GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

doi:10.1016/S2095-3119(15)61157-1

Journal of Integrative Agriculture ›› 2016, Vol. 15 ›› Issue (06): 1218-1227.DOI: 10.1016/S2095-3119(15)61157-1

GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

ZHANG Pan-pan^{1, 2*}, SONG Hui^3*, KE Xi-wang¹, JIN Xi-jun¹, YIN Li-hua¹, LIU Yang¹, QU Yang⁴, SU Wang², FENG Nai-jie¹, ZHENG Dian-feng¹, FENG Bai-li²

¹National Coarse Cereals Engineering Research Center, Heilongjiang August First Agricultural University, Daqing 163319, P.R.China
² State Key Laboratory of Crop Stress Biology in Arid Areas, Ministry of Science and Technology/Northwest A&F University, Yangling 712100, P.R.China
³ Anyang Academy of Agriculture Sciences, Anyang 455000, P.R.China
⁴ Baoji Institute of Agricultural Science, Qishan 722400, P.R.China

收稿日期:2015-04-08 出版日期:2016-06-01 发布日期:2016-06-06
通讯作者: FENG Bai-li, Tel: +86-29-87082889, E-mail: 7012766@163.com; ZHENG Dian-feng, Tel: +86-459-8979675, E-mail: zdffnj@263.net
作者简介:ZHANG Pan-pan, E-mail: zpp35@163.com
基金资助:
This research was funded by the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2014BAD07B03), the National Natural Science Foundation of China (31371529), the Postdoctoral Science Foundation of Heilongjiang Province, China (LBH-Z14177), the project of Education Department in Heilongjiang Province, China (12541599), and the China Agricultural Research System (CARS07-13.5-A9).

GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

ZHANG Pan-pan^{1, 2*}, SONG Hui^3*, KE Xi-wang¹, JIN Xi-jun¹, YIN Li-hua¹, LIU Yang¹, QU Yang⁴, SU Wang², FENG Nai-jie¹, ZHENG Dian-feng¹, FENG Bai-li²

¹National Coarse Cereals Engineering Research Center, Heilongjiang August First Agricultural University, Daqing 163319, P.R.China
² State Key Laboratory of Crop Stress Biology in Arid Areas, Ministry of Science and Technology/Northwest A&F University, Yangling 712100, P.R.China
³ Anyang Academy of Agriculture Sciences, Anyang 455000, P.R.China
⁴ Baoji Institute of Agricultural Science, Qishan 722400, P.R.China

Received:2015-04-08 Online:2016-06-01 Published:2016-06-06
Contact: FENG Bai-li, Tel: +86-29-87082889, E-mail: 7012766@163.com; ZHENG Dian-feng, Tel: +86-459-8979675, E-mail: zdffnj@263.net
About author:ZHANG Pan-pan, E-mail: zpp35@163.com
Supported by:
This research was funded by the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2014BAD07B03), the National Natural Science Foundation of China (31371529), the Postdoctoral Science Foundation of Heilongjiang Province, China (LBH-Z14177), the project of Education Department in Heilongjiang Province, China (12541599), and the China Agricultural Research System (CARS07-13.5-A9).

摘要/Abstract

Abstract: The experiments were conducted for three consecutive years across 14 locations using 9 non-waxy proso millet genotypes and 16 locations using 7 waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by analysis of variance (ANOVA) and genotype and genotype by environment interaction (GGE) biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment (E), genotype (G) and their interaction (G×E) (P<0.1%). G×E interaction effect was six times higher than G effect in non-waxy group and seven times in waxy group. N04-339 in non-waxy and Neimi 6 (NM6) in waxy showed higher grain yields and stability compared with other genotypes. Also, Neimi 9 (NM9, a non-waxy cultivar) and 90322-2-33 (a waxy cultivar) showed higher adaptability in 7 and in 11 locations, respectively. For non-waxy, Dalat, Inner Mongolia (E2) and Wuzhai, Shanxi (E5) were the best sites among all the locations for maximizing the variance among candidate cultivars, and Yanchi, Ningxia (E10) had the best representativeness. Wuzhai, Shanxi (e9) and Yanchi, Ningxia (e14) were the best representative locations, and Baicheng, Jilin (e2) was better discriminating location than others for waxy genotypes. Based on our results, E10 and e14 have enhanced efficiency and accuracy for non-waxy genotypes and waxy genotypes selection, respectively in national regional test of proso millet varieties.

Key words: proso millet , GGE biplot , yield stability , test location representativeness

ZHANG Pan-pan, SONG Hui, KE Xi-wang, JIN Xi-jun, YIN Li-hua, LIU Yang, QU Yang, SU Wang, FENG Nai-jie, ZHENG Dian-feng, FENG Bai-li. GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes[J]. Journal of Integrative Agriculture, 2016, 15(06): 1218-1227.

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GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes

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