The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials

被引:82
|
作者
Sabaghnia, N. [1 ]
Sabaghpour, S. H. [2 ]
Dehghani, H. [1 ]
机构
[1] Tarbiat Modares Univ, Fac Agr, Dept Plant Breeding, Tehran, Iran
[2] Dry Land Agr Res Inst, Kermanshah, Iran
来源
关键词
D O I
10.1017/S0021859608007831
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Genotype by environment (G x E) interaction effects are of special interest for breeding programmes to identify adaptation targets and test locations. Their assessment by additive main effect and multiplicative interaction (AMMI) model analysis is currently defined for this situation. A combined analysis of two former parametric measures and seven AMMI stability statistics was undertaken to assess G x E interactions and stability analysis to identify stable genotypes of 11 lentil genotypes across 20 environments. G x E interaction introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. The combined analysis of variance for environments (E), genotypes (G) and G x E interaction was highly significant (P<0.01). suggesting differential responses of the genotypes and the need for stability analysis. The parametric stability measures of environmental variance showed that genotype ILL 6037 was the most stable genotype, whereas the priority index measure indicated genotype FLIP 82-1L to be the most stable genotype. The first seven principal component (PC) axes (PC1-PC7) were significant (P<0.01), but the first two PC axes Cumulatively accounted for 71% of the total G x E interaction. In contrast, the AMMI stability statistics suggested different genotypes to be the most stable. Most of the AMMI stability statistics showed biological stability, but the SIPCF statistics of AMMI model had agronomical concept stability. The AMMI stability value (ASV) identified genotype FLIP 92-12L as a more stable genotype, which also had high mean performance. Such an outcome could be regularly employed in the future to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts for recommendations for lentil and other crops in the Middle East and other areas of the world.
引用
收藏
页码:571 / 581
页数:11
相关论文
共 50 条
  • [1] AMMI Model for Yield Estimation in Multi-Environment Trials: A Comparison to BLUP
    Sa'diyah, Halimatus
    Hadi, Alfian Futuhul
    INTERNATIONAL CONFERENCE ON FOOD, AGRICULTURE AND NATURAL RESOURCES, IC-FANRES 2015, 2016, 9 : 163 - 169
  • [2] AMMI Model to Assess Durum Wheat Genotypes in Multi-Environment Trials
    Tekdal, S.
    Kendal, E.
    JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY, 2018, 20 (01): : 153 - 166
  • [3] APPLICATION OF AMMI MODEL FOR EVAULATION SPRING BARLEY GENOTYPES IN MULTI-ENVIRONMENT TRIALS
    Kendal, Enver
    Tekdal, Sertac
    BANGLADESH JOURNAL OF BOTANY, 2016, 45 (03): : 613 - 620
  • [4] EVOLOTION BARLEY GENOTYPES IN MULTI-ENVIRONMENT TRIALS BY AMMI MODEL AND GGE BIPLOT ANALYSIS
    Oral, Erol
    Kendal, Enver
    Kilic, Hasan
    Dogan, Yusuf
    FRESENIUS ENVIRONMENTAL BULLETIN, 2019, 28 (4A): : 3186 - 3196
  • [5] Multi-environment field trials for wheat yield, stability and breeding progress in Germany
    Wang, Tien-Cheng
    Rose, Till
    Zetzsche, Holger
    Ballvora, Agim
    Friedt, Wolfgang
    Kage, Henning
    Leon, Jens
    Lichthardt, Carolin
    Ordon, Frank
    Snowdon, Rod J.
    Stahl, Andreas
    Stuetzel, Hartmut
    Wittkop, Benjamin
    Chen, Tsu-Wei
    SCIENTIFIC DATA, 2025, 12 (01)
  • [6] PARAMETRIC STABILITY ANALYSES OF MULTI-ENVIRONMENT YIELD TRIALS IN TRITICALE (xTriticosecale Wittmack)
    Kaya, Yuksel
    Ozer, Emel
    GENETIKA-BELGRADE, 2014, 46 (03): : 705 - 718
  • [7] Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China
    Fan, Xing-Ming
    Kang, Manjit S.
    Chen, Hongmei
    Zhang, Yudong
    Tan, Jing
    Xu, Chuxia
    AGRONOMY JOURNAL, 2007, 99 (01) : 220 - 228
  • [8] Genotype × Environment Interacion in multi-environment Trials using shrinkage factors for ammi models
    J. Moreno-González
    J. Crossa
    P.L. Cornelius
    Euphytica, 2004, 137 : 119 - 127
  • [9] Bayesian AMMI applied to food-type soybean multi-environment trials
    Freiria, Gustavo Henrique
    Azeredo Goncalves, Leandro Simoes
    Zeffa, Douglas Mariani
    Lima, Wilmar Ferreira
    Fonseca Junior, Nelson da Silva
    Cavenaghi Prete, Cassio Egidio
    de Batista Fonseca, Ines Cristina
    REVISTA CIENCIA AGRONOMICA, 2020, 51 (04):
  • [10] Application of AMMI Model to Assess Spring Maize Genotypes under Multi-Environment Trials in Hebei Province
    Ye, Meijin
    Wei, Jianwei
    Bu, Junzhou
    Gu, Zenghui
    Wang, Yanbing
    Chen, Shuping
    Peng, Haicheng
    Yue, Haiwang
    Xie, Junliang
    INTERNATIONAL JOURNAL OF AGRICULTURE AND BIOLOGY, 2019, 21 (04) : 827 - 834