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作物学报 ›› 2026, Vol. 52 ›› Issue (10): 2886-2897.doi: 10.3724/SP.J.1006.2026.61020

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

基于BLUP-GGE双标图的北部冬麦区小麦品种试验环境评价

周华1(), 刘丽华2(), 张笑晴1(), 许乃银3, 屈平平2, 李宏博2, 刘阳娜2, 张明明2, 李亚会2, 许栩3, 徐剑文3,*(), 庞斌双2,*()   

  1. 1 全国农业技术推广服务中心, 北京 100125
    2 北京市农林科学院杂交小麦研究所 / 农业农村部农作物DNA指纹创新利用重点实验室(部省共建) / 作物分子设计与智慧育种北京市重点实验室 / 杂交小麦分子遗传北京市重点实验室, 北京 100097
    3 江苏省农业科学院经济作物研究所, 江苏南京 210014
  • 收稿日期:2026-03-10 接受日期:2026-07-15 出版日期:2026-10-12 网络出版日期:2026-07-22
  • 通讯作者: 徐剑文, E-mail: xujianwen@jaas.ac.cn;庞斌双, E-mail: 1492196201@qq.com
  • 作者简介:周华, E-mail: zhouhua@agri.gov.cn;
    刘丽华, E-mail: llh216@163.com;
    张笑晴, E-mail: zhangxiaoqing@agri.gov.cn** 同等贡献
  • 基金资助:
    北京市农林科学院科技创新能力建设专项(KJCX20261407);北京市农林科学院科技创新能力建设专项(KJCX20261405);江苏省农业科技自主创新项目(CX(24)3120);北京市农林科学院杂交小麦研究所改革与发展课题(XMZZKT202606);北京市农林科学院杂交小麦研究所改革与发展课题(XMZZKT202607);农业生物育种国家科技重大专项(2022ZD04019)

Evaluation of wheat variety test environments in the Northern Winter Wheat Region based on BLUP-GGE biplot analysis

Zhou Hua1(), Liu Li-Hua2(), Zhang Xiao-Qing1(), Xu Nai-Yin3, Qu Ping-Ping2, Li Hong-Bo2, Liu Yang-Na2, Zhang Ming-Ming2, Li Ya-Hui2, Xu Xu3, Xu Jian-Wen3,*(), Pang Bin-Shuang2,*()   

  1. 1 National Extension and Service Center of Agricultural Technology, Beijing 100125, China
    2 Institute of Hybrid Wheat, Beijing Academy of Agriculture and Forestry Sciences / Key Laboratory of Crop DNA Fingerprinting Innovation and Utilization of the Ministry of Agriculture and Rural Affairs (Co-construction by Ministry and Province) / Beijing Key Laboratory of Crop Molecular Design and Intelligent Breeding / Beijing Key Laboratory of Molecular Genetics in Hybrid Wheat, Beijing 100097, China
    3 Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, Jiangsu, China
  • Received:2026-03-10 Accepted:2026-07-15 Published:2026-10-12 Published online:2026-07-22
  • Contact: Xu Jian-Wen, E-mail: xujianwen@jaas.ac.cn;Pang Bin-Shuang, E-mail: 1492196201@qq.com
  • About author:** Contributed equally to this work
  • Supported by:
    Science and Technology Innovation Project of BAAFS, China(KJCX20261407);Science and Technology Innovation Project of BAAFS, China(KJCX20261405);Jiangsu Agricultural Science and Technology Innovation Fund(CX(24)3120);Reform and Development Project of Hybrid Wheat Institute, BAAFS, China(XMZZKT202606);Reform and Development Project of Hybrid Wheat Institute, BAAFS, China(XMZZKT202607);Biological Breeding-National Science and Technology Major Project(2022ZD04019)

