Response of Quality Protein Maize (QPM) hybrids for grain yield in diverse environments

Ramesh Kumar; Jyoti Kaul; Ya shmeet Kaur; A K Das; Avinash Singode; Mukesh Choudhary; R B Dubey; D Sravani; G Mukri; S Rakshit

doi:10.56093/ijas.v90i4.102218

Authors

Ramesh Kumar ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
Jyoti Kaul ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
Ya shmeet Kaur ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
A K Das ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
Avinash Singode ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
Mukesh Choudhary ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
R B Dubey ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
D Sravani ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
G Mukri ICAR-Indian Institute of Maize Research, Ludhiana 141004, India
S Rakshit ICAR-Indian Institute of Maize Research, Ludhiana 141004, India

https://doi.org/10.56093/ijas.v90i4.102218

Keywords:

Additive main effects and multiplicative interaction (AMMI), Genotype and environment interaction (GÃ—E), GGE biplot, PCA, QPM

Abstract

Genotype (G) × environment (E) interaction is very important for the evaluation of cultivars in breeding programmes. Present study was conducted to assess the effect of environment and yield stability of 68 quality protein maize (QPM) hybrids at three test environments [Begusarai (E1), Udaipur (E2) and Karim Nagar (E3)] in randomized block design (RBD). Data was analysed using the additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype by environment (GGE) biplot methods. The variation in genotypes, environments and genotype × environment interactions (G×E) was highly significant. Maximum variation was explained by G×E interactions (53.84%) and least by differences in environmental conditions (2.36%). Genotype × environment interaction was main source of variation followed by genotypes and environments. Together the two AMMI principal coordinates axes (PCA) explained 100% of phenotypic variation. AMMI Stability value (ASV) was calculated using ASV scores. Among these QPM hybrids, G59 was found to be the most stable with ASV of 0.174. The GGE biplot explained 77.41% of the total variation relative to G and GEI. Superior cross combinations for specific locations were also identified, viz. DQL2053 ×CML161, DQL2028 × CML161, DQL2047 × CML165, DQL2037 × CML161, DQL2042 × CLQRCY40, DQL2032 × CML165, DQL2047 × CML165 and DQL2072 ×CLQRCY40 in E1, DQL2063 × CML161, DQL2057 × CML161, DQL2053 × CML165, DQL2080 × CLQRCY40 and DQL2065 × CLQRCY40 in E2 and DQL2063 × CML161, DQL2039 × CML165, DQL2140 × CML161, DQL2082 × CLQRCY40 and DQL2024 × CML165 in E3.

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