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

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

[https://doi.org/10.5061/dryad.8sf7m0cvn](https://doi.org/10.5061/dryad.8sf7m0cvn)

The data is related to paper \"Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (*Zea mays L*.)\". The paper explores the mega environment pattern of Northern Karnataka region.

## Description of the data and file structure

The data contains four columns,

YEAR: contains year of experiment. The experiment was conducted from year 2016 to 2019.

ENV: environment, the locations of experiment for the given year. The number of locations vary year to year

GEN: genotypes, maize hybrids involved in experiments. The number of hybrids vary year to year

REP: replication, three replications for all the experiments

GY: grain yield, unit is Quintals/Hectare

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