Morpho-Agronomic Characterization, Sample Size, and Plot Size for the Evaluation of Capsicum chinense Genotypes

• Autor(a) principal: Alves, Silfran Rogério Marialva
• Data de Publicação: 2022
• Outros Autores: Lopes, Ricardo, Meneses, Carlos, Valente, Magno Sávio Ferreira, Martins, Cibele Chalita [UNESP], Ramos, Santiago Ferreyra, Oliveira, Izamara, de Jesus Pinto Fraxe, Therezinha, Costa, Lucifrancy, Lopes, Maria Teresa Gomes
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/horticulturae8090785; http://hdl.handle.net/11449/249194
• topic: experimental precision; plant breeding; “pimenta-de-cheiro” Capsicum chinense

The Amazon is a center of diversity for Capsicum chinense Jacq., with wide genetic and morphological variability, but little exploration has been performed there to facilitate their improvement. This study aimed to characterize and evaluate C. chinense genotypes for the development of cultivars by determining the optimal size of the experimental plot and the minimum sample size to ensure a precise estimation of yield. A total of 23 genotypes were evaluated, and in multivariate analyses, the plants were characterized by 21 morphological descriptors and eight quantitative traits related to biometry and yield. The recommended sample size for fruit evaluation was defined based on simulations with subsample resampling and evaluation of the semi-amplitudes of the confidence interval of the mean estimate. The optimal plot size was estimated by the modified maximum curvature method. The similarity coefficients among the genotypes ranged from 0.54 to 0.93, indicating that the established clusters contained important information for future crosses. According to the sample size methodologies, 25 to 40 fruits should be sampled for valid evaluations of biometric traits. Experiments with five to eight plants per plot are recommended to test progenies of the species, ensuring good experimental precision combined with high selection accuracy for yield traits.