AMMI and SREG analysis for protein content in Vigna unguiculata (L.) Walp

Carlos Enrique Cardona Ayala, Hermes Araméndiz Tatis, Miguel Mariano Espitia Camacho

Resumo


Identification of cowpea genotypes with high protein content for specific environments, based on the genotype-environment interaction, has a positive impact in places where access to protein for human consumption is deficient. The objective of the study was to analyze the protein content of 10 cowpea bean genotypes in five environments in the Caribbean Region of Colombia. The randomized complete block design with four replications at each site was used. The analysis of the genotype-environment interaction (GEI) was performed using the AMMI (additive main effects and multiplicative interaction) and SREG (regression in sites) models, in which the main effects of genotypes (G) + GEI are part of the bilinear term of the model. The AMMI and SREG models and their biplots were useful in the analysis and interpretation of the protein content of cowpea beans from experiments carried out in multiple environments. The AMMI model identified genotypes 1, 4 and 8 as those with the greatest adaptability and stability, and the Montería (MO7B), Mahates (MA7B) and Cereté (CE7B) environments as the most favorable. The SREG model identified a potential mega-environment constituted by the PN7B, MA7B and CE7B environments, in which genotypes 1, 2 and 3 presented greater adaptability and stability, while genotype 8 showed specific adaptability in MO7B. In both models, genotypes 6, 7 and 10 showed absence of adaptability and stability in the studied environments.

Palavras-chave


Nutritional quality, genotype-environment interaction, adaptability, stability.

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Referências


ALIDU, M.S.; ASANTE, I.K.; MENSAH, H.K. Evaluation of nutritional and phytochemical variability of cowpea Recombinant Inbred Lines under contrasting soil moisture conditions in the Guinea and Sudan Savanna Agro-ecologies. Helion, 6: eo3406, 2020.

BAPTISTA, A. et al. Characterization of protein and fat composition of seeds from common beans (Phaseolus vulgaris L.), cowpea (Vigna unguiculata L. Walp) and bambara groundnuts (Vigna subterranea L. Verdc) from Mozambique. Journal of Food Measurement and Characterization, v. 11, n. 02, p.442–450, 2017.

BENTON-JONES, J. Plant analysis techniques. Benton-Jones Laboratories, Georgia.1989, 384p.

CROSSA, J.; CORNELIUS, P.L.; YAN, W. Biplots of linear bilinear models for studying crossover genotype × environment interaction. Crop Science, v. 42, n. 01, p. 619-633, 2002.

DARAI, R. et al. AMMI biplot analysis for genotype environment interaction on yield trait of high Fe content lentil genotypes in Terai and Mid-Hill environment of Nepal. Annals of Agricultural and Crop Sciences, v. 2, n. 01, p. 1026, 2017.

DDAMULIRA, G., et al. Grain Yield and protein content of brazilian cowpea genotypes under diverse ugandan environments. American Journal of Plant Sciences, v. 6, p. 2074-2084, 2015.

EBDON, J.S.; GAUCH, J. R. Additive main and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype x environment interaction. Crop Science, v. 42, n. 2, p.489-496, 2002.

FAO. Estadísticas de cultivo. 2020, Disponivel em http://www.fao.org/faostat/es/#data/QC. ( Acceso em: 18 agosto de 2020).

GAUCH Jr., H.G. A Simple Protocol for AMMI Analysis of Yield Trials. Crop Science, v. 53, n. 1, p.1860-1869, 2013.

GAUCH Jr., H.G. Statistical analysis of yield of trials by AMMY and GGE. Crop Science, v. 46, n. 1, p.1488-1500, 2006.

GERRANO, A.S. et al. Selection of cowpea genotypes based on grain mineral and total protein content, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, v. 69, n. 2, p.155-166, 2018.

GERRANO, A.S.; RENSBRURG, W.S.; ADEBOLA, P.O. Nutritional composition of immature pods in selected cowpea [Vigna unguiculata (L.) Walp.] genotypes in South Africa. Australian Journal of Crop Science, v. 11, n. 02, p.134-141, 2017.

