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Acta Agronomica Sinica ›› 2025, Vol. 51 ›› Issue (1): 30-43.doi: 10.3724/SP.J.1006.2025.44093

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

Comprehensive evaluation of regional trial varieties of medium mature hybrid cotton in the Yellow River Basin based on GYT biplot

LI Chao1,2(), FU Xiao-Qiong1,*()   

  1. 1Institute of Cotton Research, Chinese Academy of Agricultural Sciences / State Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Anyang 455000, Henan, China
    2State Key Laboratory of North China Crop Improvement and Regulation / Key Laboratory for Crop Germplasm Resources of Hebei / North China Key Laboratory for Crop Germplasm Resources of Education Ministry / Hebei Agricultural University, Baoding 071001, Hebei, China
  • Received:2024-06-07 Accepted:2024-09-18 Online:2025-01-12 Published:2024-10-11
  • Contact: *E-mail: m13663726262@163.com
  • Supported by:
    Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences and the Central Public-interest Scientific Institution Basal Research Fund(1610162024048)

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Abstract:

The analysis and evaluation of the regional cotton trials for medium-maturing hybrid varieties in the Yellow River Basin provide a scientific basis for optimizing varietal distribution and improving trait characteristics. This study comprehensively evaluated the yield, quality, agronomic traits, and disease resistance of 30 tested varieties in the Yellow River Basin from 2022 to 2023 using the GYT biplot method. An in-depth analysis was conducted on key traits, including lint yield, boll weight, number of bolls per plant, pre-frost yield rate, lint percentage, first fruiting branch node, number of fruiting branches, growth period, plant height, seed index, fiber length, fiber strength, micronaire, fiber elongation, fiber uniformity, Fusarium wilt index, and Verticillium wilt index. The joint analysis of variance showed that genotype and environmental effects were highly significant, and most genotype × environment interaction effects were significant or highly significant over the two-year experiment. Notably, the sum of squares for interaction effects exceeded that for genotype effects. Among the tested varieties, ZMS 9B07 demonstrated wider adaptability and higher lint yield compared to the control variety ZMS 9711. Compared to the GT biplot, the GYT biplot method exhibited a higher proportion of explained variation, better model fit, and greater reliability. This method more intuitively displays the characteristics of the tested varieties, providing a valuable reference for the comprehensive evaluation of multiple crop traits in China.

Key words: cotton, GT biplot, GYT biplot, yield-trait combination

Table 1

Information of the tested varieties"

序号
Number		 品种
Cultivar		 品种缩写
Cultivar abbreviation		 试验年份
Test year		 备注
Note
1 邯杂棉17号 Hanzamian 17 HZ17 2022
2 鲁杂棉1178 Luzamian 1178 LZ1178 2022
3 中棉所9D05 Zhongmiansuo 9D05 ZMS9D05 2022
4 中棉5026 Zhongmian 5026 ZM5026 2022
5 中棉所95614 Zhongmiansuo 95614 ZMS95614 2022
6 邯杂棉16号 Hanzamian 16 HZ16 2022
7 邯杂棉20号 Hanzamian 20 HZ20 2022
8 衡杂69 Hengza 69 HZ69 2022
9 中棉所9B07 Zhongmiansuo 9B07 ZMS9B07 2022-2023
10 中棉所1811 Zhongmiansuo 1811 ZMS1811 2022
11 中棉1279 Zhongmian 1279 ZM1279 2022
12 中棉所99033 Zhongmiansuo 99033 ZMS99033 2022-2023
13 中棉所96007 Zhongmiansuo 96007 ZMS96007 2022
14 中棉所9711 Zhongmiansuo 9711 ZMS9711 2022-2023 对照品种Control variety
15 瑞杂816 Ruiza 816 RZ816 2022 另一对照品种Another control variety
16 冀杂299 Jiza 299 JZ299 2023
17 邯杂棉19号 Hanzamian 19 HZ19 2023
18 衡杂21 Hengza 21 HZ21 2023
19 国欣棉38 Guoxinmian 38 GX38 2023
20 鲁杂206 Luza 206 LZ206 2023
21 鲁杂235 Luza 235 LZ235 2023
22 中棉所9B08 Zhongmiansuo 9B08 ZMS9B08 2023
23 中棉所1810 Zhongmiansuo 1810 ZMS1810 2023
24 中棉CJ79 Zhongmian CJ79 ZMCJ79 2023
25 中棉5031 Zhongmian 5031 ZM5031 2023
26 中棉所95623 Zhongmiansuo 95623 ZMS95623 2023
27 中M03 Zhong M03 ZM03 2023

