Purebreeding of Red Maasai and crossbreeding with Dorper sheep in different environments in Kenya

The aim of this article was to study opportunities for improvement of the indigenous and threatened Red Maasai sheep (RM) in Kenya, by comparing purebreeding with crossbreeding with Dorper sheep (D) as a terminal breed, in two different environments (Env. A and a harsher Env. B), assuming different levels of genotype‐by‐environment interaction (G × E). Breeding goals differed between environments and breeds. Four scenarios of nucleus breeding schemes were stochastically simulated, with the nucleus in Env. A. Overall, results showed an increase in carcass weight produced per ewe by more than 10% over 15 years. Genetic gain in carcass weight was 0.17 genetic SD/year (0.2 kg/year) across scenarios for RM in the less harsh Env. A. For survival and milk yield, the gain was lower (0.04–0.05 genetic SD/year). With stronger G × E, the gain in the commercial tier for RM in the harsher Env. B became increasingly lower. Selection of females also within the commercial tier gave slightly higher genetic gain. The scenario with purebreeding of RM and a subnucleus in Env. B gave the highest total income and quantity of meat. However, quantity of meat in Env. A increased slightly from having crossbreeding with D, whereas that in Env. B decreased. A simple and well‐designed nucleus breeding programme would increase the genetic potential of RM. Crossbreeding of RM with D is not recommended for harsh environmental conditions due to the large breed differences expected in that environment.     

Genetic and economic analyses of female replacement rates in the dam-daughter pathway of a hierarchical swine breeding structure.

A stochastic life-cycle swine production model was used to study the effect of female replacement rates in the dam-daughter pathway for a tiered breeding structure on genetic change and returns to the breeder. Genetic, environmental, and economic parameters were used to simulate characteristics of individual pigs in a system producing F1 female replacements. Evaluated were maximum culling ages for nucleus and multiplier tier sows. System combinations included one- and five-parity alternatives for both levels and 10-parity options for the multiplier tier. Yearly changes and average phenotypic levels were computed for performance and economic measures. Generally, at the nucleus level, responses to 10 yr of selection for sow and pig performance in five-parity herds were 70 to 85% of response in one-parity herds. Similarly, the highest selection responses in multiplier herds were from systems with one-parity nucleus tiers. Responses in these were typically greater than 115% of the response for systems with the smallest yearly change, namely, the five-parity nucleus and five- and 10-parity multiplier levels. In contrast, the most profitable multiplier tiers (10-parity) had the lowest replacement costs. Within a multiplier culling strategy, rapid genetic change was desirable. Differences between systems that culled after five or 10 parities were smaller than differences between five- and one-parity multiplier options. To recover production costs, systems with the lowest returns required 140% of market hog value for gilts available to commercial tiers, whereas more economically efficient systems required no premium.     

Genotyping strategies of selection candidates in livestock breeding programmes

Benefits of genomic selection (GS) in livestock breeding operations are well known particularly where traits are sex‐limited, hard to measure, have a low heritability and/or measured later in life. Sheep and beef breeders have a higher cost:benefit ratio for GS compared to dairy. Therefore, strategies for genotyping selection candidates should be explored to maximize the economic benefit of GS. The aim of the paper was to investigate, via simulation, the additional genetic gain achieved by selecting proportions of male selection candidates to be genotyped via truncation selection. A two‐trait selection index was used that contained an easy and early‐in‐life measurement (such as post‐weaning weight) as well as a hard‐to‐measure trait (such as intra‐muscular fat). We also evaluated the optimal proportion of female selection candidates to be genotyped in breeding programmes using natural mating and/or artificial insemination (NatAI), multiple ovulation and embryo transfer (MOET) or juvenile in vitro fertilization and embryo transfer (JIVET). The final aim of the project was to investigate the total dollars spent to increase the genetic merit by one genetic standard deviation (SD) using GS and/or reproductive technologies. For NatAI and MOET breeding programmes, females were selected to have progeny by 2 years of age, while 1‐month‐old females were required for JIVET. Genomic testing the top 20% of male selection candidates achieved 80% of the maximum benefit from GS when selection of male candidates prior to genomic testing had an accuracy of 0.36, while 54% needed to be tested to get the same benefit when the prior selection accuracy was 0.11. To achieve 80% of the maximum benefit in female, selection required 66%, 47% and 56% of female selection candidates to be genotyped in NatAI, MOET and JIVET breeding programmes, respectively. While JIVET and MOET breeding programmes achieved the highest annual genetic gain, genotyping male selection candidates provides the most economical way to increase rates of genetic gain facilitated by genomic testing.     

