Resumen

TORRES-MORALES, Braulio et al. Morphological and agronomic diversity of seven corn races from the state of Chiapas. Rev. Mex. Cienc. Agríc [online]. 2022, vol.13, n.4, pp.687-699.  Epub 22-Ago-2022. ISSN 2007-0934.  https://doi.org/10.29312/remexca.v13i4.2956.

The centers of origin and genetic diversity of corn are located in Mexico, in the state of Chiapas is one of the areas with greater diversity and great social roots towards this crop. Several previous studies of this diversity have been carried out, which have had the limitation that they include a small number of representative populations of the reported races. The objective of this study was to analyze the morphological and agronomic diversity of 42 accessions belonging to seven of the main corn races grown in the state of Chiapas. Field evaluations were established under random complete block designs with two repetitions, in this way, they were sown in warm, semi-warm and temperate zones, under rainfed conditions in the spring-summer cycle of 2018. The analyses of variance detected significant differences between accessions in 79.1% of the variables analyzed and, through the principal component analysis, three groups were formed: group I was compactly integrated by populations of the Zapalote Grande race, with a greater morphological similarity between their accessions, group II with the Comiteco and Tehua races, with morphological amplitude between their populations and group III with the Olotón, Negrito, Negro de Chimaltenago and Motozinteco races, grouped by their phenological cycle and plant size. The groupings observed were consistent with those obtained in the cluster analysis, which reflects consistency in the associations and confirms the identity of the corn races previously reported for the state of Chiapas, they also confirm the complexity of the corn germplasm of that area, finding a wide variation and interrelations between the different populations and races.

Palabras llave : Zea mays L.; Chiapas; multivariate analysis; phenotypic characterization.