Crops ›› 2023, Vol. 39 ›› Issue (3): 27-34.doi: 10.16035/j.issn.1001-7283.2023.03.004

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AMMI Model Analysis of Grain Yield of Wheat Varieties (Lines) in Inner Mongolia Regional Trials

Zhang Haibin1,2(), Wu Xiaohua1, Yu Meiling1(), Wang Xiaobing1, Ye Jun1,2, Cui Siyu1, Li Yuanqing1, Wang Zhanxian3, Zhang Hongxu4, Xue Wei5, Li Yan6, Cui Guohui1, Zhao Xuanwei1, Liu Juan1   

  1. 1Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot 010031, Inner Mongolia, China
    2Hebei Agricultural University, Baoding 071001, Hebei, China
    3Ordos Academy of Agricultural and Animal Husbandry Sciences, Ordos 017000, Inner Mongolia, China
    4Bayannur Academy of Agricultural and Animal Husbandry Sciences, Linhe 015000, Inner Mongolia, China
    5Chifeng Academy of Agricultural and Animal Husbandry Sciences, Chifeng 024000, Inner Mongolia, China
    6Tongliao Academy of Agricultural Sciences, Tongliao 028000, Inner Mongolia, China
  • Received:2021-07-28 Revised:2021-12-02 Online:2023-06-15 Published:2023-06-16

Abstract:

To objectively evaluate the high and stable yield and adaptability of wheat varieties (lines) participating in the Inner Mongolia regional trial, prove the analysis method suitable for Inner Mongolia wheat regional trial test results, the grain yield data of wheat varieties (lines) participating in the Inner Mongolia regional trial from 2016 to 2020 were analyzed using AMMI model. The results showed that environmental differences were the main reason for the yield differences of wheat varieties (lines) in the Inner Mongolia regional trial, and there was interaction between genotype and environment. By combining the two indicators of grain yield and stability parameter (Dg) to measure the high yield and stability of varieties (lines), Nongmai 482, Nongmai 300, Chimai 8 and Nongmai 016 were the high yield varieties suitable for Inner Mongolia. By analyzing the interaction effect values of wheat varieties (lines) in the regional trials in Inner Mongolia and the pilots, it was found that Nongmai 482 is more suitable for Yuquan district, Hohhot, and Nongmai 300 was more suitable for Shulinzhao town, Dalad banner, Chimai 8 was more suitable for Yuquan district, Hohhot and Songshan district, Chifeng, and Nongmai 016 was more suitable for Qianjiadian town, Tongliao. The analysis of the grain yields of wheat varieties (lines) in the Inner Mongolia regional trials by the AMMI model was proved that the AMMI model could objectively evaluate the high yield stability and adaptability of wheat varieties (lines) in Inner Mongolia wheat regional trials. The results can provide the theoretical evidence for breeders to choose good varieties and areas suitable for popularization planting of new wheat varieties.

Key words: Wheat, Grain yield, AMMI model, High and stable yield, Adaptability

Table 1

Wheat varieties (lines) grain yield analysis of variance and AMMI model analysis"

