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Research Article

Legume Research, volume 44 issue 12 (december 2021) : 1413-1418

Stability for Grain Yield using AMMI Bi Plot and Disease Reaction Studies in Pigeonpea [Cajanus cajan (L.) Millsp.]

S. Muniswamy1,*, Praveen Kumar1, P. Kuchanur1, L.N. Yogesh2, T. Annaray3, Sidramappa4, Sunil Kulkarni4, D.M. Mahalinga1
1Zonal Agricultural Research Station, Kalaburagi-585 101, Karnataka, India.
2College of Agriculture, Bheemarayanagudi, Shahapur-585 287, Karnataka, India.
3Agricultural Research Station, Hagari, Bellary-583 138, Karnataka, India.
4Agricultural Research Station, Malnoor, Yadagir-585 224, Karnataka, India.
5Agricultural Research Station, Bidar-585 401, Karnataka, India.

  • Submitted14-10-2019|

  • Accepted20-12-2019|

  • First Online 15-04-2020|

  • doi 10.18805/LR-4259

Cite article:- Muniswamy S., Kumar Praveen, Kuchanur P., Yogesh L.N., Annaray T., Sidramappa, Kulkarni Sunil, Mahalinga D.M. (2021). Stability for Grain Yield using AMMI Bi Plot and Disease Reaction Studies in Pigeonpea [Cajanus cajan (L.) Millsp.] . Legume Research. 44(12): 1413-1418. doi: 10.18805/LR-4259.

ABSTRACT

The genetic material for finding the genotype × environment (G × E) interaction comprised of 15 advanced genotypes, which were tested in five environments, during kharif-2018. In totalling to these genotypes used for stability, four more genotypes (total 19 genotypes) were used for studying the disease reaction to Fusarium wilt (FW) and Sterility Mosaic Disease (SMD) in respective sick plots. The AMMI (Additive main effects and multiplicative interaction) analysis of variance for grain yield publicized highly significant (p<0.01) for environments, G × E interaction, PCA I and PCA II significant (p<0.05) for genotypes. First principal component axis (PCA 1) of the interaction captured 60 % of the interaction sum of squares. In AMMI analysis AMMI 1 bi plot showed that the genotypes viz., GRG-152 (G1), GRG-811 (G15), KRG-244 (G9), AGL-1603-2 (G12) and TS-3R (G14) recorded higher average mean yield with high main (additive) effects coupled with positive IPCA 1 score. While the genotypes KRG-224 (G5) though showed highest yield, but recorded negative IPCA 1 score demonstrating its environment sensitivity. Environments, such as Bheemarayana gudi (E2), Hagari (E4) and Malnoor (E5) could be regarded as more stable site for high yielding pigeonpea genotypes than other locations for grain yield as indicated IPCA scores. The disease screening revealed that the genotype AGL-1603-2 (G12) was resistant to both FW, SMD attached with high yield as indicated by its per se performance and the genotype GPT-1 showed resistance to both FW, SMD. Two genotypes viz., GRG-152 (G1) and GRG-811 (G15) showed resistance to FW and moderate resistance to SMD and possess high yielding as indicated by their per se performance. Hence, these genotypes could be used unswervingly as a varieties or choice of parents for hybridization programme.

INTRODUCTION

Pigeonpea [Cajanus cajan (L.) Millsp.] is one of the major pulse crops of the tropics and sub-tropics. Pigeonpea belongs to agriculturally most important tribe Phaseoleae of the family Fabaceae (Leguminosae). It ranks sixth in global grain legume production and worldwide it is cultivated in about more than 4.7 m ha area. India is the largest producer and consumer of pigeonpea with an area of 4.4 m ha, with annual production of 3.68 m t and productivity of 832 kg/ha (Anonymous, 2019). In India, after chickpea, pigeonpea is the second most important pulse crop and India contributed about 72% of global pigeonpea production. The average yield in India remained around 900 kgs/ha for the past six decades (FAOSTAT, 2017). This yield gap is mainly due to the exposure of the crop to many biotic and abiotic stresses as well as due to its cultivation in marginal environments with limited inputs (Sharma and Upadhyaya, 2016). Pigeonpea stands ahead of all the pulses due to its drought tolerance and environment-friendly low cost cultivation. Performance of genotypes in terms of productivity without stability serves no purpose. The AMMI model describes the GE interaction in more than one dimension and it offers better opportunities for interpreting GE interaction than analysis of variance (ANOVA) and regression of the mean (Vargas et al., 2001). AMMI analysis reveals a highly significant constituent that has clear agronomic meaning (Zobel et al., 1988).
        
