Yield and agronomic characteristics of purple corn (Zea mays L.) hybrids with different inbreeding using the AMMI model
DOI:
https://doi.org/10.21704/ac.v84i1.1681Keywords:
maize, grain yield, hybrids, stability and adaptability, biplotAbstract
The production of corn (Zea mays L.) in Mexico is deficient despite producing 27 million tons, in the last four years the production of corn in Sonora has increased, producing from 544 to 680 thousand tons in 2018 and 2021 respectively, 97% of the production corresponds to white corn, 3% to yellow corn; however, in colored maize there are no planting records. The objective of this research was to estimate grain yield and its agronomic components in improved purple maize hybrids in southern Sonora. A randomized complete block experimental design was used with three repetitions with the AMMI model, the plots were two rows four meters long with a population density of 100,000 plants per hectare, the variables were: grain yield, seed weight, thousand grains, ear length, grains per row, rows per ear and test weight. The results showed statistical significance between hybrids, years and their interaction in all the variables. The AMMI model was useful to understand the existing genotype-environment interaction between hybrids of mode color in grain yield. The AMMI allowed hybrids 10, 1, 17, 4 and 2 to be identified as more stable in RG, GH, HM and LM for the year 2020. On the other hand, hybrids 9, 12, 3 and 13 in 2019; however, those with the greatest interaction were 7, 16 and 4 presented performances below the general average; hybrids 16, 10, 14 and 19 obtained higher yield, as well as higher PMG, PH GH, and LM. There was a correlation between grain yield with PMG, PH, LM, GH and HM; in PMG there was more association with PH, GH and HM.
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Acevedo, B.M., Álvarez, R., Silva, O., Torres, E. (2019). Interacción genotipo ambiente en arroz para identificar mega-ambientes y ambientes ideales mediante el modelo de regresión por sitios y biplot. Bioagro 31(1): 35-44. Bioagro 33(2): 67-78. 2021. doi: http://www.doi.org/10.51372/bioagro332.1.
Crossa, J., Gauch, H., Zobel, R. (1990). Additive main effects and multiplicative interaction analysis of two international maize ultivar trials. Crop Sci. 30: 493-500. https://doi.org/10.2135/cropsci1990.0011183X003000030003xCitations: 159.
Eberhart, S.A., and Russel, W.A. (1966). Stability parameters for comparing varieties. Crop Sci. 6(1):36-40. Doi.org/10.2135/cropsci1966.0011183X000600010011x.
Fayeun, L.S., Alake, C.G., Akinlolu, O.A. (2018). GGE biplot analysis of fluted pumpkin (Telfairia occidentalis) landraces evaluated for marketable leaf yield in Southwest Nigeria. J. Saudi Soc. Agric. Sci. 17(4):416-423. Doi.org/10.1016/j.jssas.2016.10.001.
García, M.P.J., Pérez, C.A.A., Silva, D.R.J., Álvarez, P.R.M., Monasterio, P.P., Taramona, R.L.A. (2020). Evaluación del potencial agronómico de híbridos de maíz amarillo basado en el análisis GGE Biplot y el modelo AMMI. Bioagro 32(2): 95-106. https://www.redalyc.org/articulo.oa?id=476047390006.
García-Mendoza, P.J., Pérez-Almeida, I.B., Prieto-Rosales, G.P., Medina-Castro, D.E., Manayay-Sánchez, D., Marín-Rodríguez, C.A., Ricse-Nestares, J.C., Ortecho-Llanos, R., Medina-Hoyos, A.E. (2021). Interacción genotipo ambiente y potencial productivo de 25 variedades de maíz amiláceo en la provincia de Tayacaja, Perú. Revista Bioagro 33(2): 67-78. https://doi.org/10.29312/remexca.v13i7.3070.
García, E. (1973). Modificaciones al Sistema de Clasificación Climática de Köppen. http://cdigital.dgb.uanl.mx/la/1020081042/1020081042_011.pdf. (Noviembre, 2021).
Gauch, H.G. (2006). Statistical Analysis of Yield Trials by AMMI and GGE. Crop Science, 46: 1488-1500. https://doi.org/10.2135/cropsci2005.07-0193
Gauch, H.G., Zobel, R.W. (1996). AMMI analysis of yield trials. In: M.S. Kang y H. G. Gauch. (eds.). Genotype-by-Environment interaction. CRC Press. Boca Raton, FL, USA. pp. 85-122. http://dx.doi.org/10.1201/9781420049374.ch4.
Gómez, M.Y., Boicet, F.T., Tornés, O.N., Meriño, H.Y. (2018). Interacción genotipo ambiente de cuatro variedades de tomate en la provincia Granma. Rev. Centro Agrícola. 45(2):21-28.