年份
Year
变异来源
Source of variation
自由度
Degree of freedom
平方和
Sum of squares
均方
Mean square
解释变异比例
Ratio of explaining the variation (%)
F
F-value
2016 总方差 69 185 577 072.90 2 689 522.80
处理 34 176 339 953.80 5 186 469.23 19.65**
基因型(G) 6 3 507 633.26 584 605.54 2.99 9.22*
环境(E) 4 165 003 966.01 41 250 991.50 92.57 156.30**
交互作用(G×E) 24 7 828 354.55 326 181.44 4.44 1.55*
PCA1 9 3 502 526.40 389 169.60 44.74 1.47*
PCA2 7 2 553 073.12 364 724.73 32.61 1.38*
残差 8 1 772 755.03 221 594.38 22.65
2017 总方差 83 113 877 538.22 1 372 018.53
处理 41 112 203 999.27 2 736 682.91 68.68**
基因型(G) 6 3 545 073.63 590 845.61 3.16 14.83**
环境(E) 5 100 316 374.52 20 063 274.90 89.41 503.52**
交互作用(G×E) 30 8 342 551.11 278 085.04 7.44 6.98**
PCA1 10 6 747 978.86 674 797.89 80.89 16.94**
PCA2 8 846 321.39 105 790.17 10.14 2.66*
残差 12 748 250.87 62 354.24 8.97
2018 总方差 89 308 973 740.19 3 471 615.06
处理 44 301 610 893.97 6 854 793.04 41.89**
基因型(G) 8 12 621 4580.20 15 776 822.53 41.85 96.42**
环境(E) 4 131 596 473.64 32 899 118.41 43.63 201.07**
交互作用(G×E) 32 43 799 840.10 1 368 745.00 14.52 8.37**
PCA1 11 40 176 230.76 3 652 384.61 91.73 22.32**
PCA2 9 2 790 708.52 310 078.72 6.37 1.90*
残差 12 832 900.82 69 408.40 1.90
2019 总方差 139 2 160 626 804.91 15 544 077.73
处理 34 74 059 050.64 2 178 207.37 39.60**
基因型(G) 6 1 197 739.36 199 623.23 1.62 8.91*
环境(E) 4 68 077 038.52 17 019 259.63 91.92 302.10**
交互作用(G×E) 24 4 784 272.75 199 344.70 6.46 3.53**
PCA1 9 3 613 505.34 401 500.59 75.53 5.61**
PCA2 7 806 293.27 115 184.75 16.85 2.70*
残差 8 364 474.14 45 559.27 7.62
2020 总方差 215 3 424 422 257.81 15 927 545.39
处理 53 171 185 989.04 3 229 924.32 20.56**
基因型(G) 8 3 384 344.62 173 043.08 1.98 9.03*
环境(E) 5 163 315 350.78 33 063 070.16 95.40 256.10**
交互作用(G×E) 40 4 486 293.63 112 157.34 2.62 1.53*
PCA1 12 2 692 078.36 224 339.86 60.01 5.72**
PCA2 10 967 217.94 96 721.79 21.56 3.32*
残差 18 826 997.33 45 944.30

Table 2

The scores and Dg of wheat varieties (lines) on the principal component axis of significant interaction"

年份
Year
品种(系)
Variety (line)
平均产量
Average yield (kg/hm2)
PCA1 PCA2 稳定性参数
Dg
稳产性排序
Dg sequence
2016 农麦730 7292.31a 22.20 18.03 28.60 6
农麦482 7089.54ab -0.43 -8.13 8.14 3
巴麦15号 6896.11abc -0.89 -1.89 2.09 1
农麦979 6848.09bc 10.12 -1.04 10.17 4
永良4号 6791.39bc 0.77 -6.92 6.97 2
巴麦14号 6790.73bc -5.34 -18.55 19.30 5
赤麦8号 6532.60c -26.42 18.49 32.25 7
2017 农麦482 6882.88a 29.77 -3.49 29.97 7
巴麦15号 6814.52a -8.96 17.22 19.41 5
赤麦8号 6629.98b -17.24 -7.12 18.65 4
巴麦15品9 6443.22c 4.37 9.35 10.32 2
巴麦15品11 6410.98c 10.65 -10.18 14.74 3
赤麦9号 6375.41c -20.87 -9.34 22.87 6
永良4号 6332.61c 2.28 3.58 4.24 1
2018 赤麦8号 7029.51a 10.43 4.42 11.33 3
农麦300 6977.49ab 6.13 -2.50 6.62 1
巴麦15品9 6930.13ab 5.23 13.41 14.40 5
农麦326 6740.04abc 9.73 -11.36 14.96 6
永良4号 6687.34abc 9.78 9.34 13.52 4
巴麦17号 6682.01abc 8.02 -3.88 8.91 2
巴麦16号 6639.32bc 5.41 -23.62 24.23 8
鄂麦1608 6569.28c 8.17 13.64 15.90 7
农大3753 3044.86d -62.91 0.54 62.91 9
2019 农麦016 7757.21a 7.08 4.05 8.16 2
农麦832 7670.50ab 23.92 3.37 24.16 7
农麦300 7657.16ab 0.83 -12.47 12.50 5
赤麦9号 7650.49ab -1.56 2.67 3.09 1
巴麦19号 7597.13ab -13.64 13.63 19.29 6
巴麦18号 7583.79ab -8.83 -3.45 9.48 3
永良4号 7143.57b -7.79 -7.80 11.02 4
2020 农麦016 7792.78a -6.27 3.94 7.41 3
巴麦18号 7726.08ab 8.99 0.70 9.02 4
巴麦20号 7670.50ab -2.48 16.16 16.35 8
巴麦19号 7620.48ab 11.14 -8.25 13.86 7
农麦125 7620.48ab -2.65 5.43 6.05 2
赤麦9号 7420.38ab -18.65 -7.50 20.11 9
兆丰10号 7414.82ab 13.32 -0.95 13.36 6
赤18鉴3 7353.68b -5.68 -7.45 9.37 5
永良4号 7337.00b 2.28 -2.09 3.09 1

