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Universitas Hasanuddin
Research output:Contribution to journalArticlepeer-review

Stability and adaptability analyses to identify suitable high-yielding maize hybrids using PBSTAT-GE

Azrai M.

Open Agriculture

Q2
Published: 2025

Abstract

Abstract An assessment of the stability and adaptability of released varieties is needed to ensure their potential. Analysis of both approaches can be performed through PBSTAT-GE. However, the application of PBSTAT-GE in combination with index selection for elucidating stability and adaptability in hybrid maize has not been reported in depth. Therefore, this study aimed to identify suitable high-yielding maize hybrids based on stability and adaptability analyses using PBSTAT-GE software followed by index selection. The study was conducted in eight locations having different agro-climates in 2023, including eight test hybrids and two check varieties. The experiment used a randomized complete block design with three replications in each environment, so there are 300 experimental units in this study. This study focused on the grain yield, which was analyzed for potential stability and adaptability in the PBSTAT-GE. Based on the results of this study, PBSTAT-GE has the potential to be applied for comprehensive stability and adaptability analysis. The max–min standardization-based accumulation index can combine parametric stability-based assessment, non-parametric stability, and productivity potential of a genotype. Based on this approach, MAI-UH 08 and MAI-UH 03 are recommended for hybrid maize variety release with good stability and adaptability potential in both. In addition, lines MAI-UH 01, MAI-UH 02, and MAI-UH 05 can be recommended in Tomohon and Boyolali based on good adaptability potential. In conclusion, PBSTAT-GE is highly suitable and recommended for stability and adaptability analysis in identifying high-yielding maize hybrids, especially using a max–min standardization-based accumulation index.

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10.1515/opag-2025-0447

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AdaptabilitySciences
HybridSciences
BiologySciences
Stability (learning theory)Sciences
AgronomySciences
BiotechnologySciences
Computer scienceSciences
EcologySciences
Machine learningSciences