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© 2019 by the authors; licensee Asian Online Journal Publishing Group 
 

Agriculture and Food Sciences Research 
Vol. 6, No. 1, 79-84, 2019 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/journal.512.2019.61.79.84 

© 2019 by the authors; licensee Asian Online Journal Publishing Group 

   
 

 
 
Evaluation of Extra-early Maturing Maize Genotypes for Grain Yield and Stability 
in the Groundnut Basin Agro-ecological Zone of Senegal 

 
 Georgina Lala Ehemba1 

 Yedomon Ange Bovys Zoclanclounon2 

 Moustapha Gueye3 

 Cyril Diatta4 

 Martial Barnabe Etienne Diatta5 

 Cheikh Thiaw6 

 Alfred Kouly Tine7 

 Alpha Bocar Baldé8 

 Ghislain Kanfany9 

 

 
( Corresponding Author) 

 
1National Agricultural Research Center of Bambey, Senegalese Institute of Agricultural Research, BP 211 
Bambey, Senegal; Regional Study Center for the Improvement of Drought Adaptation, BP 3320, Khombole Road, 
Thiès, Senegal. 

 
2Regional Study Center for Drought Adaptation Improvement, BP 3320, Khombole Road, Thiès, Senegal. 

 
3,4,5,6,7,8National Agricultural Research Center of Bambey, Senegalese Institute of Agricultural Research, BP 211 
Bambey, Senegal. 

 
9National Agricultural Research Center of Bambey, Senegalese Institute of Agricultural Research, BP 211 
Bambey, Senegal; West African Center for Crop Improvement (WACCI), University of Ghana. 

 

 
Abstract 

Maize is a highly valuable staple food crop in Sub-Saharan Africa regions. Its production is 
hampered by both biotic and abiotic stress. Early-maturing genetic resources appear as an 
excellent choice to manage adverse climatic change’s effects. This study aimed to find the best 
extra-early maize genotypes based on genotype by environment interactions in the groundnut 
agro-ecological zone of Senegal. The trials were conducted in Paoskoto, Ndiedieng, Nioro and 
Keur Sene during the rainy seasons of 2016 and 2017. Six extra-early maize genotypes were 
evaluated following a randomized complete block design with three replications. Data on grain 
yield were recorded and analysed using additive main effect and multiplicative interaction and 
genotype plus genotype by environment biplot methods. Analysis of variance revealed a 
significant (P < 0.01) genotype by environment effect on grain yield. The sum of squares variation 
was higher (77.2%) explained by the environment effect. The genotype 2008-TZEE-W-STR 
showed the highest yield performance (3299 kg ha-1) and lowest yield stability index (3). This 
genotype can be suggested to farmers for its cultivation and a good candidate for early maturing 
maize breeding program in Senegal. 

 
Keywords: AMMI, G x E, Rainfed, Early-maturation, Yield, Senegal, Zea mays. 

 
Citation | Georgina Lala Ehemba; Yedomon Ange Bovys 
Zoclanclounon; Moustapha Gueye; Cyril Diatta; Martial Barnabe 
Etienne Diatta; Cheikh Thiaw; Alfred Kouly Tine; Alpha Bocar 
Baldé; Ghislain Kanfany (2019). Evaluation of Extra-early Maturing 
Maize Genotypes for Grain Yield and Stability in the Groundnut 
Basin Agro-ecological Zone of Senegal. Agriculture and Food 
Sciences Research, 6(1): 79-84. 
History:  
Received: 14 February 2019 
Revised: 18 March 2019 
Accepted: 22 April 2019 
Published: 4 July 2019 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Contribution/Acknowledgement: All authors contributed to the conception 
and design of the study. The authors thank gratefully farmers for field 
facilities and their collaboration. 
Funding: This study was supported by the West Africa Agricultural 
Productivity Program (WAAP).  
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

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Contents 
1. Introduction ...................................................................................................................................................................................... 80 
2. Materials and Methods ................................................................................................................................................................... 80 
3. Results and Discussion ................................................................................................................................................................... 81 
4. Conclusion ......................................................................................................................................................................................... 83 
References .............................................................................................................................................................................................. 83 
 

Contribution of this paper to the literature 
This study contributes to existing literature by finding the best extra-early maize genotypes based on 
genotype by environment interactions in the groundnut agro-ecological zone of Senegal. 

