Crops ›› 2022, Vol. 38 ›› Issue (5): 27-33.doi: 10.16035/j.issn.1001-7283.2022.05.004

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Evaluation on Salt Tolerance of 42 Sea-Island Cotton (Gossypium barbadense) Varieties in Xinjiang during Germination Period

Zhao Kang(), Yang Tao, Wang Honggang, Li Shengmei, Pang Bo, Ma Shangjie, Gao Wenwei()   

  1. College of Agriculture, Xinjiang Agricultural University/Engineering Research Centre of Cotton, Ministry of Education, Urumqi 830052, Xinjiang, China
  • Received:2021-07-14 Revised:2021-08-19 Online:2022-10-15 Published:2022-10-19

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Abstract:

To understand the salt-resistance difference of Xinjiang sea-island cotton germplasms in the germination period, 42 sea-island cotton varieties in Xinjiang were evaluated and screened by using 150mmol/L NaCl. The results showed that under salt stress, the germination rate, germination vigor, germination index and fresh weight of 42 sea-island cotton varieties were inhibited to varying degrees. There were very significant correlations among the indicators. Two principal components were determined through principal component analysis, reflecting germination, dry matter accumulation and water content. There was a significant positive correlation between the principal component comprehensive score value and membership function mean value. Using the comprehensive evaluation values of the two, 42 sea-island cotton varieties were grouped into four categories, namely high salt-tolerant varieties, medium salt-tolerant varieties, weakly salt-tolerant varieties and salt-sensitive varieties. This study provided a theoretical basis for the evaluation and identification of salt tolerance of sea-island cotton varieties.

Key words: Sea-island cotton, Evaluation of salt-tolerance, Principal component analysis, Membership function

Table 1

Salt tolerance coefficient of each index during the germination period of sea-island cotton"

指标
Index		 极小值
Minimum
(%)		 极大值
Maximum
(%)		 均值
Mean
(%)		 标准差
Standard
deviation		 变异系数
Coefficient of
variation (%)
RGR 6.70 93.30 50.83 21.19 41.70
RGP 3.40 83.30 30.19 20.01 66.30
RGI 5.00 81.20 34.80 17.39 49.96
RFW 19.80 100.00 47.87 24.35 52.62
SIRGR 6.70 93.30 49.17 21.19 43.10
SIRGP 16.70 96.60 69.81 20.01 28.67
SIRGI 18.80 95.00 65.20 17.39 26.67
SIRFW 0.00 80.20 52.12 24.35 46.72

Table 2

Correlation analysis of cotton indexes under salt stress"

指标Index RGR RGP RGI RFW SIRGR SIRGP SIRGI SIRFW
RGR 1.000
RGP 0.724** 1.000
RGI 0.914** 0.910** 1.000
RFW 0.417** 0.440** 0.472** 1.000
SIRGR -1.000** -0.724** -0.914** -0.417** 1.000
SIRGP -0.724** -1.000** -0.910** -0.440** 0.724** 1.000
SIRGI -0.914** -0.910** -1.000** -0.472** 0.914** 0.910** 1.000
SIRFW -0.417** -0.440** -0.472** -1.000** 0.417** 0.440** 0.472** 1.000

Fig.1

Frequency distribution of relative salt tolerance indexes of different sea-island cotton varieties"

Fig.2

Frequency distribution of salt damage rates of different sea-island cotton varieties"

Table 3

Eigenvalues and contribution rates of two principal components"

主成分
Principal
component		 特征根
Eigenvalue		 贡献率
Contribution
(%)		 累计贡献率
Cumulative
contribution (%)
Ⅰ 5.993 74.91 74.91
Ⅱ 1.411 17.63 92.54

Table 4

Load matrix of each factor"

主成分Principal component RGR RGP RGI RFW SIRGR SIRGP SIRGI SIRFW
Ⅰ 0.908** 0.911** 0.978** 0.621* -0.908** -0.911** -0.978** -0.621*
Ⅱ -0.196 -0.154 -0.173 0.783* 0.196 0.154 0.173 -0.783*

Table 5

Component score system matrix"

主成分Principal component RGR RGP RGI RFW SIRGR SIRGP SIRGI SIRFW
Ⅰ 0.151 0.152 0.163 0.104 -0.151 -0.152 -0.163 -0.104
Ⅱ -0.139 -0.109 -0.122 0.555 0.139 0.109 0.122 -0.555

Table 6

Score and order of main components of salt tolerance of sea-island cotton varieties"