Table S1

Variety data for medium maturity hybrid regional trials in the Yellow River Basin during 2022-2023"

品种
Cultivar		 年份
Year		 皮棉产量LY
(kg hm−2)		 铃重
BW
(g)		 单株铃数
NBPP
(个)		 霜前花率
PFR
(%)		 衣分
LP
(%)		 第一果枝节位FFBN
(节)		 果枝数
NFB
(个)		 生育期
GP
(d)		 株高
PH
(cm)		 子指
SI
(g)		 纤维
长度
FL
(mm)		 断裂比
强度
FS
(cN tex−1)		 马克隆值
MIC		 伸长率
FE
(%)		 整齐度
FU
(%)		 枯萎病指数 FWI 黄萎病指数 VWI
中棉所9B07 ZMS9B07 2022 1648.5 6.7 19.3 95.2 41.5 7.1 13.3 115 104.4 11.0 29.5 30.3 5.0 5.2 84.0 19.6 22.1
邯杂棉16号 HZ16 2022 1584 6.5 18.6 92.8 42.7 7.1 13.2 117 119.9 10.7 27.4 30.7 5.2 5.2 83.5 34.3 43.7
中棉所1811 ZMS1811 2022 1557 6.2 19.5 94.1 40.6 6.9 13.9 115 106.1 10.7 29.9 31.3 4.8 5.0 83.5 35.4 25.2
中棉所9D05 ZMS9D05 2022 1534.5 6.3 19.2 95.4 40.1 7.3 13.5 115 110.4 10.9 30.3 30.5 4.7 5.0 83.9 9.3 29.0
中棉所99033 ZMS99033 2022 1509 6.5 19.2 94.7 39.1 7.0 13.6 116 111.3 12.0 28.6 30.4 4.9 5.7 83.5 2.6 22.5
邯杂棉20号 HZ20 2022 1506 6.5 20.4 93.2 37.9 7.5 14.2 116 118.4 11.1 28.8 33.1 5.1 5.3 84.1 50.1 21.2
中棉1279 ZM1279 2022 1503 6.3 18.0 92.0 40.0 6.8 13.6 117 112.3 11.9 29.5 29.9 4.7 5.8 83.6 3.8 41.4
鲁杂1178 LZ1178 2022 1474.5 6.1 19.8 91.8 40.3 7.4 13.6 117 108.1 11.1 31.0 33.3 4.9 4.7 84.4 14.2 23.8
邯杂棉17号 HZ17 2022 1465.5 6.7 18.8 92.1 39.9 7.3 13.0 117 118.3 11.3 29.6 29.3 4.5 5.8 83.4 8.2 32.1
中棉5026 ZM5026 2022 1464 6.3 19.4 94.3 37.6 6.9 13.5 115 106.2 11.9 29.7 32.2 5.0 5.0 84.3 11.5 30.8
中棉所95614 ZMS95614 2022 1431 6.8 18.3 93.7 38.8 7.0 13.4 117 110.6 11.4 29.2 31.5 4.9 5.4 84.0 5.9 28.5
瑞杂816 RZ816 2022 1428 6.5 18.1 94.9 39.2 6.6 12.8 114 100.6 11.9 29.2 32.0 5.1 4.7 84.1 45.1 27.1
衡杂69 HZ69 2022 1425 6.4 18.0 94.4 39.0 7.0 12.9 115 104.8 11.4 29.0 32.4 5.1 5.0 83.0 6.4 29.3
