Spatial Dependence of Check Plot Yield and Local Control

Abstract

AbstractFor maximizing genetic gains, with given resources, plant breeders generally conduct large but un‐ or inadequately‐replicated field trials especially during early generation evaluations and population improvement programmes. Checks arc often used to improve the precision of test plot comparisons. Adjustments by moving or weighted means etc. arc, however, empirical or based on assumptions too difficult to verify. An alternative stochastically best linear unbiased optimal locally weighted method of spatial prediction of micro‐environmental heterogeneity indices of treatment plots was examined. Two checks used in an unreplicated evaluation of 1560 test lines of winter wheat expressed the micro‐environmental variation in similar ways. About 46, 38 and 16 percent of the total variation in yield was a simple row and column effects, spatially dependent, and random errors, respectively. Optimal weights derived to simulate the micro‐environmental heterogeneity indices were sensitive to the position of the test plot and thus provided a better local control. The model was validated, and prediction standard deviations of simulations computed.