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Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.) - MaRDI portal

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Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.)

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(Redirected from Dataset:6686681)



DOI10.5281/zenodo.8414465Zenodo8414465MaRDI QIDQ6686681

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 6 October 2023



Description

Multi-location experiments on maize were conducted from 2016 to 2019 at ten locations distributed across two agro-climatic zones (ACZ) i.e., ACZ-3 and ACZ-8 of Karnataka, India. Individual analysis of variance for each location-year combination showed significant differences among the hybrids; similarly, combined analysis showed a higher proportion of GE interaction variance than due to genotype. Mega-environments were identified using biplot approaches such as AMMI, GGE, and WAASB methodologies for the years 2016 to 2019. The BLUP method revealed a high correlation between grain yield and stability indices ranging from 0.67 to 1.0. Considering all three methods together, the three location pairs Arabhavi-Belavatagi, Bailhongal-Belavatagi, and Hagari-Sirguppa had three occurrences in the same mega-environment with a value of 0.67, and these location combinations consistently produced winning genotypes. Among the common winning genotypes identified, it was G7 during 2016 and 2017 and G10 during 2018 and 2019, based on WAASBY. The likelihood of Arabhavi-Nippani, Hagari-Mudhol, and Dharwad-Hagari occurring in the same mega-environment is minimal because they did not share the same winning genotype, with the exception of a small number of events. Despite being in the same agro-climatic zone, Arabhavi, Hagari, and Mudhol rarely had a winning genotype in common. An agro-climatic zone is grouped based on climatic and soil conditions which doesn't consider GE interaction of cultivars thus, releasing the cultivars for commercial cultivation considering mega environments pattern would enhance the yield for the given target region.






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