Validating superior maize hybrids in all India coordinated trials using REMATTOOL-R: a decision support approach

Area of Science:

• Agricultural Science
• Plant Breeding
• Genetics

Background:

• Maize hybrid advancement relies on multi-environment trials (METs) integrating grain yield, maturity, and agronomic performance.
• Interpreting large MET datasets is complex, time-consuming, and subjective, necessitating objective decision-support tools.
• The All India Coordinated Research Project (AICRP) on Maize seeks to improve hybrid evaluation frameworks.

Purpose of the Study:

• To evaluate the effectiveness of REMATTOOL-R in validating the existing maize hybrid advancement framework under the AICRP on Maize.
• To assess REMATTOOL-R's ability to objectively identify superior maize hybrids by integrating multiple traits.

Main Methods:

• Analysis of multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late (Kharif 2020-21) across five Indian locations.
• Utilized REMATTOOL-R to integrate grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand.
• Identified superior hybrids using least-square means and a ≥5% grain yield superiority threshold over the standard check.

Main Results:

• REMATTOOL-R enabled rapid visualization and integrated assessment of agronomic traits for objective hybrid identification.
• Five experimental hybrids (PM 21109L, R8050, PM 21111L, BIO 978, DKC 9226) showed ≥5% higher grain yield than the standard check with comparable maturity and agronomic traits.
• REMATTOOL-R identified hybrids that corresponded with official AICRP advancement decisions, with three progressing to advanced testing stages.

Conclusions:

• REMATTOOL-R offers an efficient, transparent, and reproducible framework for evaluating maize hybrids in METs.
• The tool complements existing AICRP procedures, facilitating objective advancement decisions and reducing subjectivity.
• REMATTOOL-R is a valuable decision-support tool for coordinated maize breeding programs and large-scale hybrid evaluation systems.