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摘要：

本研究以2021—2025年北部冬麦区水地组国家小麦品种区域试验产量数据为基础, 比较分析原始数据与最佳线性无偏预测值(BLUP)的基因型主效应加基因型与环境互作效应(GGE)双标图在品种生态区划分上的可靠性, 并利用BLUP-GGE双标图对试点进行综合评价和生态亚区划分。结果表明, BLUP-GGE双标图模型拟合度比原始数据GGE双标图提高了13.5%, 对试点的评价更精确可靠。原始数据GGE双标图将试点划分为2个品种生态区, 而在BLUP-GGE双标图中并不存在, 表明传统GGE双标图在分析多年多点品种试验数据上存在一定的局限性。通过BLUP-GGE双标图的试点评价发现, 河北滦南试点综合表现最优秀; 河北固安、天津宝坻和河北遵化试点综合表现优良; 北京昌平、北京顺义、天津武清和河北保定试点综合表现较好; 山西屯留、山西祁县和山西太原试点综合表现一般。采用BLUP-GGE双标图的“哪个赢在哪里”功能图划分的生态亚区仅在试点代表性上有显著差异, 而鉴别力和理想指数无显著差异。采用试点聚类功能图划分的生态亚区在代表性、鉴别力和理想指数上均存在显著差异, 对生态特征表达更加全面, 实用性更强。其中, 第一生态亚区包括河北和天津的全部试点, 占试点总数的60%以上, 是北部冬麦区水地的主体生态亚区, 代表性和理想指数表现最好, 鉴别力较好, 是区域试验的理想试点; 第二生态亚区代表北京市郊的生态环境, 其鉴别力强、代表性中等, 更适用于早期品种选择; 而第三生态亚区包括的山西省屯留和祁县试点因海拔较高和气候冷凉, 鉴别力较差。本研究证实了BLUP-GGE双标图在多年多环境品种试验数据分析中具有一定的优越性, 并提出了一种稳健的试点聚类划分生态亚区的方法, 为优化北部冬麦区水地组小麦区域试验方案和精准推广品种提供了科学依据。

关键词: 北部冬麦区, 区域试验, 最佳线性无偏预测, GGE双标图, 品种生态区

Abstract:

Based on yield data from the national wheat regional variety trials conducted in the irrigated group of the Northern Winter Wheat Region (NWWR) from 2021 to 2025, this study compared the reliability of genotype plus genotype-by-environment interaction (GGE) biplots constructed from raw data and best linear unbiased prediction (BLUP) values for mega-environment (ME) investigation. In addition, comprehensive evaluation of trial locations and subdivision of ecological subregions were performed using the BLUP-GGE biplot. The results showed that the goodness of fit of the BLUP-GGE biplot was 13.5 percentage points higher than that of the GGE biplot based on raw data, resulting in more accurate and reliable evaluation of test locations. Notably, two spurious MEs identified by the raw data-based GGE biplot were not detected in the BLUP-GGE biplot, highlighting the limitations of traditional GGE biplot analysis for multi-year and multi-location variety trial data. Location evaluation based on the BLUP-GGE biplot showed that Luannan, Hebei, had the best overall performance, followed by Gu’an, Hebei, Baodi, Tianjin, and Zunhua, Hebei, which also performed excellently. Changping, Beijing, Shunyi, Beijing, Wuqing, Tianjin, and Baoding, Hebei, showed favorable performance, whereas Tunliu, Qixian, and Taiyuan in Shanxi showed moderate performance. Ecological subregions delineated using the which-won-where view of the BLUP-GGE biplot differed significantly only in location representativeness, with no significant differences in discriminating ability or desirability index. In contrast, subregions defined using the trial-location clustering view of the BLUP-GGE biplot showed significant differences in all three indices, namely representativeness, discriminating ability, and desirability index, thereby providing a more comprehensive reflection of ecological characteristics and greater practical value. Among these subregions, the first ecological subregion, encompassing all trial locations in Hebei and Tianjin and accounting for more than 60% of the total locations, represented the major ecological subregion of the irrigated group in the NWWR. This subregion had the best representativeness and desirability index, together with favorable discriminating ability, making it an ideal area for regional variety trials. The second ecological subregion, representing the ecological conditions of suburban Beijing, had strong discriminating ability and moderate representativeness and was therefore more suitable for early-stage variety screening. The third ecological subregion, including Tunliu and Qixian in Shanxi province, was characterized by high altitude and a cool climate, resulting in weak discriminating ability and limited suitability for variety evaluation. This study confirms the superiority of the BLUP-GGE biplot in analyzing multi-year and multi-environment variety trial data and proposes a robust method for ecological subregion delineation based on trial-location clustering. These findings provide a scientific basis for optimizing wheat regional trial schemes and supporting the precise recommendation and deployment of varieties in the irrigated group of the NWWR.

