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Standard Cultivar Selection and Digital Quantification for Precise Classification of Maturity Groups in Soybean.

Authors :
Song, Wenwen
Sun, Shi
Ibrahim, Seifeldin Elrayah
Xu, Zejun
Wu, Haiying
Hu, Xingguo
Jia, Hongchang
Cheng, Yanxi
Yang, Zhonglu
Jiang, Shibo
Wu, Tingting
Sinegovskii, Mikhail
Sapey, Enoch
Nepomuceno, Alexandre
Jiang, Bingjun
Hou, Wensheng
Sinegovskaya, Valentina
Wu, Cunxiang
Gai, Junyi
Han, Tianfu
Source :
Crop Science; Sep/Oct2019, Vol. 59 Issue 5, p1997-2006, 10p
Publication Year :
2019

Abstract

The maturity group (MG) system is widely used to group soybean [Glycine max (L.) Merr.] varieties based on their growth periods and photothermal responses. However, there is still no universal standard or quantifiable methodology for MG classification. In this study, phenological traits of 107 Chinese, 4 Far East Russian representative soybean varieties, and 113 North American reference varieties covering 13 MGs were evaluated at eight locations (ranging from 30 to 50° N) in four ecoregions of China for two consecutive years (2014 and 2015). Relative maturity groups (RMGs) were attributed to all the varieties based on the linear regression models. To decimalize the RMG values of the early‐maturing varieties belonging to MGs below 0, negative values were defined for MGs 00, 000, and 0000. The additive main effects and multiplicative interaction (AMMI) model was used to screen 185 standard candidate varieties for MGs 0000 to VIII. This study provided a systematic and quantifiable methodology for RMG identification in soybeans. The methodology is expected to be widely adopted by soybean regionalization and germplasm exchanges throughout the world and will be helpful for characterizing the photothermal sensitivity and adaptability of the given soybean varieties. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0011183X
Volume :
59
Issue :
5
Database :
Complementary Index
Journal :
Crop Science
Publication Type :
Academic Journal
Accession number :
141997745
Full Text :
https://doi.org/10.2135/cropsci2019.02.0095