López, M.F., Chura, C.J., García, P.G. (2019). Interacción genotipo por ambiente del rendimiento de maíz amarillo en híbridos trilineales, Perú. Rev. Mex. Cienc. Agríc. 10(4):859-872. Doi.org/10.29312/remexca. v10i4.1696.
Lozano, R.Á., Santacruz, V.A., San-Vicente, G.F., Crossa, J., Burgueño, J., Molina, G.J.D. (2015). Modelación de la interacción genotipo ambiente en rendimiento de híbridos de maíz blanco en ambientes múltiples. Rev. Fitotec. Mex. 38(4):337-347. Doi.org/10.35196/rfm.2015.4.337.
Martínez, G.A., Zamudio, G.B., Tadeo, R.M., Espinosa, C.A., Cardoso, G.J., Vázquez, C.G., Turrent, F.A. (2018). Rendimiento de híbridos de maíz grano blanco en cinco localidades de valles Altos de México. Rev. Mex. de Cienc. Agríc. 7(9):1447-1458. https://doi.org/10.29312/remexca.v9i7.1357.
Neisse, A.C., Kirch, L.J., Hongyu, K. (2018). AMMI and GGE biplot for genotype x environment interaction: a medoid-based hierarchical cluster analysis approach for high dimensional data. Biometrical Letters. 55(2):97-121. Doi.org/10.2478/bile-2018-0008.
Ponce-Encinas, M.C., Chura-Chuquija, J., Hernández-Leal, E., Hernández-Salinas, G., Aragón-García, A. (2022). Interacción genotipo-ambiente del rendimiento en híbridos de maíz amarillo mediante AMMI y SREG. Revista Mexicana de Ciencias Agrícolas, 13(7): 1247-1258. DOI: https://doi.org/10.29312/remexca.v13i7.3070.
SAS Institute. (2012). User’s Guide of SAS. SAS Institute Inc. Cary, North Carolina, USA. 550 p.
SIAP, Servicio de Información Agroalimentaria y Pesquera (2022) Avance de siembras y cosechas. Resumen nacional por estado. http://infosiap.siap.gob.mx:8080/agricola_siap_gobmx/AvanceNacio-nalCultivo.do (Consultado Mayo, 2022).
Salinas, M.Y., Gómez, M., Cervantes, J.M., Sierra, M.M., Palafox, A.C., Betanzos M.E., Coutiño, E.B. (2010). Calidad nixtamalera y tortillera en maíces del trópico húmedo y sub-húmedo de México. Revista Mexicana de Ciencias Agrícolas 1:509-523. https://www.redalyc.org/articulo.oa?id=263120639005.
Steel, R., Torrie, J. (1988). Bioestadística:Principios y Procedimientos. McGraw-Hill /Interamericana, México. Doi: https://arveja.awardspace.com.
Vázquez-Carrillo, M.G., Santiago-Ramos, D., Gaytán-Martínez, E., Morales-Sánchez, E., Guerrero-Herrera, M.J. (2018). High oil content maize: Physical, thermal and rheological properties of grain, masa, and tortillas. LWT-Food Science and Technology 60:156-161. DOI:10.1016/j.lwt.2014.07.043.
Vázquez, C.G., Martínez, G.A., Zamudio, G.B., Espinosa, C.A., Tadeo, R.M., Turrent, F.A. (2020). Estabilidad de rendimiento y características fisicoquímicas de grano de híbridos de maíz en Valles Altos de México. Rev. Mex. Cienc. Agríc. 8(11):1803-1814. DOI: https://doi.org/10.29312/remexca.v11i8.1990.
Vargas, H.M., Crossa, J. (2000). El análisis AMMI y la gráfica del biplot en SAS. Universidad Autónoma de Chapingo (UACh) y Unidad de Biometría y Estadística, CIMMYT. México, DF. 42 p. https://reposito ry.cimmyt.org/bitstream/handle/10883/3489/23. 73248.pdf?sequence=1&isAllowed=y (consulta de mayo, 2022).
Yan, W., Frégeau, R.J., Pageau, D., Martin, R. (2016). Genotype-by-environment interaction and trait associations in two genetic populations of oat. Crop Sci. 56(3):1136-1145. Doi.org/10.2135/cropsci2015.11.0678.
Zobel, R.W., Wright, M.J., y Gauch, H.G. (1988). Statistical analysis of a yield trial. Agron. J. 80: 388-393. https://doi.org/10.2134/agronj1988.00021962008000030002x.
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Copyright (c) 2023 Gilberto Rodríguez-Pérez, Alejandro García-Ramirez, Felipe de Jesús Reynaga-Franco, Jaime Edzael Mendívil-Mendoza, Alba Rocío Ochoa Meza, Francisco Cervantes-Ortiz, Enrique Andrio Enriquez
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