The early stages of cereal and pulse breeding programs typically involve in excess of 500 test lines. The test lines are promoted through a series of trials based on their performance (yield) and other desirable traits such as heat/drought tolerance, disease resistance, etc. It is therefore important to ensure the design (and analysis) of these trials are efficient in order to appropriately and accurately guide the breeders through their selection decisions, until only a small number of elite lines remain.
The design of early stage variety trials in Australia provided the motivation for developing a new design strategy. The preliminary stages of these programs have limited seed supply, which limits the number of trials and replicates of test lines that can be sown. Traditionally, completely balanced block designs or grid plot designs were sown at a small number of environments in order to select the highest performing lines for promotion to the later stages of the program. Given our understanding of variety (i.e. line) by environment interaction, this approach is not a sensible or optimal use of the limited resources available.
A new method to allow for a larger number of environments to be sampled for situations where seed supply is limited and number of test lines is large will be discussed. This strategy will be referred to as sparse phenotyping, which is developed within the linear mixed model framework as a model-based design approach to generating optimal trial designs for early stage selection experiments.