It was reasoned that, incorporating reproductive technologies and genomic selection in the current dairy goats breeding programme would generate higher response to selection compared to use of natural mating in the current conventional breeding programme. This premise was tested by deterministic simulation approach and compared to the current breeding programme where natural mating and conventional breeding programme is used in the tropics. Two breeding schemes with three breeding strategies were simulated. The first scheme was conventional breeding scheme (CS) which represented the current dairy goat-breeding programme in the tropics. An alternative scheme was genomic breeding scheme (GS). Each scheme was evaluated with three mating strategies. They include; conventional scheme using natural mating (CNM), AI-Liquid semen (CLS) and AI-Frozen semen (CFS), and genomic scheme using natural mating (GNM), AI-Liquid semen (GLS) and AI-Frozen semen (GFS) strategies, with 5% of the total population in the nucleus and 95 % in the commercial. The current study found that CLS and GLS were superior compared to CFS and GFS, and CNM and GNM strategies in terms of annual genetic gain, returns and profit per doe per year. The CLS realised additional Kenya Shillings (KES) 3.00 and 3.61, 151.31 and KES 46.72, 148.89 and 47.81, in genetic gain, returns and profitability compared to CNM and CFS, respectively. The CLS incurred low costs KES 23.38 compared to CFS with KES 24.47 but higher than CNM with KES 20.96. On the other hand, CFS strategy has generated additional returns KES 104.59 and profit KES 101.08 but lower by KES 0.61 in genetic gain compared to CNM strategy. Implementation of genomic scheme has generated additional improvement across the three mating strategies in all the parameters measured compared to the conventional scheme. The GS realised additional KES 11.54, 10.80 and 10.21 in genetic gain, KES 161.30, 183.48 and 175.90 returns, and 136.30, 158.48 and 150.90 profit in the GNM, GLS and GFS, respectively, compared to those realised in CNM, CLS and CFS. Genetic gain increased with increased nucleus population size up to 15 % and thereafter, it declines in both CS and GS schemes with optimal nucleus size ranging between 14 % and 16 %. In conclusion, the current study demonstrated that adoption of reproductive technologies such as AI would optimize response to selection in dairy goat breeding programs in the tropics. The response to selection in such breeding programmes could be maximized in combination with genomic selection.

有理由认为，与当前常规育种计划中的自然交配相比，将繁殖技术和基因组选择纳入当前的奶山羊育种计划将对选择产生更高的响应。通过确定性模拟方法对该前提进行了测试，并将其与热带地区使用自然交配和常规育种计划的当前育种计划进行了比较。模拟了两种育种方案和三种育种策略。第一个计划是常规育种计划（CS），它代表了热带地区当前的奶山羊育种计划。另一种方案是基因组育种方案（GS）。每个方案都通过三种交配策略进行了评估。他们包括; 使用自然交配（CNM）的常规方案，AI液体精液（CLS）和AI冷冻精液（CFS），以及使用自然交配（GNM），AI液体精液（GLS）和AI冷冻精液（GFS）策略的基因组方案，占总人口的5％在细胞核中占95％，在商业领域中占95％。当前的研究发现，CLS和GLS在年度遗传增益，回报和每头母猪的利润方面均优于CFS和GFS以及CNM和GNM策略。与CNM和CFS相比，CLS分别在遗传收益，回报和获利能力上分别实现了3.00和3.61，肯尼亚151.31和肯尼亚先令46.72、148.89和47.81肯尼亚先令。与CFS的KES 24.47相比，CLS的成本低KES 23.38，但高于CNS的KES 20.96。另一方面，CFS策略产生了额外的收益104.59肯尼亚先令，利润为101.08肯尼亚先令，但降低了0肯尼亚先令。与CNM策略相比，遗传增益为61。与常规方案相比，基因组方案的实施在所有三种参数的交配策略上均产生了额外的改进。与CNM，CLS和CFS相比，GS在GNM，GLS和GFS上分别实现了11.54、10.80和10.21的KES遗传增益，161.30、183.48和175.90的KES回报，以及GN。，GLS和GFS的136.30、158.48和150.90利润。遗传增益随着核种群数量的增加而增加，最高可达15％，此后，在CS和GS方案中遗传增益均下降，最佳核大小在14％和16％之间。总之，当前的研究表明，采用诸如AI之类的生殖技术将优化热带地区奶山羊育种计划对选择的反应。