Among the biotic stresses, Fusarium wilt (FW) and sterility mosaic diseases (SMD) are measured to be the most important diseases of pigeonpea in India. SMD and FW cause sizeable losses to pigeonpea production and have been recognized as the “must-have” traits for pigeonpea in India. As the diseases are widespread in the subcontinent and persist to be responsible for greater losses (Reddy et al., 1988).  Breeding resistant varieties is considered to be one of the most valuable and financially viable methods of reducing crop losses and has received top priority. In view of this, in the present research we evaluated 15 advanced genotypes for G × E to identify high yielding, consistent and disease resistant genotypes of pigeonpea.

MATERIALS AND METHODS

The experiments were conducted at five locations viz., Bidar (E1), Bheemarayanagudi (E2), Kalaburagi (E3), Hagari (E4) and Malnoor (E5), belong to three Agro Climate Zones (AEZ) of Karnataka (Table 1). Fifteen genotypes for stability consisted of 13 advanced lines viz., GRG 152, GRG 177, GRG 222, GRG 617, KRG-224, KRG-223, KRG-33, KRG-221, KRG-244, KRG-155, KRG-251, WRP-R-29-4 and AGL-1603-2 and two checks (TS-3R and GRG-811). The experiments were carried out during kharif 2018 in a randomized complete block design (RCBD) with three replications. Each entry was sown in 6 rows of 4 meters length with a spacing of 90 × 30 cm. Standard agronomic practices were followed and plant protection measures were taken as and when required by following the recommended package of practices (Anonymous, 2017).  Grain yield per plot was recorded and scaled to kg/ha at 10 per cent moisture. The grain yield data of 15 genotypes at five locations were subjected to AMMI analysis of variance using statistical analysis package software Windostat 9.2 (Table 2).

Table 1: Agro-climatic characteristics of testing environments.


 

Table 2: Additive main effects and multiplicative interaction (AMMI) analysis of variance for grain yield (q/ha) of 15 genotypes across 5 environments.


        
The experiment for screening Fusarium wilt and SMD was laid out at Agricultural Research Station, Kalaburagi and Bidar respectively. A total of 19 genotypes including the (15 genotypes used for stability) were used as experimental material (Table 4). All the genotypes were sown in single row of 4 m length with two replications and susceptible check was sown after every 5th row and screened during kharif 2018.  A spacing of 75 cm and 30 cm between the rows and plants respectively was followed. The per cent wilt was recorded at flowering and at physiological maturity by counting number of dead plants (due to Fusarium wilt) among the total number of plants present per genotype and per cent disease incidence (PDI) was estimated. Similarly, observations on SMD were recorded by counting number of plants infected with sterility mosaic virus among total number of plants present per genotype and PDI was calculated. The categorization of PDI value was carried out according to the scale given by (Singh et al., 2003) viz., 0-10% = Resistant, 10.1-30% = Moderately resistant, 30.1-100% Susceptible.
 

Table 4: Disease reaction of pigeonpea genotypes for Fusarium wilt and sterility mosaic disease under field condition for Kharif 2018-19.

RESULTS AND DISCUSSION

AMMI analysis of variance
 
Additive main effects and multiplicative interaction (AMMI) analysis unfolded large influence of environment and genotype × environment interaction for variations in grain yield. The AMMI analysis of variance for grain yield (q/ha) of 15 genotypes tested in five environments showed that the main effects of Genotypes, Environments and G × E interaction. Environment accounted maximum variation (69.44%) followed by G × E interaction (13.28%) and genotypes. The analysis revealed that variances due to environments, genotype × environment interaction, PCA I and PCA II are highly significant (P<0.01), whereas significant (p<0.05) for genotypes. The large sum of squares for environments indicated that the testing locations were diverse and large differences among environmental means causing most of the variation in grain yield, which is in harmony with the findings of Zobel et al.,(1988). Further, genetic variability among the genotypes was indicated by large sum of squares for genotypes as reported by the Akter et al., (2014), Singh et al., (2018). The presence of genotype-environment interaction (GEI) was clearly demonstrated by the AMMI model, when the interaction was partitioned among the first three interaction principal component axis (IPCA), first two PCA axis declared significant by an F test and PCA III was statistically non significant. The IPCA1 explained 7.97% of interaction sum of squares with 17% of the interaction degree of freedom (DF). Similarly, the second and third principal component axis (IPCA 2 and 3) explained further 3.55% and 1.29% of the GEI sum of squares respectively (Table 2). This implied that the interaction of the pigeonpea genotypes with five environments was predicted by the first three components of genotypes and environments. This is in agreement with the recommendation of Sivapalan et al., (2000). However, findings of Zobel et al., (1988) recommended that most accurate model for AMMI  can be predicted using the first two IPCAs.
 