Table 3

Interaction effect values of wheat varieties (lines) and test sites"

年份
Year
品种(系)
Variety (line)
试验地点Test site
HT HS DS HY CS TQ
2016 农麦730 55.42 - -26.15 -8.93 6.35 -26.69
农麦979 23.00 - 24.81 -19.78 -18.06 -9.97
农麦482 -12.88 - -11.96 33.04 -2.81 -5.39
巴麦14号 -20.75 - 21.96 9.61 -28.25 17.43
巴麦15号 -1.10 - -20.63 10.14 -5.71 17.29
赤麦8号 -36.67 - -3.07 -34.32 54.32 19.74
永良4号 -7.01 - 15.03 10.24 -5.84 -12.41
2017 农麦482 -18.96 -7.49 64.04 25.73 -18.19 -45.13
巴麦15号 -3.28 -0.49 -7.22 -20.40 -2.07 33.47
巴麦15品9 1.46 -5.97 8.42 12.13 -22.68 6.64
巴麦15品11 10.28 15.74 17.01 -2.84 -7.64 -32.57
赤麦8号 2.35 1.37 -39.83 -7.21 26.23 17.09
赤麦9号 8.20 -5.46 -52.43 2.65 25.86 21.17
永良4号 -0.06 2.29 10.01 -10.06 -1.52 -0.66
2018 巴麦16号 -3.11 -18.23 39.78 -35.99 17.55 -
赤麦8号 -24.67 -26.23 6.65 18.48 25.77 -
农麦326 -20.94 -22.27 24.84 -13.58 31.95 -
巴麦17号 -19.29 -21.52 30.48 10.97 -0.64 -
鄂麦1608 -36.23 -6.67 3.54 36.27 3.10 -
农麦300 -16.76 -13.42 19.01 5.95 5.23 -
巴麦15品9 -11.38 -14.71 -22.52 23.77 24.84 -
农大3753 160.93 143.36 -103.45 -72.94 -127.91 -
永良4号 -28.54 -20.31 1.67 27.06 20.12 -
2019 农麦300 -486.91 18.10 - 60.98 232.50 175.33
农麦832 100.05 -228.69 - -686.06 -414.49 1229.19
农麦016 213.44 -182.00 - -239.17 -101.00 308.73
巴麦19号 273.47 211.53 - 587.91 -374.47 -698.44
巴麦18号 -180.09 58.12 - 367.80 139.12 -384.95
赤麦9号 286.81 -75.28 - -165.80 172.47 -218.20
永良4号 -206.77 198.19 - 74.32 345.89 -411.63
2020 农麦125 59.91 26.56 -114.25 -114.25 -125.37 267.42
农麦016 -245.80 87.70 -286.56 80.29 69.17 295.21
巴麦20号 9.88 -56.82 -30.88 35.82 -508.90 550.89
兆丰10号 -334.74 332.26 591.65 -42.00 -119.81 -427.37
巴麦18号 -45.70 87.70 413.79 146.99 -264.33 -338.44
巴麦19号 126.61 -140.19 552.75 -347.70 174.78 -366.23
赤18鉴3 260.01 -306.94 -247.65 -80.90 341.53 33.97
赤麦9号 93.26 93.26 -1014.70 419.35 374.88 33.97
永良4号 76.58 -123.52 135.87 -97.58 58.05 -49.41

Table 4

The scores and De of the significant interaction principal component axis of the wheat regional test sites"

