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Acta Agronomica Sinica ›› 2023, Vol. 49 ›› Issue (5): 1262-1271.doi: 10.3724/SP.J.1006.2023.24118

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

Construction of cotton fiber quality index and weighted genotype by trait (WGT) biplot analysis

XU Nai-Yin1(), WANG Yang2, WANG Dan-Tao2, NING He-Jia2, YANG Xiao-Ni1, QIAO Yin-Tao1   

  1. 1Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, Jiangsu, China
    2China Fiber Quality Monitoring Center, Beijing 100007, China
  • Received:2022-05-13 Accepted:2022-09-05 Online:2023-05-12 Published:2022-09-13
  • Supported by:
    Project from China Fiber Quality Monitoring Center(ZQJ4-2021-040)

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Abstract:

Cotton fiber quality is determined by multiple fiber traits. Construction of a cotton fiber evaluation index is important for comprehensive and objective evaluation of cotton cultivars for their fiber quality. A cotton fiber quality index model was constructed based on the cotton fiber quality monitoring data collected by the China Fiber Quality Monitoring Center in the main cotton producing regions in Xinjiang during 2019-2021, and a weighted genotype by trait (WGT) biplot method was developed for visual evaluation of cotton cultivars for their fiber quality index (FQI) and trait profile. The results showed that (1) The key traits and assigned weights were fiber length (weight = 0.451, the same below), uniformity (0.173), strength (0.285), micronaire value (-0.117) and color grade ordination (-0.526), respectively. (2) The FQI was constructed based on fiber comprehensive evaluation index (CEI) and fiber trait consistency index (TCI), and the cotton cultivars classified as having superior FQI included Xinluzhong 64, Xinluzao 78, and Zhongmian 113, while Xinluzao 67, Xinluzao 54, Jiumian 18, and Xinluzhong were identified as poor in FQI. (3) WGT biplot was developed and used to graphically display the fiber quality comprehensive evaluation index, trait consistency index, and fiber quality index. (4) 52 cotton varieties sampled in the fiber quality monitoring action were clustered into four variety type groups. Cluster I was the best in almost all fiber traits, while cluster II was the worst in most traits. Cluster III showed best performance in fiber trait consistency index, but poor in other traits or indexes. Cluster IV was high in fiber color grade but low in other aspects. The methods and results reported in this research will provide a theoretical support for the construction of nationwide cotton fiber quality index model and serve as a model for other crops kinds.

Key words: cotton (Gossypium hirsutum L.), fiber quality index (FQI), genotype by trait (GT) biplot, weighted genotype by trait (WGT) biplot, stepwise regression analysis, clustering analysis

Table 1

Main fiber trait values of 52 cotton cultivars sampled in the cotton quality monitoring and inspection project in Xinjiang during 2019-2021"