Stability analysis by AMMI model

The presence of GEI was realized when the interaction was partitioned into the first two interaction PC axis (IPCA) (Table 2). IPCA1 and IPCA2 scores were highly significant, explaining 60.07% and 26.71% of the variability, respectively. These results are in agreement with Singh et al., (2018) and Zobel et al., (1988). In AMMI 1 biplot where the main effects (genotype mean and environment mean) and IPCA1 scores for both genotypes and environments are plotted against each other. On the other hand, the second biplot is AMMI 2 where scores for IPCA1 and IPCA2 are plotted (Fig 2). Different genotypes showed incoherent performance across all environments (Table 3). The mean grain yield value of genotypes averaged over locations ranged between 9.59 q/ha [KRG-221 (G8)] to 12.26 q/ha [GRG-152 (G1)]. Whereas, environments mean grain yield ranged from 14.24 (q/ha) for E2 to 5.19 (q/ha) for E1. The averaged grain yield over environments and genotypes was 10.64 (q/ha).
 

Table 3: Stability parameters for grain yield (q/ha) of 15 pigeonpea genotypes across 5 environments.


       
On the basis of environmental index value in terms of negative and positive, Bidar (E1) and Kalaburagi (E3) were deprived and Bheemarayana gudi (E2), Hagari (E4) and Malnoor (E5) were affluent environments. Among the genotypes GRG-152 (G1), GRG-811 (G15), KRG-244 (G9), AGL-1603-2 (G12) and TS-3R (G14) recorded higher average yields, while genotypes viz., KRG-251 (G11), KRG-155 (G10), GRG-222 (G3), KGR-33 (G7), GRG-177 (G2), KRG-223 (G6), GRG-617 (G4), WRP-R-29-4 (G13) and KRG-221 (G8) showed less than normal yields. The findings of Akter et al., (2014), Singh et al., (2018) revealed the variable performance of genotypes in different locations.
 

AMMI 1 biplot display Biplots are graphs where aspects of both genotypes and environments are plotted on the same axis, so that inter relationships can be visualized. Genotypes that group together have similar adaptation while environments which group together influences the genotypes in the same way. If a genotype or an environment has a IPCA1 score of nearly zero, it has small interaction effects and considered as stable. When a genotype and environment have the same sign on the PCA axis, their interaction is positive and if different, their interaction is negative. The AMMI 1 biplot expected yield clearly indicated for any genotype and environment combination can be calculated from Fig 1 following standard procedures suggested by Zobel et al., (1988).
 

Fig 1: AMMI 1 biplot for grain yield (q/ha) of 15 pigeonpea genotypes (G) and five environments (E) using genotypic and environmental scores.


       
The genotype KRG-224 (G5) though having highest yield, but recorded negative IPCA1 score indicating it’s environment sensitivity. The environments Hagari (E4) and Malnoor (E5) had positive IPCA 1 score, though Bheemarayana Gudi (E2) has highest environment mean yield, negative IPCA1 score were observed indicating the interaction effect on the genotype, among all environments. Malnoor (E5) had positive IPCA1 score near zero and hence had small interaction effects and which was favourable environment for the genotypes viz., GRG-152 (G1), GRG-811 (G15), KRG-244 (G9), AGL-1603-2 (G12) and TS-3R (G14). The genotype GRG-152 (G1) showed IPCA1 score close to zero, indicating that the variety was stable and less influenced by the environments. Similarly, the genotype WRP-R-29-4 (G13) was moderately stable across environments (low positive IPCA1 score) but possessed below average yield. On the other hand, KRG-155 (G10), KRG-251 (G11), GRG-617 (G4), KRG-223 (G6), GRG-222 (G3) and GRG-177 (G2) and environments like, Bidar (E1) and Kalaburagi (E3) had below average yield with negative IPCA1 score indicating that these varieties were less influenced by the environments. Thanki et al., (2010); Sawargaonkar et al., (2011) and Niranjana et al., (2014) identified genotypes with average responsiveness and also genotypes with higher environmental sensitivity.
 

