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Optimal designs in sugarcane breeding experiments

Grant number: 16/26003-4
Support type:Scholarships abroad - Research
Effective date (Start): August 01, 2017
Effective date (End): July 27, 2018
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics
Principal Investigator:Renata Alcarde Sermarini
Grantee:Renata Alcarde Sermarini
Host: Chris Brien
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Local de pesquisa : University of Adelaide, Australia  

Abstract

Sugarcane is an important crop to Brazilian and Australian's economy, both countries with the largest production. Improving this production is the objective of several plant-breeding programs, which at present, involves seven steps before getting a new variety. In the early stages of a plant-breeding program, many crosses are made, and many lines are developed, but usually the genetic material allows only few seeds of each and because of this the early-generation trials are often laid out in unreplicated designs. The first experiments used a check-plot design, e.g. a known variety, named check variety, was included each five or six plots, besides the test lines; however, they have a lower selection intensity. A commonly used design is the systematic arrangement of check-plots and the unreplicated test lines are added in the blocks, for example, without the use of randomization, this design being the most used in Brazil for several years. Many authors studied how to allocate the test lines to the plots and proposed different designs. Some authors suggested the augmented designs using known varieties and others not to include them in the trials, but evaluate only the test lines, allowing replication for part of them, characterizing the partially replicated (p-rep) designs, which has been considered an optimal design under some assumptions. In this context, the objective of this project is to investigate optimal designs for sugarcane experiments, including Brazilian and Australian ones, evaluating them under a wide range of models, spatial and genetic, and optimality criteria, like A-optimal, average prediction error variance and realized genetic gain. (AU)