Projeção GGE biplot na inferência de adaptabilidade e estabilidade da soja em um centro agrícola do Paraná
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AMIRA, J. O. et al. Relative Discriminating Powers of GGE and AMMI Models in the Selection of Tropical Soybean ( Glycine max L. Merr. ) Genotypes. International Journal of Plant Breeding and Genetics, v. 7, n. 2, p. 139-145, 2013.
AREGA, A. et al. Genotype and genotype by environment interaction and grain yield stability of medium maturity groups of soybean [Glycine max (L.) Merrill] varieties in Western Oromia, Ethiopia. African Journal of Plant Science, v. 12, n. 9, p. 227-237, 2018.
CHEELO, P. et al. GGE biplot analysis for identification of ideal soybean [Glycine max L. Merrill] test and production locations in Zambia. Journal of Experimental Agriculture International,v.15, n. 3, p. 1-15, 2017.
GASPARRI N. I. et al. The emerging soybean production frontiers in Southern Africa: Conservation challenges and the role of South-South Telecouplings. Conservation Letters. v. 9, n. 1, p. 21-31, 2016.
GRZEGOZEWSKI, D. M. et al. (2017). Spatial correlation of soybean productivity, enhanced vegetation index (EVI) and agrometeorological variables. Engenharia Agrícola, v. 37, n. 3, p. 541-555, 2017.
HARTMAN G. L. et al. Crops that feed the World 2. Soybean—worldwide production, use, and constraints caused by pathogens and pests. Food Security v. 3, p. 5-17, 2011.
KANG, B. K. et al. Genetic and environmental variation of first pod height in soybean [Glycine max (L.) Merr.]. Plant Breeding and Biotechnology, v. 5, n. 1, p. 36-44, 2017.
KARIMIZADEH, R. et al. GGE biplot analysis of yield stability in multi-environment trials of lentil genotypes under rainfed condition. Notulae Scientia Biologicae, v. 5, n. 2, p. 256-262, 2013.
PASSOS, M. L. V. et al. (2019). Growth, Productivity and Quality of Soybean Grains, Submitted to Different Seed Treatments. Journal of Agricultural Science, v. 11, n. 11, p. 174-284, 2019.
PIMENTEL, A. J. B. et al. Estimação de parâmetros genéticos e predição de valor genético aditivo de trigo utilizando modelos mistos. Pesquisa Agropecuária Brasileira, v. 49, n. 11, p. 882-890, 2014.
QIN, J. et al. Evaluation of productivity and stability of elite summer soybean cultivars in multi-environment trials. Euphytica, v. 206, n. 3, p. 759-773, 2015.
R DEVELOPMENT CORE TEAM. R: A language and environment for statistical computing Vienna: R Foundation for Statistical Computing, Vienna, 2014.
RESENDE, M. D. V.; DUARTE, J. B. Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesquisa Agropecuária Tropical, v. 37, n. 3, p. 182-194, 2007.
SANTOS, A. et al. Adaptability and stability of erect cowpea genotypes via REML/BLUP and GGE Biplot. Bragantia, v. 75, n. 3, p. 299-306, 2016.
SANTOS, A. et al. GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen. Ciência e Agrotecnologia, v. 41, n. 1, p. 22-31, 2017.
TESELLE, A. et al. Adaptability and stability of soybean cultivars under different times of sowing in Southern Brazil. Journal of Plant Sciences, v. 4, n. 2, p. 17-22, 2016.
YAN, W. et al. Cultivar evaluation and mega-environment investigation based on the GGE Biplot. Crop Science, v. 40, n. 3, p. 597-605, 2000.
YAN, W. et al. GGE Biplot vs AMMI graphs for genotype-by-environment data analysis. Journal of the India Society of Agricultural Statistics, v. 65, n. 2, p. 181-193, 2011.
YAN, W. Mega‐environment analysis and test location evalua‐ tion based on unbalanced multiyear data. Crop Science, v. 55, p. 113–122, 2015.
YAN, W.; TINKER, N.A. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, v. 86, n. 3, p. 626-645, 2006.
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