Evaluation of productivity and stability of elite summer soybean cultivars in multi-environment trials

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• 作者：Jun Qin ; Ran Xu ; Haichao Li ; Chunyan Yang ; Duan Liu ; Zhangxiong liu…
• 关键词：Summer soybean ; GGEbiplot?/li> Yield ; Adaptability
• 刊名：Euphytica
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：206
• 期：3
• 页码：759-773
• 全文大小：1,001 KB
• 参考文献：Bradu D, Gabriel KR (1978) The biplot as a diagnostic tool for models of two-way tables. Technometrics 20:47-8CrossRef
  Chen SL, Li YR, Cheng ZS, Liu JS (2009) GGE biplot analysis of effects of planting density on growth and yield components of high oil peanut. Acta Agonomica Sinica. 35(7):1328-335CrossRef
  Gabriel KR (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58(3):453-67CrossRef
  Gauch HGJ (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sciences. 46:1488-500CrossRef
  Jin YY, Gao XM, Ge RD (2011) A mathematical model of soybean yield based on the regression analysis. Jilin Agriculture. 12(262):295-96
  Li WD, Zhang MC (2006) Summer soybean varieties and parameters of Huang Huai Hai Region. Chinese Agricultural Science and Technology Press, Beijing, pp 1-
  Martin JS, Rubiales D, Flores F, Emran AA, Shtaya MJY, Sillero JC, Allagui MB, Prats E (2014) Adaptation of oat (Avena sativa) cultivars to autumn 439 sowings in Mediterranean environments. Field Crops Res 156:111-22CrossRef
  Munawar M, Hammad G, Shahbaz M (2013) Evaluation of maize (Zea mays L.) hybrids under different environments by GGE biplot analysis. Am-Eurasian J Agric Environ Sci 13(9):1252-257
  Qiu LJ, Chang RZ (2006) Descriptors and data standard for soybean (Glycine spp.). China Agriculture Press, Beijing
  Rakshit S, Ganapathy KN, Gomashe SS, Rathore A, Ghorade RB, Nagesh Kumar MV, Ganesmurthy K, Jain SK, Kamtar MY, Sachan JS, Ambekar SS, Ranwa BR, Kanawade DG, Balusamy M, Kadam D, Sarkar A, Tonapi VA, Patil JV (2012) GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica 185:465-79CrossRef
  Shan CY, Wei YG, Zhang YJ, Li XH, Nie L, Dong XL, Liu C (2009) Grey correlation degree analysis on main traits of soybean varieties in Helongjiang province. Soybean Sci. 28(5):945-48
  Shang Y, Jia QJ, Zhu JH, Hua W, Lin F, Wang JM, Yang JM (2011) Optimal use of biplots in analysis of multi-location variety test data. Acta Agriculturae Zhejiangensis. 23(2):197-02
  Sun HC, Wan JH, Guo AB, Lu DW, Cui JM, Song CJ, Pei ZQ, Liu ZP, Lu LY, Niu YF, Zheng LM (2006) The application of the gray related degree analysis to themaize combination and app raises test. J Maize Sci. 14(2):47-9
  Wu XL, Bao WK (2012) Gray relational grade analysis of synthetically traits related to yield in different winter wheat. Southwest China Journal of Agricultural Sciences. 25(2):372-78
  Xu ZR, Cao JF, Wang RF, Hu TH, Lu SH, Gao GJ, Wu FX (2010) Grey relational grade analysis on yield andma in agronomic characters of soybean. J Hebei Agric Sci 14(2):1-
  Xu SX, Li ZG, Zhang G (2012) Grey relations analysis of yield and agricultural characters for soybean plot trial. Soybean Sci Technol 1:28-0
  Yan WK (2001) GGE biplot: a windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron J 93:1111-118CrossRef
  Yan WK (2010) Optimal use of biplots in analysis of multi-location variety test data. Acta Agonomica Sin. 36(11):1805-819
  Yan W, Holland JB (2010) A heritability-adjusted GGE Biplot for test environment evaluation. Euphytica 171:355-69CrossRef
  Yan WK, Tinker NA (2005) An integrated biplot analysis system for displaying, interpreting, and exploring genotype environment interaction. Crop Sci 45:1004-016CrossRef
  Yan W, Hunt LA, Sheng QL, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci 40:596-05CrossRef
  Yan WK, Sheng QL, Hunt HuYGLA (2001) GGE biplot-an ideal tool for studying genotype by environment interaction of regional yield trial data. Acta Agonomica Sinica. 27(1):21-8
  Yan W, Kang MS, Ma BL, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643-55CrossRef
  Yan L, Feng Y, Yang CY, Shi XL, Zhang MC (2012) The character of protein and oil content distribution among introgression lines using Jidou12 as recipient parent. Acta Agriculturae Boreali Sinica. 27(1):87-2
  Yang CY, Zhang MC, Zhao SJ, Wang WX (2003) The breeding and characteristics of high oil soybean variety Jihuang 13. J Hebei Agric Sci 7(2):36-8
  Yang CY, Zhang MC, Zhao SJ, Wang WX (2004) Study on breeding methods of a new soybean variety-Jidou 12 with high protein and high yield. J Agric University of Hebei. 27(4):8-1
  Yang RC, Crossa J, Cornelius PL, Burgue?o J (2009) Biplot analysis of genotype × environment interaction: proceed with caution. Crop Sci 49:1564-576CrossRef
  Zhang ZF, Fu XF, Liu JQ, Yang HS (2010) Yield stability and testing-site representativeness in national regional trials for oat lines based on GGE-biplot analysis. Acta Agonomica Sin 36(8):1377-385CrossRef
  Zhong L (2012) Gray relational grade analysis between the rapeseed yield and related traits. J South Agric 43(4):421-24
  Zhou CJ, Tian ZY, Li JY, Yang L,
• 作者单位：Jun Qin (1)
  Ran Xu (4)
  Haichao Li (3)
  Chunyan Yang (1)
  Duan Liu (5)
  Zhangxiong liu (2)
  Lifeng Zhang (4)
  Weiguo Lu (3)
  Terrence Frett (7)
  Pengyin Chen (6)
  Mengchen Zhang (1)
  Lijuan Qiu (2)
  