中棉所9711 ZMS9711 2022 1410 6.3 18.8 93.4 38.5 6.7 13.9 115 106.7 11.2 30.3 33.5 4.6 4.7 84.4 11.8 30.7
中棉所96007 ZMS96007 2022 1399.5 6.4 18.1 94.6 38.2 7.0 13.3 116 111.5 11.0 28.7 29.8 5.0 5.0 83.4 12.4 27.8
中棉所9B07 ZMS9B07 2023 1786.5 6.9 19.7 96.9 42.1 7.0 13.1 115 100.9 11.2 29.4 31.6 5.2 5.9 85.5 10.1 28.1
国欣棉38 GX38 2023 1743 6.4 19.8 96.3 39.9 6.8 13.2 116 97.6 12.0 30.6 31.8 5.1 5.0 85.5 3.4 27.1
衡杂21 HZ21 2023 1725 6.5 19.7 96.6 41.1 7.0 12.4 115 102.7 11.5 30.8 31.9 5.1 5.3 85.5 17.0 34.1
邯杂棉19号 HZ19 2023 1720.5 6.6 20.5 96.1 39.1 7.2 13.0 115 106.1 11.1 29.5 32.2 5.3 6.1 85.4 6.7 23.9
中棉5031 ZM5031 2023 1713 6.6 20.2 96.3 39.8 6.9 13.2 115 103.5 12.1 30.6 34.0 5.1 5.0 85.9 9.6 21.5
中棉所99033 ZMS99033 2023 1711.5 6.6 20.4 96.4 40.2 6.9 13.3 116 102.8 12.5 30.5 32.8 5.1 5.3 85.9 6.1 24.6
中棉所9B08 ZMS9B08 2023 1707 6.8 19.2 97.2 41.6 7.0 13.0 116 106.2 11.3 30.5 33.8 5.1 4.9 85.8 5.0 26.5
鲁杂206 LZ206 2023 1707 6.8 19.3 97.3 41.1 6.8 13.3 115 103.9 11.1 29.4 32.7 5.1 5.3 85.9 18.9 25.6
中棉CJ79 ZMCJ79 2023 1693.5 6.7 18.9 96.1 42.0 6.9 13.0 115 102.0 10.8 30.2 32.3 5.3 5.3 85.4 8.4 32.9
冀杂299 JZ299 2023 1693.5 6.1 20.4 95.7 40.7 6.8 13.3 115 104.9 11.0 29.6 33.1 5.3 5.6 85.7 11.8 19.2
中M03 ZM03 2023 1656 6.6 20.3 96.4 39.7 7.2 13.3 115 107.1 11.3 31.0 33.8 5.0 4.9 85.7 6.8 24.0
中棉所1810 ZMS1810 2023 1639.5 7.1 19.0 96.1 38.9 6.9 12.9 115 104.6 12.6 31.3 31.9 4.8 5.0 84.8 5.4 26.7
中棉所95623 ZMS95623 2023 1627.5 5.9 21.3 96.5 40.8 6.8 13.3 114 103.8 10.6 30.0 31.8 5.2 5.1 85.2 5.7 33.7
鲁杂235 LZ235 2023 1552.5 6.7 19.7 92.9 41.5 6.8 13.6 118 108.4 12.1 30.1 32.4 5.3 5.1 85.3 8.1 23.3
中棉所9711 ZMS9711 2023 1551 6.7 20.1 96.0 38.9 6.4 13.3 116 103.5 11.7 30.4 31.5 5.1 5.4 85.4 8.1 28.6

