FACTOR ANALYSIS AND GGE BIPLOT FOR ENVIRONMENTAL AND GENOTYPIC EVALUATION IN SUNFLOWER TRIALS
Palabras clave:
Mega-environment, genotype by environment interaction, stability, adaptabilityResumen
The definition of mega-environments is of critical relevance for a more accurate cultivar recommendation. This study aimed to verify the potentialities of the GGE biplot and factor analysis for environmental evaluation, to investigate possible mega-environment, and to evaluate the adaptability and stability of sunflower genotypes. A factor analysis and GGE biplot were used for evaluating the individual effects of genotypes, environments, interactions and mega-environment discrimination based on the data from 16 sunflower genotypes evaluated in 14 environments. The factor analysis was able to identify mega-environment inconsistency and, consequently, excluded a mega-environment for recommendation. The genotype BRS 387 showed wide adaptability and high stability in the mega-environment to which it belonged, indicative of its efficiency for the region to which it is being tested. Although the GGE biplot has many interpretation possibilities, extra care is needed when making decisions because important phenomena may be left unidentified in this analysis.
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ABATE, F.; MEKBIB, F.; DESSALEGN, Y. 2015. GGE Biplot Analysis of Multi-environment Yield Trials of Durum Wheat (Triticum turgidum Desf.) Genotypes in North Western Ethiopia. American Journal of Experimental Agriculture, 8(2):120-129.
BECKER, H.C.; LÉON, J. 1988. Stability Analysis in Plant Breeding. Plant Breeding, 101(1):1-23.
BEZERRA, F.T.C.; DUTRA, A.S.; BEZERRA, M.A.F.; OLIVEIRA FILHO, F.O.; BARROS, G.L. 2014. Vegetative behavior and productivity of the sunflower depending on the spatial arrangement of the plants. Revista Ciência Agronômica, 45(2):335-343.
BHARTIYA, A.; ADITYA, J.P.; KUMARI, V.; KISHORE, N.; PURWAR, J.P.; AGRAVAL, A.; KANT, L. 2017. GGE Biplot & AMMI Analysis of Yield Stability in Multi-environment Trial of Soybean [Glycine max (L.) Merril] Genotypes Under Rainfed Condition of North Western Himalayan Hills. The Journal of Animal & Plant Sciences, 27(1):227-238.
CRUZ, C.D. 2016. Genes Software – extended and integrated with the R, Matlab and Selegen. Acta Scientiarum Agronomy, 38(4):547-552.
CRUZ, C.D.; CARNEIRO, P.C.S.; REGAZZI, A.J. 2014. Modelos Biométricos Aplicados ao Melhoramento Genético. 3rd ed. v.3. Editora UFV, Viçosa, 668p.
CRUZ, C.D.; TORRES, R.A.; VENCOVSKY, R. 1989. An alternative approach to the stability analysis proposed by Silva e Barreto. Revista Brasileira de Genética, 12(2):567-580.
DZIUBAN, C.D.; SHIRKEY, E.C. 1974. When a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 31(6): 358-361.
EBERHART, S.A.; RUSSELL, W.A. 1966. Stability parameters for comparing varieties. Crop Science, 6(1):36-40
FINLAY, K.W.; WILKINSON, G. N. 1963. The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research, 14(6):742-754.
GARBUGLIO, D.D.; FERREIRA, D.F. 2015. FGGE method: description and application in data from maize cultivars. Euphytica, 204(3):723-737.
GAUCH, H.G.; PIEPHO, H.P.; ANNICCHIARICO, P. 2008. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48(3):866-889.
GAUCH, H.G.; ZOBEL, R.W. 1996 AMMI analysis of yield trials. In: Kang, M.S. and H.G. Gauch, editors, Genotype- by-environment Interaction, CRC Press, Boca Raton. p. 85-122.
GRUNVALD, A.K.; CARVALHO, C.G.P.; OLIVEIRA, A.C.B.; PIRES, J.L.F.; CARVALHO, H.W.L.; OLIVEIRA, I.R. 2014. Adaptability and stability of conventional and high oleic sunflower genotypes cultivated in southern Brazil. Revista Ciência Agrária, 573(3):217-223.
HADI, A.F.; SA’DIYAH, H. 2016. On The Development of Statistical Modeling in Plant Breeding: An Approach of Row-Column Interaction Models (RCIM) For Generalized AMMI Models with Deviance Analysis. Agriculture and Agricultural Science Procedia. 9(1):134-145.



