| 摘要: |
| 为有效鉴定和评价河北省冬小麦品种的丰产性、稳定性和适应性，筛选出适宜河北省推广种植的优良小麦品种，采用稳定性指数（WAASB）和同时选择指数（WAASBY）对2018—2019年河北省冬小麦节水组14个参试品种在10个试点产量数据信息进行综合分析。结果表明，环境效应是引起产量变异的主要来源，其次是基因型与环境互作效应，基因型效应所占比例最小。通过卡方检验发现，AMMI 2和GGE 2分别为AMMI家族和GGE模型的适宜模型，基因型与环境互作信号和GGE信号比例分别占84.06%和85.25%。各种稳定性因子主成分分析表明，前2个主成分轴共计解释了方差变量的72.3%。产量与基于混合效应模型稳定因子以及几何适应性指数均呈现出高度正相关。‘衡H1704’‘中农202’‘众信8482’ ‘MG729’‘金禾16415’和‘石Ta14’等品种被籽粒产量×稳定性指数双标图选为丰产性突出、稳定性好的品种。其中，‘众信8482’的WAASBY同时选择指数得分最高，是参试品种中表现最好的。而‘邯科4242’的WAASBY得分最低，是表现最差的品种。大曹庄、深州、永年等试点适宜种植的冬小麦品种为‘衡H1704’和‘中农202’，藁城、武邑、南皮和邢台等试点适宜种植的冬小麦品种为‘博麦11号’‘邯生414’和‘科茂60’，邯郸和鹿泉等试点适宜种植的品种是‘邯科4242’和‘MG729’，‘众信8482’适宜在深州、辛集、邢台和永年等试点种植。综上，采用混合线性模型稳定性指数（WAASB）和同时选择指数（WAASBY）可为河北省冬小麦品种示范和推广提供依据。 |
| 关键词: AMMI模型 BLUP方法 稳定性指数 同时选择指数 多环境测试 |
| DOI：10.11841/j.issn.1007-4333.2024.08.04 |
| 投稿时间：2023-11-15 |
| 基金项目:河北省农林科学院创新工程（2022KJCXZX-RW-11） |
|
| High yield and stability evaluation of winter wheat varieties in Hebei Province by combining AMMI model and BLUP method |
|
YUE Haiwang1, BU Junzhou1, WEI Jianwei1, LIU Pengcheng1, WANG Yanbing2*, LI Yuan3*
|
| (1.Dryland Farming Institute/Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hebei Academy of Agriculture and Forestry Sciences,Hengshui 053000, China;2.Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China;3.Hebei Seed General Station, Shijiazhuang 050031, China) |
| Abstract: |
| In order to effectively identify and evaluate the yield， stability and adaptability of winter wheat varieties in Hebei Province， and to select superior wheat varieties suitable for widespread cultivation in this region， a new variety evaluation system， namely WAASB （weighted average of absolute scores） and WAASBY （considers both stability （WAASB） and mean performance （dependent variable， Y）） were employed. Comprehensive analysis was then conducted on the yield data of 14 trial varieties from the water-saving group in 10 test sites in Hebei Province during 2018—2019 winter wheat season. The AMMI （additive main effect and multiplicative interaction） model showed that environment effects were the main source of variation， followed by the interaction between genotype and environment， and genotype effects accounted for the smallest proportion. In terms of model diagnosis， AMMI 2 and GGE 2 were used as suitable models for the AMMI family and GGE models by FR test. The ratios of genotype and environment interaction （GE） signals and GGE signals accounted for 84.06% and 85.25%， respectively. The principal component analysis of various stability factors showed that the first two principal component axes collectively explained 78.5% of the variance. The grain yield was highly positively correlated with stability factors based on the mixed effects model and the geometric adaptability index. According to the GY ×WAASB biplot， ‘HH1704’ ‘ZN202’ ‘ZX8482’ ‘MG729’ ‘JH16415’ and ‘Sta14’ were selected as the varieties with outstanding high yield and good stability. Among the selected varieties， ‘ZX8482’ had the highest WAASBY score， which performed the best among the tested varieties， while ‘HK4242’ had the lowest WAASBY score， which performed the worst. The suitable varieties to be promoted in Dacaozhuang， Shenzhou and Yongnian were ‘HH1704’ and ‘ZN202’. The suitable varieties for planting in Handan and Luquan were ‘HK4242’ and ‘MG729’. It was also found that variety ‘ZX8482’ was suitable for planting in Shenzhou， Xinji， Xingtai and Yongnian. In conclusion， the use of the mixed linear model stability index （WAASB） and simultaneous selection index （WAASBY） can provide a basis for the demonstration and promotion of winter wheat varieties in Hebei Province. |
| Key words: AMMI model BLUP technology WAASB WAASBY METs |