Key words: Northern Winter Wheat Region, regional trials, best linear unbiased prediction, GGE biplot, mega-environment

表1

2021-2025年北部冬麦区水地组国家小麦品种试验点的主要环境因子"

省(市)
Province
(municipality)		 试点
Trial site		 土壤类型
Soil type		 经度Longitude 纬度Latitude 海拔Altitude (m) 年降水量
Annual
precipitation (mm)		 年平均气温Average annual temperature (℃)
北京Beijing 昌平Changping 沙壤土Sandy loam 116°13′ 40°10′ 31.3 550.3 11.9
顺义Shunyi 轻壤土Light loam 116°52′ 40°17′ 35.0 580.3 11.5
天津Tianjin 宝坻Baodi 沙壤土Sandy loam 117°28′ 39°45′ 6.0 612.5 11.6
武清Wuqing 重壤土Heavy loam 116°58′ 39°30′ 4.6 557.3 12.2
河北Hebei 保定Baoding 黏壤土Clay loam 115°36′ 39°06′ 14.7 503.2 12.3
固安Gu’an 重壤土Heavy loam 116°33′ 39°29′ 11.0 548.6 11.5
滦南Luannan 重壤土Heavy loam 118°46′ 39°35′ 13.1 648.1 11.9
遵化Zunhua 重壤土Heavy loam 117°33′ 40°11′ 30.0 724.7 10.9
山西Shanxi 祁县Qixian 轻壤土Light loam 112°23′ 37°23′ 755.3 433.7 10.2
太原Taiyuan 轻壤土Light loam 113°08′ 37°58′ 798.6 480.0 11.5
屯留Tunliu 重壤土Heavy loam 112°56′ 36°18′ 896.0 580.5 9.8

表2

2021-2025年北部冬麦区水地组国家小麦品种试验产量性状混合线性模型的方差组分"

变异来源
Source of variation		 方差组分
Variance component		 标准误差
Standard error		 Z比率
Z-ratio		 占总变异百分比
Percentage of total (%)
年份Year 0.0591 0.0872 0.6778 3.52
试点Location 0.6332 0.3455 1.8327 37.72
试点内区组Block within location 0.0000 0.0004 0.0000 0.00
品种Cultivar 0.0645 0.0174 3.7069 3.84
试点×品种Location×cultivar 0.0718 0.0123 5.8374 4.28
年份×品种Year×cultivar 0.0104 0.0063 1.6508 0.62
年份×试点Year×location 0.6499 0.1493 4.3530 38.72
年份×试点×品种Year×location×cultivar 0.0857 0.0107 8.0093 5.11
残差Residual 0.1040 0.0034 30.5882 6.20
总和Total 1.6786 — — 100.00

图1

原始数据GGE双标图的试点间关系功能图(a)和品种生态区功能图(b) 以“+”为前缀的红色字母组合为试点名称, 从原点到每个试点的射线代表试点向量。为清晰起见, 品种标记以数字表示, 1到5分别表示品种的试验年份为2021年到2025年; 1/2表示品种试验年份为2021-2022年, 其余类推。根据品种试验年份和分布情况用椭圆形虚线大致划分为3个品种类群, 分别用I、II和III表示。图b将试点划分为2个生态区, 分别用ME1和ME2表示。“Scaling = 0”表示品种-试点矩阵未定标; “Centering = 2”表示品种-试点矩阵环境中心化; “SVP = 2”表示奇异值全部分配到试点, 使双标图最适合用于试点评价。"

