文献类型： 外文期刊

作者： Yue, Haiwang ¹ ; Wang, Yanbing ² ; Chen, Zhaoyang ¹ ; Zhu, Jiashuai ³ ; Behera, Partha Pratim ⁴ ; Liu, Pengcheng ¹ ; Yang, Haoxiang ¹ ; Wei, Jianwei ¹ ; Bu, Junzhou ¹ ; Jiang, Xuwen ⁵ ; Ma, Wujun ⁵ ;

作者机构： 1.Hebei Acad Agr & Forestry Sci, Dryland Farming Inst, Hebei Prov Key Lab Crops Drought Resistance Res, Hengshui, Peoples R China

2.Inst Cereal & Oil Crops Hebei Acad Agr & Forestry, Shijiazhuang, Peoples R China

3.Univ Melbourne, Fac Sci, Parkville, Vic, Australia

4.Assam Agr Univ, Dept Plant Breeding & Genet, Jorhat, India

5.Qingdao Agr Univ, Coll Agron, Qingdao, Peoples R China

6.Shandong Bairuijia Food Co Ltd, Food Proc Engn Technol Res & Dev Ctr, Laizhou, Peoples R China

关键词： mega-environment; GGE biplot; mixed model; grain yield; envirotyping techniques

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： Introduction Winter wheat is a crucial crop extensively cultivated in northern China, where its grain yield is influenced by genetic factors (G), environmental conditions (E), and their interactions (GEI). Accurate yield estimation depends on understanding the patterns of GEI in multi-environment trials (METs).Methods From 2014 to 2018, continuous experiments were conducted in the Heilonggang region of the North China Plain (NCP), evaluating 71 winter wheat genotypes across 16 locations over five years. Leveraging 30 years of environmental data, including 19 meteorological parameters and 6 soil physicochemical properties, the study analyzed GEI and identified four distinct mega-environments (MEs) using advanced environmental classification techniques.Results Variance analysis of genotype-year combinations at individual locations revealed significant differences among genotypes. Furthermore, the joint analysis showed that GEI variance exceeded the variance attributed to genotypic effects alone. The Additive Main Effects and Multiplicative Interaction (AMMI) model indicates that the first three interaction principal component axes (IPCAs) account for over 70% of the GEI variance, thereby demonstrating the relevance of this model to the current study. Principal Component Analysis (PCA) across the five-year study period revealed positive correlations between grain yield and vapor pressure deficit (VPD), evapotranspiration potential (ETP), temperature range (TRANGE), available soil water (ASKSW), and sunshine duration. Conversely, negative correlations were observed with relative humidity at 2 meters (RH2M), total precipitation (PRECTOT), potential evapotranspiration (PETP), and dew point temperature at 2 meters (T2MDEW). Among the meteorological and soil variables, minimum temperature (TMIN), fruiting rate (FRUE), temperature at 2 meters (T2M), and clay content (CLAY) emerged as the most significant contributors to yield variation during the study period. Based on GGE biplot analysis, superior genotypes were identified for their respective regions: JM196, WN4176, and HN6119 in 2014; ZX4899, H9966, and LM22 in 2015; BM7, KN8162, and KM3 in 2016; HH14-4019, HM15-1, and HH1603 in 2017; and S14-6111 and JM5172 in 2018. Feixiang and Shenzhou were identified as the most discriminative and representative locations.Discussion These findings provide a scientific basis for optimizing winter wheat cultivation strategies in northern regions. Based on long-term data from the North China Plain, future work can further validate their applicability in other regions.