Crops ›› 2018, Vol. 34 ›› Issue (5): 40-44.doi: 10.16035/j.issn.1001-7283.2018.05.007

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Analysis of Stability and Adaptability of Winter Wheat Varieties in the Regional Trials of the Northern Wheat Region of China

Wang Hanxia,Shan Fuhua,Tian Liping,Ma Qiaoyun,Zhao Changping,Zhang Fengting   

  1. Beijing Academy of Agriculture and Forestry Sciences/Beijing Engineering and Technique Research Center for Hybrid Wheat, Beijing 100097, China
  • Received:2018-04-23 Revised:2018-08-17 Online:2018-10-15 Published:2018-10-12
  • Contact: Fengting Zhang

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Abstract:

To study the yield potential and stability of winter wheat varieties in regional trials, the data of grain yield were collected from irrigated land group of winter wheat varieties regional test in the Northern Winter Region of China during 2014-2015 cropping season and analyzed by additive main effects and multiplication interaction (AMMI) model. The results could explain clearly the genotype, environment and interaction effect of genotype by environment (G×E) of wheat yield. The Nongda 3486 and Jingnong 12-79 showed the best stability, followed by Keyi 4174, Chang 6794 and Zhongmai 93, whereas Hangmai 109, Jintai 102 and Zhongxin 7198. Wuqing, Yuci, Alaer, Zunhua and Tunyu had the best resolution, while Gu'an, Luanxian, Shunyi of Beijing, Changping of Beijing were of lower resolution. The combination of AMMI biplot and stable parameters is intuitive and accurate for evaluating the stability and adaptability of winter wheat varieties in regional trial.

Key words: AMMI model, Winter wheat, Regional trial, Stability, Adaptability

Table 1

Codes and yields of varieties and sites"

编号Code 品种(系)/地点Variety (line)/Site 产量Yield (kg/hm2)
V1 京农12-79 8 386.696
V2 农大3486 8 427.004
V3 科遗4174 7 759.475
V4 航2566 8 264.429
V5 中麦93 8 060.883
V6 航麦109 8 023.096
V7 长6794 7 847.296
V8 晋太102 7 082.863
V9 众信7198 7 688.358
V10 (CK) 中麦175 7 815.550
E1 北京昌平 8 632.270
E2 北京顺义 7 251.425
E3 河北保定 6 901.085
E4 河北遵化 7 028.770
E5 河北滦县 7 306.625
E6 河北固安 9 233.355
E7 山西屯玉 8 826.450
E8 山西榆次 6 681.750
E9 山西介休 7 052.950
E10 新疆阿拉尔 9 310.985
E11 天津宝坻 9 362.895
E12 天津武清 7 638.220

Table 2

Combined analysis of variance of the yield"

变异来源Source of variation df SS MS F P
总变异Total variation 359 534 194 764.34 1 488 007.70 - -
处理Treatment 119 513 215 271.13 4 312 733.37 49.34 0.0001
基因型Genotype 9 51 046 735.63 5 671 859.51 64.88 0.0001
环境Environment 11 362 636 951.30 32 966 995.57 377.13 0.0001
基因型×环境交互作用G×E interaction 99 99 531 584.20 1 005 369.54 11.50 0.0001
误差Error 240 20 979 493.22 87 414.56 - -

Table 3

Comparison between linear regression analysis and AMMI model in wheat regional trials"

模型
Model		 变异来源
Source of variation		 df SS F P 占互作平方和百分比(%)
Percentage of G×E
线性回归Linearregression 联合回归United regression 1 66 107.00 0.76 - 0.07
基因型回归Genotype regression 8 4 087 617.89 5.85 0.0001 4.11
环境回归Environment regression 10 21 176 499.50 24.23 0.0001 21.28
残差Residual 80 74 201 359.80 - - 74.54
AMMI模型AMMI model IPCA1 19 51 722 434.68 6.35 0.0001 51.97
IPCA2 17 16 930 737.91 2.32 0.0027 17.01
IPCA3 15 10 291 945.47 1.60 - 10.34
残差Residual 48 20 586 466.14 - - 20.68

Fig.1

AMMI biplot between averaged yield and environments and varieties (lines) "

Fig.2

AMMI biplot between varieties (lines) and environment (IPCA1 and IPCA2)"

Table 4

Parameter of stability of varieties (lines) and environments"

变量Variable IPCA1 IPCA2 De 变量Variable IPCA1 IPCA2 Dg
V1 -1.8958 2.7265 3.3208 E1 -7.6516 10.5916 13.0663
V2 0.4491 0.5075 0.6777 E2 -2.1702 9.0817 9.3374
V3 4.9879 4.6747 6.8361 E3 13.2894 -15.1040 20.1181
V4 26.0995 -2.5683 26.2256 E4 -1.4525 -20.2987 20.3506
V5 -2.5333 12.1800 12.4407 E5 5.1066 -5.5336 7.5298
V6 18.8603 30.1119 35.5308 E6 -0.7144 -1.9717 2.0971
V7 -8.7016 0.8185 8.7400 E7 -11.5653 -16.6191 20.2472
V8 -42.2024 -7.0073 42.7802 E8 -38.7882 15.0362 41.6006
V9 27.0778 -34.4303 43.8024 E9 -14.3418 11.5153 18.3926
V10 -22.1416 -7.0131 23.2257 E10 16.9895 19.4828 25.8500
E11 -0.5432 -18.4644 18.4724
E12 41.8418 12.2839 43.6077
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