-environment trail an historical
perspective. In: Plant adaptation and crop improvement (Cooper M., Hammer G.L.,
eds). CAB International: Wallingford, UK. pp. 39-124
Gauch H.G. and Zobel R.W. 1988. Predictive
and postdictive success of statistical analysis of yield trials. Theor. Appl.
Genet., 76: 1-10.https://doi.org/10.1007/BF00288824
Gulluoglu L., Bakal H., Onat B., El Sabagh A.
and Arioglu H. 2016. Characterization of peanut (Arachis hypogaea L.) seed oil
and fatty acids composition under different growing season under Mediterranean
environment. J. Exp. Biol. Agric. Sci., 4(5): 64-571. http://dx.doi.org/10.18006/2016.4(5S).564.571
Karimizadeh R., Mohammadi M., Sabaghni N.,
Mahmoodi A.A., Roustami B., Seyyedi F. and Akbari F. 2013. GGE Biplot Analysis
of Yield Stability in Multi-environment Trials of Lentil Genotypes under
Rainfed Condition. Not. Sci. Biol., 5(2): 256-262.
Kasno A. and Trustinah M. 2015. Genotype
environment interaction analysis of peanut in Indonesia. SABRAO J. Breed.
Genet., 47: 482–492
Purchase, H., 1997, September. Which
aesthetic has the greatest effect on human understanding? In International
Symposium on Graph Drawing (pp.248-261). Springer, Berlin, Heidelberg.
Samonte, S., Wilson, LT., McClung AM., and
Medley JC. (2005). Targeting cultivars onto rice growing environments using
AMMI and SREG GGE biplot analysis. Crop Sci. 45: 2414- 2424.
Yan, W. and Kang, M.S., 2002. GGE biplot
analysis: A graphical tool for breeders, geneticists, and agronomists. CRC
press.
Cite this Article:Firdisa, G; Tesiso, M; (2024).
Performance Evaluation of Groundnut (Arachis
hypogea L.) Varieties in Buno Bedele, South Western Oromia, Ethiopia. Greener
Journal of Agricultural Sciences, 14(4): 203-210, https://doi.org/10.15580/gjas.2024.4.121924203.
