Multi-environment evaluation of winter bread wheat genotypes under rainfed conditions of Iran-using AMMI model

Authors

1 Dryland Agricultural Research Institute (DARI), Agricultural Research, Education and Extension Organization (AREEO), Maragheh, Iran

2 Dryland Agricultural Research Institute (DARI), Agricultural Research, Education and Extension Organization (AREEO), Kermanshah, Iran

3 Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Sanandaj, Iran

4 Seed and Plant Certification and Registration Institute (SPCRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

5 North Khorasan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Bojnord, Iran

6 Zanjan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Zanjan, Iran

7 West Azarbaijan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Uromieh, Iran

8 Ardabil Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Ardabil, Iran

9 Markazi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Arak, Iran

Abstract

Genotype × environment interaction is an important and challenging issue for plant breeders in developing new improved varieties. This study aimedto estimate the impact of genotype × environment interactions for grain yield in winter wheat under rainfed conditions using the additive main effects and multiplicative interaction (AMMI) model, and to select genotypes with high grain yield, yield stability, and adaptation for cold rainfed environments in Iran. Twenty-two breeding lines and two commercial winter wheat cultivars, representing winter wheat-growing cold rainfed areas of Iran, were tested in eight locations over three crop cycles (2011-14). Environment was the pre dominant source of variation, accounting for 84.8% of the total sum of squares, with the remainder due to the genotype × environment interaction effect (which was almost four times that of the genotype effect). Average grain yield varied from 1125 to 1608 kg ha-1 across the 24 environments, with an average of 1385 kg ha-1. The AMMI biplots identified genotypes with wide and specific adaptation as well as environments with high and low genotype discrimination and characterization. Relative humidity, freezing days, and plant height were among the environmental factors and genotypic co-variables that contributed highly to genotype × environment interactions for grain yield. These findings could identify breeding lines as potential genetic resources for improving and stabilizing grain yield in winter bread wheat breeding programs for cold rainfed areas of Iran, through exploitingand minimizing thegenotype × environment interaction.