 
1. Introduction 

Maize is one of the most important cereal grown in sub-Sahara African countries, feeding more than 300 
millions of people [1]. Its grains are staple food crop and livestock feed [2]. In Senegal, due to the rainfall 
distribution, maize production is restricted in the southern part of the groundnut basin, Casamance and Senegal 
oriental agro-ecological zones where annual rainfall may reach more than 800 mm per year. Drought and low 
nitrogen soil, as well as parasitic weeds, seriously affect cereal production, particularly maize ones [3]. The 
invasion of the parasitic weed Striga hermontica (Del.) Benth coupled with severe drought conditions can cause 50% 
of yield loss [4]. Striga alfera Willd., known as the pink-flowered was also reported destructive for maize 
production in Senegal Parker [5]. However, Okuyama, et al. [6] reported that the potential of maize yield in 
Senegal may be increased in the range of 6% on the basis of the current farming practices. 

In order to address drought stress issue in maize production in West African savannah zones, development of 
extra-early-maturing varieties have been implemented by the joint effort of the International Institute of Tropical 
Agriculture and National Agricultural Research Systems [4]. The basis of early flowering strategy as a drought 
escape mechanism enables the plant to complete its life cycle before drought stress exacerbation [7]. In addition, 
sowing date flexibility regarding non-desirable climatic events (for instance short period of rainfall and delay of the 
rains) is an important advantage of extra-early maturing varieties. Thus, environmental conditions variation can 
influence maize growth and yield [8]. Variation in phenotypic expression in a target environment for the same 
genotypes can be related to the genotype, the environment and genotype by environment interaction [9]. Thereby, 
multi-environment trials constitute a relevant strategy for accurate identification of high-yielding and stable 
genotype [10]. Yield stability and adaptation spectrum of genotypes over the years, in fluctuating environments, 
have become an important criteria for plant breeders [11].  

Numerous models were proposed to investigate genotype by environment interaction patterns, including 
Additive Main effect and Multiplicative Interaction (AMMI) and Genotype plus genotype by environment (GGE) 
biplot. The AMMI model deals with the traditional analysis of variance for additive main effects in combination 
with principal component analysis [12]; [13]. This model was widely used in the identification of superior 
genotypes of barley [14] cassava [15] wheat [16] and rice [17]. The GGE biplot method proposed by Yan and 
Tinker [18] unravels both genotype and genotype by environment interactions based on Gabriel [19] biplot 
methodology. GGE biplot displays a powerful graphic on genotype stability and performance, genotype by 
environment interaction and mega-environment identification through “which on where” pattern graph [20]. This 
two-year study conducted in four locations in the Groundnut Basin agro-ecological of Senegal, aimed to identify 
high yielding and stable genotypes using both AMMI and GGE biplot methods. 
 

2. Materials and Methods 
2.1. Experimental Sites 

Experimentation was carried out during the rainy seasons of 2016 and 2017 in four sites (Nioro, Ndiedieng, 
Paoskoto and Keur Sene) located in the “Groundut basin” of Senegal. Details of testing environments are given in 
Table 1.  
 

Table-1. Information on testing environments during rainy seasons 2016 and 2017. 

