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Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency. [ABSTRACT FROM AUTHOR]

Profitable goat production is explained by an increased ratio of outputs (meat, milk, flees and skin) per production inputs (labour, feed and management costs). The productive potential of goats depends on their productive efficiency, which is associated with their reproductive rates. Moreover, the reproductive efficiency of individual breeding females determines the production potential of the flock. This paper reviewed the major reproductive performances of Ethiopian indigenous goat populations reported by several scholars under different management conditions. The evidence from the literature indicated that the reproductive performances of the Ethiopian goats ranged from 6.61 to 14.83 months, 11.82 to 28.46 months, 5.47 to 12.07 months, 1.00 to 2.15 kids and 4.72 to 10.2 years for pubertal age of females, age at first kidding, kidding interval, litter size at birth, and does’ average productive life, respectively. This indicates that there is considerable variation within and among the Ethiopian indigenous goat populations regarding the aforementioned reproductive parameters. Similarly, some reproductive traits including age at first kidding, kidding interval and litter size at birth have shown potential differences between management conditions. Hence, reproductive performances of the Ethiopian indigenous goat populations can be optimized using improved management, and within and between breed selective breeding schemes.

Profitable goat production is explained by an increased ratio of outputs (meat, milk, flees and skin) per production inputs (labour, feed and management costs). The productive potential of goats depends on their productive efficiency, which is associated with their reproductive rates. Moreover, the reproductive efficiency of individual breeding females determines the production potential of the flock. This paper reviewed the major reproductive performances of Ethiopian indigenous goat populations reported by several scholars under different management conditions. The evidence from the literature indicated that the reproductive performances of the Ethiopian goats ranged from 6.61 to 14.83 months, 11.82 to 28.46 months, 5.47 to 12.07 months, 1.00 to 2.15 kids and 4.72 to 10.2 years for pubertal age of females, age at first kidding, kidding interval, litter size at birth, and does' average productive life, respectively. This indicates that there is considerable variation within and among the Ethiopian indigenous goat populations regarding the aforementioned reproductive parameters. Similarly, some reproductive traits including age at first kidding, kidding interval and litter size at birth have shown potential differences between management conditions. Hence, reproductive performances of the Ethiopian indigenous goat populations can be optimized using improved management, and within and between breed selective breeding schemes. [ABSTRACT FROM AUTHOR]

Objective To solve the problems of delayed growth and development and insufficient spawning of experimental Zebrafish, so as to improve the reproductive efficiency and service life of experimental Zebrafish.Methods The zebrafish at the age of 2 months after fertilization were divided into two groups. The experimental group was fed with dry commercial diets specifically designed for ornamental fish or frozen adult brine shrimp, while control group was fed with live laval brine shrimp. Within a period of 70 days, the growth performance of the zebrafish was evaluated by measuring body length and weight, and the reproductive performance was assessed by measuring the fecundity and spawning rate. Zebrafish with apparent goiter disease were fed with dry commercial diets, and the inhibitory effect of the pellets on this disease was evaluated by measuring the diameter of the thyroid enlargement lesion. The three feeding methods were combined, and the feeding plan was optimized. The actual effects of the plan on zebrafish rearing were validated through reproductive performance tests.Results Starting from 60 days post-fertilization (dpf) until 111 dpf, the body length and weight of the dry commercial diets feed group gradually surpassed those of control group (all P

