欢迎访问作物学报,今天是

作物学报 ›› 2014, Vol. 40 ›› Issue (11): 1936-1945.doi: 10.3724/SP.J.1006.2014.01936

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

棉花区试中品种多性状选择的理想试验环境鉴别

许乃银,李健   

  1. 江苏省农业科学院经济作物研究所 / 农业部长江下游棉花和油菜重点实验室, 江苏南京 210014
  • 收稿日期:2014-04-16 修回日期:2014-09-16 出版日期:2014-11-12 网络出版日期:2014-09-26
  • 基金资助:

    本研究由国家转基因生物新品种培育重大专项(2012ZX08013016)和江苏省农业科技自主创新资金项目(CX-12-5035)资助。

Identification of Ideal Test Environments for Multiple Traits Selection in Cotton Regional Trials

XU Nai-Yin,LI Jian   

  1. Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences / Key Laboratory of Cotton and Rapeseed, Ministry of Agriculture, Nanjing 210014, China?
  • Received:2014-04-16 Revised:2014-09-16 Published:2014-11-12 Published online:2014-09-26

摘要:

 

农作物品种选育通常需要对多目标性状综合选择, 依据育种目标性状和权重建立品种选择指数, 选择遗传差异鉴别力强和目标环境代表性好的试验点, 有助于提高品种选育的效率并节省试验成本。本研究依据国家棉花品种审定标准构建针对性和实用性强的品种选择指数, 即SI = 0.40´皮棉产量+0.13´纤维比强度+0.09´(纤维长度+马克隆值)+0.11´抗枯萎病+0.09´抗黄萎病+0.10´霜前花率,采用GGE双标图方法, 对2000—2013年期间39组(含585个单点试验)长江流域国家棉花区域试验中的15个试验点,综合评价品种选择指数的鉴别力、代表性和理想指数。结果表明,湖北黄冈和江苏南京试验环境被评为最理想的试验环境, 湖北荆州、湖北武汉和江苏盐城江苏盐城为理想的试验环境, 而河南南阳、湖北襄阳、湖南常德、四川简阳和四川射洪试验环境为不理想试验环境。可以看出,理想的试验环境均位于长江流域的中下游棉区, 而不理想的试验环境中四川简阳和四川射洪位于上游的四川盆地、河南南阳和湖北襄阳位于长江流域北缘的南襄盆地、湖南常德虽然位于长江流域中游但栽培密度偏低。本研究构建的选择指数采用与国家棉花品种审定中品种评价准则相统一的目标性状和权重分配策略, 理想试验环境对我国长江流域棉区的棉花生态育种试验点的选择提供了切实可行的决策方案。

关键词: 棉花(Gossypium hirsutum L.), GGE双标图, 多性状, 品种选择指数, 理想试验环境, 长江流域, 区域试验

Abstract:

Crop breeding needs an integrating selection for the desirable traits. Screening locations with high discriminating ability and typical environments in light of cultivar selection index established by target traits and weights facilitates improving breeding efficiency and saving cost. Cultivar selection index was constructed on the basis of the National Register Criteria for Cotton Variety, namely, SI=0.40´lint cotton yield+0.13´fiber strength+0.09´(fiber length+micronaire value)+0.11´Fusarium wilt+0.09´Verticillium wilt+0.10´harvesting ratio of seed cotton before frost. GGE biplot method was adopted to evaluate the identification, representativeness, and ideal index of cultivar selection index by using 15 locations data from 39 sets of national cotton variety regional trials including 585 trials in the Yangtze River Valley (YaRV) during 2000–2013. The results showed that Huanggang in Hubei Province and Nanjing in Jinagsu province were evaluated as the most desirable trial locations; Jingzhou and Wuhan in Hubei Province and Yancheng in Jiangsu Province were desirable trial locations; while Nanyang in Henan Province, Xiangyang in Hubei Province, Changde in Hunan Province, Janyang and Shehong in Sichuan Province were considered as undesirable locations for cotton cultivar selection. The ideal environments were all located in the Middle and Lower Reaches of YaRV, while the undesirable locations included Jianyang and Shehong in the Sichuan basin in the upper reaches of YaRV, and Nanyang and Xiangyang in Nan-Xiang basin at the northern border of YaRV. Changde in Hunan Province was also considered as undesirable location although it is located in the Middle Reaches of YaRV, probably because of the significantly low plant density used in the farming. In conclusion, this study has established a feasible selection index according to the national cotton registration criteria, and identified desirable test locations for reliable and effective variety evaluation in the area of YaRV. This study sets an example of test location evaluation utilizing historical data for the similar studies in other regions and for other crops.

