Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia

Abstract

Abstract: To explore the effect of genotype and genotype × environment interaction on Fe concentration in rice grains, Fe concentrations of 10 genotypes were analyzed across eight paddy field environments during 2007–2008 using the AMMI-biplot method. Experiments were conducted using a randomized completely block design with three replications in eight environments. Results indicated that environment (E), genotype (G) and genotype × environment interaction (GE) significantly affected Fe concentration in rice grains. Environment explained 74.43 % of total (G+E+GE) variation, whereas G and GE captured 5.60% and 19.67%, respectively. Rice genotype Barumun was desirable in terms of the highest ability and stability for Fe concentration in rice grains. Environment in genotype Cilongok was the best representative of the overall environments and the most powerful to discriminate rice genotypes.

Key words: Fe concentration, genotype, genotype ×, environment interaction, AMMI-biplot method, rice

Suwarto, Nasrullah. Genotype × Environment Interaction for Iron Concentration of Rice in Central Java of Indonesia[J]. RICE SCIENCE, 2011, 18(1): 75-78 .

References

Bridge W C Jr. 1989. Analysis of plant breeding experiment with heterogeneous variance using mixed model equation. In: Applications of Mixed Models in Agriculture and Related Dicipline. Southern Cooperative Series Bulletin No. 343. Baton Rouge, LA: Lousiana Agricultural Experiment Station: 145–151.
Crossa J. 1997. Statistical analyses of multilocation trials. Adv Agron, 44: 55–85.
Crossa J, Cornelius P L, Yan W. 2002. Biplot of linier-bilinier models for stability crossover genotype × environment interaction. Crop Sci, 42: 619–633.
Ebdon J S, Gauch H G. 2002. Additive main effect and multiplicative interaction analysis of national turgrass performance trials: Interpretation of genotype × environment interaction. Crop Sci, 42: 489–496.
Gauch H G Jr, Zobel R W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci, 37: 311–326.
Hernandez M V, Crossa J. 2000. The AMMI analysis and graphing the biplot. In: Biometrics and Statistics Unit. CIMMYT INT. Mexico.
Joshi A K, Crossa J, Arun B, Chad R, Trethowan R, Vargas M, Ortiz-Monasterio I. 2010. Genotype × environment interaction for zinc and iron concentration of wheat grain in eastern Gangetic plains of India. Field Crops Res, 116: 268–277.
Oury F X, Leenhardt F, Remesy C, Chanliaud E, Duperrier B, Balfourier F, Charmet G. 2006. Genetic variability and stability of grain magnesium, zinc and iron concentration in bread wheat. Eur J Agron, 25: 177–185.
Shafii B, Price W J. 1998. Analysis of genotype-by-environment interaction using additive main effects and multiplicative interaction model and stability estimates. J Agric Biol Environ Stat, 3: 335–345.
Ma B L, Yan W, Dwyer L M, Fregeau-Reid J, Voldeng H D, Dion Y, Nass H. 2004. Graphic analysis of genotype, environment, nitrogen fertilizer, and their interactions on spring wheat yield. Agron J, 96: 169–180.
Samonte S O P B, Wilson L T, McClung A M, Medley J C. 2005. Targeting cultivar onto rice growing environment using AMMI and SREG GGE biplot analysis. Crop Sci, 45: 2414–2424.
Zobel R W, Wright M J, Gauch H G Jr. 1988. Statistical analysis of a yield trial. Agron J, 80: 388–393.

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