AMMI 2 biplot display

 
In AMMI 2 biplot, (Fig 2) the environmental scores are joined to the origin by side lines. Sites with short spokes do not exert strong interactive forces. Those with long spokes exert strong interaction. In Fig 2 where the points representing the environments E1, E2, E3, E4 and E5 are connected to the origin. The environments Bidar (E1) and Kalaburagi (E3) had short spokes and they do not exert strong interactive forces. The genotypes occurring close together on the plot will tend to have similar yields in all environments, while genotypes far apart may either differ in mean yield or show a different pattern of response over the environments. Hence, the genotypes near the origin are not sensitive to environmental interaction and those distant from the origins are sensitive and have large interaction.
 

Fig 2: AMMI 2 Biplot for grain yield (q/ha) showing the interaction of IPCA 2 against IPCA 1 scores of 15 pigeonpea genotypes (G) in five environments (E).


 
       
In the present study, KRG-221 (G8), KGR-33 (G7), GRG-617 (G4), AGL-1603-2 (G12) and GRG-811 (G15) were more responsive since they were away from the origin whereas the genotypes viz., KRG-251 (G11), KRG-223 (G6) and KRG-224 (G5) were close to the origin and hence they were non sensitive to environmental interactive forces. Among the environment Bidar (E1) and Kalaburagi (E3) were near to the origin and they do not exert strong interactive forces compared to Bheemarayana gudi (E2), Hagari (E4) and Malnoor (E5). Similar results were obtained by Jogendara et al., 2018; Akter et al., 2014.
 
Reaction of genotypes to diseases
 
The disease reaction study revealed that a total of 14 (including two check varieties) out of 19 genotypes showed resistant reaction for Fusarium wilt (Table 4), with PDI range of 3.39 (GPT-1) to 9.14 (WRP-R-29-4). Moderately resistant reaction for Fusarium wilt was observed in 1 out of 19 genotypes with PDI 14.79 per cent. The genotypes GRG-152 (G1), GRG-811 (G15), KRG-244 (G9), AGL-1603-2 (G12) and TS-3R (G14) were wilt resistant as well as in the track of high yield as indicated by their per se performance. Hence, these genotypes could be used as variety or choice of parent for hybridization programme. Sharma et al., (2012), evaluated the pigeonpea to identify the resistance to Fusarium wilt under artificial field epiphytotic conditions. Prashanti et al., (2009) screened 88 lines of pigeonpea and identified 14 resistant lines for Fusarium wilt.

Table 4: Disease reaction of pigeonpea genotypes for Fusarium wilt and sterility mosaic disease under field condition for Kharif 2018-19.


       
Sterility mosaic disease was observed in four out of 19 genotypes with PDI range of 9.50 (JSA-59-2) to 10.00 per cent (AGL-1603-2 and ICPL-15017). Moderate resistant disease reaction for sterility mosaic disease was observed in five genotypes (including one check variety) with PDI range of 11.43 (GRG-152) to 20.00 per cent (GRG-177). Sharma et al., (2013) identified pooled resistance to Fusarium wilt and sterility mosaic disease in 54 lines, out of 3000 germplasm evaluated for three consecutive years. Muniswamy et al., (2017) screened 23 pigeonpea genotypes and identified combined resistant lines for Fusarium wilt and sterility mosaic disease.
       
The genotype AGL-1603-2 (G12) was resistant to  both Fusarium wilt and sterility mosaic disease coupled with high yield as indicated by its per se performance and the genotype  GPT-1 showed resistance to both Fusarium wilt, sterility mosaic disease. Two genotypes viz., GRG-152 (G1) and GRG-811 (G15) showed resistance to Fusarium wilt and moderate resistance to sterility mosaic disease and possess high yielding as indicated by their per se performance.

CONCLUSION

The genotypes GRG-152 (G1), GRG-811 (G15), KRG-244 (G9), AGL-1603-2 (G12) and TS-3R (G14) were hardly affected by the G ´ E interaction and thus would perform well across a wide range of environments. The genotype AGL-1603-2 (G12) showed resistance to both Fusarium wilt and sterility mosaic disease besides high yielding potential as indicated by its per se performance and the genotype GPT-1 showed resistance to both Fusarium wilt and sterility mosaic disease. Two genotypes viz., GRG-152 (G1) and GRG-811 (G15) showed resistance to Fusarium wilt and moderate resistance to sterility mosaic disease and high yielding. Hence, these genotypes could be used directly as a varieties or preference of parent in hybridization programme.

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