Adaptability and grain yield stability of rice hybrids and varieties in Venezuela
DOI:
https://doi.org/10.51372/bioagro333.4Keywords:
AMMI, GGE Biplot, Oryza sativa, REML/BLUPAbstract
The development of new high-performance and stable cultivars requires test multi-environmental validation to deal with the effect of genotype by environment interaction (GEI). With the objective to determine adaptability and stability for grain yield in hybrids and rice varieties through the models AMMI, SREG and REML/BLUP. Six experiments were evaluated during the 2015-2016 dry season in the main producing regions of Venezuela. The ANOVA detected differences for genotype (G), environment (E) and their interaction (GEI), representing 19, 65 and 16 % of the total variation, respectively, with prevalence of hybrid by localities interaction. The first major components of the AMMI and GGE biplot models explained 77 and 83 % of GEI, respectively. The three models coincided and identified the hybrid RHA-180 (H6) with improved average performance, adapted and stable. The hybrid HIAAL (H3) was the most prominent. Among the checks, 'Pionero FL' (V3) was the most stable with moderate yield; the opposite occurred with ‘Soberana FL’ (V4) and ‘SD-20A’ (V1), that the AMMI and GGE biplot models identified with high and unstable performances and specific adaptation to locality INIA Guárico (L1), not coinciding with the mixed model. Two mega-environments were identified with the winning genotypes H6 and V4. There was divergence between AMMI and GGE biplot to identify discriminatory and representative locations. The Plot 199 (L3) was the most representative, while the location L1 discriminated better the genotypes. The GGE biplot analysis was more informative and complete for the GEI analysis.
Downloads
References
Acevedo-Barona, M., E. Reyes, W. Castrillo, O. Torres, C. Marín, R. Álvarez et al. 2010 Estabilidad fenotípica de arroz de riego en Venezuela utilizando los modelos Lin-Binns y AMMI. Agronomía Tropical 60: 131-138.
Acevedo-Barona, M., R. Álvarez, R. Silva, O. Torres and E. Reyes. 2019. Interacción genotipo ambiente en arroz para identificar mega-ambientes y ambientes ideales mediante el modelo de Regresión por sitios (SREG) and biplot GGE. Bioagro 31(1): 35-44.
Acevedo-Barona, M., R. Silva-Díaz, R. Álvarez-Parra, O. Torres-Angarita and E. Reyes-Ramone. 2020. Environmental stratification of rice by genotype x environment interaction analysis using five methods. Agronomía Mesoamericana 31(1): 43-57.
Camargo-Buitrago, I., E. Mc Intire and R. Gordón-Mendoza. 2011. Identificación de mega-ambientes para potenciar el uso de genotipos superiores de arroz en Panamá. Pesquisa Agropecuaria Brasileira 46(9): 1601-1069.
Colombari-Filho, J., M. de Resende, O. de Morais, A. de Castro, E. Guimarães, J. Pereira et al. 2013. Upland rice breeding in Brazil: a simultaneous genotypic evaluation of stability, adaptability and grain yield. Euphytica 192: 117-129.
Costa-Neto, G.M., J.B. Duarte, A.P. de Castro and A.B. Heinemann. 2020. Uso de infromação ambientais na modelagem e interpretação da interação genótipo x ambiente. Revisao bibliográfica. Boletim de pesquisa e desenvolvimento 56. Embrapa arroz e feijão. 46 p.
Crossa, J., H. Gauch Jr. and R. Zobel. 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science 30(3): 493- 500.
FAO. 2020. FAOSTAT. Roma. http://www.fao.org/faostat/es/#compare (retrieved May 15, 2020).
Fujimoto, R., K. Uezono, S. Ishikura, K. Osabe, W. Peacock and E. Dennis. 2018. Recent research on the mechanism of heterosis is important for crop and vegetable breeding systems. Breeding Science 68(2): 145-158.
