| 本文已被：浏览 394次 下载 243次 |
码上扫一扫！ |
| 基于AMMI模型和GGE双标图的西北春玉米品种区域试验综合评价 |
|
朱艳彬1,樊晓琴2,吉闻天3,宝春雨1,徐长成1,朱宏宇1,李晓雯1,郜睿1,张琪1,郭晋杰3*,郭延玲1*
|
|
|
| (1.辽宁东亚种业有限公司/玉米生物育种全国重点实验室/农业农村部东北主要作物遗传育种重点实验室, 沈阳 110164;2.新疆农业科学院 玛纳斯农业试验站, 新疆 昌吉 832200;3.河北农业大学 农学院/国家玉米改良中心河北分中心/河北省作物种质资源实验室, 河北 保定 071001) |
|
| 摘要: |
| 为筛选出西北地区高产稳产的品种和理想的测试点，利用AMMI模型和GGE双标图对2022年西北春玉米品种区域试验中的12个品种在19个试点的丰产性、稳产性和适应性进行分析，评价试点的区分力和代表性。结果表明，产量变异主要包括基因型、环境以及基因型与环境互作，分别占产量变异总平方和的4.39%、74.50%和10.08%。AMMI模型解释了82.76%的品种与试点互作效应。GGE双标图将19个试点分成3个生态区域；其中，甘肃省平凉市、天水市、内蒙古自治区巴彦淖尔、宁夏回族自治区平罗县、陕西省延安市、新疆维吾尔自治区昌吉州和可克达拉市7个试点的区分力和代表性较强。通过AMMI模型和GGE双标图，鉴定出适合西北地区高产稳产的品种为‘DK 2207’和‘玺旺188’。AMMI模型能够充分分解互作效应，并着重评价品种的稳定性，而GGE双标图则在评价试点的区分力和代表性方面有优势。综上，AMMI模型和GGE双标图的综合应用有助于提高西北春玉米品种和测试点评价的可靠性。 |
| 关键词: 玉米 区域试验 AMMI模型 GGE双标图 产量分析 |
| DOI：10.11841/j.issn.1007-4333.2023.12.02 |
| 投稿时间：2023-05-10 |
| 基金项目:农业生物育种重大项目(2022ZD0400604)；辽宁省农业重大专项(2022 JH1/10200001)；辽宁省中试基地公共服务平台能力建设项目(1686644347948)；玛纳斯县育种团队引进项目(MNSZZDX-2021-01)；海南省科技计划项目(2021 JJLH0087) |
|
| Comprehensive evaluation of regional trials for the spring maize hybrids in Northwest China based on AMMI model and GGE biplot |
|
ZHU Yanbin1,FAN Xiaoqin2,JI Wentian3,BAO Chunyu1,XU Changcheng1,ZHU Hongyu1,LI Xiaowen1,GAO Rui1,ZHANG Qi1,GUO Jinjie3*,GUO Yanling1*
|
| (1.Liaoning Dongya Seed Co., Ltd./State Key Laboratory of Maize Bio-Breeding/State Key Laboratory of Genetics and Breeding of Major Crops in Northeast China, Ministry of Agriculture and Rural Affairs, Shenyang 110164, China;2.Manas Agricultural Experimental Station of Xinjiang Academy of Agricultural Science, Changji 832200, China;3.College of Agronomy/Hebei Sub-center of National Maize Improvement Center/Key Laboratory for Crop Germplasm of Hebei, Hebei Agricultural University, Baoding 071001, China) |
| Abstract: |
| In order to screen out the high and stable-yield hybrids and ideal testing sites in Northwest China, the AMMI model and GGE biplot were used to analyze the fertility, stability and adaptability of 12 hybrids in 19 testing sites and evaluate the discrimination and representativeness of the testing sites in the regional trial of spring maize in Northwest China in 2022. The results showed that the sources of variation in yield mainly included genotype, environment and genotype-environment interaction, accounting for 4. 39%, 74. 50% and 10. 08% of the total square sum of yield variation, respectively. The AMMI model explained 82. 76% of the genotype-environment interactions. The GGE biplot showed that the 19 testing sites were divided into three ecological region and the seven ideal testing sites of Pingliang and Tianshui in Gansu Province, Bayannur in Inner Mongolia Autonomous Region, Pingluo County in Ningxia Hui Autonomous Region, Yan'an in Shaanxi Province, Changji Prefecture and Kokdala in Xinjiang Uygur Autonomous Region had strong discrimination and representativeness. The high and stable-yield hybrids, ‘DK 2207' and ‘Xiwang 188' were identified by AMMI model and GGE biplot in Northwest China. The AMMI model could fully resolve the genotype-environment interactions and concentrate on evaluating the stability of hybrids, while the GGE biplot had obvious advantages in evaluating the discrimination and representativeness of the testing sites. In summary, the comprehensive application of AMMI model and GGE biplot could improve the reliability of the evaluation of the hybrids and ideal testing sites in the spring maize regional of Northwest China. |
| Key words: maize regional trial AMMI model GGE biplot yield analysis |