GERRANO, A.S.; RENSBURG, W.S.; KUTU, F.R. Agronomic evaluation and identification of potential cowpea (Vigna unguiculata L. Walp) genotypes in South Africa, Acta Agriculturae Scandinavica, Section B — Soil & Plant Science, v. 69, n.4, p. 295-303, 2019.

LOZANO-RAMÍREZ, A. et al. Modelación de la interacción genotipo x ambiente en rendimiento de híbridos de maíz blanco en ambientes múltiples. Revista fitotecnia mexicana, v. 38, n. 4, p. 337–347, 2015.

MÁRQUEZ-QUIROZ, C. et al. Biofortification of cowpea beans with iron: iron´s influence on mineral content and yield. Journal of Soil Science and Plant Nutrition, v. 15, n. 4. p. 839-847, 2015.

MEBRATU, A. et al. Genotype ´ environment interaction of quality protein maize hybrids under contrasting management conditions in eastern and southern Africa. Crop Science, v. 59, n. 4, p.1576–1589, 2019.

OCHOA-CADAVID, I.; PRECIADO-ORTÍZ, R.E.; BAYUELO-JIMENEZ, J.S. Interacción genotipo × ambiente y estabilidad en rendimiento de variedades de maíz en condiciones contrastantes de fósforo. Agrociencia, v. 53, n. 3, p. 337-353, 2019.

OLIVEIRA, D.S. et al. Adaptability and stability of the zinc density in cowpea genotypes through GGE-Biplot method. Revista Ciência Agronômica, v. 48, n. 5, p.783-791, 2017. Número especial

PACHECO, A. et al. 2017. "GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1", hdl:11529/10203, CIMMYT Research Data & Software Repository Network, V16., 2017 Disponivel em https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10203. (Acceso em 19 abril de 2020).

PAULA, C.D.; JARMA - ARROYO, S.; ARAMENDIZ-TATIS, H. 2018. Caracterización nutricional y determinación de ácido fítico como factor antinutricional del frijol caupí. Agronomía Mesoamericana, v. 29., n. 1, 29-40, 2018.

POLANIA, J. A. et al. Desarrollo y distribución de raíces bajo estrés por sequía en frijol común (Phaseolus vulgaris L.) en un sistema de tubos con suelo. Agronomía Colombiana, v. 27, n. 1, p. 25-32, 2009.

ROCHA, M.M. et al. Yield adaptability and stability of semi-erect cowpea genotypes in the Northeast region of Brazil by REML/BLUP. Revista Ciência Agronômica, v. 48, n.5, p.862-871, 2017. Número especial

SAMONTE, S.O. et al. Targeting Cultivars onto rice growing Environment Using AMMI and SREG GGE Biplots Analyses. Crop Science., v. 45, n.1, p.2414-2424, 2005.

SANTOS, A. et al. Adaptability and stability of cowpea genotypes to Brazilian Midwest. African Journal of Agricultural Research, v.10, n. 41, p.3901-3908, 2015.

SILVA, D.O.M.; SANTOS, C.A.F.; BOITEUX, L.S. Adaptability and stability parameters of total seed yield and protein content in cowpea (Vigna unguiculata) genotypes subjected to semi-arid conditions. Australian Journal of Crop Science, v.10, n. 8, p.1164-1169, 2016.

SINGH, S.K. et al. Assessing genotypic variability of cowpea (Vigna unguiculata [L.] Walp.) to current and projected ultraviolet-B radiation. Journal of Photochemistry and Photobiology B: Biology, v. 93, n. 2, p.71-78, 2015.

SINGH, V. et al. Stability analysis in mungbean (Vigna radiata (L) Wilczek) for nutritional quality and seed yield. Legume Research, v.36, n.1, p.56-61, 2013.

TOLESSA, T.T.; Gela, T.S. Sites regression GGE biplot analysis of haricot bean (Phaseolus vulgaris L.) genotypes in three contrasting environments. World Journal of Agricultural Research, v.2, n. 5, p. 228-236, 2014.

ZOBEL, R.; WRIGHT, M.; GAUCH, H. Statistical analysis of a yield trial. Agronomy Journal, v. 80, n. 3, p.388-393, 1988.




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