Objective To solve the problems of delayed growth and development and insufficient spawning of experimental Zebrafish, so as to improve the reproductive efficiency and service life of experimental Zebrafish. Methods The zebrafish at the age of 2 months after fertilization were divided into two groups. The experimental group was fed with dry commercial diets specifically designed for ornamental fish or frozen adult brine shrimp, while control group was fed with live laval brine shrimp. Within a period of 70 days, the growth performance of the zebrafish was evaluated by measuring body length and weight, and the reproductive performance was assessed by measuring the fecundity and spawning rate. Zebrafish with apparent goiter disease were fed with dry commercial diets, and the inhibitory effect of the pellets on this disease was evaluated by measuring the diameter of the thyroid enlargement lesion. The three feeding methods were combined, and the feeding plan was optimized. The actual effects of the plan on zebrafish rearing were validated through reproductive performance tests. Results Starting from 60 days postfertilization (dpf) until 111 dpf, the body length and weight of the dry commercial diets feed group gradually surpassed those of control group (all P<0.000 1). From 60 dpf to 96 dpf, the growth trend in body length of the adult brine shrimp group was similar to that of control group, but the female fish in the adult brine shrimp group had significantly higher body weight than the female fish in control group at 75-82 dpf (P< 0.000 1). Compared to control group, there was a significant difference in body color between males and females in the adult brine shrimp group, and at 75 dpf, gender could be accurately distinguished by body color differences. Furthermore, the spawning rate of the zebrafish in the adult brine shrimp group at 3 months of age was significantly higher than that of control group (94.44% vs. 27.78%, P<0.05). Additionally, after feeding with the dry commercial diets for 130 days, all thyroid enlargement lesions in the experimental zebrafish disappeared. Based on the above results, the three feeding methods were combined and the feeding plan for zebrafish older than 2 months of age was optimized as follows: feed live brine shrimp in the morning, and alternate between dry commercial diets and adult brine shrimp in the afternoon. This feeding plan lasts until the age of 12 months. The spawning rate of Zebrafish can maintan 70%, and the spawning amount can reach (233.6±3.95) eggs. The fertilization rate and hatching rate were 97.47% and 90.24%, respectively, both significantly higher than those of control group (P<0.001, or P=0.01). Conclusion Compared to live brine shrimp feed, the dry commercial diets feed significantly improves the growth performance of zebrafish and has a therapeutic effect on thyroid enlargement disease. On the other hand, adult brine shrimp feed significantly enhances the early reproductive performance of zebrafish. The optimized feeding plan successfully improves the spawning efficiency of laboratory zebrafish, prolonging their reproductive lifespan and better supporting relevant scientific research. [ABSTRACT FROM AUTHOR]

In this study, breeding objective traits were identified, and alternative breeding schemes were simulated and evaluated for two goat populations. The traits were as follows: body size, twinning ability and kidding interval (KI) for Arab goats and body size, twinning ability and mothering ability for Oromo goats. The selection criteria were six-month weight (6mw, kg), litter size at birth (LSB), litter size at weaning (LSW) and KI (days). The schemes were as follows: (1) Scheme 1: 2 years of buck use and 10% selection proportion, (2) Scheme 2: 2 years of buck use and 15% selection proportion, (3) Scheme 3: 3 years of buck use and 10% selection proportion, and (4) Scheme 4: 3 years of buck use and 15% selection proportion. The predicted annual genetic gain (PAGG) for 6mw ranged from 0.29 to 0.32 kg for Arab goats while it varied from 0.34 to 0.38 kg for Oromo goats. On the contrary, the PAGGs for LSB and LSW for both populations were considerably small regardless of the different schemes. The economic return (Euro/doe) ranged from 0.99 to 1.15 for Arab goats and from 0.60 to 0.70 for Oromo goats. SCM2 is recommended over other schemes.

ABSTRACT: This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Introduction: Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation. Methods: Breeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraints, and 4 can be optimised. Two optimisation methods are used to optimise those parameters, Bayesian optimisation and random optimisation. Results: The results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms random optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints. Discussion: This study is one of the first to apply Bayesian optimisation to the design of breeding schemes while considering constraints. The presented approach has some limitations and should be considered as a first proof of concept that demonstrates the potential of Bayesian optimisation when applied to breeding schemes. Determining a general "rule of thumb" for breeding optimisation may be difficult and considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme. [ABSTRACT FROM AUTHOR]