序号
Number		 品种
Variety		 F值
F-value		 排序
Rank		 序号
Number		 品种
Variety		 F值
F-value		 排序
Rank		 序号
Number		 品种
Variety		 F值
F-value		 排序
Rank		 序号
Number		 品种
Variety		 F值
F-value		 排序
Rank
1 新海14 -17.18 29 12 新海33 10.66 7 23 孔雀202 -7.07 17 34 吐75-238 -9.87 21
2 新海13 -23.71 36 13 新海34 -15.23 26 24 孔雀201 9.05 8 35 V9-2 -9.18 20
3 新海21 -8.71 18 14 新海35 17.53 4 25 孔雀200 -21.07 33 36 巴州-3021 -25.04 39
4 新海24 -1.35 14 15 新海36 -32.40 41 26 吐75-6 4.58 11 37 吐75-193 3.39 12
5 新海25 -11.34 22 16 新海32 5.66 9 27 墨-1413 4.96 10 38 新海2号 -18.67 31
6 新海26 -3.00 16 17 N9247 -2.66 15 28 新海5号 -9.07 19 39 塔海-901 -19.22 32
7 新海27 -0.16 13 18 塔07-152 -32.74 42 29 巴238 -12.06 23 40 巴州-3244 -13.54 25
8 新海28 -16.97 28 19 N9107 -24.39 38 30 吐79-713 -26.52 40 41 巴州-3761 -24.35 37
9 新海29 -21.55 34 20 06-146 -23.49 35 31 吐77-104 -13.45 24 42 吐77-55 12.40 6
10 新海30 18.81 3 21 军海1号 24.89 2 32 巴66-284 -15.57 27
11 新海31 16.86 5 22 吐82-5-9 37.59 1 33 巴-5507 -18.00 30

Table 7

membership function mean of salt tolerance indexes of sea-island cotton varieties"

序号
Number		 品种
Variety		 隶属函数均值
Membership
function mean		 排名
Rank		 序号
Number		 品种
Variety		 隶属函数均值
Membership
function mean		 排名
Rank		 序号
Number		 品种
Variety		 隶属函数均值
Membership
function mean		 排名
Rank
1 新海14 0.271 29 15 新海36 0.033 42 29 巴238 0.357 24
2 新海13 0.168 38 16 新海32 0.546 11 30 吐79-713 0.143 40
3 新海21 0.391 19 17 N9247 0.426 17 31 吐77-104 0.362 23
4 新海24 0.446 13 18 塔07-152 0.044 41 32 巴66-284 0.306 27
5 新海25 0.368 22 19 N9107 0.152 39 33 巴-5507 0.252 31
6 新海26 0.384 21 20 06-146 0.176 36 34 吐75-238 0.406 18
7 新海27 0.438 16 21 军海1号 0.788 2 35 V9-2 0.385 20
8 新海28 0.272 28 22 吐82-5-9 0.996 1 36 巴州-3021 0.170 37
9 新海29 0.207 33 23 孔雀202 0.442 14 37 吐75-193 0.583 8
10 新海30 0.775 3 24 孔雀201 0.580 9 38 新海2号 0.257 30
11 新海31 0.733 5 25 孔雀200 0.202 34 39 塔海-901 0.216 32
12 新海33 0.610 7 26 吐75-6 0.551 10 40 巴州-3244 0.347 25
13 新海34 0.318 26 27 墨-1413 0.461 12 41 巴州-3761 0.193 35
14 新海35 0.696 6 28 新海5号 0.439 15 42 吐77-55 0.762 4

Fig.3

Correlation analysis of membership function mean values and F-values of different sea-island cotton varieties"

Table 8

Comprehensive evaluation value and ranking of salt tolerance of sea-island cotton varieties"

序号
Number		 品种
Variety		 综合评价值
Comprehensive
evaluation value		 排名
Rank		 序号
Number		 品种
Variety		 综合评价值
Comprehensive
evaluation value		 排名
Rank		 序号
Number		 品种
Variety		 综合评价值
Comprehensive
evaluation value		 排名
Rank
1 新海14 -0.588 29 15 新海36 -1.602 42 29 巴238 -0.233 23
2 新海13 -1.025 37 16 新海32 0.744 10 30 吐79-713 -1.168 40
3 新海21 -0.052 19 17 N9247 0.213 15 31 吐77-104 -0.264 24
4 新海24 0.300 14 18 塔07-152 -1.587 41 32 巴66-284 -0.458 27
5 新海25 -0.186 22 19 N9107 -1.082 39 33 巴-5507 -0.657 30
6 新海26 0.106 17 20 06-146 -1.000 36 34 吐75-238 -0.054 20
7 新海27 0.318 13 21 军海1号 1.890 2 35 V9-2 -0.081 21
8 新海28 -0.579 28 22 吐82-5-9 2.757 1 36 巴州-3021 -1.061 38
9 新海29 -0.869 34 23 孔雀202 0.115 16 37 吐75-193 0.76 9
10 新海30 1.674 3 24 孔雀201 0.926 8 38 新海2号 -0.666 31
11 新海31 1.517 4 25 孔雀200 -0.866 33 39 塔海-901 -0.777 32
12 新海33 1.045 7 26 吐75-6 0.723 11 40 巴州-3244 -0.302 25
13 新海34 -0.42 26 27 墨-1413 0.527 12 41 巴州-3761 -0.987 35
14 新海35 1.453 5 28 新海5号 0.047 18 42 吐77-55 1.447 6

Fig.4

Cluster diagram of 42 sea-island cotton varieties"

