Adaptability and stability of soybean for grain yield in shaded environments

dc.contributor.authorGloria, Paulo Ricardo Américo
dc.contributor.authorPereira, Lucas Gomes da Silva
dc.contributor.authorZanuncio, José Cola
dc.contributor.authorMatsuo, Eder
dc.contributor.authorBonafé, Cristina Moreira
dc.contributor.authorEvaristo, Anderson Barbosa
dc.date.accessioned2026-09-22T22:15:02Z
dc.date.issued2024
dc.description.abstractThe aim of this study was to identify, through different methodologies, soybean cultivars with adaptability and stability for grain yield in environments with different levels of light restriction. The grain yield of sixteen cultivars was evaluated in environments with 25% and 48% restriction of photosynthetically active radiation (PAR) in the agricultural years 2019/2020 and 2021/2022. Based on the results, an adaptability and stability analysis was performed using the Eberhart and Russell, ANN (Artificial Neural Network) and GGE (Genotype plus Genotype-Environment interaction) methods. Grain yield varied with the levels of PAR restriction and agricultural years, being higher in environments A3 (2021/2022 25% PAR) and A1 (2019/2020 25% PAR), respectively. Cultivars NS7780, 8579RSF, NS8338 and RK6718 showed higher yield. The adaptability of cultivars AS3680, M7110, and 74177RSF was low, while that of NS8338 and NS7780 was high. Cultivars NS8338, 74177RSF, RK7518, and M6210 showed high phenotypic stability to environments.en
dc.identifier.citationZANUNCIO, José Cola. et al. Adaptability and stability of soybean for grain yield in shaded environments. Crop Breeding and Applied Biotechnology, Viçosa, v. 24, n. 4, p. 01-09, 2024. DOI: http://dx.doi.org/10.1590/1984-70332024v24n4a54.
dc.identifier.doihttp://dx.doi.org/10.1590/1984-70332024v24n4a54
dc.identifier.issn1984-7033
dc.identifier.urihttps://locus.ufv.br/handle/123456789/35910
dc.language.isoeng
dc.publisherCrop Breeding and Applied Biotechnology
dc.relation.ispartofseriesv. 24 ; n. 4
dc.rightsCreative Commons Attribution License
dc.subjectGlycine maxen
dc.subjectArtificial neural networken
dc.subjectPhotosynthetically active radiationen
dc.titleAdaptability and stability of soybean for grain yield in shaded environmentsen
dc.typeArtigo

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