ABSTRACT: We tested the hypothesis that the size of a beef cattle population destined for use on dairy females is smaller under optimum-contribution selection (OCS) than under truncation selection (TRS) at the same genetic gain (ΔG) and the same rate of inbreeding (ΔF). We used stochastic simulation to estimate true ΔG realized at a 0.005 ΔF in breeding schemes with OCS or TRS. The schemes for the beef cattle population also differed in the number of purebred offspring per dam and the total number of purebred offspring per generation. Dams of the next generation were exclusively selected among the one-year-old heifers. All dams were donors for embryo transfer and produced a maximum of 5 or 10 offspring. The total number of purebred offspring per generation was: 400, 800, 1,600 or 4,000 calves, and it was used as a measure of population size. Rate of inbreeding was predicted and controlled using pedigree relationships. Each OCS (TRS) scheme was simulated for 10 discrete generations and replicated 100 (200) times. The OCS scheme and the TRS scheme with a maximum of 10 offspring per dam required approximately 783 and 1,257 purebred offspring per generation to realize a true ΔG of €14 and a ΔF of 0.005 per generation. Schemes with a maximum of 5 offspring per dam required more purebred offspring per generation to realize a similar true ΔG and a similar ΔF. Our results show that OCS and multiple ovulation and embryo transfer act on selection intensity through different mechanisms to achieve fewer selection candidates and fewer selected sires and dams than under TRS at the same ΔG and a fixed ΔF. Therefore, we advocate the use of a breeding scheme with OCS and multiple ovulation and embryo transfer for beef cattle destined for use on dairy females because it is favorable both from an economic perspective and a carbon footprint perspective.

IntroductionAdvances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation.MethodsBreeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraints, and 4 can be optimised. Two optimisation methods are used to optimise those parameters, Bayesian optimisation and random optimisation.ResultsThe results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms random optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints.DiscussionThis study is one of the first to apply Bayesian optimisation to the design of breeding schemes while considering constraints. The presented approach has some limitations and should be considered as a first proof of concept that demonstrates the potential of Bayesian optimisation when applied to breeding schemes. Determining a general "rule of thumb" for breeding optimisation may be difficult and considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme.

In this study, breeding objective traits were identified, and alternative breeding schemes were simulated and evaluated for two goat populations. The traits were as follows: body size, twinning ability and kidding interval (KI) for Arab goats and body size, twinning ability and mothering ability for Oromo goats. The selection criteria were six-month weight (6mw, kg), litter size at birth (LSB), litter size at weaning (LSW) and KI (days). The schemes were as follows: (1) Scheme 1: 2 years of buck use and 10% selection proportion, (2) Scheme 2: 2 years of buck use and 15% selection proportion, (3) Scheme 3: 3 years of buck use and 10% selection proportion, and (4) Scheme 4: 3 years of buck use and 15% selection proportion. The predicted annual genetic gain (PAGG) for 6mw ranged from 0.29 to 0.32 kg for Arab goats while it varied from 0.34 to 0.38 kg for Oromo goats. On the contrary, the PAGGs for LSB and LSW for both populations were considerably small regardless of the different schemes. The economic return (Euro/doe) ranged from 0.99 to 1.15 for Arab goats and from 0.60 to 0.70 for Oromo goats. SCM2 is recommended over other schemes. [ABSTRACT FROM AUTHOR]

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future. [ABSTRACT FROM AUTHOR]

Optimizing the construction and update strategies for reference and candidate populations is the basis of the application of genomic selection (GS). In this study, we first simulated1200-purebred-pigs population that have been popular in China for 20 generations to study the effects of different population sizes and the relationship between individuals of the reference and candidate populations. The results showed that the accuracy was positively correlated with the size of the reference population within the same generation (r = 0.9366, p < 0.05), while was negatively correlated with the number of generation intervals between the reference and candidate populations (r = −0.9267, p < 0.01). When the reference population accumulated more than seven generations, the accuracy began to decline. We then simulated the population structure of 1200 purebred pigs for five generations and studied the effects of different heritabilities (0.1, 0.3, and 0.5), genotyping proportions (20, 30, and 50%), and sex ratios on the accuracy of the genomic estimate breeding value (GEBV) and genetic progress. The results showed that if the proportion of genotyping individuals accounts for 20% of the candidate population, the traits with different heritabilities can be genotyped according to the sex ratio of 1:1male to female. If the proportion is 30% and the traits are of low heritability (0.1), the sex ratio of 1:1 male to female is the best. If the traits are of medium or high heritability, the male-to-female ratio is 1:1, 1:2, or 2:1, which may achieve higher genetic progress. If the genotyping proportion is up to 50%, for low heritability traits (0.1), the proportion of sows from all genotyping individuals should not be less than 25%, and for the medium and high heritability traits, the optimal choice for the male-to-female ratio is 1:1, which may obtain the greatest genetic progress. This study provides a reference for determining a construction and update plan for the reference population of breeding pigs. [ABSTRACT FROM AUTHOR]

