Background Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. Conclusions Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.

Seed orchards represent the link between tree breeding and silvicultural activities. Their genetic efficiency is of vital importance as it determines the extent of genetic gain and diversity of future forest tree plantations. Given their importance, seed orchard genetics has received increased scientific and managerial scrutiny. Virtually all aspects affecting seed orchard genetic efficiency have been thoroughly investigated, including their biological model, underpinning assumptions and management practices developed and implemented to improve crop genetic quality. In this review, we systematically address these topics starting with the position of seed orchards in the tree improvement cycle, their population genetics model and the biological factors affecting this model; namely, reproductive investment and success, reproductive phenology, inbreeding, gene flow and finally the biology of the seed. Management practices are reviewed including those implemented during the establishment phase (orchard size, design, number of parents and their representation) and those implemented for enhanced crop management (crown management, supplemental mass pollination, bloom delay, selective seed harvesting and the production of designer crops). The genetic consequences of these issues are discussed. The intention of this article is to produce a state-of-art review of this vital component of every tree improvement delivery system and to facilitate and encourage further research and development for present and future seed orchards.