Site Soil type 

Geographical position 
Cropping 

season 

Total 
rainfall 
(mm) 

Temperature (°C) 

Latitude 
(N) 

Longitude 
(W) 

Altitude 
(m.a.s.l) 

Minimum Maximum 

Nioro Sandy clay 13°43'48" 15°46'48" 28 
2016 663 23.9 31.4 
2017 1103 23.0 31.6 

Ndiedieng, 

Sandy clay 

13°34'12" 16°05'24" 18 
2016 377 22.6 31.8 

2017 257 24.5 35.7 

Paoskoto 
Sandy clay 

13°46'48" 15°47'24" 28 
2016 1269 22.3 35.2 
2017 1307 23.6 36.0 

Keur Sene 
Sandy clay 

13°50'60" 16°02'24" 25 
2016 330 23.3 35.6 
2017 360 24.3 37.9 

    Source: National Agency of Civil Aviation and Meteorology of Senegal. 
 

2.2. Plant Materials and Test-Environments 
Six extra-early maturing maize genotypes Table 2 were evaluated in a randomized complete block design with 

three replications in each of the environments. Sowing was done with a spacing of 0.75 m between rows and 0.25 m 
between plants within a row in plots of 6 × 5.25 m2 area. Each plot contained 8 rows.  A basal 15N-15P-15K 
fertilizer at a rate of 200 kg ha-1 was applied before sowing. Recommended cultural practices were applied for crop 
management. At the maturity stage, grains data were recorded and converted to kg ha-1 using the plot size as a 
factor. 



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Table-2. Information on extra-early maize genotypes used in this study. 

Genotypes code Pedigree/original name Origin Type 

G1 2008-TZEE-W-STR IITA-Ibadan OPV 
G2 2009-TZEE-OR1-STR IITA-Ibadan OPV 
G3 TZEE-WPop-STRC5 IITA-Ibadan OPV 
G4 TZEE-Y ISRA-Senegal OPV 
G5 TZEE-W ISRA-Senegal OPV 

Note: IITA: International Institute of Tropical Agriculture. OPV: Open Pollinated 
Variety. 
ISRA: Institut Sénégalais de Recherches Agricoles. 

 
2.3. AMMI Model 

Analysis of variance was performed using AMMI model implemented in statistical software GenStat [21]. 

Assuming 𝜇 is the grand mean of extra early maize genotypes yield,  𝛼𝑔  is the genotype deviation of the grand 

mean, 𝛽𝑒 is the environment deviation,  𝜆𝑛 is singular value for interaction principal component (IPC) n and 𝜆𝑛
2  is 

the corresponding eigenvalue, 𝛾𝑔𝑛 is the eigenvector for genotype g and component n, 𝛿𝑒𝑛 is the eigenvector for 

environment e, 𝜌𝑒 is the residual, 𝜅𝑟(𝑒) is the block effect for replication r within environment e and 휀𝑔𝑒𝑟 is the 

error, the yield of genotype g in environment e for replication r (𝑌𝑔𝑒𝑟) is expressed by Gauch [22] model: 

Yger= μ + αg + β
e
+ ∑ λn γ

gn 
δen

n
+ ρ

e
+ κr(e)+ εger 

Based on the relative contributions of the IPC 1 and 2 axis scores to genotype by environment interaction 
effect, the AMMI stability value (ASV) was computed using the following formula described by Danquah, et al. 
[15]: 

ASV = √[
IPC1Sum of squares

IPC2Sum of squares

 (IPC1score)]

2

+ (IPC2score)
2 

Using based-yield genotypes ranking and ranking based on AMMI stability value, yield stability index was 
calculated as follows:  

Yger= μ + αg + β
e
+ ∑ λn γ

gn 
δen

n
+ ρ

e
+ κr(e)+ εger 

Lower is AMMI stability value and ranking based on yield values, more stable and high yielding is the 
genotype [23]. 
 

2.4. GGE Biplot Analysis 
Data recorded on grain yield were arranged in site-maize genotypes two-way table containing mean yields. 

Biplot graphs were plotted using statistical software GenStat 18 [21] following the GGE biplot model defined by 
Yan, et al. [24] as follows:  

y
ij
= ∑ λnξin

 η
jn

r

n=1

 

where r is the number of principal components required to approximate the original data, λn is the singular value of 

principal components, ξin and ξjn are the ith genotype score and the jth environment score for principal components 
respectively and yij the response yield according to ith genotype in jth environment. 
 