We tested the hypothesis that the size of a beef cattle population destined for use on dairy females is smaller under optimum-contribution selection (OCS) than under truncation selection (TRS) at the same genetic gain (ΔG) and the same rate of inbreeding (ΔF). We used stochastic simulation to estimate true ΔG realized at a 0.005 ΔF in breeding schemes with OCS or TRS. The schemes for the beef cattle population also differed in the number of purebred offspring per dam and the total number of purebred offspring per generation. Dams of the next generation were exclusively selected among the one-year-old heifers. All dams were donors for embryo transfer and produced a maximum of 5 or 10 offspring. The total number of purebred offspring per generation was: 400, 800, 1,600 or 4,000 calves, and it was used as a measure of population size. Rate of inbreeding was predicted and controlled using pedigree relationships. Each OCS (TRS) scheme was simulated for 10 discrete generations and replicated 100 (200) times. The OCS scheme and the TRS scheme with a maximum of 10 offspring per dam required approximately 783 and 1,257 purebred offspring per generation to realize a true ΔG of €14 and a ΔF of 0.005 per generation. Schemes with a maximum of 5 offspring per dam required more purebred offspring per generation to realize a similar true ΔG and a similar ΔF. Our results show that OCS and multiple ovulation and embryo transfer act on selection intensity through different mechanisms to achieve fewer selection candidates and fewer selected sires and dams than under TRS at the same ΔG and a fixed ΔF. Therefore, we advocate the use of a breeding scheme with OCS and multiple ovulation and embryo transfer for beef cattle destined for use on dairy females because it is favorable both from an economic perspective and a carbon footprint perspective. [ABSTRACT FROM AUTHOR]

Optimizing the construction and update strategies for reference and candidate populations is the basis of the application of genomic selection (GS). In this study, we first simulated1200-purebred-pigs population that have been popular in China for 20 generations to study the effects of different population sizes and the relationship between individuals of the reference and candidate populations. The results showed that the accuracy was positively correlated with the size of the reference population within the same generation (r = 0.9366, p < 0.05), while was negatively correlated with the number of generation intervals between the reference and candidate populations (r = −0.9267, p < 0.01). When the reference population accumulated more than seven generations, the accuracy began to decline. We then simulated the population structure of 1200 purebred pigs for five generations and studied the effects of different heritabilities (0.1, 0.3, and 0.5), genotyping proportions (20, 30, and 50%), and sex ratios on the accuracy of the genomic estimate breeding value (GEBV) and genetic progress. The results showed that if the proportion of genotyping individuals accounts for 20% of the candidate population, the traits with different heritabilities can be genotyped according to the sex ratio of 1:1male to female. If the proportion is 30% and the traits are of low heritability (0.1), the sex ratio of 1:1 male to female is the best. If the traits are of medium or high heritability, the male-to-female ratio is 1:1, 1:2, or 2:1, which may achieve higher genetic progress. If the genotyping proportion is up to 50%, for low heritability traits (0.1), the proportion of sows from all genotyping individuals should not be less than 25%, and for the medium and high heritability traits, the optimal choice for the male-to-female ratio is 1:1, which may obtain the greatest genetic progress. This study provides a reference for determining a construction and update plan for the reference population of breeding pigs.

