Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual

Abstract

This study explores the use of a mixed linear model including spatially correlated residuals in addition to the traditional randomized complete block (RCB) analysis in 12 forest genetic trials. The analysis of early height data from progeny and clonal tests of three species (Picea sitchensis (Bong.) Carr., Pinus pinaster Ait., and Pinus radiata D. Don) showed that there was significant spatial variation in all trials. Adding a basic first-order separable autoregressive error term more effectively modelled the spatial variation than the RCB model and greatly reduced the block and plot variances. There was no evidence that extended spatial modelling was required. The spatial analysis greatly improved the accuracy of genetic value estimation in some trials and was accompanied by large changes in rank of the genetic entries and by greater gains in selection relative to the RCB analysis.

Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden

“…However, experimental studies with large number of trials and variables have shown that either an increase or decrease is possible for σ 2 A using spatial analyses. Our observations of increased additive variance in most cases and decreased variance in some cases are in line with those reports with many trials and variables (Costa e Silva et al 2001;Dutkowski et al 2002Dutkowski et al , 2006Ye and Jayawickrama 2008). There may several possibilities with decreased genetic variance in spatial model.…”

“…(2) A spatial model using an autoregressive spatial component and an independent error instead of just an independent error in base model (Costa e Silva et al 2001;Dutkowski et al 2002) as…”

“…Spatial analysis using first-order autocorrelation has been used as a common method to analyze forest genetic trials (Costa e Silva et al 2001; Costa e Silva and Kerr 2013; Dutkowski et al 2006Dutkowski et al , 2002Ye and Jayawickrama 2008). In Norway spruce, we have used spatial analyses as a first-stage analyses for individual sites, and adjusted data have been used for genotype-bysite (G×E) interaction study to dissect the patterns and causes of significant G×E (Chen et al 2017).…”

“…Such error variance is often significant but usually small, so the independent error is removed from the model (Cullis et al 1998;Gilmour et al 1997). In most forestry trials, it is considered that both an independent and an autoregressive error are necessary (Costa e Silva et al 2001;Dutkowski et al 2002;Kusnandar and Galwey 2000). If the model is fitted without the independent error, the additive genetic variance could be substantially inflated and the actual patterns of spatial variation could be obscured when real independent errors are ignored.…”

“…The relative genetic gain was estimated as 100 * (G S − G B )/G B , where G S and G B are the expected genetic response from selecting a proportion of individuals using the spatial and base models, respectively (Costa e Silva et al 2001). The selection proportion was set at the top 20% for parents and top 5% for offspring or genotypic values, based on estimated breeding values or clonal genotypic values.…”