[1] 杨真, 王宝山. 中国盐渍土资源现状及改良利用对策. 山东农业科学, 2015, 47(4):125-130.
[2] 努尔沙吾列·哈斯木汉. 新疆土壤盐渍化成因及其防治对策. 科学技术创新, 2020(9):52-53.
[3] 李菲, 龚记熠, 李欲柯, 等. 抗旱耐盐植物功能基因发掘及其在棉花育种中的应用. 分子植物育种, 2019, 17(22):7395-7400.
[4] 卢秀茹, 贾肖月, 牛佳慧. 中国棉花产业发展现状及展望. 中国农业科学, 2018, 51(1):26-36.
[5] 张雯丽, 沈贵银, 曹慧, 等. “十三五”时期我国重要农产品消费趋势、影响与对策. 农业经济问题, 2016, 37(3):11-17.
[6] 刘贞贞, 平文超, 张忠波, 等. 河北省棉花战略东移存在的主要问题及对策. 安徽农业科学, 2014, 42(26):9125-9128.
[7] 朱建峰, 杨秀艳, 武海雯, 等. 植物种子萌发期耐盐碱性提高技术研究进展. 生物技术通报, 2020, 36(2):158-168.
doi: 10.13560/j.cnki.biotech.bull.1985.2019-0727
[8] 严勇亮, 张金波, 路子峰, 等. 大豆种质资源耐盐性鉴定与评价. 新疆农业科学, 2021, 58(1):65-71.
doi: 10.6048/j.issn.1001-4330.2021.01.008
[9] 库尼都孜阿依·吐尔汗, 任毅, 颜安, 等. 新疆冬小麦品种萌发期耐盐性综合评价及耐盐种质的筛选. 新疆农业科学, 2020, 57(1):20-31.
doi: 10.6048/j.issn.1001-4330.2020.01.003
[10] 邓杰, 孙丽芳, 王霞, 等. 89份玉米自交系萌发期耐盐碱性综合评价. 玉米科学, 2020, 28(4):15-21.
[11] 杨涛, 李生梅, 黄雅婕, 等. 海岛棉品种(系)萌发期盐胁迫综合评价. 新疆农业大学学报, 2019, 42(5):313-319.
[12] 季波, 徐金鹏, 时龙, 等. 10种禾本科牧草种子萌发期耐盐性. 新疆农业科学, 2021, 58(2):342-351.
doi: 10.6048/j.issn.1001-4330.2021.02.016
[13] 叶武威. 棉花种质的耐盐性及其耐盐基因表达的研究. 北京: 中国农业科学院, 2007.
[14] 张国伟, 路海玲, 张雷, 等. 棉花萌发期和苗期耐盐性评价及耐盐指标筛选. 应用生态学报, 2011, 22(8):2045-2053.
[15] 吴文超, 曲延英, 高文伟, 等. 不同棉花品种对盐、旱胁迫的光合响应及抗逆性评价. 新疆农业科学, 2016, 53(9):1569-1579.
[16] 高春华, 朱金英, 张华文, 等. 38个粒用高粱品种芽期耐盐性的综合鉴定及评价. 核农学报, 2019, 33(9):1841-1855.
doi: 10.11869/j.issn.100-8551.2019.09.1841
[17] Sikder R K, Wang X R, Jin D S, et al. Screening and evaluation of reliable traits of upland cotton (Gossypium hirsutum L.) genotypes for salt tolerance at the seedling growth stage. Journal of Cotton Research, 2020, 3(2):25-37.
doi: 10.1186/s42397-020-00063-3
[18] 温文保. 模糊数学在农业科学中的应用. 湖南农学院学报, 1983(4):87-97.
[19] 陶向新. 模糊数学在农业科学中的初步应用. 沈阳农学院学报, 1982(2):96-107.
[20] 苏秀娟, 徐海江, 邓晓娟, 等. 盐胁迫对海岛棉种子活力的影响. 新疆农业科学, 2020, 57(9):1604-1614.
doi: 10.6048/j.issn.1001-4330.2020.09.005
[21] 郑巨云, 曾辉, 王俊铎, 等. 陆地棉品种资源萌发期耐盐性的隶属函数法评价. 新疆农业科学, 2018, 55(9):1579-1592.
doi: 10.6048/j.issn.1001-4330.2018.09.002
[22] 王宁, 冯克云, 南宏宇, 等. 甘肃河西走廊棉区棉花萌发期和苗期耐盐性鉴定与评价. 干旱地区农业研究, 2018, 36(1):148-155.
[23] 任毅, 颜安, 张芳, 等. 国内外301份小麦品种(系)种子萌发期抗旱性鉴定及评价. 干旱地区农业研究, 2019, 37(3):1-14.
[24] 林海明, 杜子芳. 主成分分析综合评价应该注意的问题. 统计研究, 2013, 30(8):25-31.
[25] 杨涛, 李生梅, 黄雅婕, 等. 海岛棉资源自然复合盐胁迫综合评价. 核农学报, 2021, 35(7):1507-1521.
doi: 10.11869/j.issn.100-8551.2021.07.1507
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