• Genotyping purebred parents can imrpove genetic gain of organic pigs. • Using organic boars comes with opportunities and risks. • Organic pig breeders should carefully consider population size and rate of inbreeding when developing a breeding program for organic pigs. Optimizing organic pig breeding schemes depends on how to best allocate resources to phenotyping and genotyping. This study investigates how phenotyping and genotyping strategies affect genetic gain in a small organic pig population. We used stochastically simulated breeding schemes aimed at producing three-way crossbred finisher pigs. The simulated breeding schemes varied in the availability of phenotypes and genotypes for purebreds and crossbreds, the origin of the sire, and the genetic correlation between purebred and crossbred performance. For each simulated breeding scheme, genetic gain and rate of inbreeding were collected for up to 100 replicates. Phenotyping of crossbred individuals did not improve the genetic gain in the purebreds or the crossbreds. Genotyping purebred sows did improve genetic gain in both purebreds and crossbreds. Genetic gain and rate of inbreeding were maximized when internal sires were used, assuming strong GxE between organic and conventional production systems. Yet, breeding schemes that used internal sires had a larger variance in the genetic gain than those that used external sires. In conclusion, breeding schemes for organic pigs can be optimized by using internal sires and by genotyping the purebred parents. Yet, the rate of inbreeding and population size should be taken into careful consideration when developing an independent breeding program for a small (organic) population. [ABSTRACT FROM AUTHOR]

Formalized breeding schemes are a key component of breeding program design and a gateway to conducting plant breeding as a quantitative process. Unfortunately, breeding schemes are rarely defined, expressed in a quantifiable format, or stored in a database. Furthermore, the continuous review and improvement of breeding schemes is not routinely conducted in many breeding programs. Given the rapid development of novel breeding methodologies, it is important to adopt a philosophy of continuous improvement regarding breeding scheme design. Here, we discuss terms and definitions that are relevant to formalizing breeding pipelines, market segments and breeding schemes, and we present a software tool, Breeding Pipeline Manager, that can be used to formalize and continuously improve breeding schemes. In addition, we detail the use of continuous improvement methods and tools such as genetic simulation through a case study in the International Institute of Tropical Agriculture (IITA) Cassava east-Africa pipeline. We successfully deploy these tools and methods to optimize the program size as well as allocation of resources to the number of parents used, number of crosses made, and number of progeny produced. We propose a structured approach to improve breeding schemes which will help to sustain the rates of response to selection and help to deliver better products to farmers and consumers. [ABSTRACT FROM AUTHOR]

For breeding schemes based on a diversity of production systems, there is a growing challenge in combining standardised tools for the collective genetic improvement of a breed and the selection of a flock able to respond to the specific constraints of the farm and the farmer’s objectives. In order to progress on this question, it is necessary to gain better knowledge of how farmers who use one or several of these tools select the future breeding animals of their flocks, and the conditions under which they use these collective tools. We took the example of dairy sheep farming in Corsica (France) and of two collective tools for the Corsican breed’s management: the use of milk recording data within and outside of the breeding scheme and the buying of rams from the breeding scheme’s cooperative. The tools’ use and perception were described by means of interviews with farmers (n = 40). Cross-analysis of farms’ descriptors and the use of collective tools was performed by statistical analysis using available databases on dairy sheep flocks in Corsica. A diversity of breeding practices was associated to the use of milk recording data and to the purchase of rams sold by the breeding scheme’s cooperative. Flock size, milking method and type of land use influenced these breeding practices and the adoption or mistrust of a collective genetic improvement tool. Dissemination of genetic progress through rams from the breeding scheme is possible, provided that a diversity of breeding practices and production environments is represented in the nucleus flocks.Highlights Participating in the breeding scheme of the Corsican breed did not hinder the use of various replacement and culling practices in dairy sheep farms Modalities of use of the milk recording tool are multiple and driven by work organisation, perception of the breeding scheme and the farm project Rams sold by the breeding scheme’s cooperative are present in several commercial farms with varying characteristics

