Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest

Abstract

Single-trial and across-trial spatial analyses using autoregressive error structures were conducted for growth traits based on 1,146 data sets from 275 Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] progeny trials in 45 first-generation breeding zones in the US Pacific Northwest. The breeding zones encompassed a wide range of latitude, longitude, and elevation. Efficiency of using spatial analysis in reducing variation due to site heterogeneity, estimating genetic parameters, and increasing prediction accuracy was compared among different experimental designs, traits, assessment ages, and tree spacings. More than 97% of the data sets showed significant model improvement with spatial analysis, and height showed more improvement than diameter or volume. Spatial analysis on average removed 14~34% of residual variance due to spatial heterogeneity, which resulted in an up to 20% increase in accuracy of breeding value prediction. The coefficient of variation decreased substantially due to spatial adjustment. Rank correlation between predicted gains before and after spatial analysis was about 0.96, and spatial analysis had little effect on the average predicted gain of the top 20% of parents. We did not observe substantial geographic trends in improvements due to spatial adjustment. Across-site spatial analysis had almost no effect on genotype-by-environment interaction but tended to increase among-trial heterogeneity of residual variance. Two different methods for across-trial spatial analysis were compared and discussed.