PERFORMANCE OF UPLAND NERICA AND NON -NERICA RICE ENOTYPES IN MULTI-ENVIRONMENT YIELD TRIALS AS ANALYSED USING GGEBIPLOT MODEL

Authors

  • Sewagegne Tariku Adet Agricultural Research Center, P.O.Box=8, Bahir Dar, ETHIOPIA
  • TadesseLakew Adet Agricultural Research Center, P.O.Box=8, Bahir Dar, ETHIOPIA

DOI:

https://doi.org/10.29121/granthaalayah.v4.i3.2016.2796

Keywords:

Multi-Environment Trials, GGE Biplot Analysis, G × E Interaction, Upland NERICA Rice

Abstract [English]

Ten upland New Rice for Africa (NERICA) and three upland non-NERICA rice genotypes were evaluated at three locations of six environments in north western Ethiopia from 2009 to 2011 to identify stable and high yielding genotypes and mega environments. Randomized complete block design with three replications was used.  GGE biplot methodology was used for graphically display of yield data. The combined analysis of variance revealed that environment (E) accounted for 32.2% of the total variation while G and GEI captured 20.3% and 21.1%, respectively. The first 2 principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 56.9 % and 20.6% of GGE sum of squares (SS), respectively. Genotypic PC1 scores >0 detected the adaptable and/or higher-yielding genotypes, while PC1 scores <0 discriminated the non-adaptable and/or lower-yielding ones. Unlike genotypic PC1 scores, near-zero PC2 scores identified stable genotypes, whereas absolute larger PC2 scores detected the unstable ones. On the other hand, environmental PC1 scores were related to non-crossover type GEIs and the PC2 scores to the crossover type. Among the tested genotypes 3, 2, 11, 13, 8 were found to be desirable in terms of higher yielding ability and stability in descending order. Based on GGEbiplot analysis, the test environments were classified in to three mega-environments. Mega -1  included environment  WO-1 (Woreta) with  genotype 9 as  a winner; Mega-2 constituted  environments such as  WO-3 and WO-5 (Woreta)  with  genotype 2 as a winner  and  Mega-3 contained  environments including  PA-2,PA-6(Pawe)  and ME-7(Metema) with  genotype 8 as winner. However, it is not justifiable to consider two mega-environments within one specified area. So that mega environments 1 and 2 should be treated as one. The result of this study can be used as a driving force for the national rice breeding program to design breeding strategy that can address the request of different stakeholders for improved varieties. Among the tested genotypes in this study, three candidate genotypes (2, 3 and 8) were selected and verified considering their better performance. Of which, genotype 2 has been officially released for large scale production with the common name ‘’NERICA-12’’.

Downloads

Download data is not yet available.

References

Balestre M, Vanderley BDS, Antonio AL, and Moises SR (2010). Stability and adaptability of upland rice genotypes .Crop breeding and applied biotechnology 10:357-363. DOI: https://doi.org/10.1590/S1984-70332010000400011

Ding M, B.Tier and W. Yan (2007). Application of GGE biplot analysis to evaluate Genotype (G) Environment (E) and G × E interaction on P. radiata: a case study. Paper presented to Australasian Forest Genetics Conference Breeding for Wood Quality, 1114 April 2007, Hobart, Tasmania, Australia.

Fan XM, MS 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. DOI: https://doi.org/10.2134/agronj2006.0144

Flores F, MT. Moreno and JI. Cubero (1998). A comparison of uni¬variate and multivariate methods to analyze environments. Field Crops Res 56:271-286. DOI: https://doi.org/10.1016/S0378-4290(97)00095-6

Gabriel, K. R. (1971). The biplot graphic display of matrices withapplication to principal DOI: https://doi.org/10.1093/biomet/58.3.453

component analysis. Biometrika 58: 453-467.

GauchHG and RW. Zobel (1996): AMMI analysis of yield trials. In M.S. Kang &Gauch H.G. eds. Genotype-by-environment interaction, pp 85-122. Boca Raton, FL, CRC Pres GauchHG, Piepho HP, Annicchiarico P (2008). Statistical analysis of yield trials by AMMI and GGE: Further consid¬erations. Crop Sci 48:866-889. Gauch, GH. and RW. Zobel (1997). Interpreting mega-environments and targeting genotype Crop Sci. 37: 311-326. DOI: https://doi.org/10.1201/9781420049374.ch4

GGE-biplot,(2009). GGE-biplot software version 5.2. The completebiplot analysis system: GGEbiplot pattern explorer. Copy right Weikai Yan, 2001-2009,USA.

Gomez KA. and Gomez AA. (1994). Statistical procedures for agricultural research. 2nd edition. John Willey and Sons.

Kaya Y, M. Akcura, and S .Taner (2006). GGE-biplot analysis of multi-environment yield trials in bread wheat. Turk J. Agric. 30:325-337.

Kroonenberg PM. (2005) Introduction to biplots for G×E tables. Department of Mathematics, Research Report #51, University of Queensland, 22.

Matus-Cadiz MA, P. Hucl, CE. Perron and RT. Tyler (2003) Genotype x environment interaction for grain color in hard white spring wheat. Crop Sci 43: 219-226 DOI: https://doi.org/10.2135/cropsci2003.0219

Ministry of Agriculture and Rural Development (MoARD), (2010). National Rice Research and Development Strategy of Ethiopia. Addis Ababa, Ethiopia, pp. 48.

SAS., (2004). System analysis software. Version 9.1.2. SAS institute Inc., Cary, North

Carolina, USA.

Yan W. (2001). GGEbiplot- a Windows application for graphi¬cal analysis of multi-environment trial data and other types of two-way data. Agron J 93:1111-1118. DOI: https://doi.org/10.2134/agronj2001.9351111x

Yan, W.(2011). GGEbiplot vsAMMI graphs for genotype-by-environment data analysis.

Journal of IIndian Society of Agricultural Statistic . 65(2):181-193.

Yan W and LA. Hunt (2002). Biplot analysis of diallel data. Crop Sci 42:21-30. DOI: https://doi.org/10.2135/cropsci2002.0021

Yan, W and I. Rajcan. (2002). Biplot analysis of test sites and trait relations of soybean in DOI: https://doi.org/10.2135/cropsci2002.0011

Ontario. Crop Sci. 42: 11-20.

Yan, W and L.A. Hunt. (2001). Interpretation of genotype x environment interaction for winter wheat yield in Ontario. Crop Sci. 41: 19-25. DOI: https://doi.org/10.2135/cropsci2001.41119x

Yan, W., L.A. Hunt, Q. Sheng and Z. Szlavnics. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40: 597-605. DOI: https://doi.org/10.2135/cropsci2000.403597x

Yan W. and MS Kang (2003) GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL. DOI: https://doi.org/10.1201/9781420040371

Yan W. and NA. Tinker (2006) Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci 86: 623-645 DOI: https://doi.org/10.4141/P05-169

Zoble RW, MJ. Wright and HG. Gauch (1988). Statistical analysis of a yield trial. AgronJ 80:388- 393. DOI: https://doi.org/10.2134/agronj1988.00021962008000030002x

Downloads

Published

2016-03-31

How to Cite

Tariku, S., & Lakew, T. (2016). PERFORMANCE OF UPLAND NERICA AND NON -NERICA RICE ENOTYPES IN MULTI-ENVIRONMENT YIELD TRIALS AS ANALYSED USING GGEBIPLOT MODEL. International Journal of Research -GRANTHAALAYAH, 4(3), 146–158. https://doi.org/10.29121/granthaalayah.v4.i3.2016.2796