Formalized breeding schemes are a key component of breeding program design and a gateway to conducting plant breeding as a quantitative process. Unfortunately, breeding schemes are rarely defined, expressed in a quantifiable format, or stored in a database. Furthermore, the continuous review and improvement of breeding schemes is not routinely conducted in many breeding programs. Given the rapid development of novel breeding methodologies, it is important to adopt a philosophy of continuous improvement regarding breeding scheme design. Here, we discuss terms and definitions that are relevant to formalizing breeding pipelines, market segments and breeding schemes, and we present a software tool, Breeding Pipeline Manager, that can be used to formalize and continuously improve breeding schemes. In addition, we detail the use of continuous improvement methods and tools such as genetic simulation through a case study in the International Institute of Tropical Agriculture (IITA) Cassava east-Africa pipeline. We successfully deploy these tools and methods to optimize the program size as well as allocation of resources to the number of parents used, number of crosses made, and number of progeny produced. We propose a structured approach to improve breeding schemes which will help to sustain the rates of response to selection and help to deliver better products to farmers and consumers.

Anatomical adaptation is an important feature that allows plants to mitigate drought stress. A comparative leaf anatomy of two drought-tolerant sugarcane cultivars, KKU-1999-02 and KKU-1999-03, was studied in early drought stress between 30 and 90 days after planting using peeling and freehand sectioning methods. KKU-1999-02 and KKU-1999-03 showed different anatomical adaptation features, such as increase in cuticle thickness, bulliform cell size, vascular bundle, and stomatal density, and decreases in leaf thickness and stomatal size. KKU-1999-02 showed more remarkable anatomical changes than KKU-1999-03. The results provide important information that can be applied in combination with other agronomic traits in sugarcane breeding programs to expand the adaptation devices of tolerant cultivars under preliminary drought stress.

National and international across-population selection is often recommended and fairly common in the current breeding practice of dairy cattle, with the primary aims to increase genetic gain and genetic variability. The aim of this study was to test the hypothesis that the strategy of truncation selection of sires across populations [i.e., competitive gene flow strategy (CGF)] may not necessarily maximize genetic gain in the long term in the presence of genotype-by-environment interaction (G×E). Two alternative strategies used to be compared with CGF were forced gene flow (FGF) strategies, with 10 or 50% of domestic dams forced to be mated with foreign sires (FGF10%, FGF50%). Two equal-size populations (N dams = 1,000) that were selected for the same breeding goal trait (h2 = 0.3) under G×E correlation (r g) of either 0.9 or 0.8 were simulated to test these 3 different strategies. Each population first experienced either 5 or 20 differentiation generations (G d), then 15 migration generations. Discrete generations were simulated for simplicity. Each population performed a within-population conventional breeding program during differentiation generations and the 3 across-population sire selection strategies based on joint genomic prediction during migration generations. The 4 G d_ r g combinations defined 4 different levels of differentiation degree between the 2 populations at the start of migration. The true rate of inbreeding over the last 10 migration generations in each scenario was constrained at 0.01 to provide a fair basis for comparison of genetic gain across scenarios. Results showed that CGF maximized the genetic gain after 15 migration generations in 5_0.9 combination only, the case of the lowest differentiation degree, with a superiority of 0.4% (0.04 genetic SD units) over the suboptimal strategy. While in 5_0.8, 20_0.9, and 20_0.8 combinations, 2 FGF strategies had a superiority in genetic gain of 2.3 to 12.5% (0.21–1.07 genetic SD units) over CGF after 15 migration generations, especially FGF50%. The superiority of FGF strategies over CGF was that they alleviated inbreeding, introduced new genetic variance in the early migration period, and improved accuracy in the entire migration period. Therefore, we concluded that CGF does not necessarily maximize the genetic gain of across-population genomic breeding programs given moderate G×E. The across-population selection strategy remains to be optimized to maximize genetic gain. [ABSTRACT FROM AUTHOR]

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hamazaki and Iwata.)