Keywords


Anandan, A., T. Sabesan, R. Eswaran, G. Rajiv, N. Muthalagan, and R. Suresh. 2009. Appraisal of environmental interaction on quality traits of rice by additive main effects and multiplicative interaction analysis. Cereal Res. Commun. 37(1): 131–140.
Annicchiarico, P. 1997. Joint regression vs. AMMI analysis of genotype–environment interactions for cereals in Italy. Euphytica 94: 53–62.
Atanasova, D., V. Dochev, N. Tsenov, and I. Todorov. 2009. Influence of genotype and environments on quality of winter wheat varieties in Northern Bulgaria. Agric. Sci. Technol. 1(4): 121–125.
Baril, C. P., J. B. Denis, R. Wustrnan, and F. A. van Eeuwijk. 1995. Analyzing genotype by environment interaction in Dutch potato variety trials using factorial regression. Euphytica 82: 149– 155.
Becker, H. C., and J. Leon. 1988. Stability analysis in plant breeding. Plant Breed. 101: 1–23.
Bidinger, F. R., G. L. Hammer, and R. C. Muchow. 1996. The physiological basis of genotype by environment interaction in crop adaptation. Pp. 329-347. In Cooper M., and G. L. Hammer (eds.). Plant adaptation and crop improvement. CABI, Wallingford, UK.
Bramel-Cox, P. J. 1996. Breeding for reliability of performance across unpredictable environments. Pp. 309–339. In Kang, M. S., and H. H. Gauch (eds.). Genotype-by-Environment Interaction. CRC Press, Bota Raton, Florida.
Caliskan, M. E., E. Erturk, T. Sogut, E. Boydak, and H. Arioglu. 2007. Genotype × environment interaction and stability analysis of sweet potato (Ipomoea batatas) genotypes. N. Z. J. Crop Hort. Sci. 35:87–99.
Crossa, J. 1990. Statistical analysis of multilocation trials. Adv. Agron. 44: 55-85.
Dias, C., and W. J. Krzanowski. 2003. Model selection and cross validation in additive main effect and multiplicative interaction models. Crop Sci. 43: 865-873.
Ebdon, J. S., and H. G. Gauch. 2002. Additive main effects and multiplicative interaction analysis of National Turfgrass performance trials: II. Genotype recommendation. Crop Sci. 42: 497–506.
Eberhart, S. A., and W. A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci. 6: 36–40.
Fan, X. M., M. S. Kang, H. Chen, Y. Zhang, J. Tan, and C. Xu. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron. J. 99: 220–228.
FAO. 2012. FAOSTAT agriculture data. Agricultural production 2009. FAO, Rome. Available at: http://faostat.fao.org
Fischer, R. A. 1985. Number of kernels in wheat crops and the influence of solar radiation and temperature. J. Agric. Sci. 105: 447-461.
Finlay, K.W., and G. N. Wilkinson. 1963. The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res. 14: 742–754.
Fox, P. N., J. Crossa, and I. Ramagos. 1997. Multi-environment testing and genotype × environment interaction. Pp.117-138. In Kempton, R. A., and P. N. Fox (eds.). Statistical methods for plant variety evaluation. London: Chapman & Hall.
Gabriel, K. R. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453–467.
Gauch, H. G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44: 705–715.
Gauch, H. G. 1992. Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier, New York.
Gauch, H. G., and R. W. Zobel. 1990. Imputing missing yield trial data. Theor. Appl. Genet. 79: 753–761.
Gauch, H. G., and R. W. Zobel. 1996. AMMI analysis of yield trials. Pp. 85-122. In Kang, M.S., and H. G. Gauch (eds.). Genotype-by-environment interaction. CRC Press, Boca Raton, Florida, USA.
Gauch, H. G., and R. W. Zobel. 1997. Identifying mega-environment and targeting genotypes. Crop Sci. 37:381–385.
Gollob, H. F. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33: 73–155.
Grausgruber, H., M. Oberforster, M. Werteker, P. Ruckenbauer, and J. Vollmann. 2000. Stability of quality traits in Austrian grown winter wheats. Field Crops Res. 66: 257–267.
Gruneberg, W. J., Manrique, K., Zhang, D. and Hermann, M. 2005. Genotype × environment interactions for a diverse set of sweet potato clones evaluated across varying eco-geographic conditions in Peru. Crop Sci. 45: 2160-2171.
Hristov, N., N. Mladenov, V. Djuric, A. Kondic-Spika, A. Marjanovic-Jeromela, and D. Simic. 2010. Genotype by environment interactions in wheat quality breeding programs in southeast Europe. Euphytica 174: 315–324.
Kanatti, A., K. N. Rai, K. Radhika, M. Govindaraj, K. L. Sahrawat, and A. S. Rao. 2014. Grain iron and zinc density in pearl millet: combining ability, heterosis and association with grain yield and grain size. Springer Plus. 3: 763. doi:10.1186/2193-1801-3-763.
Kang, M. S. 1997. Using genotype-by-environment interaction for crop cultivar development. Adv. Agron. 62: 199–252.
Kroonenberg, P. M. 1995. Introduction to biplots for G × E tables. Dep. of Mathematics Research. Report. No. 51, U. Queensland, Australia.
Li, W., Z. H. Yan, Y. M. Wei, X. J. Lan, and Y. L. Zheng. 2006. Evaluation of genotype × environment interaction in Chinese spring wheat by the AMMI model, correlation, and path analysis. J. Agron. Crop Sci. 192: 221–227.
Lin, C. S., and G. Butler. 1990. Cluster analyses for analyzing two-way classification data. Agron J. 82: 344–348.
Lin, C. S., and M. R. Binns. 1994. Concepts and methods for analysis regional trial data for cultivar and location selection. Plant Breed. Rev. 11: 271–297.
Moghaddam, M., B. Ehdaie, and J. G. Waines. 1997. Genetic variation and inter relationships of agronomic characters in landraces of bread wheat from southeastern Iran. Euphytica 95: 361–369.
Mohammadi, R., and A. Amri. 2013. Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192: 227–249.
Nowosad, K., A. Liersch, W. Poplawska, and J. Bocianowski. 2016. Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208: 187–194.
Purchase, J. L., H. Hatting, and C. S. Van Deventer. 2000. Genotype × environment interaction of winter wheat in South Africa: II. Stability analysis of yield performance. S. Afr. J. Plant Soil 17: 101–107.
Rodriguez, M., D. Rao, R. Papa, and G. Attene. 2008. Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica 163: 231-247.
Samonte, S. O. P. B., L. T. Wilson, A. M. McClung, and J. C. Medley. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Sci. 45: 2414–2424.
Shafii B, K. A. Mahler, W. J. Price, and D. L. Auld. 1992. Genotype x environment interaction effects on winter rapeseed yield and oil content. Crop Sci. 32:922–927.
Sibiya, J., P. Tongoona, J. Derera, and N. Rij. 2012. Genetic analysis and genotype by environment (G × E) for grey leaf spot disease resistance in elite African maize (Zea mays L.) germplasm. Euphytica 185: 349–362.
Tai, G. C. C. 1971. Genotypic stability analysis and its application to potato regional trials. Crop Sci. 11: 184–190.
Thomason, W. E., and S. B. Philips. 2006. Methods to evaluate wheat cultivar testing environment and improve cultivar selection protocols. Field Crops Res. 99: 87-95.
van Eeuwijk, F. A., and A. Elgersma. 1993. Incorporating environmental information in an analysis of genotype by environment interaction for seed yield in perennial rye grass. Heredity 70: 447-457.
van Eeuwijk F. A., J. B. Denis, and M. S. Kang. 1996. Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. Pp. 15–50.  In M. S. Kang and H. G. Gauch (eds.). Genotype-by-Environment Interaction. CRC Press, Boca Raton, Florida, USA.
van Eeuwijk, F. A., M. Malosetti, X. Yin, P. C. Struik, and Stam, P. 2005. Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models. Aust. J. Agric. Res. 56: 1–12.
van Oosterom, E. J., V. Mahalakshmi, F. R. Bidinger, and K. P. Rao. 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. 
Vargas, M., J. Crossa, F. A. van Eeuwijk, E. Ramirez, and K. Sayre. 1999. Using partial least squares regression, factorial regression, and AMMI models for interpreting genotype × environment interaction. Crop Sci. 39:  955-967.
 
Vargas, M., J. Crossa, F. V. Eeuwijk, K. D. Sayre, and M. P. Reynolds. 2001. Interpreting treatment × environment interaction in agronomy trials. Agron. J. 93: 949-960.
Yan, W., and L. A. Hunt. 1998. Genotype by environment interaction and crop yield. Plant Breed. Rev. 16:135–178.
Yan, W., and L. A. Hunt. 2001. Interpretation of genotype × environment interaction for winter wheat in Ontario. Crop Sci. 41: 19–25.
Yan, W., and M. S. Kang. 2002. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, Florida, USA.
Yan, W., and I. Rajcan. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 42:11–20.
Yan, W., L. A. Hunt, Q. Sheng, and Z. Szlavnics. 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci. 40:596-605.
Yates, F., and W. G. Cochran. 1938. The analysis of groups of experiments. J. Agric. Sci. 28:556–580.
Zobel, R. W., M. J. Wright, and H. G. Gauch. 1988. Statistical analysis of a yield trial. Agron. J. 80: 388–393.