图2

BLUP-GGE双标图的试点代表性和鉴别力功能图(a)和理想试点功能图(b) 以“+”为前缀的红色字母组合为试点名称, 从原点到每个试点的射线代表试点向量。红色圆圈表示平均环境坐标, 通过原点和红色圆圈的射线为平均环境轴。图a中品种标记以数字表示, 1到5分别表示品种的试验年份为2021年到2025年; 1/2表示品种试验年份为2021-2022年, 其余类推。图b中品种图标简化为“*”, 同心圆圆心(蓝色圆圈)为理想试点图标, 试点越接近理想试点越理想。“Scaling = 0”表示品种-试点矩阵未定标; “Centering = 2”表示品种-试点矩阵环境中心化; “SVP = 2”表示奇异值全部分配到试点, 使双标图最适合用于试点评价。"

表3

2021-2025年北部冬麦区水地组国家小麦品种试验的试点评价量化指标"

试点
Trial site		 鉴别力
Discrimination ability		 代表性
Representativeness		 理想指数
Desirability index
向量长度
Vector length		 排名
Rank		 与AEA轴相关系数
Correlation with AEA		 排名
Rank		 距理想试点距离
Distance to ideal		 排名
Rank
河北滦南Luannan, Hebei 0.735 5 0.999 1 0.483 1
河北遵化Zunhua, Hebei 0.870 4 0.874 6 0.580 2
河北固安Gu’an, Hebei 0.626 8 0.997 2 0.583 3
天津宝坻Baodi, Tianjin 0.687 6 0.968 3 0.592 4
北京昌平Changping, Beijing 1.025 3 0.868 7 0.653 5
天津武清Wuqing, Tianjin 0.661 7 0.883 5 0.674 6
北京顺义Shunyi, Beijing 1.182 2 0.863 8 0.675 7
河北保定Baoding, Hebei 1.210 1 0.811 10 0.696 8
山西太原Taiyuan, Shanxi 0.499 9 0.934 4 0.755 9
山西祁县Qixian, Shanxi 0.283 10 0.832 9 0.996 10
山西屯留Tunliu, Shanxi 0.282 11 0.695 11 1.044 11

图3

BLUP-GGE双标图的“哪个赢在哪里”功能图(a)和试点聚类功能图(b) 以“+”为前缀的红色字母组合为试点名称。位于多边形角顶的图标为品种编号, 其余品种图标简化为“*”。椭圆形虚线包围的试点属于同一生态亚区, 分别用ESR1、ESR2和ESR3表示。“Scaling = 0”表示品种-试点矩阵未定标; “Centering = 2”表示品种-试点矩阵环境中心化; “SVP = 2”表示奇异值全部分配到试点, 使双标图最适合用于试点评价。"

表4

基于不同分类方法的生态亚区主要特征参数多重比较"

分类方法
Classification method		 生态亚区
Ecological subregion		 鉴别力
Discrimination ability		 代表性
Representativeness		 理想指数
Desirability index
哪个赢在哪里
Which-won-where		 ESR1 0.685±0.510 aA -0.856±0.039 bAB 0.650±0.062 aA
ESR2 0.637±0.102 aA 0.975±0.031 aA 0.603±0.112 aA
ESR3 0.693±0.478 aA 0.815±0.081 bB 0.842±0.207 aA
试点聚类
Location grouping		 ESR1 -0.755±0.230 aAB 0.924±0.071 aA 0.623±0.091 bB
ESR2 1.104±0.111 aA -0.866±0.004 abA 0.664±0.015 bB
ESR3 0.283±0.001 bB 0.764±0.097 bA 1.020±0.034 aA
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