Key words: Cotton (Gossypium hirsutum L.), GGE biplot, Multiple traits, Cultivar selection index, Ideal test environment, Yangtze River Valley (YRaV), Regional crop trial

[1]Gauch H G, Zobel R W. Identifying mega-environments and targeting genotypes. Crop Sci, 1997, 37: 311–326



[2]Cooper M, Woodruff D R, Eisemann R L, Brennan P S, Delacy I H. A selection strategy to accommodate genotype-by- environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor Appl Genet, 1995, 90: 492–502



[3]Heslot N, Akdemir D, Sorrells ME, Jannink J. Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet, 2014, 127: 463–80



[4] Ramburan S. A multivariate illustration and interpretation of non-repeatable genotype × environment interactions in sugarcane. Field Crops Res, 2014, 157: 57–64



[5]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)



[6]Badu-Apraku B, Oyekunle M, Menkir A, Obeng-Antwi K, Yallou C G, Usman I S, Alidu H. Comparative performance of early-maturing maize cultivars developed in three eras under drought stress and well-watered environments in West Africa. Crop Sci, 2013, 53: 1298–1311



[7]Badu-Apraku B, Akinwale R O, Obeng-Antwi K, Haruna A, Kanton R, Usman I, Ado S G, Coulibaly N, Yallou G C, Oyekunle M. Assessing the representativeness and repeatability of testing sites for drought-tolerant maize in West Africa. Can J Plant Sci, 2013, 93: 699–714



[8]Yan W K, Pageau D, Fregeau-Reid J, Durand J. Assessing the representativeness and repeatability of test locations for genotype evaluation. Crop Sci, 2011, 51: 1603–1610



[9]Mohammadi R, Amri A. Analysis of genotype × environment interactions for grain yield in durum wheat. Crop Sci, 49: 1177–1186



[10]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



[11]Blanche S B, Myers G O. Identifying discriminating locations for cultivar selection in Louisiana. Crop Sci, 2006, 46: 946–949



[12]Ober E S, Bloa M L, Clark C J A, Royal A, Jaggard K W, Pidgeon J D. Evaluation of physiological traits as indirect selection criteria for drought tolerance in sugar beet. Field Crops Res, 2005, 91: 231–249



[13]Yan W K. GGE biplot: a windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron J, 2001, 93: 1111–1118



[14]Yan W K, Kang M S. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. Boca Raton, London, New York, Washington D.C: CRC Press, 2003



[15]罗俊, 张华, 邓祖湖, 许莉萍, 徐良年, 袁照年, 阙友雄. 应用GGE双标图分析甘蔗品种(系)的产量和品质性状. 作物学报, 2013, 39: 142–152



Luo J, Zhang H, Deng Z H, Xu L P, Xu L N, Yuan Z N, Que Y X. Analysis of yield and quality traits in sugarcane varieties (lines) with GGE-biplot. Acta Agron Sin, 2013, 39: 142–152 (in Chinese with English abstract)



[16]张志芬, 付晓峰, 刘俊青, 杨海顺. 用GGE双标图分析燕麦区域试验品系产量稳定性及试点代表性. 作物学报, 2010, 36: 1377–1385



Zhang Z F, Fu X F, Liu J Q, Yang H S. Yield stability and testing-site representativeness in national regional trials for oat lines based on GGE-biplot analysis. Acta Agron Sin, 2010, 36: 1377–1385(in Chinese with English abstract)



[17]Jamshidmoghaddam M, Pourdad S S. Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica, 2013, 190: 357–369



[18]Flores F, Hybl M, Knudsen J C, Marget P, Muel F, Nadal S, Narits L, Raffiot B, Sass O, Solis I, Winkler J, Stoddard F L, Rubiales D. Adaptation of spring faba bean types across European climates. Field Crops Res, 2013, 145: 1–9



[19]Amira J O, Ojo D K, Ariyo O J, Oduwaye O A, Ayo-Vaughan M A. Relative discriminating powers of GGE and AMMI models in the selection of tropical soybean genotypes. Afr Crop Sci J, 2013, 21: 67–73



[20]Farshadfar E, Mohammadi R, Aghaee M, Vaisi Z. GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Aust J Crop Sci, 2012, 6: 1074–1079



[21]Yan W K. GGE Biplot vs. AMMI graphs for genotpe-by-environment data analysis. J Ind Soc Agric Statist, 2011, 65: 181–193



[22]Glaz B, Kang M S. Location contributions determined via GGE biplot analysis of multienvironment sugarcane genotype-performance trials. Crop Sci, 2008, 48: 941–950



[23]许乃银, 李健, 张国伟, 周治国. 基于HA-GGE双标图的长江流域棉花区域试验环境评价. 作物学报, 2012, 38: 2229–2236



Xu N Y, Li J, Zhang G W, Zhou Z G. Evaluation of cotton regional trial environments based on HA-GGE biplot in the Yangtze River valley. Acta Agron Sin, 2012, 38: 2229–2236 (in Chinese with English abstract)



[24]中华人民共和国农业部. 农作物品种审定规范棉花. 北京: 中国农业出版社, 2007



Ministry of Agriculture, China. Standards of Registration for Cotton Varieties. Beijing: China Agriculture Press, 2007 (in Chinese)



[25]Yan W K, Holland J B. A heritability-adjusted GGE biplot for test environment evaluation. Euphytica, 2010, 171: 355–369