年份
Year
试点
Test site
平均产量
Average
yield (kg/hm2)
PCA1 PCA2 鉴别力
参数
De
鉴别力
排序
De sequence
2016 HT 8352.75a 28.83 -11.12 30.90 1
DS 7495.17b -3.75 12.34 12.89 5
HY 8490.91a 5.01 17.42 18.13 3
CS 5146.86c -15.02 -23.04 27.51 2
TQ 4972.01c -15.08 4.41 15.71 4
2017 HT 6527.07c 5.15 4.03 6.54 5
HS 8569.52a -0.15 3.43 3.44 6
DS 6225.97d -32.60 -8.45 33.68 1
HY 7150.72b -8.31 7.11 10.94 4
CS 5668.55e 13.87 12.46 18.65 3
TQ 5192.12f 22.04 -18.58 28.83 2
2018 HT 7611.21b -38.47 -6.44 39.00 1
HS 7984.73a -34.11 3.62 34.3 2
DS 5927.41c 24.51 -20.98 32.26 3
HY 5069.57d 17.62 26.10 31.49 4
CS 5240.40d 30.46 -2.29 30.54 5
2019 HT 8180.28b 1.33 -15.59 15.65 2
HS 8308.91b -6.09 0.89 6.15 5
HY 9333.24a -15.59 -1.88 15.71 3
CS 5326.47d -5.01 13.95 14.82 4
TQ 6750.99c 25.36 2.63 25.50 1
2020 HT 8974.86b -3.14 -4.11 5.17 5
HS 10 075.41a 1.77 2.36 2.95 6
DS 8048.47c 24.73 2.13 24.82 1
HY 7448.17d -6.70 0.96 6.76 4
CS 5758.43e -5.89 -15.88 16.94 3
TQ 4998.79f -10.78 14.54 18.10 2
[1] 刘志勇, 王道文, 张爱民, 等. 小麦育种行业创新现状与发展趋势. 植物遗传资源学报, 2018, 19(3):430-434.
[2] 崔国惠, 李元清, 于美玲, 等. “十五”以来内蒙古小麦育种研究进展及发展建议. 内蒙古农业科技, 2012(1):1-3.
[3] 常磊, 柴守玺. GGE双标图在我国旱地春小麦稳产性分析中的应用. 中国生态农业学报, 2010, 18(5):988-994.
[4] 陈志德. 水稻品种区域试验精度和稳定性分析模型的研究. 南京:南京农业大学, 2004.
[5] 姚金保, 张鹏, 余桂红, 等. 江苏省小麦品种(系)籽粒产量基因型与环境互作分析. 麦类作物学报, 2020, 40(12):1-12.
[6] 王瑞, 李加纳, 唐章林, 等. 优质油菜新品种(系)的稳定性和适应性分析. 西南农业大学学报, 2003, 25(1):45-47.
[7] Gauch H G. Model selection and validation for yield trials with interaction. Biometrics, 1988, 44:705.
doi: 10.2307/2531585
[8] 王磊, 杨仕华, 谢芙贤, 等. AMMI模型及其在作物区试数据分析中的应用. 应用基础工程科学学报, 1997, 5(1):39-46.
[9] 陈双龙. 福建省水稻品种区试点综合评价研究. 福建农业学报, 2005, 20(1):1-5.
[10] 高海涛, 王书子, 王翠玲, 等. AMMI模型在旱地小麦区域试验中的应用. 麦类作物学报, 2003, 23(4):43-46.
[11] 岳海旺, 李春杰, 李媛, 等. 河北省春播玉米品种产量稳定性及试点辨别力综合分析. 核农学报, 2018, 32(7):1267-1280.
doi: 10.11869/j.issn.100-8551.2018.07.1267
[12] 刘雪艳. 不同基因型玉米品种抗旱性评价. 杨凌:西北农林科技大学, 2017.
[13] 吴雯雯, 欧杨虹. 应用AMMI模型对玉米杂交组合多点试验的稳定性分析. 山东农业科学, 2016, 48(4):24-27.
[14] 许乃银, 陈旭升, 郭志刚, 等. AMMI模型在棉花区试数据分析中的应用. 江苏农业学报, 2001, 17(4):205-210.
[15] 秦军红, 张婷婷, 孟丽丽, 等. 引进马铃薯种质资源抗旱性评价. 植物遗传资源学报, 2019, 20(3):574-582.
[16] 施万喜. 利用AMMI模型分析陇东旱地冬小麦新品种(系)丰产稳产性. 干旱地区农业研究, 2009, 27(3):37-43.
[17] Mladenov V, Banjacb, Milosevic M. Evaluation of yield and seedrequirements stability of bread wheat (Triticum aestivum L.) via AMMI model. Turkish Journal of Field Crops, 2012, 17(2):203-216.
[18] Singh C, Gupta A, Gupta V, et al. Genotype×environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models. Crop Breeding and Applied Biotechnology, 2019, 19(3):309-318.
[19] 赵玉坤, 宁东贤, 杨秀丽, 等. 基因型、环境及其交互效应对长胚芽鞘小麦产量稳定性的影响. 山西农业科学, 2017, 45 (9):1397-1400.
[20] 包齐军. AMMI模型在啤酒大麦区域试验中的应用. 作物杂志, 2012(2):130-134.
[21] 乔祥梅, 黄锦, 程加省, 等. 利用AMMI模型分析旱地小麦新品种云麦70的稳定性及适应性. 安徽农业科学, 2015, 43(34):38-40.
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