Table 2

Joint analysis of variance for various traits of tested varieties in 2022"

变异来源
SV		 自由度
DF		 皮棉产量LY (kg hm-2) 铃重BW (g) 单株铃数NBPP (个) 霜前花率PFR (%) 衣分LP (%)
SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%)
基因型
Genotype (G)		 14 889,096.45** 8.12 6.15** 14.58 98.02** 1.52 254.37** 4.52 351.97** 36.13
环境Environment (E) 12 8,686,177.15** 79.38 24.39** 57.87 5760.98** 89.06 4632.06** 82.37 489.87** 50.29
交互作用G×E 168 1,367,668.85 12.50 11.61** 27.54 609.89** 9.43 737.12** 13.11 132.25** 13.58
残差Residual 141 993,319.14 6.42 301.19 212.36 75.03
总变异Total 194 10,942,942.45 42.15 6468.89 5623.56 974.09
变异来源
SV		 自由度
DF		 第一果枝节位
FFBN (节)		 果枝数
NFB (台)		 生育期
GP (d)		 株高
PH (cm)		 子指
SI (g)
SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%)
基因型Genotype (G) 14 11.35** 14.96 28.51** 3.04 218.58** 2.57 5687.29** 12.80 36.47** 20.64
环境Environment (E) 12 45.10** 59.46 803.74** 85.62 7698.33** 90.59 32,031.14** 72.07 104.51** 59.17
交互作用G×E 168 19.40* 25.58 106.43* 11.34 580.75** 6.83 6727.05** 15.14 35.66* 20.19
残差Residual 141 10.32 66.81 267.41 3197.56 21.10
总变异Total 194 75.84 938.68 8497.66 44,445.49 176.64
变异来源
SV		 自由度
DF		 纤维长度
FL (mm)		 断裂比强度
FS (cN tex-1)		 马克隆值
MIC		 伸长率
FE (%)		 整齐度
FU (%)
SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%)
基因型Genotype (G) 14 130.99** 44.47 333.12** 37.28 7.58** 29.35 25.09** 53.54 33.85** 5.29
环境Environment (E) 12 98.84** 33.55 186.51** 20.87 10.75** 41.61 13.75** 29.35 415.85** 65.03
交互作用G×E 168 64.74 21.98 373.97 41.85 7.50* 29.04 8.02* 17.11 189.76* 29.67
残差Residual 141 42.97 252.08 3.86 4.94 96.38
总变异Total 194 294.57 893.61 25.83 46.86 639.45

Table 3

Joint analysis of variance for traits of tested varieties in 2023"

变异来源
SV		 自由度
DF		 皮棉产量LY (kg hm-2) 铃重BW (g) 单株铃数NBPP (个) 霜前花率PFR (%) 衣分LP (%)
SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%)
基因型Genotype (G) 14 847,924.99** 3.46 18.04** 25.88 85.20** 0.79 196.28** 4.64 228.18** 31.98
环境Environment (E) 13 21,720,980.23** 88.66 40.17** 57.62 10,247.27** 95.17 3354.86** 79.34 379.95** 53.25
交互作用G×E 182 1,931,122.31* 7.88 11.50* 16.50 435.11** 4.04 677.58** 16.02 105.37* 14.77
残差Residual 154 1,146,086.04 7.47 250.31 192.63 67.70
总变异Total 209 24,500,027.53 69.71 10,767.58 4228.72 713.50
变异来源
SV		 自由度
DF		 第一果枝节位FFBN (节) 果枝数NFB (台) 生育期GP (d) 株高PH (cm) 子指SI (g)
SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%)
基因型Genotype (G) 14 8.11** 4.73 13.98** 2.54 104.60** 0.72 1365.04** 2.52 75.73** 48.81
环境Environment (E) 13 145.03** 84.51 474.27** 86.16 13,931.96** 96.43 46,542.18** 85.76 52.32** 33.72
交互作用G×E 182 18.47* 10.76 62.22** 11.30 410.47 2.84 6363.20** 11.72 27.11** 17.47
残差Residual 154 11.81 26.76 295.72 2956.26 13.97
总变异Total 209 171.61 550.47 14,447.03 54,270.43 155.16
变异来源
SV		 自由度
DF		 纤维长度
FL (mm)		 断裂比强度
FS (cN tex-1)		 马克隆值
MIC		 伸长率
FE (%)		 整齐度
FU (%)
SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%) SS PSS (%)
基因型Genotype (G) 14 70.31** 31.85 135.30** 11.11 3.31** 16.37 25.07** 45.78 17.96** 5.54
环境Environment (E) 13 37.85** 17.15 547.73** 44.98 7.93** 39.28 9.11** 16.63 122.32** 37.76
交互作用G×E 182 112.60** 51.00 534.68** 43.91 8.96** 44.35 20.59** 37.60 183.62** 56.69
残差Residual 154 51.24 283.60 5.22 6.96 73.41
总变异Total 209 220.76 1217.71 20.20 54.77 323.90

