1- Urmia University
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Extended Abstract
Background: The selection of superior maize (Zea mays L.) genotypes for achieving high grain yield is of great importance. Maize production is increasingly affected by abiotic stresses such as salinity and phosphorus deficiency, which threaten yield stability and food security in stressed environments. Genetic diversity and selection are pivotal components in maize breeding, contributing to the maximization of genetic gain and productivity. The genotype × environment interaction (G×E) is one of the most significant limiting factors in breeding programs, as it reduces the correlation between genotypic and phenotypic parameters and complicates selection progress. Among the various models proposed for evaluating G×E interactions, the AMMI and GGE biplot models are among the most practical. The objective of this study was to analyze the genotype-by-environment interaction for grain yield stability and several physiological traits in maize across different climatic conditions. The research specifically emphasized identifying adapted and stable genotypes for the target environments using the Additive Main Effects and Multiplicative Interaction (AMMI) model and the graphical GGE biplot method.
Methods: In this study, 86 maize genotypes obtained from several research centers (Razi University of Kermanshah, Khorasan Razavi Agricultural and Natural Resources Research Center, and the Seed and Plant Improvement Institute of Karaj) were evaluated under four environmental conditions: non-saline, salinity stress, optimal phosphorus, and phosphorus deficiency stress. The objective was to investigate the stability of their grain yield (GY), biological yield (BY), canopy temperature (CT), and relative water content (RWC). The pot experiment was conducted in an open area at the Faculty of Agriculture, Urmia University, using a completely randomized design with three replications during the 2017 growing season. Stability analysis methods—Additive Main Effects and Multiplicative Interaction (AMMI) and GGE biplot—were employed to analyze the genotype-by-environment interaction for the physiological traits and grain yield.
Results: Given the significant genotype-by-environment interaction, stability analysis was conducted using two multivariate methods: AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype and Genotype-by-Environment) biplot. The results demonstrated that the first principal component accounted for the majority of the genotype-by-environment interaction variation for all four traits in both analytical methods. The results from the AMMI Stability Value (ASV) and mean performance comparisons indicated that Genotypes 21 and 14 for grain yield (GY), Genotype 63 for biological yield (BY), Genotype 40 for canopy temperature (CT), and Genotype 13 for relative water content (RWC) are ideal candidates for utilization in breeding programs. Genotype 11 (P14L2) was identified as the most stable genotype across biological yield and canopy temperature triats using both AMMI and GGE biplot analyses. Additionally, Genotype 77 (Line 11) demonstrated stability for both grian yield and biological yield traits across both analytical approaches. Genotype 4 (P9L3Kahriz) was recognized as a genotype with general adaptability for all four traits across all four environments in both analytical methods. Furthermore, several genotypes exhibited specific adaptations: Genotype 25 (P13L1) for grain yield under non-saline conditions, Genotype 10 (P11L7) for both grain and biological yield under salinity stress, Genotype 28 (P10L9) for both grain and biological yield under non-saline conditions, and Genotype 16 (P16L4Kahriz) for all four traits under salinity stress were identified as well-adapted genotypes in both analytical approaches.
Conclusion: The studied genotypes showed considerable genetic diversity for the investigated traits: grain yield (GY), biological yield (BY), canopy temperature (CT), and relative water content (RWC). Based on the results of this study, genotype 11 (P14L2) was identified as the most stable genotype for biological yield and canopy temperature triats and Genotype 77 (Line 11) for both grian yield and biological yield traits by both Additive Main effects and Multiplicative Interaction (AMMI) and GGE biplot analyses. These findings demonstrate the efficacy of AMMI and GGE analyses in explaining complex stress responses and identifying resilient genotypes. Introducing new germplasm is essential for maintaining genetic diversity, improving traits, enhancing adaptation to environmental changes, accelerating crop improvement, protecting crops, and responding to market demands. This action is a fundamental pillar for addressing the needs of food security, sustainable agriculture, and plant breeding. By introducing genotypes resistant to salinity and phosphorus deficiency stress, this research takes a step towards addressing these environmental challenges and promoting sustainable agriculture.
