Estratégias de seleção de milho doce por fenotipagem convencional e de alto desempenho

• Autor(a) principal: Silva, Marina Freitas e
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/29778; http://doi.org/10.14393/ufu.di.2020.3913
• topic: Zea mays L. subsp. sacharatta; melhoramento de plantas; índice de seleção; fenômica; plant breeding; selection index; phenomics; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::FITOTECNIA::MELHORAMENTO VEGETAL; Agronomia

Sweet corn (Zea mays L. subsp. Saccharata) is a vegetable with high added value, intended mainly for industrial processing. In Brazil, this segment generates approximately R$ 550 million per year. Despite the expansion of the sweet corn market in Brazil and the advent of new technologies in the canned food industries, the commercial production of sweet corn has not met the current Brazilian demand. As a result, the demand for new cultivars that are more productive and adapted in Brazil has increased. To select superior sweet corn genotypes and meet the requirements for productivity and disease resistance, it is also necessary to meet requirements imposed by the processing industries, which seek consumers’ approval. In addition, evaluation processes need to be more efficient. Important strategies for simultaneous character selection that ensure efficiency in evaluations include (i) the use of selection indices and (ii) high-throughput phenotyping. The selection index allows combining multiple information contained in the experimental unit, so that we can select characters from a set of important variables. In turn, phenomics is a set of methodologies and protocols used to evaluate various characteristics in the plant fast, with high precision and low cost. It provides phenotypic information during the culture cycle in a non-destructive way and in many individuals. However, few studies have reported the efficiency of both tools in improving sweet corn. Given the need for improvement to obtain and analyze phenotypic data on sweet corn, this study aimed to (i) compare the efficiency of different selection indices based on the characters that have a direct effect on grain yield in segregating sweet corn populations and (ii) verify whether high-performance phenotyping by digital image analysis is efficient in the selection process of superior sweet corn individuals.