[26]许乃银, 李健. 利用GGE双标图划分长江流域棉花纤维品质生态区. 作物学报, 2014, 40: 866–873



Xu N Y, Li J. Ecological regionalization of cotton fiber quality based on GGE biplot in Yangtze River Valley. Acta Agron Sin, 2014, 40: 866–873 (in Chinese with English abstract)



[27]Anothai J, Patanothai A, Pannangpetch K, Jogloy S, Boote K J, Hoogenboom G. Multi-environment evaluation of peanut lines by model simulation with the cultivar coefficients derived from a reduced set of observed field data. Field Crops Res, 2009, 110: 111–122



[28]Yan W K. Singular-value partioning in biplot analysis of multienvironment trial data. Agron J, 2002, 94: 990–996



[29]许乃银, 李健, 张国伟, 周治国. 基于GGE双标图和马克隆值选择的棉花区域试验环境评价. 中国生态农业学报, 2013, 21: 1241–1248



Xu N Y, Li J, Zhang G W, Zhou Z G. Evaluation of regional cotton trial environments based on cotton fiber micron-aire selection by using GGE biplot analysis. Chin J Eco-Agric, 2013, 21: 1241–1248 (in Chinese with English abstract)



[30]汤飞宇, 程锦, 黄文新, 莫旺成, 肖文俊. 陆地棉高品质系数量性状的遗传变异与选择指数. 棉花学报, 2009, 21: 361–365



Tang F Y, Cheng J, Huang W X, Mo W C, Xiao W J. Genetic variation and selection indices of quantitative traits in upland cotton (Gossypium hirsutum L.) lines with high fiber quality. Cotton Sci, 2009, 21: 361–365 (in Chinese with English abstract)



[31]Yan W K, Hunt L A. Interpretation of genotype × environment interaction for winter wheat yield in Ontario. Crop Sci, 2001, 41: 19–25

[1] 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[J]. 作物学报, 2021, 47(4): 660-671.
[2] 张毅,许乃银,郭利磊,杨子光,张笑晴,杨晓妮. 我国北部冬麦区小麦区域试验重复次数和试点数量的优化设计[J]. 作物学报, 2020, 46(8): 1166-1173.
[3] 叶夕苗,程鑫,安聪聪,袁剑龙,余斌,文国宏,李高峰,程李香,王玉萍,张峰. 马铃薯产量组分的基因型与环境互作及稳定性[J]. 作物学报, 2020, 46(3): 354-364.
[4] 胡海燕,刘迪秋,李允静,李阳,涂礼莉*. 一个棉花纤维伸长期优势表达启动子pGhFLA1的克隆与鉴定[J]. 作物学报, 2017, 43(06): 849-854.
[5] 许乃银,金石桥,李健. 我国棉花品种区域试验重复次数和试点数量的设计[J]. 作物学报, 2016, 42(01): 43-50.
[6] 罗俊,许莉萍,邱军,张华,袁照年,邓祖湖,陈如凯,阙友雄. 基于HA-GGE双标图的甘蔗试验环境评价及品种生态区划分[J]. 作物学报, 2015, 41(02): 214-227.
[7] 许乃银,李健. 利用GGE双标图划分长江流域棉花纤维品质生态区[J]. 作物学报, 2014, 40(05): 891-898.
[8] 杨长琴,刘瑞显,张国伟,徐立华,周治国. 花铃期渍水对棉铃对位叶蔗糖代谢及铃重的影响[J]. 作物学报, 2014, 40(05): 908-914.
[9] 刘敬然,刘佳杰,孟亚利,王友华,陈兵林,张国伟,周治国. 外源6-BA和ABA对不同播种期棉花产量和品质及其棉铃对位叶光合产物的影响[J]. 作物学报, 2013, 39(06): 1078-1088.
[10] 王春平,胡希远,沈琨仑. 玉米区域试验中误差方差的异质性及其对品种评价的影响[J]. 作物学报, 2013, 39(03): 449-454.
[11] 罗俊,张华,邓祖湖,许莉萍,徐良年,袁照年,阙友雄. 应用GGE双标图分析甘蔗品种(系)的产量和品质性状[J]. 作物学报, 2013, 39(01): 142-152.
[12] 许乃银,张国伟,李健,周治国. 基于HA-GGE双标图的长江流域棉花区域试验环境评价[J]. 作物学报, 2012, 38(12): 2229-2236.
[13] 吴存祥,李继存,沙爱华,曾海燕,孙石,杨光明,周新安,常汝镇,年海,韩天富. 国家大豆品种区域试验对照品种的生育期组归属[J]. 作物学报, 2012, 38(11): 1977-1987.
[14] 关荣霞,方宏亮,何艳琴,常汝镇,邱丽娟. 国家大豆区域试验品种(系) SSR位点纯合度分析[J]. 作物学报, 2012, 38(10): 1760-1765.
[15] 王洁, 廖琴, 胡小军, 万建民. 北方稻区国家水稻品种区域试验精确度分析[J]. 作物学报, 2010, 36(11): 1870-1876.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!