| 摘要: |
| 为有效鉴定和评价黄淮海玉米品种产量丰产性、稳定性和适应性，筛选出适宜黄淮海地区推广种植的优良玉米品种，采用AMMI模型稳定性参数和非参数方法对黄淮海玉米区域试验15个参试品种在6个省12个试点进行产量稳定性评价，同时利用GGE双标图对参试品种进行适应性分析。结果表明：方差分析变异来源中基因型（G）、环境（E）以及基因型与环境互作（GEI）均达到了极显著差异（P＜0.01）和显著差异（P＜0.05），其中环境效应在总平方和中占比最大，为85.02%，其次是基因型与环境互作效应，占比为10.74%，基因型所占比例最小，为4.25%。对基因型与环境互作效应进行分解，前5个交互主成分（IPCA 1~IPCA 5）差异达到了显著和极显著水平，共计解释了85.41%的互作效应。基于Spearman相关分析，非参数统计因子和参数统计因子内部各指标之间存在着显著的相互关系。生物干重与非参数统计因子S（6）、Z1、Z2、NP（2）、NP（3）、NP（4）、KR间均呈显著负相关（P＜0.05），与S（1）和S（2）则呈显著正相关（P＜0.05）。AMMI双标图显示，参试品种整株生物干重范围在18 000~22 000 kg/hm2 ，‘渝单805’‘正大511’‘青秀001’和‘皖农科青贮8号’丰产性较好，‘金诚6’丰产性最差。‘正大511’‘雅玉7758’‘KNX22002’和‘青秀001’等参试品种稳定性较强，山东德州较其他试点有较好的辨别力，而山东泰安和河南濮阳等试点辨别力较差。GGE双标图分析表明‘正大511’属于丰产性和稳定性均较好的品种，为黄淮海地区的理想品种，其次为‘青秀001’和‘渝单805’，而‘安科青2号’离圆心位置最远，为参试品种中最不理想的品种。综上，采用AMMI模型和GGE双标图评价可为黄淮海地区玉米品种示范和推广提供依据。 |
| 关键词: AMMI模型 GGE双标图 稳定性统计 基因型与环境互作 适应性 |
| DOI：10.11841/j.issn.1007-4333.2024.09.03 |
| 投稿时间：2024-03-05 |
| 基金项目:河北省农林科学院科技创新专项（2023KJCXZX-HZS-1， 2023KJCXZX-HZS-12）；国家玉米产业技术体系（CARS-02）；国家重点研发计划（2022YFD1201002-3）；河北省玉米现代种业科技创新团队（21326319D） |
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| Stability and adaptability assessment for the selection of elite maize cultivars based on AMMI model and GGE-biplot |
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YUE Haiwang, WEI Jianwei, LIU Pengcheng, CHEN Shuping, BU Junzhou
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| (Dryland Farming Institute / Hebei Provincial Key Laboratory of Crops Drought Resistance Research， Hebei Academy of Agriculture and Forestry Sciences， Hengshui 053000， China) |
| Abstract: |
| In order to effectively identify and evaluate the high yield， stability and adaptability of maize hybrids growing in the Huanghuaihai Plain in China， and to select superior maize hybrids suitable for widespread cultivation in this region， the stability parameters of AMMI model and non-parametric methods were used to evaluate the yield stability of 15 evaluated cultivars in 12 locations across 6 provinces of a regional trial of summer maize in the Huanghuaihai Plain. Meanwhile， the adaptability of the evaluated hybrids was analyzed by using the GGE biplot. The results of combined analysis of variance （ANOVA） and additive main effect and multiplicative interaction （AMMI） analysis showed that environments （E）， genotypes （G）， GE interaction， accounted for 85.02%， 4.25% and 10.74% of the total variation， respectively. The GE interaction effect was divided into five interaction principal component axes （IPCAs）， among them the first five IPCAs were highly significant， and a total of 85.41% of the GE interaction effects were explained. Based on Spearman correlation analysis， there were significant interrelationships within each of the non-parametric and parametric statistical statistics. Significant negative correlations were discovered between the whole plant dry yield and the non-parametric statistical statistics S（6）， Z1， Z2， NP（2）， NP（3）， NP（4） and KR， while significant positive correlations were found with S（1） and S（2）. The results of AMMI biplot analysis showed that the whole plant dry weight of the evaluated hybrids ranged from 18 000 kg/hm2 to 22 000 kg/hm2. ‘Yudan 805’ ‘Zhengda 511’ ‘Qingxiu 001’ and ‘Wannongkeqingzhu 8’ were more productive and ‘Jincheng 6’ was the least productive. ‘Zhengda 511’ ‘Yayu 7758’ ‘KNX 22002’ and ‘Qingxiu 001’ showed more stability than the other hybrids. Dezhou of Shandong Province had better discriminating power than other sites， while sites such as Tai’an and Puyang had poor discriminating power. The GGE biplot analysis showed that ‘Zhengda 511’ was a good cultivar in terms of high-yielding and stability and the ideal hybrid for this study， followed by ‘Qingxiu 001’ and ‘Yudan 805’. ‘Ankeqing 2’ was the furthest from the center of the circle and the least desirable cultivar in this study. In conclusion， the use of the AMMI model stability analysis and GGE biplot can provide a basis for the demonstration and promotion of maize hybrids in Huanghuaihai Plain. |
| Key words: AMMI model GGE Biplot stability statistics genotype × environment interaction adaptability |
