Advanced strategies in soybean breeding for enhanced oil, yield and balanced protein in the face of climate change

• Ano de defesa: 2024
• Autor(a) principal: Fagundes, Talieisse Gomes
• 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: Glycine max; Aprendizado de máquina; Bioinformática; Bioinformatics; Clusterização; Clusterization; Drought tolerance; Machine learning; Predição genômica; Tolerância à seca
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11137/tde-13032025-142733/

In an era marked by the pressures of a rapidly expanding global population and escalating environmental challenges, the imperative for sustainable agricultural practices has never been more critical. With the global population projected to reach 9.8 billion by 2050, the demand for food, particularly nutrient-rich sources like soybeans (Glycine max (L.) Merrill), is set to surge dramatically. Soybeans, a pivotal crop of global agriculture, play an indirect, yet a crucial role in combating hunger and malnutrition through their significant contributions to the world\'s protein and oil reserves, while also bolstering economic and environmental sustainability. However, the cultivation of soybeans is confronted with multifaceted challenges, notably the threat of environmental stressors such as drought, which are exacerbated by climate change. These conditions not only diminish yields but also impair the quality of soybeans, affecting important traits like protein and oil content. Addressing these issues necessitates a profound understanding of soybean\'s physiological and genetic mechanisms of resilience against such stressors. Concurrently, the evolving global demand for soybeans, fueled by their varied applications from nutrition to biofuel production, necessitates the optimization of yield, oil, and protein content. This scenario presents unique breeding challenges that require innovative solutions. The integration of advanced genomic research, bioinformatics, and machine learning emerges as a pivotal strategy, offering new opportunities to enhance crop traits while upholding principles of sustainability and stewardship. This study undertakes a comprehensive examination of the challenges and opportunities in soybean breeding, focusing on mitigating the impacts of environmental stressors like drought and leveraging genetic and agronomic advancements to optimize yield and quality.