代码
Code		 品种
Cultivar name		 LEN UNI STR MIC COL CPI CEI TCI FQI TYPE
G1 17-68 29.5 83.3 29.6 4.8 4.9 22,991 0.47 0.15 0.49 I
G2 J206-5 29.8 83.2 30.2 4.6 6.2 22,791 0.44 0.25 0.48 IV
G3 J8031 29.1 80.5 29.1 4.4 7.3 22,541 -0.51 0.30 0.18 III
G4 巴43541 Ba 43541 29.7 83.0 28.1 4.2 4.1 22,991 0.65 0.27 0.55 I
G5 冀杂708 Jiza 708 28.7 82.0 28.0 4.8 4.7 22,491 -0.17 0.22 0.29 II
G6 金垦108 Jinken 108 28.7 82.3 28.8 4.8 6.9 22,191 -0.47 0.13 0.20 III
G7 酒棉18号 Jiumian 18 28.5 83.0 27.6 5.5 7.6 22,091 -0.93 0.22 0.05 III
G8 鲁泰19-1 Lutai 19-1 29.0 82.8 30.4 4.8 3.1 23,091 0.63 0.36 0.53 I
G9 鲁泰19-2 Lutai 19-2 28.9 82.1 30.2 4.9 3.0 22,891 0.47 0.41 0.48 I
G10 瑞杂818 Ruiza 818 28.9 82.8 28.4 4.8 6.8 22,191 -0.33 0.13 0.24 III
G11 瑞杂820 Ruiza 820 28.7 83.2 28.0 4.8 5.4 22,641 -0.14 0.14 0.30 II
G12 神牛17 Shenniu17 27.5 81.4 28.1 4.8 3.2 22,491 -0.45 0.64 0.18 II
G13 神牛18 Shenniu 18 27.1 81.2 27.6 4.2 3.0 22,591 -0.54 0.77 0.14 II
G14 塔河2号 Tahe 2 29.5 83.6 30.8 4.3 6.8 22,691 0.41 0.26 0.47 IV
G15 天丰67 Tianfeng 67 28.7 82.8 27.2 5.0 6.9 22,091 -0.64 0.14 0.14 III
G16 新陆早42 Xinluzao 42 29.5 83.4 29.9 4.7 8.7 22,441 -0.18 0.50 0.27 IV
G17 新陆早49 Xinluzao 49 29.6 83.1 29.7 4.5 7.0 22,691 0.15 0.28 0.39 IV
G18 新陆早54 Xinluzao 54 28.0 82.3 28.5 5.3 7.0 22,091 -0.96 0.24 0.04 III
G19 新陆早57 Xinluzao 57 28.6 83.0 29.1 4.6 6.9 22,491 -0.35 0.20 0.24 III
G20 新陆早63 Xinluzao 63 28.9 83.1 29.9 4.6 7.0 22,491 -0.15 0.21 0.30 III
G21 新陆早64 Xinluzao 64 29.3 83.2 28.6 4.8 7.0 22,541 -0.13 0.22 0.30 III
G22 新陆早65 Xinluzao 65 28.9 82.5 29.4 4.9 6.9 22,341 -0.31 0.16 0.25 III
G23 新陆早67 Xinluzao 67 27.9 81.2 27.8 4.9 6.8 21,941 -1.10 0.24 0 III
G24 新陆早71 Xinluzao 71 29.6 83.6 30.2 4.5 7.0 22,691 0.27 0.29 0.43 IV
G25 新陆早72 Xinluzao 72 28.7 83.8 28.5 4.0 7.0 22,441 -0.12 0.27 0.31 III
G26 新陆早78 Xinluzao 78 29.8 83.2 30.4 4.5 3.7 23,041 0.99 0.25 0.65 I
G27 新陆早80 Xinluzao 80 28.5 81.9 27.6 4.6 3.0 22,741 0.08 0.48 0.36 II
G28 新陆早84 Xinluzao 84 28.9 82.7 28.8 4.8 7.0 22,191 -0.38 0.15 0.23 III
G29 新陆中32 Xinluzhong 32 29.2 82.9 27.8 4.5 6.7 22,391 -0.20 0.19 0.28 III
G30 新陆中37 Xinluzhong 37 28.4 83.4 28.6 4.5 6.9 22,341 -0.41 0.23 0.21 III
G31 新陆中40 Xinluzhong 40 29.8 83.5 26.4 4.5 2.5 23,091 0.82 0.53 0.57 I
G32 新陆中55 Xinluzhong 55 29.1 83.6 28.8 4.7 6.8 22,541 -0.12 0.18 0.31 III
G33 新陆中58 Xinluzhong 58 28.2 81.7 27.4 4.5 3.0 22,741 -0.10 0.53 0.30 II
G34 新陆中62 Xinluzhong 62 28.5 83.1 29.8 4.2 4.8 22,891 0.22 0.23 0.41 II
G35 新陆中64 Xinluzhong 64 30.4 85.0 33.5 4.9 1.9 23,791 2.07 0.49 0.84 I
G36 新陆中66 Xinluzhong 66 29.7 82.5 29.6 4.6 4.1 22,891 0.63 0.25 0.54 I
G37 新陆中67 Xinluzhong 67 28.7 81.5 27.1 4.7 4.4 22,541 -0.23 0.30 0.27 II
G38 新陆中68 Xinluzhong 68 28.3 82.5 29.2 5.1 7.5 22,241 -0.76 0.24 0.11 III
G39 新陆中70 Xinluzhong 70 28.5 82.6 27.4 4.9 6.9 22,191 -0.71 0.15 0.12 III
G40 新陆中71 Xinluzhong 71 29.7 84.6 32.1 5.1 6.8 22,791 0.57 0.40 0.51 IV
G41 新陆中73 Xinluzhong 73 28.7 83.1 28.2 4.7 6.2 22,441 -0.26 0.10 0.26 III
G42 新陆中75 Xinluzhong 75 29.1 84.0 31.6 5.1 3.6 23,141 0.81 0.32 0.59 I
G43 新陆中78 Xinluzhong 78 28.2 82.4 26.9 5.2 6.5 22,091 -0.91 0.16 0.06 III
代码
Code		 品种
Cultivar name		 LEN UNI STR MIC COL CPI CEI TCI FQI TYPE
G44 新陆中80 Xinluzhong 80 29.2 82.9 28.8 4.6 6.8 22,391 -0.15 0.17 0.30 III
G45 新陆中81 Xinluzhong 81 29.1 82.7 28.1 5.0 6.3 22,391 -0.28 0.10 0.26 III
G46 新陆中82 Xinluzhong 82 29.2 83.2 28.5 4.5 6.7 22,541 -0.10 0.17 0.32 III
G47 新陆中84 Xinluzhong 84 29.7 82.9 33.2 4.5 7.2 22,741 0.52 0.45 0.49 IV
G48 新陆中85 Xinluzhong 85 30.4 82.3 27.8 4.1 5.8 22,791 0.53 0.46 0.50 IV
G49 新陆中87 Xinluzhong 87 28.8 82.3 28.4 4.4 5.4 22,491 -0.11 0.12 0.31 II
G50 兆丰28 Zhaofeng 28 29.6 83.4 26.3 4.5 2.4 23,241 0.74 0.54 0.55 I
G51 中棉113 Zhongmian 113 30.4 83.9 33.4 4.6 6.9 23,091 1.05 0.53 0.64 IV
G52 中棉96A Zhongmian 96A 28.8 82.7 27.2 4.5 6.4 22,291 -0.35 0.15 0.24 III

