Abstract
The aim of this study was to generate and evaluate the efficiency of improved field experiments while simultaneously accounting for spatial correlations and different levels of genetic relatedness using a mixed models framework for orthogonal and non-orthogonal designs. Optimality criteria and a search algorithm were implemented to generate randomized complete block (RCB), incomplete block (IB), augmented block (AB) and unequally replicated (UR) designs. Several conditions were evaluated including size of the experiment, levels of heritability, and optimality criteria. For RCB designs with half-sib or full-sib families, the optimization procedure yielded important improvements under the presence of mild to strong spatial correlation levels and relatively low heritability values. Also, for these designs, improvements in terms of overall design efficiency (ODE%) reached values of up to 8.7%, but these gains varied depending on the evaluated conditions. In general, for all evaluated designs, higher ODE% values were achieved from genetically unrelated individuals compared to experiments with half-sib and full-sib families. As expected, accuracy of prediction of genetic values improved as levels of heritability and spatial correlations increased. This study has demonstrated that important improvements in design efficiency and prediction accuracies can be achieved by optimizing how the levels of a treatment are assigned to the experimental units.
Highlights
Designing an experiment is an essential stage in any research settings
The evaluated conditions related to varying levels of heritability, genetic structure and spatial correlations for the randomized complete block (RCB) designs are shown in Table 1 and Figure 1
D-optimality criterion based on the IDE% and ODE% design efficiency measures
Summary
Important planning decisions are taken in order to choose the most appropriate layout out of an array of design alternatives. Generating experimental designs relies on three basic principles: randomization, replication and blocking [1,2]. Replication enables estimation of experimental error variance and, adequate number of replicates provides with precise inferences. Randomization ensures that all experimental units are likely to receive any treatment, it minimizes systematic errors or bias induced by the experimenter. Blocking controls for different sources of natural variation among experimental units, and when applied appropriately, controls for field variations and helps to reduce background noise. The generation of an optimal or near-optimal experimental design requires making best use of available information and resources with a goal of estimating statistical parameters of interest with the best accuracy and precision possible
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