For breeding schemes based on a diversity of production systems, there is a growing challenge in combining standardised tools for the collective genetic improvement of a breed and the selection of a flock able to respond to the specific constraints of the farm and the farmer's objectives. In order to progress on this question, it is necessary to gain better knowledge of how farmers who use one or several of these tools select the future breeding animals of their flocks, and the conditions under which they use these collective tools. We took the example of dairy sheep farming in Corsica (France) and of two collective tools for the Corsican breed's management: the use of milk recording data within and outside of the breeding scheme and the buying of rams from the breeding scheme's cooperative. The tools' use and perception were described by means of interviews with farmers (n = 40). Cross-analysis of farms' descriptors and the use of collective tools was performed by statistical analysis using available databases on dairy sheep flocks in Corsica. A diversity of breeding practices was associated to the use of milk recording data and to the purchase of rams sold by the breeding scheme's cooperative. Flock size, milking method and type of land use influenced these breeding practices and the adoption or mistrust of a collective genetic improvement tool. Dissemination of genetic progress through rams from the breeding scheme is possible, provided that a diversity of breeding practices and production environments is represented in the nucleus flocks. Participating in the breeding scheme of the Corsican breed did not hinder the use of various replacement and culling practices in dairy sheep farms Modalities of use of the milk recording tool are multiple and driven by work organisation, perception of the breeding scheme and the farm project Rams sold by the breeding scheme's cooperative are present in several commercial farms with varying characteristics [ABSTRACT FROM AUTHOR]

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions. [ABSTRACT FROM AUTHOR]

Grain phosphorus (P) and phytate contents are important for early growth and quality of wheat, respectively. We studied seed P and phytate contents of 57 wheat genotypes released during last 6 decades. The grain total P and phytate P concentrations ranged from 2.0 to 8.9 mg g−1 and 1.6 to 4.7 mg g−1, respectively. Grain P was higher in genotypes released in current or last decade than pre-green revolution period and it correlated well with grain phytate contents. Indicating that breeders worked only for increased grain P and did not consider the phytate contents. Twenty-five genotypes were selected based on differences in grain P and phytate contents to observe the P use efficiency under field conditions. Genotypes differed significantly for grain P and yield at both 0 and 60 kg ha−1 P levels. In conclusion, phytate fraction in grains of recent wheat genotypes was more as compared to old genotypes and genotype Inqlab-91 and Chakwal-86 had maximum grain total P. Moreover, number of productive tillers, spikelet per spike, grain weight, harvest index and grain P concentration and uptake were found responsible for P tolerance under P deficiency. [ABSTRACT FROM AUTHOR]

Agroecology uses ecological processes and local resources rather than chemical inputs to develop productive and resilient livestock and crop production systems. In this context, breeding innovations are necessary to obtain animals that are both productive and adapted to a broad range of local contexts and diversity of systems. Breeding strategies to promote agroecological systems are similar for different animal species. However, current practices differ regarding the breeding of ruminants, pigs and poultry. Ruminant breeding is still an open system where farmers continue to choose their own breeds and strategies. Conversely, pig and poultry breeding is more or less the exclusive domain of international breeding companies which supply farmers with hybrid animals. Innovations in breeding strategies must therefore be adapted to the different species. In developed countries, reorienting current breeding programmes seems to be more effective than developing programmes dedicated to agroecological systems that will struggle to be really effective because of the small size of the populations currently concerned by such systems. Particular attention needs to be paid to determining the respective usefulness of cross-breeding v. straight breeding strategies of well-adapted local breeds. While cross-breeding may offer some immediate benefits in terms of improving certain traits that enable the animals to adapt well to local environmental conditions, it may be difficult to sustain these benefits in the longer term and could also induce an important loss of genetic diversity if the initial pure-bred populations are no longer produced. As well as supporting the value of within-breed diversity, we must preserve between-breed diversity in order to maintain numerous options for adaptation to a variety of production environments and contexts. This may involve specific public policies to maintain and characterize local breeds (in terms of both phenotypes and genotypes), which could be used more effectively if they benefited from the scientific and technical resources currently available for more common breeds. Last but not least, public policies need to enable improved information concerning the genetic resources and breeding tools available for the agroecological management of livestock production systems, and facilitate its assimilation by farmers and farm technicians.

