Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy r_A of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

通过采用基因组选择 (GS) 和表型组学 ( Ph )等新技术来提高当前饲草育种计划的效率，如果没有概念证明证明具有成本效益的遗传增益 (ΔG)，则具有挑战性。本文使用决策支持软件DeltaGen（战术工具）和QU-GENE（战略工具），对五种育种方法的相对效率进行建模和评估。整合 GS 和Ph对 ∆G 和成本 ($) 的影响研究了半同胞（HS）家族表型选择育种策略。确定性和随机建模分别使用 200 和 1000 个多年生黑麦草 HS 科的模拟数据集，分别使用按季节和地点的干物质 (DM) 产量数据和计算机生成的数据进行。结果表明育种策略和关键技术、GS 和博士的整合的短期（确定性）和长期（随机）影响, 在 ∆G 上。这些技术显着提高了 ∆G 的速率，在某些情况下还提高了成本效率。在 HS 系列 GS 中应用 1%，预测 200 HS 和 1000 HS 的 DM 产量每个周期分别为 6.35 和 8.10% ΔG。即使在0.12 的低 GS 精度 r _A下，GS 在 HS 选择之间和内部的应用都显着提高了年度总 ΔG 。尽管 ∆G 有所减少，但使用Ph来评估季节性 DM 产量，通过降低相对于标准 DM 削减的每百分比 ∆G 的成本，清楚地证明了其影响。开源软件工具 DeltaGen 和 QuLinePlus/QU-GENE 提供了在一系列育种场景下模拟育种方法和技术集成影响的方法。