Efficiency of using spatial analysis for Norway spruce progeny tests in Sweden
ANNALS OF FOREST SCIENCE 2018, 75 (2)
Chen ZQ, Helmersson A, Westin J, Karlsson B, Wu HX

Abstract
Key message: Spatial 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.
Context: Spatial 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 eva:luated.
Aims: This study aims to examine the effectiveness of spatial analysis in improving the accuracy of predicting breeding values and genetic gain.
Methods: Spatial 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.
Results: Ninety-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.
Conclusion: The spatial analysis approach is more efficient in Swedish Norway spruce trials than the conventional methods using models based on the experimental design.

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