作物杂志,2020, 第2期: 60–64 doi: 10.16035/j.issn.1001-7283.2020.02.010

• 遗传育种·种质资源·生物技术 • 上一篇    下一篇

大粒花生品种区域试验的AMMI模型分析

刘卫星,贺群岭,张枫叶,范小玉,陈雷,李可,吴继华()   

  1. 商丘市农林科学院,476000,河南商丘
  • 收稿日期:2019-08-05 修回日期:2019-11-21 出版日期:2020-04-15 发布日期:2020-04-13
  • 通讯作者: 吴继华 E-mail:wjihua122@163.com
  • 作者简介:刘卫星,主要从事花生遗传育种与栽培研究,E-mail:cotton@sina.cn
  • 基金资助:
    河南省重大科技专项(161100111000);河南省花生产业技术体系专项资金(S2012-05-G01)

AMMI Model Analysis on Regional Trials of Large-Seeded Peanut Varieties

Liu Weixing,He Qunling,Zhang Fengye,Fan Xiaoyu,Chen Lei,Li Ke,Wu Jihua()   

  1. Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, Henan, China
  • Received:2019-08-05 Revised:2019-11-21 Online:2020-04-15 Published:2020-04-13
  • Contact: Jihua Wu E-mail:wjihua122@163.com

摘要:

运用AMMI模型分析了2015-2016年国家北方片花生区域试验荚果产量数据,结果表明,品种、试验地点、品种与试验地点互作、交互效应主成分值(IPCA)均达极显著水平。不同花生品种在各试验地点的稳定性和不同试验地点对品种的分辨力差异较大,开农705的稳定性参数最大(Dg=32.9718),对照品种花育33号的稳定性参数最小(Dg=11.3287);8个花生品种中高产稳产的品种是商花11号和郑农花15号,产量高而稳定性一般的品种是开农705和龙花二号,稳产但不高产的品种是花育33号;19个试验地点中山东日照和河南洛阳的分辨力强,河南漯河和安徽固镇的分辨力较差。

关键词: AMMI模型, 大粒花生, 产量, 稳定性, 适应性

Abstract:

The AMMI model was employed to analyze the pod yield data of regional trials for large-seeded peanut from 2015 to 2016 in north China. The results showed that the varieties, test sites, the variety×test site interaction and the interaction principal component axis (IPCA) reached at extremely significant level. There were significant differences in stability of the eight peanut varieties and resolution of the nineteen testing sites. Among the eight varieties, the stability parameter of Kainong 705 was the highest (Dg=32.9718) and that of Huayu 33 was the lowest (Dg=11.3287); Shanghua 11 and Zhengnonghua 15 were the varieties with high-yield and high-stability, Kainong 705 and Longhua 2 were the varieties with high-yield and low-stability, Huayu 33 was the variety with low-yield and high stability. As for the nineteen testing sites, Rizhao in Shandong and Luoyang in Henan had the best resolution, while Luohe in Henan and Guzhen in Anhui were of lower resolution.

Key words: AMMI model, Large-seeded peanut, Yield, Stability, Adaptability

表1

2015-2016年花生品种荚果平均产量"

品种Variety 2015 2016
G1 (CK) 5 207.58 4 769.17
G2 5 073.41 4 749.99
G3 5 747.23 5 400.39
G4 5 312.19 5 331.21
G5 5 514.17 5 071.06
G6 5 639.91 5 201.49
G7 5 319.20 5 312.29
G8 5 540.31 5 106.67

表2

2015-2016年各试验地点花生荚果平均产量"

试验地点Testing site 2015 2016
E1 6 226.59 8 031.26
E2 3 637.69 3 457.97
E3 4 825.13 5 027.44
E4 5 733.26 5 708.31
E5 6 411.39 5 295.30
E6 3 603.86 4 299.11
E7 4 699.22 7 292.06
E8 4 558.84 3 983.55
E9 3 723.43 3 933.83
E10 6 147.49 3 971.59
E11 5 664.68 5 470.91
E12 5 932.54 4 619.40
E13 6 708.69 5 896.65
E14 4 808.74 4 908.56
E15 6 386.08 4 346.87
E16 6 960.00 3 696.32
E17 5 834.38 6 735.96
E18 6 430.29 5 014.39
E19 4 673.42 5 548.41