畜禽基因组选择方法研究进展

畜禽的选种选育在生产中至关重要,育种值估计是选种选育的核心。基因组选择（genomic selection,GS）是利用全基因组范围内的高密度标记估计个体基因组育种值的一种新型分子育种方法,目前已在牛、猪、鸡等畜禽育种中得到应用并取得了良好的效果。该方法可实现畜禽育种早期选择,降低测定费用,缩短世代间隔,提高育种值估计准确性,加快遗传进展。基因组选择主要是通过参考群体中每个个体的表型性状信息和单核苷酸多态性（single nucleotide polymorphism,SNP）基因型估计出每个SNP的效应值,然后测定候选群体中每个个体的SNP基因型,计算候选个体的基因组育种值,根据基因组育种值的高低对候选群体进行合理的选择。随着基因分型技术快速发展和检测成本不断降低,以及基因组选择方法不断优化,基因组选择已成为畜禽选种选育的重要手段。作者对一些常用的基因组选择方法进行了综述,比较了不同方法之间的差异,分析了基因组选择存在的问题与挑战,并展望了其在畜禽育种中的应用前景。     

Research Progress on Genomic Selection Methods in Livestock and Poultry

Selection and breeding are very important in production of livestock and poultry,and breeding value estimation is the core of selection and breeding.Genomic selection (GS) is a novel molecular breeding method to estimate genomic breeding value using high-density markers across the whole genome.At present,GS has been successfully applied in cattle,pig,chicken and so on,and made significant progress.This method can achieve early selection,decrease the testing costs,shorten generation interval,improve the accuracy of breeding value estimation and accelerate genomic progress.GS estimates the effect of SNP by phenotype information and SNP genotype of each individual in the reference population,and measures the SNP genotype to calculate the genomic estimated breeding value in the candidate population,then selects the best individuals according to the genomic estimated breeding value.With the rapid development of genotyping technology and the decrease of detection cost,and the continuous optimization and high efficiency of genomic selection methods,genomic selection has become an important research method in the selection and breeding of livestock and poultry.The authors reviewed some of the widely used genomic selection methods,compared the differences between different methods,analyzed the problems and challenges of genomic selection,and looked forward to its application prospects in breeding.     

Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population‐wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome‐wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long‐term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree‐based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome‐wide selection holds promise for genetic improvement of pigs in the tropics.     

Evaluation of the efficiency of alternative two-tier nucleus breeding systems designed to improve meat sheep in Kenya

A deterministic approach was used to genetically and economically evaluate the efficiency of five two‐tier nucleus breeding systems for meat sheep in Kenya. The nucleus breeding systems differed in terms of whether the system was closed or open, in the type of animals that were involved in the movement of genetic superiority and in the number of selection pathways in each system. These systems were compared under four alternative breeding objectives based on monetary genetic gain and profit per ewe. The first objective simulated a situation where the flock size cannot be increased due to non‐feed related constraints (FLOCK). The second specifically assumed that the flock size is restricted due to limited amount of feed resources (FEED). The third and fourth objectives assumed that sheep performed only tangible roles (TR) and both tangible and intangible roles (IR) in the production system respectively. Monetary genetic gains were highest for all objectives in an open nucleus system with a certain proportion of commercial‐born ewes being introduced in the nucleus while at the same time utilizing young rams from the nucleus to breed sires and dams for the nucleus and commercial sector (ONyre). Utilizing young rams in a closed nucleus system for the dissemination of superior genes resulted in higher annual monetary genetic gain than utilization of old rams. Profit per ewe was significantly higher for FLOCK and IR in ONyre. In a closed system that allowed for downward movement of dams from the nucleus to the commercial sector to breed sires and dams, profit per ewe was highest for FEED and TR. The success of a nucleus breeding system should also focus on the profitability and logistics of establishing it. The implication of these results on the choice of two‐tier nucleus breeding systems for the improvement of meat sheep is discussed.     

