Hostname: page-component-686fd747b7-mrck6 Total loading time: 0 Render date: 2025-02-13T12:19:39.245Z Has data issue: false hasContentIssue false
  • English
  • Français

THE USE OF AMMI MODEL FOR INTERPRETING GENOTYPE × ENVIRONMENT INTERACTION IN DURUM WHEAT

Published online by Cambridge University Press:  03 July 2017

REZA MOHAMMADI ,
MOHAMMAD ARMION ,
ESMAEIL ZADHASAN ,
MALEK MASOUD AHMADI  and
AHMED AMRI
REZA MOHAMMADI*
Affiliation:
Dryland Agricultural Research Institute (DARI), AREEO, Sararood Branch, Kermanshah, Iran
MOHAMMAD ARMION
Affiliation:
Center of Agricultural Research and Natural Resources, AREEO, Ilam, Iran
ESMAEIL ZADHASAN
Affiliation:
Dryland Agricultural Research Institute (DARI), AREEO, Maragheh, Iran
MALEK MASOUD AHMADI
Affiliation:
Center of Agricultural Research and Natural Resources, AREEO, North Khorasan, Iran
AHMED AMRI
Affiliation:
International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
*
‡‡Corresponding author. Email: r.mohammadi@areeo.ac.ir
Get access Rights & Permissions [Opens in a new window]

Summary

Durum wheat (Triticum durum) is one of the most important cereal crops in the Mediterranean region; however, its cultivation suffers from low yield due to environmental constrains. The main objectives of this study were to (i) assess genotype × environment (GE) interaction for grain yield in rainfed durum wheat and to (ii) analyse the relationships of GE interaction with genotypic/meteorological variables by the additive main effects and multiplicative interaction (AMMI) model. Grain yield and some related traits were evaluated in 25 durum wheat genotypes (landrace, breeding line, old and new varieties) in 12 rainfed environments differing in winter air temperature. The AMMI analysis of variance indicated that the environment had highest contribution (84.3% of total variation) to the variation in grain yield. The first interaction principal component axis (IPCA1) explained 77.5% of GE interaction sum of squares (SS), and its effect was 5.5 times greater than the genotype effect, indicating that the IPCA1 contributed remarkably to the total GE interaction. Large GE interaction for grain yield was detected, indicating specific adaptation of genotypes. While the postdictive success method indicated AMMI-4 as the best model, the predictive success one suggested AMMI-1. The AMMI biplot analysis confirmed a rank change interaction among the locations, indicating the presence of strong and unpredictable rank-change location-by-year interactions for locations. In contrast to landraces and old varieties, the breeding lines with high yield performance had high phenotypic plasticity under varying environmental conditions. Results indicated that the GE interaction was associated with the interaction of heading date, plant height, rainfall, air temperature and freezing days.

