Optimizing breeding programs by deploying modern tools: case studies in rice and raspberry

• Ano de defesa: 2024
• Autor(a) principal: Prado, Melina
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Agricultura; Agriculture; Breeding; Disease resistance; Ensaios multi-ambiente; Envirotipagem; Envirotyping; Equações de modelos mistos; Fenotipagem; Genotipagem; Genotyping; Graphical network; GWAS; Melhoramento; Mixed model equations; Multi-environment trials; Phenotyping; Redes gráficas; Resistência a doenças
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11137/tde-08102024-155059/

Agriculture currently faces several challenges associated with the imbalance between population growth and food production, as well as climate change. To overcome these challenges, breeding must utilize all necessary technologies to develop crops with higher productivity, resistance, stability, and climate-smart traits. The breeding process is generally evaluated regarding selection gain, which depends on several parameters in the breeders equation that have significantly changed in recent years. These changes have been enabled by advances in different omics such as phenomics, enviromics, and genomics. In this way, we applied some of the most recent breeding tools to contribute to agriculture in two case studies. In the second chapter, we simultaneously used association mapping and graphical networks to identify genomic regions responsible for resistance to one of the most significant fungi affecting raspberries. We used parents from more than one species, differing in economic importance and resistance, and employed both classical phenotyping techniques and highthroughput phenotyping to characterize this interspecific population. In the final chapter, we demonstrated an efficient way to optimize Multi-Environment Trials using enviromics. Additionally, we evaluated the environmental covariates that most influence rice yield in the US Rice Belt and characterized these Target Population of Environments (TPEs). Despite the US rice production representing 5% of the worlds rice production and having tripled its imports since 2001, there is still a need for work characterizing its TPEs. Thus, this work presents itself as a valuable resource for modern breeding, contributing to the production of more resistant, productive, and climate-smart varieties to address the current breeding challenges.