Cowpea [Vigna unguiculata (L.) Walp] is an annual herbaceous legume that belongs to Fabaceae family. It is one of the widely cultivated and consumed grain legumes globally, especially in the arid and semi-arid tropics (Baidoo and Mochiah, 2014; Noubissietchiagam et al., 2010).  Generally,  cowpea  production  and  utilization  in  Ethiopia is very low as compared to other African countries though the country is claimed to be the center of diversity and/or origin. The country has high potential for the production of the crop as more than 66.5% of the arable land is very suitable for cowpea production (Collaborative Crop Research Program (CCRP), 2015). It plays a critical role in the lives of millions of people in the developing world, providing them a major source of dietary protein that nutritionally complements low protein staple cereal and tuber crops. Its grain  is the most important part of the plant for human consumption (Agbogidi and Egho, 2012). Drought is the most important abiotic stress limiting production of all crops worldwide, even the most drought tolerant cowpea (Hall, 2004). More importantly, Ethiopia is known as a victim with recurrent droughts that causes partial or total crop failure, and subsequently, famine in the country. In such situations, cowpea can be a potential crop to reduce the consequences of drought because of its drought tolerant nature more than other staple crops. The relative magnitude of environment, genetic and their interaction effects are a challenge that makes production difficult (Hall et al., 2003). Therefore, in the process of developing cowpea varieties for desirable traits, it is necessary to evaluate genotypes in contrasting environments in the country. However, information on the effect of genotype, environment, and their interaction on cowpea grain yield under diversified agro-climatic conditions of Ethiopia is limited. The present study was initiated to estimate the magnitude of genotype, environment and genotype by environment interaction for grain yield of cowpea and characterize yield stability of cowpea genotypes across different environments.

 

The experiment was conducted in seven environments during 2016/17 cropping season in Ethiopia (Table 1). Sixteen cowpea genotypes (14 advanced lines and two standard checks) were used for this study (Table 2). The experiment was laid out in 4 × 4 triple lattice experimental design with three replications. The seeds of the experimental genotypes were planted on 4 m × 3.6 m plots (14.4 m²) having six rows, with inter-row spacing of 60 cm and 20 cm within rows. Fertilizer (DAP 100 kg/ha) was applied for the experiment along with other agronomic managements based on the recommendation. Data were collected on the basis of five sample plants randomly taken from the four central rows, viz. plant height at maturity, number of pods per plant, and number of seeds per pod, and on the basis of entire plot, such as days to 50% emergence, days to 50% flowering, days to 75% maturity, grain yield per net plot and 100-seeds weight. All data were subjected to analysis of variance (ANOVA) separately for individual environment and other environments.  ANOVA is important in revealing the presence of GEI,   but   it   does   not   indicate   genotypes   contribution  to  the interaction and which genotype was stable across environments. Stability was computed for grain yield by SPAR 2.0 software for Eberhart and Russell’s stability parameters along with Genstat statistical software (16^th edition) for AMMI stability parameters and GGE biplot. Mean that differ significantly were separated by Duncan Multiple Range Test. The regression coefficient (b_i) (Eberhart and Russell’s stability parameters) measures the response of genotypes to environments. When the regression coefficient of the genotype is nonsignficant from unity/one (b_i= 1), the genotype is said to be averagely responsive and suitable for both poor and good environments; when the b_i value of genotypes is signficantly different from one/unity (bi >1), the genotype is said to be highly responsive above the average and suitable only in good environment; whereas, when the genotype b_i value is signficantly different from one/unity (b < 1), it indicates the genotype is low reponsive and suitable for poor environment (Wachira et al., 2002). No significant S²di (deviation from regression) value from zero indicates stable genotypes across environments and with significant S²di value from zero considered as unstable genotypes across environments. AMMI stability value (ASV) is used to judge stable genotypes (the smaller the value, the more stable the genotype is).

