作物学报 ›› 2014, Vol. 40 ›› Issue (11): 1936-1945.doi: 10.3724/SP.J.1006.2014.01936
许乃银,李健
XU Nai-Yin,LI Jian
摘要:
| [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: 1805–1819Yan 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: 142–152Luo 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: 1377–1385Zhang 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: 2229–2236Xu 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, 2007Ministry 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: 866–873Xu 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: 1241–1248Xu 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: 361–365Tang 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] | 胡亮亮, 周洪妹, 王晓磊, 王素华, 李彩菊, 魏云山, 王丽侠, 程须珍, 陈红霖. 小豆产量相关性状的基因型与环境互作效应及稳定性分析[J]. 作物学报, 2025, 51(10): 2581-2594. |
| [2] | 谢雄泽, 谢捷, 褚乾梅, 尹羽丰, 余小红, 王盾, 冯鹏. 长江流域冬油菜需水量及水分盈亏特征分析[J]. 作物学报, 2024, 50(7): 1829-1840. |
| [3] | 许乃银, 金石桥, 晋芳, 刘丽华, 徐剑文, 刘丰泽, 任雪贞, 孙全, 许栩, 庞斌双. 基于SNP标记的小麦品种遗传相似度及其检测准确度分析[J]. 作物学报, 2024, 50(4): 887-896. |
| [4] | 邵扬, 郭延平, 周丙月, 张峰, 张兴民, 王玉萍. 蚕豆产量组分的基因型与环境互作及稳定性分析[J]. 作物学报, 2024, 50(1): 149-160. |
| [5] | 陶玥玥, 盛雪雯, 徐坚, 沈园, 王海候, 陆长婴, 沈明星. 长三角水稻-油菜周年两熟温光资源分配与利用特征[J]. 作物学报, 2023, 49(5): 1327-1338. |
| [6] | 张佳运, 马淑梅, 余常兵, 王淑彬, 魏亚凤, 杨文钰, 王小春. 长江流域旱地多熟模式水分供需平衡特征与水分生产效益[J]. 作物学报, 2022, 48(11): 2891-2907. |
| [7] | 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. |
| [8] | 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. |
| [9] | 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. |
| [10] | 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. |
| [11] | 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. |
| [12] | 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. |
| [13] | 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. |
| [14] | 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. |
| [15] | 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. |
|
||