Effect of selection and selective genotyping for creation of reference on bias and accuracy of genomic prediction

Reference populations for genomic selection usually involve selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In a simulation study, bias and accuracy of GEBV were explored for various genetic models with individuals selectively genotyped in a typical nucleus breeding program. We compared the performance of three existing methods, that is, Best Linear Unbiased Prediction of breeding values using pedigree‐based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single‐Step approach (SSGBLUP) using both. For a scenario with no‐selection and random mating (RR), prediction was unbiased. However, lower accuracy and bias were observed for scenarios with selection and random mating (SR) or selection and positive assortative mating (SA). As expected, bias disappeared when all individuals were genotyped and used in GBLUP. SSGBLUP showed higher accuracy compared to GBLUP, and bias of prediction was negligible with SR. However, PBLUP and SSGBLUP still showed bias in SA due to high inbreeding. SSGBLUP and PBLUP were unbiased provided that inbreeding was accounted for in the relationship matrices. Selective genotyping based on extreme phenotypic contrasts increased the prediction accuracy, but prediction was biased when using GBLUP. SSGBLUP could correct the biasedness while gaining higher accuracy than GBLUP. In a typical animal breeding program, where it is too expensive to genotype all animals, it would be appropriate to genotype phenotypically contrasting selection candidates and use a Single‐Step approach to obtain accurate and unbiased prediction of GEBV.     

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

Genomic selection (GS) is a statistical and breeding methodology designed to improve genetic gain. It has proven to be successful in animal breeding; however, key points of difference have not been fully considered in the transfer of GS from animal to plant breeding. In plant breeding, individuals (varieties) are typically evaluated across a number of locations in multiple years (environments) in formally designed comparative experiments, called multi‐environment trials (METs). The design structure of individual trials can be complex and needs to be modelled appropriately. Another key feature of MET data sets is the presence of variety by environment interaction (VEI), that is the differential response of varieties to a change in environment. In this paper, a single‐step factor analytic linear mixed model is developed for plant breeding MET data sets that incorporates molecular marker data, appropriately accommodates non‐genetic sources of variation within trials and models VEI. A recently developed set of selection tools, which are natural derivatives of factor analytic models, are used to facilitate GS for a motivating data set from an Australian plant breeding company. The power and versatility of these tools is demonstrated for the variety by environment and marker by environment effects.     

基因组选择及其在羊育种中的应用研究进展

数量性状是羊育种中的重要性状,受微效多基因控制、遗传力低,而传统育种方法难以提高羊的育种效率。提高动物育种效率对于选种选配工作和经济生产效益至关重要。随着育种新技术的不断革新与发展,基因组选择(genomic selection, GS)方法已成为育种技术中强大的工具,且已成功运用于个体经济价值较大的物种中,其具有缩短世代间隔、提高育种准确性、减少生产成本、提高畜禽经济效益等优势。近年来,由于基因组技术的不断成熟和各个统计模型的升级优化,以及高密度SNP芯片价格的下调,报告有关于基因组选择育种的实证和模拟研究层出不穷,且基因组选择技术已在羊育种中逐步开展,特别是在羊的重要性状中已有不少报道。由于羊的品种较多,地方性状差异化较大,个体经济价值略低,尽管基因组育种的新技术已经非常成熟,但目前仍没有在羊育种中大范围普及。为了更全面地了解该技术在羊育种中的研究现状,且基于选种选配的重要地位,作者就基因组选择在羊育种中的研究进展展开综述,主要从表型测定、基因分型、不同模型方面介绍了基因组选择在羊的重要性状中的应用和现状,讨论了其优势与挑战,并展望了基因组选择的未来发展方向。     