 

 

 

The combined analysis of variance over environments showed significant (p<0.01) mean squares of genotypes, environments and interaction of genotypes × environments (GEI) for grain yield (Table 3). The results indicated the presence of significant variations among genotypes and environments and the genotypes had inconsistent performance across the test environments for the mentioned traits. This in turn, suggested the need to conduct further GEI and thereby stability analyses to understand the nature of GEI and stability of the performance of genotypes across environments. Akande (2009) in cowpea, Kaya et al. (2002) in wheat, Solomon et al. (2008), Wende (2013) and Workie et al. (2013) in maize and Yayis et al. (2014) in field pea also reported the significant effect of genotype, environment and GEI on yield and some other yield related traits and suggested the importance of further stability analysis.

 

 

Mean performance of genotypes for grain yield

The first three genotypes (Table 4) with highest mean grain yield were IT-99K-1060a (1398.8 kg/ha) and 86D-378 (1377.1 kg/ha) without significant differences between the two followed by 95K-1095-4A (1321.8 kg/ha). The three genotypes with lowest mean grain yield were IT-96D-610 (1112.5 kg/ha), Kenketi (1128.5 kg/ha) without significant difference among the two and IT-97K-568-18 (1007.0 kg/ha).

 

 

The AMMI for grain yield showed the significant (p<0.01) effect of environment, genotype, and genotype by environment interaction. Environment, genotype, and genotype by environment interaction accounted for about 63.3, 5.3, and 29.7% of the total sum of squares, respectively. Most of the total sum of squares of the model was attributed to the environment and the interaction effect. This result is in agreement with the results reported by Akande (2009),  Sarvamangala et al. (2010) and Nunes et al. (2014) in cowpea along with Taye et al. (2000) in fieldpea which revealed that the contribution of environment to the observed variation of yield was large. The larger sum of squares of GEI compared to the genotype indicated larger differences in genotypic response across environments. In cowpea (Stanley Omar et al., 2005) and chickpea (Solomon et al., 2008), larger contribution of GEI than genotype effect  for the observed yield variation was also reported. The greater contribution of the treatment (98.3%) than the error (1.53) indicated the reliability of the multi-environment experiment. The AMMI model further partitioned the genotype by environment interaction sum of square into interaction principal component axes (IPCA) and residual term. The mean squares of the first three IPCAs were signficant and all togther contributed 79.33% of the total sum of squares of GEI. The IPCA 1, IPCA 2 and IPCA 3 accounted for 37.93, 24.67 and 16.73%, respectively, for the observed variation due to GEI. For the validation of the variation explained by GEI, the first three multiplicative component axes are adequate (Gauch, 2006). This is because of notable reduction of dimensionality and graphical visualization for the stability patterns of genotypes (Annicchiarico, 2002) (Tables 5 and 6).

 

 

Stability analysis for grain yield estimates of stability parameters from Eberhart and Russell’s model

The six genotypes viz.; IT-960-604, Kenketi, IT-99K-10609, TVU, IT-96D-604 and IT-97K-568-18 with non-significant S²di values from zero indicated the genotypes were stable. However, all genotypes had lower yield than overall mean of genotypes (1237.4 kg/ha) except TVU and IT-96D-604 which indicated the genotypes were not desirable for cultivation though they were stable. The desirable genotypes are expected not only to be stable in all environments but also have (high mean values). Ten genotypes viz.; 86D-378, IT-89KD, MEL-NURL-96-3, IT-96D-610, IT-93K-556-4, IT-99K-1060a, 95K-1095-4A, IT-87D-1137, 93K-619-1 and IT-93K-293-2-2 had significant S²di values from zero indicating the genotypes were unstable. TVU was the desirable genotype for cultivation in all environments having static stability evident from non-significant value S²di from zero, with non-significant bi value (bi=1) from unity/one and higher mean grain yield above average mean grain yield of genotypes. IT-99K-1060   was  a   low     responsive    genotype    to    varied environments and suitable only for unfavorable environments with b_i value signficantly different from one/unity (bi <1).