Background: The selection of superior maize (Zea mays L.) genotypes for achieving high grain yield is of great importance. Maize production is increasingly affected by abiotic stresses such as salinity and phosphorus deficiency, which threaten yield stability and food security in stressed environments. Genetic diversity and selection are pivotal components in maize breeding, contributing to the maximization of genetic gain and productivity. The genotype × environment interaction (G×E) is one of the most significant limiting factors in breeding programs, as it reduces the correlation between genotypic and phenotypic parameters and complicates selection progress. Among the various models proposed for evaluating G×E interactions, the AMMI and GGE biplot models are among the most practical. The objective of this study was to analyze the genotype-by-environment interaction for grain yield stability and several physiological traits in maize across different climatic conditions. The research specifically emphasized identifying adapted and stable genotypes for the target environments using the Additive Main Effects and Multiplicative Interaction (AMMI) model and the graphical GGE biplot method.
Methods: In this study, 86 maize genotypes obtained from several research centers (Razi University of Kermanshah, Khorasan Razavi Agricultural and Natural Resources Research Center, and the Seed and Plant Improvement Institute of Karaj) were evaluated under four environmental conditions: non-saline, salinity stress, optimal phosphorus, and phosphorus deficiency stress. The objective was to investigate the stability of their grain yield (GY), biological yield (BY), canopy temperature (CT), and relative water content (RWC). The pot experiment was conducted in an open area at the Faculty of Agriculture, Urmia University, using a completely randomized design with three replications during the 2017 growing season. Stability analysis methods—Additive Main Effects and Multiplicative Interaction (AMMI) and GGE biplot—were employed to analyze the genotype-by-environment interaction for the physiological traits and grain yield.
Results: Given the significant genotype-by-environment interaction, stability analysis was conducted using two multivariate methods: AMMI (Additive Main Effects and Multiplicative Interaction) and GGE (Genotype and Genotype-by-Environment) biplot. The results demonstrated that the first principal component accounted for the majority of the genotype-by-environment interaction variation for all four traits in both analytical methods. The results from the AMMI Stability Value (ASV) and mean performance comparisons indicated that Genotypes 21 and 14 for grain yield (GY), Genotype 63 for biological yield (BY), Genotype 40 for canopy temperature (CT), and Genotype 13 for relative water content (RWC) are ideal candidates for utilization in breeding programs. Genotype 11 (P14L2) was identified as the most stable genotype across biological yield and canopy temperature triats using both AMMI and GGE biplot analyses. Additionally, Genotype 77 (Line 11) demonstrated stability for both grian yield and biological yield traits across both analytical approaches. Genotype 4 (P9L3Kahriz) was recognized as a genotype with general adaptability for all four traits across all four environments in both analytical methods. Furthermore, several genotypes exhibited specific adaptations: Genotype 25 (P13L1) for grain yield under non-saline conditions, Genotype 10 (P11L7) for both grain and biological yield under salinity stress, Genotype 28 (P10L9) for both grain and biological yield under non-saline conditions, and Genotype 16 (P16L4Kahriz) for all four traits under salinity stress were identified as well-adapted genotypes in both analytical approaches.
Conclusion: The studied genotypes showed considerable genetic diversity for the investigated traits: grain yield (GY), biological yield (BY), canopy temperature (CT), and relative water content (RWC). Based on the results of this study, genotype 11 (P14L2) was identified as the most stable genotype for biological yield and canopy temperature triats and Genotype 77 (Line 11) for both grian yield and biological yield traits by both Additive Main effects and Multiplicative Interaction (AMMI) and GGE biplot analyses. These findings demonstrate the efficacy of AMMI and GGE analyses in explaining complex stress responses and identifying resilient genotypes. Introducing new germplasm is essential for maintaining genetic diversity, improving traits, enhancing adaptation to environmental changes, accelerating crop improvement, protecting crops, and responding to market demands. This action is a fundamental pillar for addressing the needs of food security, sustainable agriculture, and plant breeding. By introducing genotypes resistant to salinity and phosphorus deficiency stress, this research takes a step towards addressing these environmental challenges and promoting sustainable agriculture.
Keywords: AMMI, Genotype × Environment Interaction, GGE Biplot, Salinity Stress, Phosphorus Deficiency Stress, Yield Stability
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