基因组选择在我国种猪育种中应用的探讨

种猪育种对我国养猪业起着极其重要的作用。基因组选择在我国猪育种生产中的应用水平尚不及欧美发达国家的种猪企业。完整的性能记录、高效的数据系统和资金投入的缺乏是制约基因组选择在我国猪育种生产中应用的重要因素。基因组选择能够增加不同性状遗传评估的育种值准确性,尤其是增加低遗传力性状的准确性。基因组选择在杂交优势、选配和多品种评估方面均具有应用优势。我国种猪企业需要进一步完善表型和性能数据的收集,制定长期的育种规划。通过区域性的联合评估和基因组选择技术的应用,加速群体的遗传进展,加速提升我国商品猪的生产性能。     

Prediction of response to marker-assisted and genomic selection using selection index theory

Selection index methods can be used for deterministic assessment of the potential benefit of including marker information in genetic improvement programmes using marker-assisted selection (MAS). By specifying estimates of breeding values derived from marker information (M-EBV) as a correlated trait with heritability equal to 1, it was demonstrated that marker information can be incorporated in standard software for selection index predictions of response and rates of inbreeding, which requires specifying phenotypic traits and their genetic parameters. Path coefficient methods were used to derive genetic and phenotypic correlations between M-EBV and the phenotypic data. Methods were extended to multi-trait selection and to the case when M-EBV are based on high-density marker genotype data, as in genomic selection. Methods were applied to several example scenarios, which confirmed previous results that MAS substantially increases response to selection but also demonstrated that MAS can result in substantial reductions in the rates of inbreeding. Although further validation by stochastic simulation is required, the developed methodology provides an easy means of deterministically evaluating the potential benefits of MAS and to optimize selection strategies with availability of marker data.     

Genotyping of groups of cows to improve genomic breeding values of new traits

Genotyping females and including them into the reference set for genomic predictions in dairy cattle is considered to provide gains in reliabilities of estimated breeding values for selection candidates. This should especially be true for low heritability traits. By the use of simulation, we extended a genomic reference set for an existing trait by including a fixed number of genotyped first‐crop daughters for one or two generations of reference sires. Moreover, we calculated results for the effects of a similar strategy in a situation where for a new trait the recording of phenotypes has recently started. For this case, we compared the effect of two different genotyping strategies: first, to phenotype cows but to genotype their sires only, and second, to collect phenotypes and genotypes on the same cows. We studied the effects on validation reliabilities and unbiasedness of predicted values for selection candidates. We found that by extending the reference set with genotyped daughters it is possible to increase the validation reliability of genomic breeding values. In the case of a new trait, it is always better to collect and use genotypes and phenotypes on the same animals instead of using only sire genotypes. We found that the benefits that can be achieved are sensitive to the sampling strategy used when selecting females for genotyping.     

Effects of Cage Location and Tier Level with Respect to Light Intensity in Semiconfined Housing on Egg Production and Quality During the Late Laying Period

This experiment was conducted to determine the effects of cage location and tier level with respect to light intensity on egg production and egg quality of hens housed in a semiconfined facility. Hens (ISA Brown, n = 225) at 75 wk of age were placed into 3-tier cages as top (T), middle (M), and bottom (B) tiers located in cages illuminated artificially (EI), by window (FW), or between corridors (C) for 2 mo. Light intensity was measured monthly for each cage at 5 cm from feeders every 6 h. Egg production was recorded daily and egg quality was assessed biweekly. Light intensity was the greatest for cages in the FW group (151.9, 119.8, and 89.8 lx for tiers T, M, and B, respectively), followed by EI (52.6, 54.5, and 51.0 lx for tiers T, M, and B, respectively), and C (44.5, 23.4, and 4.7 lx for tiers T, M, and B, respectively). Hens at location EI had greater egg production than hens at FW and C. Egg production for hens at tier T was also greater than for hens at tiers M and B. Egg production for hens at EI and C decreased quadratically, whereas that for hens at FW decreased linearly from tiers T to B. Cage location, but not tier level, affected egg weight. Hens at EI and FW produced heavier eggs than hens at C. Shape index, yolk color, and yolk index were independent of cage location and tier level. Hens at EI and FW produced eggs with thinner and weaker shells than hens at C. Moreover, eggshell strength increased linearly from tier T to B. Both albumen index and Haugh unit were the greatest for hens at FW, followed by EI and C. Their responses to cage location varied with tier levels. In conclusion, variation in light intensity in multitier cage systems in semiconfined laying hen houses may be a contributing factor for depressed laying performance and egg quality.     