Type
Research Article
Information
Experimental Agriculture , Volume 54 , Issue 5 , October 2018 , pp. 670 - 683
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allard, R. W. and Bradshaw, A. D. (1964). Implication of genotype-environmental interaction in applied plant breeding. Crop Science 5:503–506.Google Scholar
Baril, C. P., Denis, J. B., Wustrnan, R. and van Eeuwijk, F. A. (1995). Analysing genotype by environment interaction in Dutch potato variety trials using factorial regression. Euphytica 82:149–155.Google Scholar
Bidinger, F. R., Hammer, G. L. and Muchow, R. C. (1996). The physiological basis of genotype by environment interaction in crop adaptation. In Plant Adaptation and Crop Improvement, 329–347 (Eds Cooper, M. and Hammer, G. L.). Wallingford, UK: CABI.Google Scholar
Ceccarelli, S. (1996). Positive interpretation of genotype by environment interactions in relation to sustainability and biodiversity. In Plant adaptation and crop improvement, 467–486 (Eds Cooper, M. and Hammer, G. L.). CABI Publishing.Google Scholar
Crossa, J., Fox, P. N., Pfeiffer, W. H., Rajaram, S. and Gauch, H. G. (1991). AMMI adjustment for statistical analysis of an internal wheat yield trial. Theoretical and Applied Genetics 81:27–37.Google Scholar
Ebdon, J. S. and Gauch, H. G. (2002). Additive main effects and multiplicative interaction analysis of National Turfgrass performance trials. II genotype recommendation. Crop Science 42:497–506.Google Scholar
Fan, X. M., Kang, M. S., Chen, H., Zhang, Y., Tan, J. and Xu, C. (2007). Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal 99:220–228.Google Scholar
Gauch, G. H. and Zobel, R. W. (1997). Interpreting mega-environments and targeting genotypes. Crop Science 37:311–326.Google Scholar
Gauch, H. G. (1992). Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. Amsterdam: Elsevier.Google Scholar
Gauch, H. G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science 46:1488–1500.Google Scholar
Gauch, H. G. (2013). A simple protocol for AMMI analysis of yield trials. Crop Science 53:1860–1869.Google Scholar
Gollob, H. F. (1968). A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33:73–155.Google Scholar
Grausgruber, H., Oberforster, M., Werteker, M., Ruckenbauer, P. and Vollmann, J. (2000). Stability of quality traits in Austrian-grown winter wheats. Field Crops Research 66:257–267.Google Scholar
Isik, K. and Kleinschmit, J. (2005). Similarities and effectiveness of test environments in selecting and deploying desirable genotypes. Theoretical and Applied Genetics 110:311–322.Google Scholar
Li, W., Yan, Z. H., Wei, Y. M., Lan, X. J. and Zheng, Y. L. (2006). Evaluation of genotype×environment interactions in Chinese Spring wheat by the AMMI model, correlation and path analysis. Journal of Agronomy and Crop Science 192:221–227.Google Scholar
Mahmoudi (2010). Meteorological statistics of stations of Dryland agricultural research institute of Iran. Agricultural Center for Information Science and Technology (ACIST), No. 42105, p. 42.Google Scholar
Mohammadi, R. and Amri, A. (2013). Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192:227–249.Google Scholar
Mohammadi, R., Haghparast, R., Amri, A. and Ceccarelli, S. (2010). Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop and Pasture Science 61:92–101.Google Scholar
Mohammadi, R., Sadeghzadeh, D., Armion, M. and Amri, A. (2011). Evaluation of durum wheat experimental lines under different climate and water regime conditions of Iran. Crop and Pasture Science 62:137–151.Google Scholar
Moragues, M., Garci´a del Moral, L. F., Moralejo, M. and Royo, C. (2006). Yield formation strategies of durum wheat landraces with distinct pattern of dispersal within the Mediterranean basin: II Biomass production and allocation. Field Crops Research 95:182–193.Google Scholar
Munoz, P., Voltas, J., Igartua, E. and Romagosa, I. (1998). Changes in adaptation of barley releases over time in north eastern Spain. Plant Breeding 117:531–535.Google Scholar
Payne, R. W., Harding, S. A., Murray, D. A., Soutar, D. M., Baird, D. B., Glaser, A. I., Welham, S. J., Gilmour, A. R., Thompson, R. and Webster, R. (2012). GenStat release 15 statistical software for windows. VSN International Ltd, Hemel.Google Scholar
Purchase, J. L., Hatting, H. and Van Deventer, C. S. (2000). Genotype x environment interaction of winter wheat in south Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil 17:95–100.Google Scholar
Ramburan, S., Zhou, M. and Labuschagne, M. (2011). Interpretation of genotype × environment interactions of sugarcane: Identifying significant environmental factors. Field Crops Research 124:392–399.Google Scholar
Sneller, C. H., Kilgore-Norquest, L. and Dombek, D. (1997). Repeatability of yield stability statistics in soybean. Crop Science 37:383–390.Google Scholar
van Eeuwijk, F. A. (1995). Linear and bilinear models for the analysis of multi-environment trials I. An inventory of models. Euphytica 84:1–7.Google Scholar
van Eeuwijk, F. A. and Elgersma, A. (1993). Incorporating environmental information in an analysis of genotype by environment interaction for seed yield in perennial ryegrass. Heredity 70:447–457.Google Scholar
van Oosterom, E. J., Mahalakshmi, V., Bidinger, F. R. and Rao, K. P. (1996). Effect of water availability and temperature on the genotype-by-environment interaction of pearl millet in semi-arid tropical environments. Euphytica 89:175–183.Google Scholar
Vargas, M., Crossa, J., Van Eeuwijk, F. A., Ramirez, E. and Sayre, K. (1999). Using partial least squares regression, factorial regression, and AMMI models for interpreting genotype x environment interaction. Crop Science 39:955–967.Google Scholar
Voltas, J., Romagosa, I., Lafarga, A., Armesto, A. P., Sombrero, A. and Araus, J. L. (1999b). Genotype by environment interaction for grain yield and carbon isotope discrimination of barley in Mediterranean Spain. Australian Journal of Agricultural Research 50:1263–1271.Google Scholar
Voltas, J., van Eeuwijk, F. A., Sombrero, A., Lafarga, A., Igartua, E. and Romagosa, I. (1999a). Integrating statistical and ecophysiological analysis of genotype by environment interaction for grain filling of barley I. Individual grain weight. Field Crops Research 62:63–74.Google Scholar
Yan, W. and Hunt, L. A. (2001). Interpretation of genotype x environment interaction for winter wheat yield in Ontario. Crop Science 41:19–25.Google Scholar
Yan, W., Hunt, L. A., Sheng, Q. and Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science 40:596–605.Google Scholar
Yang, R. C., Crossa, J., Cornelius, P. L. and Burgueno, J. (2009). Biplot analysis of genotype x environment interaction: Proceed with caution. Crop Science 49:1564–1576.Google Scholar
Zobel, R. W., Wright, M. J. and Gauch, H. G. (1988). Statistical analysis of yield trial. Agronomy Journal 80:388–393.Google Scholar