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Acta Agronomica Sinica ›› 2026, Vol. 52 ›› Issue (10): 2886-2897.doi: 10.3724/SP.J.1006.2026.61020

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

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 Online:2026-10-12 Published: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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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

Table 1

Main environmental factors of national wheat variety test locations in the irrigated group of the Northern Winter Wheat Region from 2021 to 2025"

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

Table 2

Variance components of the mixed linear model for yield traits in the national wheat variety trials of the irrigated group in the Northern Winter Wheat Region from 2021 to 2025"

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

Fig. 1

Raw-data GGE biplot showing relationships among trial locations (a) and mega-environment delineation (b) Red letter combinations prefixed with “+” indicate the test location names, and rays from the origin to each location represent location vectors. For clarity, variety markers are indicated by numbers: 1-5 represent varieties tested from 2021 to 2025, respectively; 1/2 indicates varieties tested in both 2021 and 2022, and so forth. According to the test years and distribution patterns of the varieties, the varieties were roughly divided into three clusters using dashed ellipses, denoted as I, II, and III. In panel b, the test locations were divided into two mega-environments, denoted as ME1 and ME2. “Scaling = 0” indicates that the genotype-by-location matrix was not scaled; “Centering = 2” indicates that the genotype-by-location matrix was centered by the mean of each location; and “SVP = 2” indicates that the singular values were fully partitioned to locations, making the GGE biplot most suitable for location evaluation."

Fig. 2

BLUP-GGE biplot showing test location representativeness and discriminating ability (a) and the ideal test location (b) Red letter combinations prefixed with “+” indicate the test location names, and rays from the origin to each location represent location vectors. The red circle represents the average environment coordinate, and the ray passing through the origin and the red circle represents the average environment axis. In panel a, variety markers are indicated by numbers: 1-5 represent varieties tested from 2021 to 2025, respectively; 1/2 indicates varieties tested in both 2021 and 2022, and so forth. In panel b, variety markers are simplified as “*”, and the center of the concentric circles, shown as a blue circle, represents the ideal location. The closer a location is to the blue circle, the more desirable it is. “Scaling = 0” indicates that the genotype-by-location matrix was not scaled; “Centering = 2” indicates that the genotype-by-location matrix was centered by the mean of each location; and “SVP = 2” indicates that the singular values were fully partitioned to locations, making the GGE biplot most suitable for location evaluation."

Table 3

Quantitative indices for test location evaluation in the national wheat variety trials of the irrigated group in the Northern Winter Wheat Region from 2021 to 2025"

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

Fig. 3

Which-won-where view (a) and location grouping view (b) of the BLUP-GGE biplot Red letter combinations prefixed with “+” indicate the test location names. Markers located at the vertices of the polygon indicate variety codes, whereas the markers of the remaining varieties are simplified as “*” for clarity. Locations enclosed by dashed ellipses belong to the same ecological subregion (ESR), denoted as ESR1, ESR2, and ESR3. “Scaling = 0” indicates that the genotype-by-location matrix was not scaled; “Centering = 2” indicates that the genotype-by-location matrix was centered by the mean of each location; and “SVP = 2” indicates that the singular values were fully partitioned to locations, making the GGE biplot most suitable for location evaluation."

Table 4

Multiple comparisons of main characteristic parameters of ecological subregions based on different classification methods"

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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