Fig. 1

GT biplot of tested varieties in 2022 (a) and 2023 (b) LY: lint yield; BW: boll weight; NBPP: number of bolls per plant; PFR: pre-frost yielding rate; LP: lint percent; FFBN: first fruit branch node; NFB: number of fruit branches; GP: growth period; PH: plant height; SI: seed index; FL: fiber length; FS: fiber strength; MIC: micronaire; FE: fiber elongation; FU: fiber uniformity; FWI: fusarirum wilt index; VWI: verticillium wilt index; HZ17: Hanzamian 17; LZ1178: Luzamian 1178; ZMS9D05: Zhongmiansuo 9D05; ZM5026: Zhongmian 5026; ZMS95614: Zhongmiansuo 95614; HZ16: Hanzamian 16; HZ20: Hanzamian 20; HZ69: Hengza 69; ZMS9B07: Zhongmiansuo 9B07; ZMS1811: Zhongmiansuo 1811; ZM1279: Zhongmian 1279; ZMS99033: Zhongmiansuo 99033; ZMS96007: Zhongmiansuo 96007; ZMS9711: Zhongmiansuo 9711; RZ816: Ruiza 816; JZ299: Jiza 299; HZ19: Hanzamian 19; HZ21: Hengza 21; GX38: Guoxinmian 38; LZ206: Luza 206; LZ235: Luza 235; ZMS9B08: Zhongmiansuo 9B08; ZMS1810: Zhongmiansuo 1810; ZMCJ79: Zhongmian CJ79; ZM5031: Zhongmian 5031; ZMS95623: Zhongmiansuo 95623; ZM03: Zhong M03."

Fig. 2

GYT biplot of tested varieties in 2022 (a) and 2023 (b) Y×BW: lint yield × boll weight; Y×NBPP: lint yield × number of bolls per plan; Y×PFR: lint yield × pre-frost yielding rate; Y×LP: lint yield × lint percent; Y×FFBN: lint yield × first fruit branch node; Y×NFB: lint yield × number of fruit branches; Y×GP: lint yield × growth period; Y×PH: lint yield × plant height; Y×SI: lint yield × seed index; Y×FL: lint yield × fiber length; Y×FS: lint yield × fiber strength; Y×MIC: lint yield × micronaire; Y×FE: lint yield ×fiber elongation; Y×FU: lint yield × fiber uniformity; Y×FWI: lint yield × fusarirum wilt index; Y×VWI: lint yield × verticillium wilt index; HZ17: Hanzamian 17; LZ1178: Luzamian 1178; ZMS9D05: Zhongmiansuo 9D05; ZM5026: Zhongmian 5026; ZMS95614: Zhongmiansuo 95614; HZ16: Hanzamian 16; HZ20: Hanzamian 20; HZ69: Hengza 69; ZMS9B07: Zhongmiansuo 9B07; ZMS1811: Zhongmiansuo 1811; ZM1279: Zhongmian 1279; ZMS99033: Zhongmiansuo 99033; ZMS96007: Zhongmiansuo 96007; ZMS9711: Zhongmiansuo 9711; RZ816: Ruiza 816; JZ299: Jiza 299; HZ19: Hanzamian 19; HZ21: Hengza 21; GX38: Guoxinmian 38; LZ206: Luza 206; LZ235: Luza 235; ZMS9B08: Zhongmiansuo 9B08; ZMS1810: Zhongmiansuo 1810; ZMCJ79: Zhongmian CJ79; ZM5031: Zhongmian 5031; ZMS95623: Zhongmiansuo 95623; ZM03: Zhong M03."

Table 4

Correlations among yield-trait combinations in 2022"

项目Item Y×BW Y×NBPP Y×PFR Y×LP Y×FFBN Y×NFB Y×GP Y×PH Y×SI Y×FL Y×FS Y×MIC Y×FE Y×FU Y×FWI
Y×NBPP 0.68**
Y×PFR 0.85** 0.84**
Y×LP 0.82** 0.75** 0.89**
Y×FFBN 0.75** 0.90** 0.80** 0.80**
Y×NFB 0.65** 0.93** 0.83** 0.76** 0.81**
Y×GP 0.86** 0.86** 0.92** 0.95** 0.89** 0.87**
Y×PH 0.71** 0.67** 0.61* 0.72** 0.82** 0.70** 0.81**
Y×SI 0.61* 0.52* 0.64** 0.54* 0.41 0.52* 0.63** 0.36
Y×FL 0.57* 0.83** 0.79** 0.76** 0.74** 0.80** 0.79** 0.42 0.51*
Y×FS 0.39 0.78** 0.60* 0.55* 0.59* 0.73** 0.58* 0.32 0.32 0.64**
Y×MIC 0.51* 0.59* 0.58* 0.63** 0.58* 0.65** 0.66** 0.52* 0.41 0.79** 0.22
Y×FE 0.76** 0.51* 0.59* 0.61* 0.62** 0.58* 0.74** 0.78** 0.69** 0.41 0.06 0.62
Y×FU 0.85** 0.90** 0.96** 0.94** 0.86** 0.89** 0.97** 0.71** 0.65** 0.85** 0.68** 0.65** 0.63**
Y×FWI −0.12 −0.28 −0.15 −0.13 −0.14 −0.21 −0.11 −0.14 0.10 0.01 -0.44 0.27 0.20 −0.18
Y×VWI 0.24 0.51* 0.35 0.08 0.35 0.35 0.17 −0.09 0.15 0.41 0.40 0.11 −0.01 0.28 −0.19