3. Results and Discussion 
3.1. Performance of the Tested Extra-Early Maize in Different Locations 

During the rainy season of 2016, G3 showed the highest yield (3893 kg.ha-1) at Paoskoto, followed by G2 (3846 
kg ha-1) at Keur Sene and G5 (3253 kg ha-1) at Paoskoto Table 3.  
 

Table-3. Mean grain yield (kg. ha-1) of maize genotypes in different environments during 2016 and 2017 crop seasons. 

Year Site G1 G2 G3 G4 G5 Mean Minimum Maximum 

2016          
 Nioro 2959 2951 2524 2809 2684 2785 1920 3680 
 Ndiedieng 2365 3484 3591 2187 1742 2674 747 4853 
 Paoskoto 2951 2258 3893 2382 3253 2948 1173 4053 
 Keur Sene 3715 3846 3589 2382 1742 3788 2954 4652 

2017          
 Nioro 3790 3528 3259 2829 4206 3522 1723 4794 
 Ndiedieng 3790 2957 3259 2952 2994 2998 2097 4085 
 Paoskoto 2400 925 1849 2382 978 1387 587 2667 
 Keur Sene 4886 4737 4205 3988 4574 4478 3514 5333 

                   Source: Field data. 
 

Yields ranged from 747 kg ha-1 to 4853 kg ha-1 across environments during 2016 cropping season. In 2017, G1 
and G2 showed the highest yields in Keur Sene with 4,886 kg ha-1 and 4737 kg ha-1, respectively. 
 

3.2. Additive Main Effect and Multiplicative Interaction Analysis 
The AMMI analysis of variance revealed a significant effect of genotype, environment and genotype by 

environment on yield variation Table 4.  
 
 



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Table-4.  Analysis of variance for AMMI2 model of five extra early maize genotypes in eight environments. 

Source of variation Df SS MS TVE (%) G × E (%) 

Total 119 145733332 1224650 - - 
Treatments 39 112590559 2886937*** - - 

Genotype (G) 4 4941432 1235358** 4.38 - 
Environment (E) 7 86901293 12414470*** 77.18 - 
G × E Interaction 28 20747834 740994** 18.42  

IPC1 10 9279579 927958** - 44.72 

IPC2 8 6508658 813582* - 31.37 

Residual 10 49599596 495960 - - 
Blocks within environments 16 11888665 743042* - - 

Error 64 21254108 332095 - - 
Note: *significant at 0.05 probability level. **significant at 0.01 probability level. ***significant at 0.001 1evel of probability. 
ns: non-significant. df: degree of freedom. SS: sum of square. MS: mean of square. TVE (%): percentage relative to total sum of 
square. IPC: Interaction Principal Component. G × E (%): percentage of sum of square variation explained by genotype by 
environment effect. 

 
Environment factor explained the highest (77.2%) part of total variation followed by genotype by environment 

interaction (18.4%) and genotype (4.4%). The partitioning of the sum of square variances revealed a predominant 
effect of environment. This suggests that the environment influences range was wider than genotype. As found in 
several studies [25]; [26] the variability observed in environmental parameters such as rainfall, temperature and 
soil texture can be related to the strong environmental effect.  

IPC1 and IPC2 represent 44.7% and 31.4% of genotype by environment variation, respectively. The IPC 1 
effect was highly greater (10 times) than the effect of the genotype and contributed to more than 40% of GE 
interaction variation. The AMMI IPC1 and IPC2 scores were respectively positive and negative, reflecting 
location-year crossover pattern in genotype by environment interactions [27]. 
 

Table-5. Ranking of the tested genotypes based on ASV and YSI. 

