DEVELOPING OPTIMAL SELECTION SYSTEMS IN SUGARCANE BREEDING PROGRAMS

SELECTION REPRESENTS a costly and important part of sugarcane breeding programs. Previous research showed that cane yields in small single-row plots are affected strongly by competition effects, and that a high weighting in selection indices should be placed on CCS in small single-row plots to maximise gains for economic value. This led to a new selection system being suggested, involving initial screening of large numbers of clones in 5 m plots with heavy selection pressure for CCS followed by two stages of selection in multi-row plots. A stochastic simulation model using assumptions on relevant parameters (genetic, error, competition, and GE variances, genetic correlations) was developed to predict gains from alternative selection systems. Field trials were conducted in the Burdekin region to assess realised gains from alternative selection trial designs to validate and refine assumptions important in the model. The model was then used to predict genetic gains in selection systems with a wide range of configurations (e.g. plot size, replicate number, number of sites, selection criteria, selection intensity in each stage, and number of stages). Based on the results, it was recommended that three stages of clonal selection (following current family selection in stage 1 be performed in core breeding programs. This should involve firstly screening clones in small (1 row × 5 m) plots, with a selection index biased strongly toward CCS, but also including cane yield estimated via visual grade. Selected clones should then be evaluated in two further stages – the first one consisting of 4-row plots at four sites with a single replicate per clone per site. Clones selected from this stage should then be evaluated in 4-row plots at four sites but with two replicates per site. The recommended system has been introduced in the Burdekin selection system for further practical evaluation and it is recommended that it be assessed in other regions. The research conducted here also emphasised the importance of using optimal selection indices in single-row plots, in order to maximise gains from selection.