Genotype by Environment Interaction Analysis for Grain Yield and Yield Components of Summer Maize Hybrids across the Huanghuaihai Region in China

Haiwang Yue, Hugh G. Gauch, Jianwei Wei, Junliang Xie, Shuping Chen, Haicheng Peng, Junzhou Bu and Xuwen Jiang
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Haiwang Yue: Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, China
Hugh G. Gauch: Soil and Crop Sciences, Cornell University, Ithaca, NY 14853, USA
Jianwei Wei: Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, China
Junliang Xie: Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, China
Shuping Chen: Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, China
Haicheng Peng: Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, China
Junzhou Bu: Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences, Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, China
Xuwen Jiang: Maize Research Institute, College of Agronomy, Qingdao Agricultural University, Qingdao 266109, China

Agriculture, 2022, vol. 12, issue 5, 1-17

Abstract: Increasing the maize production capacity to ensure food security is still the primary goal of global maize planting. The purpose of this study was to evaluate genotypes with high yield and stability in summer maize hybrids grown in the Huanghuaihai region of China using additive main effects and multiplicative interaction (AMMI) analysis and best linear unbiased prediction (BLUP) technique. A total of 18 summer maize hybrids with one check hybrid were used for this study using a randomized complete block design (RCBD) with three replicates at 74 locations during two consecutive years (2018–2019). A three-way analysis of variance (ANOVA) and an AMMI analysis showed that genotype (G), environment (E), year (Y) and their interactions were highly significant ( p < 0.001) except G × E × Y for all evaluated traits viz., grain yield (GY), ear length (EL), hundred seed weight (HSW) and E × Y for hundred seed weight. The first seven interaction principal components (IPCs) were highly significant and explained 81.74% of the genotype by environment interaction (GEI). By comparing different models, the best linear unbiased prediction (BLUP) was considered the best model for data analysis in this study. The combination of AMMI model and BLUP technology to use the WAASB (weighted average of absolute scores from the singular value decomposition of the matrix of BLUP for GEI effects generated by linear mixed model) index was considered promising for similar research in the future. Genotypes H321 and Y23 had high yield and good stability, and could be used as new potential genetic resources for improving and stabilizing grain yield in maize breeding practices in the Huanghuaihai region of China. Genotypes H9, H168, Q218, Y303 and L5 had narrow adaptability and only apply to specific areas. The check genotype Z958 had good adaptability in most environments due to its good stability, but it also needs the potential to increase grain yield. Significant positive correlations were also found between the tested agronomic traits.

Keywords: Zea mays L.; AMMI model; BLUP; GEI; WAASB (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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