Abstract
Key messageSpatial analysis could improve the accuracy of genetic analyses, as well as increasing the accuracy of predicting breeding values and genetic gain for Norway spruce trials.ContextSpatial analysis has been increasingly used in genetic evaluation of field trials in tree species. However, the efficiency of spatial analysis relative to the analysis using the conventional experimental designs or pre- and post-blocking method in Swedish genetic trials has not been systematically evaluated.AimsThis study aims to examine the effectiveness of spatial analysis in improving the accuracy of predicting breeding values and genetic gain.MethodsSpatial analysis, using separable first-order autoregressive processes of residuals in rows and columns, was used in nine types of trait classes from 145 field trials of Norway spruce (Picea abies (L.) Karst.) in Sweden.ResultsNinety-six percent of variables (traits) were converged for the spatial model. Large trials with a large block variance tend to have a larger improvement from the model of experimental design to spatial model in accuracy. Growth and Pilodyn measurement traits showed greater improvements in log likelihood, accuracy, and genetic gain. Block variance was reduced by more than 80% for trait height and diameter using spatial analysis, indicating that it is more effective using both pre-blocking and post-blocking analyses in Swedish Norway spruce trials. The prediction accuracy for diameter and height for progeny breeding values showed an increase of 3.6 and 3.4%, respectively. The improvement of efficiency for growth traits is also related to the geographical location of test sites, tree age, number of survival trees, and the spacing of the trial.ConclusionThe spatial analysis approach is more efficient in Swedish Norway spruce trials than the conventional methods using models based on the experimental design.
Highlights
It is well known that progeny trials in forest tree breeding programs usually cover large areas due to the large field space needed for individual trees and the large size of the testing population
To make the comparison of genetic entries more precise and increase the accuracy of estimated genetic parameters and predicted breeding values, Randomized complete block (RCB) is further advanced into a new set of design called balanced incomplete block (BIB) which includes randomized incomplete block (RIB)
The spatial models converged for 434 variables from a total of 454 variables examined in the 145 trials
Summary
It is well known that progeny trials in forest tree breeding programs usually cover large areas due to the large field space needed for individual trees and the large size of the testing population (several dozens to a few hundred families with multiple individuals from each family). The large physical area needed for a progeny trial usually exhibits considerable variation in environmental conditions (Bian et al 2017; Dutkowski et al 2006; Chen et al 2017) To reduce such environmental heterogeneity, an experimental design subdividing the trial into blocks is usually used (Williams et al 2002). To make the comparison of genetic entries more precise and increase the accuracy of estimated genetic parameters and predicted breeding values, RCB is further advanced into a new set of design called balanced incomplete block (BIB) which includes randomized incomplete block (RIB) This achieves a reduction in the overall residual error by further removal of environmental error associated with row and/or column directions and among smaller incomplete blocks. Even with traditional RCB design, implementation of spatial analysis could still improve the model by detecting microsite variations within or between block variations, covering the type of changes in soil depth and nutrition that exist in most trials (Dutkowski et al 2006; Ye and Jayawickrama 2008)
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