UNREPLICATED SPATIAL DESIGNS COMPARED USING OPTIMALITY CRITERIA

In field trials including large numbers of varieties, it is often impossible or impractical to replicate each variety. In these situations, the researcher may choose to use only one replicate of each test variety and to include a check variety every so often so that the spatial variability of the field may be determined. Five different check patterns were purposefully designed, each possessing distinct characteristics. The purpose of this study is to determine which spatial patterns for the check variety are better able to identify the spatial structure in a field and to rank the experimental varieties accurately. The problem was approached in two ways. First, the check patterns were compared using optimality criteria. Then, the patterns were applied to an actual field experiment, and the data collected was used to identify the spatial structure of variation in the field and to test for experimental variety differences. It is shown that the results from the optimality criteria were not necessarily comparable to what was actually observed in the field

UNREPLICATED VARIETY TRIALS: EFFECTS OF CHECK PLOT DENSITY AND FIXED VERSUS RANDOM TREATMENTS

Crop researchers performing germplasm screenings are often unable to replicate their plots due to scarcity of seed and the large numbers of genotypes being evaluated. The use of known check varieties is a common method of overcoming the difficulties associated with unreplicated trials. In this simulation, we explored the effect of check plot density on the effectiveness of the resulting analysis. We also explored the effect of analyzing treatments as random versus fixed. Our study considers ten different designs with check densities ranging from 5% of the plots to 50%. The designs and analyses were then compared on the basis of the correlation of the actual treatment effects with the following: observed yield, LSMEANs for treatments fixed, and BLUPs for treatments random. Finally, we observed the frequency with which the analysis ranked the top 10% of the treatments within the top 15% of the LSMEANs or BLUPs. It was found that the LSMEANs and BLUPs from the spatial analysis provide more accurate results than the observed Y-values. Also, if the treatments are analyzed as fixed and the LSMEANs are used as estimates, then there seems to be a certain point beyond which not much additional information is gained by adding more check plots. This plateau is reached near a check plot density of approximately 30%. Finally, the BLUPs seem to be a more accurate estimate of the true treatment effects than are the LSMEANs at the lower densities; in fact, the BLUPs perform relatively well even at check densities of only 5% or 10%