基于不同密度SNP芯片在杜洛克公猪中的全基因组选择效果分析

基因组选择(GS)是近些年发展起来的一项新型育种技术,目前已在动植物育种实践中应用。本研究通过在1 068头杜洛克公猪群体中使用不同密度的SNP芯片进行全基因组选择效果比较分析。结果发现:使用基因型填充后芯片以及高密度SNP芯片所获得的估计基因组育种值(GEBV)之间可以达到99%的相关,并发现个体间亲缘关系的远近对同群体内基因型填充结果的准确率影响不大。由此可见,与目标性状紧密相关的低密度SNP芯片可用于实际育种工作,在降低使用成本的同时并不影响全基因组选择效果,为实质性进行猪分子育种提供了一条可行途径。     

大白猪基因组选择在不同应用策略下的比较研究

目前,基因组选择（genomic selection,GS）技术已经在种猪育种中开展,但为获得较高的收益,还需研究一些应用策略,如确定仔猪基因分型个体比例和早期仔猪留种比例。本试验选择温氏集团出生于2011—2016年的大白种猪作为研究对象,共有超过4.5万条的生长测定记录,超过7万条繁殖记录,和2 090个个体的简化基因组测序（GBS）数据,其中,出生于2016年7~12月的440个体作为候选群体。研究性状包括两个生长性状（校正100 kg日龄和校正100 kg背膘厚）和一个繁殖性状（总产仔数）。为对比预测效果,在候选群体进行育种值预测时,按照是否利用其基因型或表型信息分为4种预测方案,比较不同方案的预测可靠性和个体选择指数的排名情况。结果显示,在预测候选群育种值时,利用其表型或基因型信息均比不利用时的预测结果更加可靠。对生长性状终测前、后进行基因组选择指数计算,发现,终测后指数排名前30%的个体都位于终测前指数排名前60%内。若仔猪出生后仅选择常规BLUP预测指数排名前60%的个体,会导致有接近15%的具有优秀潜力的个体被遗漏。本研究建议,对所有新生健康仔猪都进行基因分型并计算基因组选择指数,然后对指数排名靠前60%的个体进行性能测定。     

Reducing inbreeding rates with a breeding circle: Theory and practice in Veluws Heideschaap

Breeding circles allow genetic management in closed populations without pedigrees. In a breeding circle, breeding is split over sub‐populations. Each sub‐population receives breeding males from a single sub‐population and supplies breeding males to one other sub‐population. Donor‐recipient combinations of sub‐populations remain the same over time. Here, we derive inbreeding levels both mathematically and by computer simulation and compare them to actual inbreeding rates derived from DNA information in a real sheep population. In Veluws Heideschaap, a breeding circle has been in operation for over 30 years. Mathematically, starting with inbreeding levels and kinships set to zero, inbreeding rates per generation (ΔF) initially were 0.29%–0.47% within flocks but later converged to 0.18% in all flocks. When, more realistically, inbreeding levels at the start were high and kinship between flocks low, inbreeding levels immediately dropped to the kinship levels between flocks and rates more gradually converged to 0.18%. In computer simulations with overlapping generations, inbreeding levels and rates followed the same pattern, but converged to a lower ΔF of 0.12%. ΔF was determined in the real population with a 12 K SNP chip in recent generations. ΔF in the real population was 0.29%, based on markers to 0.41% per generation based on heterozygosity levels. This is two to three times the theoretically derived values. These increased rates in the real population are probably due to selection and/or the presence of dominant rams siring a disproportionate number of offspring. When these were simulated, ΔF agreed better: 0.35% for selection, 0.38% for dominant rams and 0.67% for both together. The realized inbreeding rates are a warning that in a real population inbreeding rates in a breeding circle can be higher than theoretically expected due to selection and dominant rams. Without a breeding circle, however, inbreeding rates would have been even higher.     

