110 years of rice breeding at LSU: realized genetic gains and future optimization

• Ano de defesa: 2025
• Autor(a) principal: Silva, Allison Vieira da
• 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: Arroz; Fenotipagem de alto rendimento; Ganho genético; Genetic gain; Genomic selection; High-throughput phenotyping; Rice; Seleção genômica; Simulação estocástica; Stochastic simulation
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11137/tde-03022026-103247/

This study assessed the genetic gains achieved by the Louisiana State University (LSU) long-grain rice breeding program over more than a century of activities. Statistical analyses of multi-environment trials conducted between 1994 and 2018, pedigree records, and stochastic simulations using the AlphaSimR package were employed. Historical genetic gains were estimated for three key traits: grain yield, plant height, and whole milling yield. Results indicated a consistent annual increase of 0.86% in grain yield (56.54 kg/ha/year), while plant height gradually decreased over time, reflecting selection for shorter and more stable cultivars. Whole milling yield showed modest progress, demonstrating the greater complexity of achieving substantial improvement in this trait. Simulations allowed the comparison of different breeding frameworks over 20 generations, showing that scenarios with larger numbers of crosses and progeny sizes led to higher genetic gains, though requiring greater resource allocation. The integration of genomic selection with high-throughput phenotyping proved particularly promising, accelerating gains without critically compromising genetic diversity. These findings demonstrate that the LSU program achieved consistent improvements in productivity and that the balanced adoption of modern tools, together with simulation-based planning, is essential to sustain future progress and address global food security and agricultural sustainability challenges.