Seven genotypes 86D-378, IT-89KD, MEL-NURL-96-3, IT-93K-556-4, 95K-1095-4A, IT-96D-604 and 93K-619-1 had  mean yield greater than the mean yield of genotypes over seven environments ranging from 2.1 to 11.3%. However, all genotypes had S²di values significantly different from zero and significant bi values (bi>1) from unity/one. This suggested that the genotypes were not stable and highly responsive to favorable environments. These were desirable genotypes for cultivation in favorable environments for the crop having dynamic stability (mean value higher in favorable environments than the average yield of favorable environments).

 Two genotypes (IT-99K-1060 and IT-97K-568-18) had non-significant S²di value from zero (S²di>0), significant bi value (bi<1) from unity/one and lower mean yield than average mean yield of genotypes. These genotypes were stable and more responsive to unfavorable environments for the crop, but the low yield of these genotypes did not promote its being recommended for cultivation in environments where they perform.

 IT-96D-604 had non-significant S²di value from zero (S²di>0), significant bi value (bi>1) from unity/one and high mean yield above average mean yield of genotypes which suggested it was a desirable genotype for cultivation in all environments and more responsive in favorable environments. TVU had yield advantage of 4.01% over grand mean yield of genotypes and fifth ranking mean yield, zero (0) IPCA 1 score and relatively low IPCA 2 (negative); also, ASV suggested that this genotype could be considered for cultivation in unfavorable environments. This result indicated a proportionate genotype response (Silveira et al., 2013).

The genotypes with lower IPCA1 scores would produce a lower G×E interaction effect than those with higher IPCA1 scores and have less variable yields (more stable) across environments (Oliveira et al., 2014). The second group of genotypes consisted of IT-99K-1060a, 86D-378, 95K-1095-4A, 93K-619-1, MEL-NURL-96-3, IT-89KD and IT-96D-604 of which the first four ranked 1 - 4 high yields in the experiment while the last three ranked 6, 7 and 9 high yields. All had higher mean yields above the grand mean yield of genotypes, negative IPCA 1 scores, low ASV ranked 1 - 6 except 95K-1095-4A and MEL-NURL-96-3 with ASV ranked 11 and 14, respectively. The first four high yielding genotypes (IT-99K-1060a, 86D-378, 95K-1095-4A, 93K-619-1) except (86D-378) had same sign of IPCA 1 and IPCA 2 scores while the other genotype was suitable in unfavorable environments with opposite sign of IPCA 1 and IPCA 2. Therefore, the three genotypes could be considered for cultivation in all environments. Other genotype (86D-378) could be considered for cultivation in environments where it performed well. Dynamic stability implies for a stable genotype, a yield response that is always parallel to the mean response of the tested environments, that is, zero GEI (Annicchiarico, 2002). The third group of genotypes consisted of IT-99K-1060, IT-960-604, IT-87D-1137, Kenketi, IT-96D-610 and IT-97K-568-18 which had mean yields lower than grand mean yield of genotypes, with mean  yield   ranked  11 - 16  having  relatively  high  and positive IPCA 1 scores, of which IT-96D-610, IT-99K-1060 and IT-87D-1137 had high ASV ranked 12, 13 and 15, respectively. The results suggested that these genotypes could not be considered for cultivation. Usually, in crop improvement programs, tests of performance across a wide range of environments is conducted to reduce the effect of GEI and to ensure that the selected genotypes have a high yield and stable performance across several environments (Stanley et al., 2005) (Table 7).