Table 5

Correlations among yield-trait combinations in 2023"

项目Item Y×BW Y×NBPP Y×PFR Y×LP Y×FFBN Y×NFB Y×GP Y×PH Y×SI Y×FL Y×FS Y×MIC Y×FE Y×FU Y×FWI
Y×NBPP 0.13
Y×PFR 0.68** 0.73**
Y×LP 0.59* 0.55* 0.85**
Y×FFBN 0.67** 0.70** 0.89** 0.79**
Y×NFB 0.51* 0.74** 0.82** 0.77** 0.75**
Y×GP 0.71** 0.68** 0.95** 0.88** 0.90** 0.86**
Y×PH 0.59* 0.59* 0.74** 0.69** 0.89** 0.65** 0.75**
Y×SI 0.61* 0.26 0.47 0.27 0.44 0.43 0.58* 0.28
Y×FL 0.67** 0.57* 0.87** 0.68** 0.84** 0.63** 0.86** 0.62* 0.69**
Y×FS 0.58* 0.64** 0.84** 0.74** 0.86** 0.80** 0.85** 0.86** 0.46 0.80**
Y×MIC 0.73** 0.43 0.80** 0.51* 0.71** 0.54* 0.72** 0.53* 0.71** 0.90** 0.67**
Y×FE 0.46 0.63** 0.65** 0.59* 0.63** 0.60* 0.66** 0.59* 0.15 0.35 0.41 0.25
Y×FU 0.67** 0.75** 0.98** 0.88** 0.91** 0.87** 0.98** 0.77** 0.51* 0.87** 0.88** 0.74** 0.67**
Y×FWI −0.07 0.07 −0.08 −0.20 0.02 0.06 −0.03 −0.14 0.19 0.10 −0.04 0.03 −0.19 −0.08
Y×VWI 0.20 0.28 0.17 0.05 0.26 0.50 0.29 0.46 0.38 0.13 0.50 0.15 0.22 0.27 0.26

Fig. 3

Genotype ranking view and which-won-where view of the GYT biplot for the tested genotypes in 2022 and 2023 Abbreviations are the same as those given in Fig. 2. a and b are genotype ranking view and which-won-where view of the GYT biplot for the tested genotypes in 2022, respectively; c and d are genotype ranking view and which-won-where view of the GYT biplot for the tested genotypes in 2023, respectively."

Fig. 4

Comparison of two genotypes in GYT biplots based on the 2022 (a) and 2023 (b) cotton regional trials ‘g’ represents other tested varieties. Abbreviations are the same as those given in Fig. 2."

Fig. 5

Comparison of two genotypes in the GGE biplot for lint yield based on the 2022 (a) and 2023 (b) datasets TJ: Tianjin; SJZ: Shijiazhuang; HD: Handan; CZ: Cangzhou; GC: Gucheng; LQ: Linqing; QH: Qihe; HM: Huimin; JX: Jinxiang; DY: Dongying; AY: Anyang; XH: Xihua; YC: Yuncheng; YL: Yangling; SY: Siyang; LB: Lingbi."

