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Navigating Hybrid-Environment Interaction in Maize Evaluation: Parametric and Non-Parametric Insights

Navigating Hybrid-Environment Interaction in Maize Evaluation: Parametric and Non-Parametric Insights

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DOI	10.20900/cbgg20250008
刊名	Crop Breeding, Genetics and Genomics CBGG
年，卷(期)	2025, 7(3)
作者	Sunil Neelam*,Jyothi Bhoga,Nagesh Kumar Mallela Venkata,Bhadru Dharavath,Vinodhana Kumari,Rajashekhar Mahantaswami Kachhapur,Sravani Dinasarapu,Ramesh Kumar Phagna,Dhandapani Appavoo,
作者单位	Winter Nursery Centre, Indian Council of Agricultural Research (ICAR), Indian Institute of Maize Research, Rajendranagar, Hyderabad 500030, India ; Maize Research Centre, Professor Jayashankar Telangana State Agricultural University, Hyderabad 500030, India ; Centre for Plant Breeding & Genetics, Department of Millets, Tamil Nadu Agricultural University, Coimbatore 641003, India ; All India Coordinated Research Project on Maize, University of Agricultural Sciences, Dharwad 580005, India ; All India Coordinated Research Project on Maize, Agricultural Research Station, Karimnagar 505002, India ; Indian Council of Agricultural Research (ICAR), Indian Institute of Maize Research, Ludhiana 141004, India ; Indian Council of Agricultural Research (ICAR), National Academy of Agricultural Research Management, Hyderabad 500030, India ;
摘要	Development of high-yielding maize (Zea mays L.) hybrids, along with being well-adapted to many environments, is the most important goal of the National Maize Research Program in India. genotype × environment interaction (GEI) continues to be a major challenging issue to plant breeders and production agronomists. The present research investigates the (GEI), specifically examining hybrid stability and yield performance across distinct environmental conditions. A total of 62 maize hybrids were evaluated using both parametric and non-parametric methodologies across the four environments (Coimbatore, Dharwad, Hyderabad, Karimnagar) during Kharif 2021. Combined analysis of variance (ANOVA) and the Additive Main Effects and Multiplicative Interaction (AMMI) model are widely employed in multi-environment trials (MET) to evaluate genotype performance and stability. The AMMI model integrates ANOVA for assessing additive main effects with principal component analysis (PCA) to explore GEI, offering a comprehensive understanding of hybrid responses across environments. Moreover, PCA and correlation analysis were utilized to elucidate the relationships between parametric and non-parametric metrics, facilitating a comprehensive understanding of hybrid performance dynamics. The findings underscored the necessity of simultaneously considering yield and stability to harness GEI effects, thereby refining the maize cultivar selection process. The stability parameters, such as S(6), NP(2), NP(3), NP(4), KR, and CVi, were identified as effective statistics for screening desirable hybrids as they had a significant positive correlation with mean yield. Furthermore, according to the static and dynamic concepts of stability, the results revealed that stability statistics clustered into five groups. The overall stability analysis following different stability methods concluded that G51, G26, G30, G31, G12, G2, G27, G20, G47, and G56, identified as high yielding and stable across the four tested environmental conditions. Through the integration of yield and stability considerations and the utilization of analytical tools like PCA, consisting of both parametric and non-parametric statistics and cluster analysis, this study contributes to identifying resilient maize cultivars capable of confronting the challenges posed by climate change.
Abstract	Development of high-yielding maize (Zea mays L.) hybrids, along with being well-adapted to many environments, is the most important goal of the National Maize Research Program in India. genotype × environment interaction (GEI) continues to be a major challenging issue to plant breeders and production agronomists. The present research investigates the (GEI), specifically examining hybrid stability and yield performance across distinct environmental conditions. A total of 62 maize hybrids were evaluated using both parametric and non-parametric methodologies across the four environments (Coimbatore, Dharwad, Hyderabad, Karimnagar) during Kharif 2021. Combined analysis of variance (ANOVA) and the Additive Main Effects and Multiplicative Interaction (AMMI) model are widely employed in multi-environment trials (MET) to evaluate genotype performance and stability. The AMMI model integrates ANOVA for assessing additive main effects with principal component analysis (PCA) to explore GEI, offering a comprehensive understanding of hybrid responses across environments. Moreover, PCA and correlation analysis were utilized to elucidate the relationships between parametric and non-parametric metrics, facilitating a comprehensive understanding of hybrid performance dynamics. The findings underscored the necessity of simultaneously considering yield and stability to harness GEI effects, thereby refining the maize cultivar selection process. The stability parameters, such as S(6), NP(2), NP(3), NP(4), KR, and CVi, were identified as effective statistics for screening desirable hybrids as they had a significant positive correlation with mean yield. Furthermore, according to the static and dynamic concepts of stability, the results revealed that stability statistics clustered into five groups. The overall stability analysis following different stability methods concluded that G51, G26, G30, G31, G12, G2, G27, G20, G47, and G56, identified as high yielding and stable across the four tested environmental conditions. Through the integration of yield and stability considerations and the utilization of analytical tools like PCA, consisting of both parametric and non-parametric statistics and cluster analysis, this study contributes to identifying resilient maize cultivars capable of confronting the challenges posed by climate change.
关键词	stability, parametric; non-parametric; AMMI; Maize
KeyWord	stability, parametric; non-parametric; AMMI; Maize
基金项目
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引用本文

Sunil Neelam*,Jyothi Bhoga,Nagesh Kumar Mallela Venkata,Bhadru Dharavath,Vinodhana Kumari,Rajashekhar Mahantaswami Kachhapur,Sravani Dinasarapu,Ramesh Kumar Phagna,Dhandapani Appavoo. Navigating Hybrid-Environment Interaction in Maize Evaluation: Parametric and Non-Parametric Insights [J]. Crop Breeding, Genetics and Genomics. 2025; 7; (3). - .

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