  1. The Institute of Cereal & Oil Crops, Hebei Academy of Agricultural and Forestry Sciences/National Soybean Improvement Center Shijiazhuang Sub-Center/North China Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, Shijiazhuang City, 050031, Hebei, People’s Republic of China
  4. Crops Institute of Shandong Academy of Agricultural Science, Jinan, 25010, People’s Republic of China
  3. Institute of Industrial Crops, Zhengzhou National Subcenter for Soybean Improvement/Key Laboratory of Oil Crops in Huanghuaihai Plains, Ministry of Agriculture, P R China, Henan Academy of Agricultural Sciences, Zhengzhou, 450002, Henan, People’s Republic of China
  5. Geochemical Environmental Research Group, Texas A&M University, 833 Graham Rd, College Station, TX, 77845, USA
  2. National Key Facility for Crop Gene Resources and Genetic Improvement, Key Laboratory of Crop Germplasm & Biotechnology, MOA, Institute of Crop Science, The Chinese Academy of Agricultural Sciences, Beijing, 100081, People’s Republic of China
  7. Horticulture Department, University of Arkansas, Fayetteville, AR, 72701, USA
  6. Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, 72701, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Plant Physiology
  Plant Sciences
  Ecology
  
• 出版者：Springer Netherlands
• ISSN：1573-5060

文摘

Spring soybean cultivars produced in moderate climates currently represent almost the entire soybean industry; however, soybean production has the potential to be extended into the summer months in different regions of the world. It is critical to select the correct soybean cultivar for production in a specific environment. The purpose of this study was therefore to evaluate the productivity (yield) and stability of the current summer soybean cultivars in multi-environment trials in the Huang–Huai–Hai region, presently the largest summer soybean-producing region in the world, to determine which cultivars will be most successful for large scale production in this region, as well as those that should be used in future breeding efforts. A total of 94 summer soybean cultivars were grown in the three major soybean production provinces, i.e., Shandong, Henan, and Hebei, over 3 years (2008-010). The GGEbiplot?software provided a ‘genotype x genotype-by-environment interaction-function to evaluate the importance of agronomic factors controlling the soybean yield of each cultivar across the nine different environments. Xudou10, Zhonghuang39, Lu93748-1 and Lu99-1 exhibited relatively high average yields. The stability among the high-yielding cultivars was ranked in decreasing order as Xudou10, Zheng99048, Jidou7, Yudou18, and Gaozuoxuan-1. Among all recorded factors, the pod number per plant was the most important factor controlling yield, followed by seed number per plant, effective branch number, and 100-seed weight, which positively affected soybean yield. In contrast, a higher bottom pod height, greater number of nodes on the main stem, and longer growth duration were negatively correlated with yield. Keywords Summer soybean GGEbiplot?/span> Yield Adaptability