Genotype Mean Rank (µ) IPC1 IPC2 ASV ASV Rank (η) YSI (µ + η) YSI rank 

G1 3299 1 8.93 -12.45 17,81 2 3 1 
G2 3086 3 -10.47 27.89 31,63 3 6 2ex 
G3 3209 2 -29.12 -19.04 45,68 5 7 4 
G4 2704 5 4.13 11.03 12,50 1 6 2ex 
G5 3065 4 26.53 -7.42 38,54 4 8 5 

                 Source: Field data. 
 
Based on AMMI stability values (ASV), G4 appeared as the most stable genotype across environments. 

However, yield stability index (YSI) revealed that G1 was the best genotype followed by G2 and G3 Table 5. 

 
3.3. Genotype Plus Genotype by Environment Biplot Analysis 

The genotype by environment patterns were graphically presented in Figure 1 and Figure 2 using GGE biplot 
model. Principal components 1 and 2 encompass 40.75% and 25.10% of total variation respectively. The Figure 1 
represents the average-environment coordination (AEC) view based on yield performance and stability. 
 

 
Figure-1. The average-environment coordination view showing mean 
performance and stability of five extra early genotypes tested in eight 
environments. The genotypes: G1(2008-TZEE-W-STR), G2(2009-
TZEE-OR1-STR), G3 (TZEE-WPop-STRC5), G4(TZEE-Y), 
G5(TZEE-W). The years: 16 (2016), 17(2017). The locations: KS (Keur 
Sene), PA (Paoskoto), ND (Ndiedieng), NI (Nioro). 

                                                           Source: Field data. 
 



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Figure-2. The which-won-where GGE biplot showing five extra 
early maize genotypes behaviour in eight environments. The 
genotypes: G1(2008-TZEE-W-STR), G2(2009-TZEE-OR1-STR), 
G3(TZEE-WPop-STRC5), G4(TZEE-Y), G5(TZEE-W). The years: 
16 (2016), 17(2017). The locations: KS (Keur Sene), PA(Paoskoto), 
ND (Ndiedieng), NI (Nioro).   

                                                            Source: Field data. 
 

 The AEC arrow indicates the highest genotype based on yield performance. The circle represents the ideal 
genotype, which combines high yield and stability. According to Figure 1, the genotype G1 was the best genotype 
followed by G5. In contrast, G4 is the most unstable genotype across all environments. 

The which-won-where polygon view Figure 2 highlighted the genotype by environment interaction pattern of 
this study. The genotypes located at the vertex of the polygon showed either best or poor performance in a related 
environment. The genotypes G1 and G5 had a higher grain yield than average in the environments NI17, KS17, 
ND17, PA17, KS16 and NI16. G3 was the best genotype in the environments PA16 and ND16. In contrast, G2 and 
G4 yields were lower than average across the tested environments. The orthogonal lines from the origin of biplot 
which cut the sides of the polygon allow the comparison between two adjacent genotypes in relationship with their 
respective environments. Thus, G1 performed better in NI17, KS17, ND17 and PA17, whereas G3 was better in 
ND16 and PA16. The orthogonal lines in the polygon also revealed the presence of location-year crossover 
patterns. The environment couples PA16-PA17 and ND16-ND17 were located on opposite sides of orthogonal 
lines, indicating a location-year effect. GGE biplot polygon view supports this crossover presence, denoting of 
adaptation of maize genotypes according to a target environment. Various studies [28-30], performed on a wide 
range of crops, successfully showed the efficacy of AMMI and GGE biplot methods to figure out genotype 
adaptation patterns.  
 

4. Conclusion 
The present study demonstrated a high environment effect in GE variation despite the fact that the 

environments were located in the same agro-ecological zone. Both AMMI and GGE biplot analysis showed that 
the genotype 2008-TZEE-W-STR had the highest grain yield and was more stable across environments. It can be 
recommended for cultivation in the groundnut basin of Senegal and used in maize breeding program as a gene 
donor regarding yield and stability. 
 

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