表3

花生品种荚果产量联合方差分析"

变异来源
Source of variation
df 平方和
Sum of squares
占总的平方和百分比(%)
Percentage of total sum of squares
均方
Mean square
F P
总变异Total variation 455 430 507 422.61 - 946 170.16 - -
处理Treatment 151 413 331 665.70 - 2 737 295.80 48.4484 0.0001
基因型Genotype (G) 7 18 856 776.40 4.38 2 693 825.20 47.6790 0.0001
环境Environment (E) 18 361 562 332.25 83.99 20 086 796.24 355.5235 0.0001
基因型×环境交互作用G×E interaction 126 32 912 557.05 7.65 261 210.77 4.6233 0.0001
误差Error 304 17 175 756.91 3.99 56 499.20 - -

表4

线性回归与AMMI模型分析结果"

模型
Model
变异来源
Source of variation
df 平方和
Sum of squares
占总的平方和百分比(%)
Percentage of total sum of squares
均方
Mean square
F P
线性回归
Linear regression
analysis
联合回归United regression 1 901 569.71 2.74 901 569.71 15.9572 0.0001
基因型回归Genotype regression 6 1 260 054.77 3.83 210 009.13 3.7170 0.0014
环境回归Environment regression 17 6 500 847.34 19.75 382 402.78 6.7683 0.0001
残差Residual 102 24 250 085.23 73.68 237 745.93 4.2080 0.0001
AMMI模型
AMMI model
IPCA1 24 12 366 546.90 37.57 515 272.79 4.4899 0.0001
IPCA2 22 8 509 099.24 25.85 386 777.24 3.3702 0.0001
IPCA3 20 5 151 121.87 15.65 257 556.09 2.2442 0.0020
残差Residual 60 6 885 789.04 20.93 114 763.15 - -

图1

平均荚果产量与IPCA1的AMMI模型双标图"

表5

花生品种的稳定性参数及排序"

品种
Variety
IPCA1 IPCA2 IPCA3 Dg 位次
Order
G1(CK) -0.4380 -11.3192 0.1501 11.3287 8
G2 -2.3195 -25.6711 4.2104 26.1173 4
G3 -23.2418 20.0340 12.0663 32.9718 1
G4 -0.3688 8.6932 -31.5322 32.7107 2
G5 -19.4645 -13.3420 -1.2394 23.6307 5
G6 3.0022 13.5149 9.5819 16.8368 6
G7 30.8842 0.3913 7.6546 31.8211 3
G8 11.9462 7.6990 -0.8917 14.2401 7

表6

试验地点的稳定性参数及排序"

试验地点
Testing site
IPCA1 IPCA2 IPCA3 De 位次
Order
E1 3.5204 -1.5893 1.2015 4.0451 18
E2 19.3002 -10.4954 4.9151 22.5124 3
E3 6.8929 13.8254 -6.7735 16.8681 6
E4 1.6036 -6.2567 6.1876 8.9445 16
E5 1.2227 7.6446 -11.9365 14.2273 9
E6 2.2034 8.7108 0.1289 8.9861 15
E7 27.0216 9.3446 4.9899 29.0239 2
E8 1.9683 -0.4205 -1.2237 2.3555 19
E9 2.4504 -3.1489 -16.5475 17.0217 5
E10 6.3001 7.3112 7.7440 12.3739 13
E11 -0.3787 -12.5732 7.5653 14.6786 8
E12 -12.0463 -7.3143 6.5748 15.5512 7
E13 -7.7215 3.2372 10.4448 13.3864 10
E14 -4.4749 -12.2357 0.4139 13.0349 12
E15 -9.9627 7.2712 -16.1485 20.3199 4
E16 -5.0310 -11.9311 -1.9425 13.0933 11
E17 -19.0959 20.3153