Comparative Analysis of Different Implementation Strategies on Genomic Selection in Large White Pigs

At present, genomic selection (GS) has been applied in pigs breeding, but some implementation strategies, such as the determination of genotyping ratios or early selection rates for piglets, are required to obtain a higher benefit using this technology. The Large White pigs born from 2011 to 2016 at WENS Foodstuff Group Co.,Ltd were chose as the research objects, including more than 45 000 growth measurement records, more than 70 000 reproduction records and 2 090 individuals with genotyping-by-sequencing (GBS) data. The 440 individuals born from July to December in 2016 were used as the candidate individuals. The traits included two growth traits, age at 100 kg and backfat thickness at 100 kg, and one reproduction trait, number of total born. To compare the prediction effects, four prediction scenarios were designed according to including or ignoring the phenotypic or genotypic information of candidate individuals when predicting their breeding values. The predictive reliability of different scenarios and rankings of selection indices of individuals would be compared. The results showed that the results using the phenotypic and genotypic information was more reliable than ignoring them to predict the breeding values of candidate individuals. When genomic selection indices were calculated before and after performances testing for the growth traits, the individuals ranking in the top 30% of indices after testing were all found in the individuals ranking in the top 60% of indices before testing. If the piglets with the top 60% of traditional BLUP indices were only selected, around 15% of individuals with good genetic potentials would be omitted. This study suggests that all healthy piglets after birth are genotyped and their genomic selection indices are calculated, and then the individuals ranking in the top 60% of indices are chose to perform growth measurement.     

Breeding programs for smallholder sheep farming systems: I. Evaluation of alternative designs of breeding schemes

Village‐ and central nucleus‐based schemes were simulated and evaluated for their relative bio‐economic efficiencies, using Ethiopia's Menz sheep as example. The schemes were: village‐based 2‐tier (Scheme‐1) and 1‐tier (Scheme‐2) cooperative village breeding schemes, dispersed village‐based nuclei scheme (Scheme‐3), conventional 2‐tier central nucleus‐based scheme (Scheme‐4), and schemes linking a central nucleus and village multiplier nuclei with selection in central nucleus (Scheme‐5) or in both central and village nuclei (Scheme‐6). Among village‐based schemes, Scheme‐1 gave the highest genetic progress, while Scheme‐2 was economically the most efficient with genetic gain in the breeding objective of Birr 5.6 and a profit of Birr 37.2/ewe/year. The central nucleus schemes were more efficient than the village schemes. Scheme‐4 was the most efficient with genetic gain in the breeding objective of Birr 13.5 and a profit of Birr 71.2, but is operationally more difficult as it requires a very large central nucleus. The choice between village and central nucleus‐based schemes would depend on local conditions (availability of infrastructure, logistics and technical knowhow and support). Linking central nucleus with village‐based nuclei (Scheme‐6) would be a feasible option to overcome the operational difficulties of the conventional central nucleus scheme. If a village‐based breeding program is envisaged as should be the 1st step in most low‐input systems, then Scheme‐2 is the most efficient. To scale out to an entire Menz breed level, Scheme‐3 would be recommended.     

Cost-benefit evaluation of artificial insemination for genetic improvement of wool-producing sheep

SUMMARY The financial costs and benefits associated with the use of artificial insemination (AI) in commercial flocks are evaluated. Benefits are calculated in terms of net present values after summing the discounted value of benefits over 20 years. Two breeding strategies are evaluated. With the first, AI is used to produce flock ewes and wethers. The method is unlikely to be profitable unless high breeding value rams are available for AI programs with fresh semen. With the second, AI is used to produce home-bred rams, which in turn sire flock ewes and wethers. This approach is more likely to be profitable. The cost of AI per lamb weaned from laparoscopic AI programs is about $100. Benefits exceed this cost for rams of very high merit when wool prices are moderate or higher. Flock structure has a significant effect on the benefits. Flocks with low wether retention rates have benefits half that of flocks that retain most wethers to 6 years of age. AI with purchased semen also provides benefits to risk management for owners of commercial flocks who wish to breed their own replacement flock rams.