 

 

Which-Won-Where” Patterns

In Figure 1, a polygon view of GGE was formed by connecting the vertex genotypes with straight lines and the rest of the genotypes were placed within the polygon. The vertex genotypes were 86D-378 (G2), IT-89KD (G3), IT-93K-556-4 (G6), IT-97K-568-18 (G7), IT-99K-1060a (G8), 95K-1095-4A (G9) and IT-93K-293-2-2 (G13) and 93K-619-1 (G12) having the largest distance from the origin which were more responsive to environmental change and gave high yield except IT-97K-568-18 (G7) which was considered as specially adapted genotypes. The vertex genotypes in each sector are the best genotype at environments whose markers fall into the respective sector. Environments within the same sector share the same winning genotypes, and environments in different sectors have different winning genotypes. The genotypes within the polygon and nearer to origin were less responsive than vertex genotypes (Yan and Hunt, 2001; Yan and Tinker, 2006). Accordingly, the genotypes Kenketi (G1), MEL-NURL-96-3 (G4), IT-96D-610 (G5), IT-87D-1137 (G10), IT-96D-604 (G11), IT-99K-1060 (G14), IT-960-604 (15) and TVU (G16) were located within polygon which were less responsive. Genotype TVU (G16), located near to the origin indicated stablity. Winner and higher yielder genotype at Jinka (E6) and Sekota (E3) was IT-93K-556-4 (G6). IT-89KD (G3) and IT-99K-1060a (G8) were winners and highest yielders at Kobo (E4) and Melkassa (E5) respectively. Genotype 93K-619-1 (G12) and IT-93K-293-2-2 (G13) were winner and high yielder genotypes at Arbaminch and Meisso, respectively. Genotype IT-97K-568-18 (G7) was winner but lowest yielder at Babile (E2) which was relatively not conducive for cowpea genotypes to express their potentials. Yan et al. (2000) and Yan and Kang (2003) reported the polygon view of GGE biplot as the best way for identification of winning genotypes with visualizing the interaction patterns between genotypes and environments. The GGE biplot has therefore, been used in crop genotypes trials to effectively identify the best-performing genotype(s) across environments, identify the best genotypes for specific environments delineation, whereby specific genotypes can be recommended to specific environments and can be used to evaluate the yield  and  stability  of  genotypes (Yan and  Kang,  2003; Yan and Tinker, 2006).

 

 

Figure 2 shows the discriminating ability and representativeness of test environments. Acordingly, Melkassa,Sekota and Jinka were more descrimnating environments with longer vector and  larger  angle  which provides much more  information about differences among genotypes. These environments cannot be used in selecting superior cowpea genotypes, but are useful in culling unstable genotypes. Babile and Meisso had relatively  short  vectors   and   close to origin that all genotypes performed similarly, and therefore provides little or no information about the genotypes difference.

Thus, it should not be used as test environments for cowpea genotypes. However, identification and removal of non-informative test environments as well as identification of test environments for yield evaluation trial requires multiyear data (Yan et al., 2007). Arbaminch and Kobo had long vectors and small angles with the abscissa and were ideal for selecting superior genotypes. If budgetary constraints allow only a few test environments, these test environments would be the first choice.

 

 

According to Yan and Hunt (2001), discriminating ability and representativeness are the important properties of test environments. An ideal environment should be highly differentiating for the tested genotypes and at the same time representative of the target environment (Yan et al., 2007). Representativeness of the test environment is visualized by the angle formed between the environment vector and abscissa of average environment axis. The smaller the angle, the more representative the environment is (Yan et al., 2007). Environments with longer vectors are more discriminating of the genotypes, whereas environments with very short vectors are little or not informative on the genotype difference (Yan et al., 2007).

 

 

 

 

 

 

 

 

 

TVU (check variety) was identified as the most stable with mean yield above the mean grain yield of genotypes. Two genotypes, IT-99K-1060a (1398.8 kg/ha) and 86D-378 (1377.1 kg/ha) had first and second highest yield, identified as responsive to favorable environments suggested the need to further test to develop as varieties. Other two genotypes, 95K-1095-4A and 93K-619-1, identified as unstable and highly responsive to environments suggested considering the genotypes as candidate varieties where they performed best. Melkassa, Sekota and Jinka were identified as more descrimnating environments, Arbaminch and Kobo were ideal for selecting superior genotypes, but Babile and Meisso were not descrimnating environments.

 

The authors have not declared any conflict of interests.