[1] 李付广, 袁有禄. 棉花分子育种进展与展望. 中国农业科技导报, 2011, 13(5): 1-8.
Li F G, Yuan Y L. Progress and prospect of cotton molecular breeding. J Agric Sci Technol, 2011, 13(5): 1-8 (in Chinese with English abstract).
[2] 徐国强, 史大坤, 李娜, 高炯浩, 李建新, 孙辰硕, 姚天茏, 李卫华, 汤继华, 张雪海. 植物杂种优势位点定位研究进展. 分子植物育种, 2023, 21: 4735-4748.
Xu G Q, Shi D K, Li N, Gao J H, Li J X, Sun C S, Yao T L, Li W H, Tang J H, Zhang X H. The research progress in localization of heterotic loci in plants. Mol Plant Breed, 2023, 21: 4735-4748 (in Chinese with English abstract).
[3] 严威凯. 品种选育与评价的原理和方法评述. 作物学报, 2022, 48: 2137-2154.
Yan W K. A critical review on the principles and procedures for cultivar development and evaluation. Acta Agron Sin, 2022, 48: 2137-2154 (in Chinese with English abstract).
[4] de Leon N, Jannink J L, Edwards J W, Kaeppler S M. Introduction to a special issue on genotype by environment interaction. Crop Sci, 2016, 56: 2081-2089.
[5] Malik W A, Forkman J, Piepho H P. Testing multiplicative terms in AMMI and GGE models for multienvironment trials with replicates. Theor Appl Genet, 2019, 132: 2087-2096.
[6] 许乃银, 李健. 基于GGE双标图的长江流域国审棉花品种分类特征评价. 中国农业科学, 2014, 47: 4780-4789.
Xu N Y, Li J. Evaluation on the classification characteristics of national registered cotton varieties in the Yangtze River valley based on GGE biplot. Sci Agric Sin, 2014, 47: 4780-4789 (in Chinese with English abstract).
[7] Yan W K. Crop Variety Trials: Data Management and Analysis. USA: John Wiley & Sons, Incorporated, 2014.
[8] 许乃银, 李健. 我国主产棉区棉花纤维品质性状的区域分布特征. 中国生态农业学报, 2016, 24: 1547-1554.
Xu N Y, Li J. Regional distribution characteristics of cotton fiber quality in main cotton production areas in China. Chin J Eco- Agric, 2016, 24: 1547-1554 (in Chinese with English abstract).
[9] Yan W K, Frégeau-Reid J. Genotype by Yield*Trait (GYT) biplot: a novel approach for genotype selection based on multiple traits. Sci Rep, 2018, 8: 8242.
[10] 严威凯. 双标图分析在农作物品种多点试验中的应用. 作物学报, 2010, 36: 1805-1819.
Yan W K. Optimal use of biplots in analysis of multi-location variety test data. Acta Agron Sin, 2010, 36: 1805-1819 (in Chinese with English abstract).
[11] Yan W K. Singular-value partitioning in biplot analysis of multienvironment trial data. Agron J, 2002, 94: 990.
[12] Xu N Y, Fok M, Li J, Yang X N, Yan W K. Optimization of cotton variety registration criteria aided with a genotype-by-trait biplot analysis. Sci Rep, 2017, 7: 17237.
[13] Tang Q Y, Zhang C X. Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research. Insect Sci, 2013, 20: 254-260.
[14] Dolatabad S S, Choukan R, Hervan E M, Dehghani H. Biplot analysis for multi-environment trials of maize (Zea mays L.) hybrids in Iran. Crop Pasture Sci, 2010, 61: 700.
[15] 岳海旺, 韩轩, 魏建伟, 郑书宏, 谢俊良, 陈淑萍, 彭海成, 卜俊周. 基于GYT双标图分析对黄淮海夏玉米区域试验品种综合评价. 作物学报, 2023, 49: 1231-1248.
Yue H W, Han X, Wei J W, Zheng S H, Xie J L, Chen S P, Peng H C, Bu J Z. Comprehensive evaluation of maize hybrids tested in Huang-Huai-Hai summer maize regional trial based on GYT biplot analysis. Acta Agron Sin, 2023, 49: 1231-1248 (in Chinese with English abstract).
[16] 许乃银, 赵素琴, 张芳, 付小琼, 杨晓妮, 乔银桃, 孙世贤. 基于GYT双标图对西北内陆棉区国审棉花品种的分类评价. 作物学报, 2021, 47: 660-671.
Xu N Y, Zhao S Q, Zhang F, Fu X Q, Yang X N, Qiao Y T, Sun S X. Retrospective evaluation of cotton varieties nationally registered for the Northwest Inland cotton growing regions based on GYT biplot analysis. Acta Agron Sin, 2021, 47: 660-671 (in Chinese with English abstract).
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