Genomic selection (GS) is currently being used in the New Zealand radiata pine (Pinus radiata D. Don) breeding program to accelerate genetic gain. GS also has the potential to accelerate the deployment of genetic gain to the production forest through early selection. The increased rate of genetic gain in the breeding cycle will need to be transferred more quickly to realise that gain in the deployment population. GS selections will have lower accuracies than selections based on phenotypic data as currently practised; however, it is unknown how this will affect the genetic gain from GS-based deployment. Moreover, census size and turnover rate need to be optimised to cope with the influx of new marker-based selected material into a commercial orchard. We utilised a stochastic simulation approach to investigate these concepts, comparing three deployment scenarios: half-sib open-pollinated orchards (OP), full-sib control-pollinated orchards (CP) and clonal deployment through somatic embryogenesis. When accounting for time, genomic selection in OP, CP and clonal deployment pathways increased genetic gain by 9.5%, 15.9% and 44.6% respectively compared to phenotypic selection. The optimal orchard scenario would be genomic-selected control-pollination with a low census size (n = 40, males and females combined), low female turnover (5%) and a high male turnover (15–25%). This scheme balances high genetic gain with high seed yield while moderating the rate of inbreeding.

基因组选择（GS）目前正用于新西兰辐射松（Pinus radiataD. Don）加速遗传增益的育种计划。GS 还具有通过早期选择加速将遗传增益部署到生产林的潜力。育种周期中增加的遗传增益率需要更快地转移，才能在部署种群中实现这种增益。GS 选择的准确性将低于目前采用的基于表型数据的选择；然而，尚不清楚这将如何影响基于 GS 部署的遗传增益。此外，需要优化普查规模和周转率，以应对新的基于标记的精选材料涌入商业果园。我们利用随机模拟方法来研究这些概念，比较三种部署场景：半同胞开放授粉果园 (OP)、全同胞对照授粉果园（CP）和通过体细胞胚胎发生进行克隆部署。当考虑时间时，与表型选择相比，OP、CP 和克隆部署途径中的基因组选择分别使遗传增益增加了 9.5%、15.9% 和 44.6%。最佳果园方案是基因组选择的控制授粉，人口普查规模较小（n  = 40，男性和女性合计），女性流动率低（5%），男性流动率高（15-25%）。该方案平衡了高遗传增益和高种子产量，同时降低了近交率。