Virtual StatsPD@Waite meeting

Every month, the professional development meetings of statisticians and data scientists at Waite, known as StatsPD@Waite, bring together specialists in various aspects of data sciences in agriculture from Waite, Roseworthy and Adelaide.

Please join us for the next virtual StatsPD@Waite meeting where Renata Alcarde Sermarini from the University of São Paulo will present on her recent collaborative work with Chris Brien on p-rep designs.  

Please note that the StatsPD@Waite meetings are recorded. If you have a question to the speaker but would rather not be recorded, please send me your question via chat during the meeting and I will ask it on your behalf. 

Please email Beata Sznajder for details of the Zoom meeting.

Impact on genetic gain from using misspecified statistical models in generating p-rep designs for early generation plant-breeding experiments 

Renata Alcarde Sermarini (University of São Paulo)

We are concerned with the generation of designs for early generation, plant-breeding experiments that use limited experimental resources as efficiently as possible to maximize the realized genetic gain (RGG) resulting from the selection of lines. A number of authors have demonstrated that partially replicated (p-rep) designs for such experiments, in which the percentage of lines that are duplicated is p, are likely to be more efficient than grid-plot designs. Therefore, our aim is to obtain the most efficient p-rep design for an experiment using one of two distinctly different criteria and employing widely or readily available statistical software packages to search for an optimal design. However, this can be difficult because knowledge of the sources of variation and their magnitudes is required and is often unavailable. To overcome this impediment, a comprehensive simulation experiment was conducted to investigate whether designs that are robust to a wide range of experimental situations can be identified. Designs with p set to 20% and for different experimental situations are generated and the performance of each tested for 24 different variation scenarios. We concluded that for large experiments, the RGG obtained with various optimal designs is indeed not affected by the different variation scenarios and that resolved designs for fixed genetic effects should be generated for robustness. On the other hand, the design assumptions affect the RGG for small p-rep designs. Even so, an overall recommendation is made.