Fig. 1

Tester vector view of GT biplot of cotton cultivars sampled in cotton quality inspection project in Xinjiang during 2019-2021 LEN: fiber length; UNI: fiber length uniformity; STR: fiber strength; MIC: micronaire value; COL: fiber color grade; CPI: cotton price index. The uppercase G followed by the numbers represents variety codes. Abbreviation of variety names are the same as those given in Table 1."

Table 2

Pearson correlations among key fiber traits across 52 cotton cultivars sampled in the cotton quality inspection project in Xinjiang during 2019-2021"

性状 Trait LEN UNI STR MIC COL CPI
纤维长度Fiber length (LEN) 1
整齐度Fiber length uniformity (UNI) 0.613** 1
比强度Fiber strength (STR) 0.516** 0.519** 1
马克隆值Micronaire value (MIC) -0.247ns 0.038ns 0.033ns 1
颜色级Fiber color grade (COL) 0.005ns 0.086ns 0.074ns 0.156ns 1
价格指数Cotton price index (CPI) 0.616** 0.449** 0.497** -0.314* -0.681** 1

Table 3

Multiple regression coefficients of key fiber traits from stepwise regression using cotton price as the response variable"

模型
Model		 标准回归系数 Standard regression coefficient 决定系数
R2		 样本容量
Sample size		 均方差根/均值
RMSE/$\bar{o}$(%)
LEN UNI STR MIC COL
模型1 Model 1 0.455 0.175 0.280 -0.116 -0.525 0.931** 7212 10.196
模型2 Model 2 0.453 0.172 0.284 -0.113 -0.529 0.932** 7212 10.108
模型3 Model 3 0.449 0.166 0.290 -0.118 -0.524 0.932** 7212 10.312
模型4 Model 4 0.456 0.180 0.279 -0.115 -0.524 0.933** 7212 10.169
模型5 Model 5 0.454 0.171 0.284 -0.115 -0.525 0.930** 7212 10.207
模型6 Model 6 0.454 0.170 0.287 -0.117 -0.525 0.931** 7211 10.294
模型7 Model 7 0.448 0.172 0.288 -0.118 -0.529 0.932** 7211 10.335
模型8 Model 8 0.448 0.171 0.291 -0.116 -0.525 0.931** 7211 10.184
模型9 Model 9 0.447 0.178 0.279 -0.119 -0.525 0.930** 7211 10.358
模型10 Model 10 0.446 0.172 0.290 -0.120 -0.528 0.932** 7211 10.401
平均值Mean 0.451 0.173 0.285 -0.117 -0.526 0.931** 7212 10.256

Fig. 2

The “mean vs. stability” view and “ideal cultivar” view of WGT biplot based on the two-way table of cotton cultivars vs. fiber traits LEN: fiber length; UNI: fiber length uniformity; STR: fiber strength; MIC: micronaire value; COL: fiber color grade. The capital G followed by numbers represents variety codes. Variety names are the same as those given in Table 1. The weight assigned to each trait is indicated in the parentheses. For example, LEN (0.451) indicates that the weight for fiber length is 0.451. The axes were rotated such that PC1 corresponds to the comprehensive evaluation index (CEI) and the absolute value of PC2 corresponds to the trait consistency index (TCI). The origin of concentric circles in Fig. 2-B is the ideal variety mark, and the Euclidean distance from the variety mark to the origin represents the distance to the ideal variety (DTI). The smaller the distance, the better the fiber quality index."

Fig. 3

“Variety clustering” view of WGT biplot based on the two-way table of cotton cultivars vs. fiber traits The varieties surrounded by the oval dotted line are of the same breed type I, II, III, and IV stand for the four variety types, respectively. The uppercase G followed by the numbers represents variety code. Abbreviation of variety names are the same as those given in Table 1. Abbreviations of traits are the same as given in Fig. 1."