García-Mendoza, P., I. Pérez-Almeida, G. Prieto-Rosales, D. Medina-Castro, D. Sánchez, C. Marín-Rodríguez et al. 2021. Interacción genotipo ambiente y potencial productivo de 25 variedades de maíz amiláceo en la provincia de Tayacaja, Perú. Bioagro 33(2): 67-78.
Gauch, H.G. 1992. Statistical Analysis of Regional Yield Trials. AMMI Analysis of Factorial Designs. Elsevier Science, Amsterdam.
Gonçalves, G.M.C., R.L.F. Gomes, A.C.A. Lopes and P.F.M. Vieira. 2020. Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot. Crop Breeding and Applied Biotechnology 20(2): 1-9.
Haider, Z., M.A. Akhter, A. Mahmood and R. Khan. 2017. Comparison of GGE biplot and AMMI analysis of multi- environment trial (MET) data to assess adaptability and stability of rice genotypes. African journal of Agricultural Research 12(51): 3542-3548.
Huang, M., T. Qi-Yuan, A. He-Jum and Z. Ying-Bin. 2017. Yield potential and stability in super hybrid rice and its production strategies. Journal of Integrative Agriculture 16(2): 1009-1017.
INIA. 2020. Instituto Nacional de Investigaciones Agrícola. Red meteorológica de Venezuela. http://www.agrometeorologia. inia.gob.ve/index.php/la-red-rai (retrieved March 20, 2020).
Liu, J., M. Li, Q. Zhang., X. Wei and X. Huang. 2019. Exploring the molecular basis of heterosis for plant breeding. Journal of Integrative Plant Biology 62(3): 287-298.
Ponnuswamy, R., A. Rathore, A. Vemula and R. Das. 2018. Analysis of multi-location data of hybrids rice trials reveals complex genotype by environment interaction. Cereal Research Communications 46(1): 146-157.
Ramalho, M., J. Dos Santos, A. Abreu and J. Nunes. 2012. Aplicação da Genética Quantitativa no Melhoramento de Plantas Autógamas. Ed-Lavras. Universidade Federal de Lavras, UFLA.
Regitano-Neto, A., E. Ramos Júnior, P. Gallo, J. de Freitas and L. Azzini. 2013. Comportamento de genótipos de arroz de terras altas no estado de São Paulo. Revista Ciência Agronômica 44: 512-519.
Samonte, S., L. Wilson, A. McClung and J. Medley. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analysis. Crop Science 45: 2414-2424.
Sincik, M., A.T. Goksoy, E. Senyigit, Y. Ulusoy, M. Acar, S. Gizlenci et al. 2021. Response and yield stability of canola (Brassica napus L.) genotypes to multi-environments using GGE biplot analysis. Bioagro 33(2): 105-114.
Torres, E. 2014. Desarrollo de híbridos de arroz para América Latina “Un desafío para la investigación en mejoramiento de arroz”. https://n9.cl/1f0fe (retrieved March 10, 2020).
Yan, W., L. Hunt, Q. Sheng and Z. Szlanvnics. 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science 40(3): 597-605.
Yan, W. and I. Rajcan. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42: 11-20.
Yan, W. and M. Kang. 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists. CRC Press. Boca Raton, FL, USA.
Yuan, L.P. 2017. Progress in super-hybrid rice breeding. The Crop Journal 5: 100-102.
Zobel, R., M. Wright and G. Gauch. 1988. Statistical analysis of a yield trial. Agronomy Journal 80: 388-393.
Published
How to Cite
Issue
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Rights of the author/s are from the year of publication
This work is under the license:
Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
The opinions expressed by the authors not necesarily reflect the position of the publisher or UCLA. The total or partial reproduction of the texts published in this journal is authorized, as long as the complete source and the electronic address of this journal is cited. Authors have the right to use their articles for any purpose as long as it is done for non-profit purposes. Authors can publish the final version of their work on internet or any other medium, after it has been published in this journal.
Bioagro reserves the right to make textual modifications and technical adjustments to the figures of the manuscripts, in accordance with the style and specifications of the journal.