Key message: There is considerable genetic gain of tree volume from clonal deployment in Norway spruce (Picea abies(L.) H. Karst) and clonal deployment will have at least 50% more or double genetic gain than the seedling deployment. Context: Genetic parameters and genetic gains for wood quality and growth traits were estimated in six large clonal progeny trials. Aims: Develop the optimal clonal deployment strategy of Norway spruce in Sweden. Methods: Wood quality and growth traits were measured in all clonal trials and additive and non-additive genetic variances are partitioned. Results: Additive and non-additive genetic variances were equally important for growth traits while non-additive variance was small or not significant for wood quality trait. The genetic gain predicted for clonal deployment was greater than any of the other four deployment strategies. Selecting the top 1% of tested clones (clonal forestry) would have 48.4% and 134.6% more gain than the gain predicted for the seedling deployment of selected full-sib families and half-sib family (family forestry), respectively, at the same selection intensity. Conclusion: This study highlights that testing of 30–40 clones per family would maximize the realized genetic gain for different clonal selection scenarios, either selecting the best ten or 20 clones without any co-ancestry restrictions or selecting the best single clone from each of the best ten or 20 families (e.g., co-ancestry restriction). Clonal mean selection and vegetative deployment are the most effective.

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 evaluated. 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.

The summer drought of 2018 was one of the most climatically severe events in Europe that led to record-breaking temperatures and wildfires in many parts of Europe. The main objective of this study was to assess the impact of the 2018 drought on the phenotypic and genetic response of Norway spruce height growth using the Standardized Precipitation-Evapotranspiration Index (SPEI). To achieve this, the total cumulative height growth of about 6000 clones from 2016 to 2019 in four full-sib trials in Sweden, aged 6–7 years, and from 2017 to 2019 in two half-sib trials in Finland, aged 8–9 years, were measured. The results indicate that the 2018 drought caused reductions in the increment of trees. Although heritability estimates were similar to other reports for Norway spruce, the additive genetic variance was highly inflated in one of the visibly drought-damaged trials in Southern Sweden. Similarly, the genotype by environment (G × E) interaction was highly significant in the drought-damaged Southern Swedish trials. Both additive genetic and phenotypic correlations obtained between height increments in 2019 and final heights were the weakest in all studied trials, implying that the drought legacies might have influenced the recovery of trees in 2019. We may conclude that the severe drought can be an underlying factor for a strong G × E interaction and changes in the ranking of genotypes. Therefore, a selection of drought-resistant genotypes with a good growth capacity tested in variables sites should be considered as an important criterion for future breeding of Norway spruce.

Height is a key trait in the indices applied when selecting genotypes for use in both tree breeding populations and production populations in seed orchards. Thus, measurement of tree height is an important activity in the Swedish Norway spruce breeding program. However, traditional measurement techniques are time-consuming, expensive, and often involve work in bad weather, so automatization of the data acquisition would be beneficial. Possibilities for such automatization have been opened by advances in unmanned aerial vehicle (UAV) technology. Therefore, to test its applicability in breeding programs, images acquired by a consumer-level UAV (DJI Phantom 4 Pro V2.0) system were used to predict the height and breast height diameter of Norway spruce trees in a 12-year-old genetic field trial established with 2.0 × 2.0 m initial spacing. The tree heights were also measured in the field using an ultrasonic system. Three additive regression models with different numbers of predictor variables were used to estimate heights of individual trees. On stand level, the average height estimate derived from UAV data was 2% higher than the field-measured average. The estimation of family means was very accurate, but the genotype-level accuracy, which is crucial for selection in the Norway spruce breeding program, was not high enough. There was just ca. 60% matching of genotypes in groups selected using actual and estimated heights. In addition, heritability values calculated from the predicted values were underestimated and overestimated for height and diameter, respectively, with deviations from measurement-based estimates ranging between −19% and +12%. However, the use of more sophisticated UAV and camera equipment could significantly improve the results and enable automatic individual tree detection.