Table 4

Comparison among different clusters of cotton cultivars according to WGT biplot analysis in key traits"

性状
Trait		 I型品种
Variety type I
(mean ± SE)		 II型品种
Variety type II
(mean ± SE)		 III型品种
Variety type III
(mean ± SE)		 IV型品种
Variety type IV
(mean ± SE)
纤维长度Fiber length (mm) 29.53±0.14 a 28.29±0.20 c 28.74±0.08 b 29.80±0.12 a
整齐度Fiber length uniformity (%) 83.28±0.25 a 82.03±0.24 c 82.72±0.14 b 83.43±0.22 a
比强度Fiber strength (cN tex-1) 29.60±0.70 a 27.99±0.27 b 28.38±0.15 b 30.81±0.60 a
马克隆值Micronaire value 4.68±0.09 ab 4.55±0.08 b 4.78±0.06 a 4.53±0.09 b
颜色级Fiber color grade 3.34±0.29 c 4.09±0.35 b 6.86±0.06 a 6.94±0.27 a
价格指数Cotton price index 23,116±82 a 22,624±47 b 22,312±36 c 22,747±56 b
综合评价指数Comprehensive evaluation index 0.83±0.15 a -0.16±0.08 c -0.44±0.06 d 0.42±0.11 b
性状协调指数Trait consistency index 0.36±0.04 a 0.38±0.08 a 0.19±0.01 b 0.38±0.04 a
纤维质量指数Fiber quality index 0.58±0.03 a 0.28±0.03 c 0.21±0.02 d 0.46±0.03 b
[1] 唐淑荣, 杨伟华. 我国主产棉省纤维品质现状分析与建议. 棉花学报, 2006, 18: 386-390.
Tang S R, Yang W H. Status and analysis of cotton fiber quality distribution in China and suggestions. Cotton Sci, 2006, 18: 386-390. (in Chinese with English abstract)
[2] 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.
doi: 10.3724/SP.J.1006.2022.11105
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)
doi: 10.3724/SP.J.1006.2022.11105
[3] Yan W K. A systematic narration of some key concepts and procedures in plant breeding. Front Plant Sci, 2021, 12: 724517.
doi: 10.3389/fpls.2021.724517
[4] Yan W K, Frégeau-Reid J. Genotype by yield × trait (GYT) biplot: a novel approach for genotype selection based on multiple traits. Sci Rep (UK), 2018, 8: 8242.
doi: 10.1038/s41598-018-26688-8
[5] Yan W K, Frégeau-Reid J. Breeding line selection based on multiple traits. Crop Sci, 2008, 48: 417-423.
doi: 10.2135/cropsci2007.05.0254
[6] 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 (UK), 2017, 7: 17237.
doi: 10.1038/s41598-017-17631-4 pmid: 29222523
[7] Baxevanos D, Goulas C, Rossi J, Braojos E. Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE biplots. Agron J, 2008, 100: 1230-1236.
doi: 10.2134/agronj2007.0363
[8] Blanche S B, Myers G O. Identifying discriminating locations for cultivar selection in Louisiana. Crop Sci, 2006, 46: 946-949.
doi: 10.2135/cropsci2005.0279
[9] 许乃银, 李健. 棉花区试中品种多性状选择的理想试验环境鉴别. 作物学报, 2014, 40: 1936-1945.
doi: 10.3724/SP.J.1006.2014.01936
Xu N Y, Li J. Identification of ideal test environments for multiple traits selection in cotton regional trials. Acta Agron Sin, 2014, 40: 1936-1945. (in Chinese with English abstract)
doi: 10.3724/SP.J.1006.2014.01936
[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.
doi: 10.3724/SP.J.1006.2010.01805
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. Crop Variety Trials:Data Management and Analysis. New York: John Wiley & Sons, 2014. pp 163-186.
[12] 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.
doi: 10.3724/SP.J.1006.2021.04135
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)
doi: 10.3724/SP.J.1006.2021.04135
[13] Michele R, Nicola L, Zina F. Evaluation and application of the OILCROP-SUN model for sunflower in southern Italy. Agric Syst, 2003, 78: 17-30.
doi: 10.1016/S0308-521X(03)00030-1
[14] Yan W K, Kang M S, Ma B, Woods S, Cornelius P L. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci, 2007, 47: 643-655.
doi: 10.2135/cropsci2006.06.0374
[15] Adugna W, Labuschagne M T. Cluster and canonical variate analyses in multilocation trials of linseed. J Agric Sci (Cambridge), 2003, 140: 297-304.
doi: 10.1017/S0021859603003174
[16] Robert L L, Michael P B, Stuart G G, Marinus H J, Geoffrey R S N, Greg A C. Fiber quality and textile performance of some Australian cotton genotypes. Crop Sci, 2010, 50: 1509-1518.
doi: 10.2135/cropsci2009.10.0600
[17] Yan W K. Singular-value partioning in biplot analysis of multienvironment trial data. Agron J, 2002, 94: 990-996.
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