To improve genetic gain of breeding programs for village poultry production, breeding schemes with observations obtained in village production systems using individual (VIO) and group recording (VGO) were examined under different levels of genotype-by-environment-interactions (GxE). GxE was modeled by varying the correlation between traits measured in the breeding station and village environments for bodyweight (rg_BW) and egg production (rg_EP). Relative and absolute genetic gains obtained from VIO and VGO were used for comparison between the schemes. Results showed that village observations significantly improved genetic gains compared to the scheme without birds tested in the village. The improvement was only slightly larger with individual observations than with group observations. Higher rg_BW and rg_EP led to lower relative genetic gain, but a higher absolute gain of VIO and VGO. It is recommended to apply a breeding scheme using group recording of village performance when strong GxE in breeding for village poultry is expected. [ABSTRACT FROM AUTHOR]

Genomic Selection (GS) is a method in plant breeding to predict the genetic value of untested lines based on genome-wide marker data. The method has been widely explored with simulated data and also in real plant breeding programs. However, the optimal strategy and stage for implementation of GS in a plant-breeding program is still uncertain. The accuracy of GS has proven to be affected by the data used in the GS model, including size of the training population, relationships between individuals, marker density, and use of pedigree information. GS is commonly used to predict the additive genetic value of a line, whereas non-additive genetics are often disregarded. In this review, we provide a background knowledge on genomic prediction models used for GS and a view on important considerations concerning data used in these models. We compare within- and across-breeding cycle strategies for implementation of GS in cereal breeding and possibilities for using GS to select untested lines as parents. We further discuss the difference of estimating additive and non-additive genetic values and its usefulness to either select new parents, or new candidate varieties.

Alternative genomic selection and traditional BLUP breeding schemes were compared for the genetic improvement of feed efficiency in simulated Norwegian Red dairy cattle populations. The change in genetic gain over time and achievable selection accuracy were studied for milk yield and residual feed intake, as a measure of feed efficiency. When including feed efficiency in genomic BLUP schemes, it was possible to achieve high selection accuracies for genomic selection, and all genomic BLUP schemes gave better genetic gain for feed efficiency than BLUP using a pedigree relationship matrix. However, introducing a second trait in the breeding goal caused a reduction in the genetic gain for milk yield. When using contracted test herds with genotyped and feed efficiency recorded cows as a reference population, adding an additional 4,000 new heifers per year to the reference population gave accuracies that were comparable to a male reference population that used progeny testing with 250 daughters per sire. When the test herd consisted of 500 or 1,000 cows, lower genetic gain was found than using progeny test records to update the reference population. It was concluded that to improve difficult to record traits, the use of contracted test herds that had additional recording (e.g., measurements required to calculate feed efficiency) is a viable option, possibly through international collaborations. [ABSTRACT FROM AUTHOR]

We simulated a genomic selection pig breeding schemes containing nucleus and production herds to improve feed efficiency of production pigs that were cross-breed. Elite nucleus herds had access to high-quality feed, and production herds were fed low-quality feed. Feed efficiency in the nucleus herds had a heritability of 0.3 and 0.25 in the production herds. It was assumed the genetic relationships between feed efficiency in the nucleus and production were low ( rg = 0.2), medium ( rg = 0.5) and high ( rg = 0.8). In our alternative breeding schemes, different proportion of production animals were recorded for feed efficiency and genotyped with high-density panel of genetic markers. Genomic breeding value of the selection candidates for feed efficiency was estimated based on three different approaches. In one approach, genomic breeding value was estimated including nucleus animals in the reference population. In the second approach, the reference population was containing a mixture of nucleus and production animals. In the third approach, the reference population was only consisting of production herds. Using a mixture reference population, we generated 40-115% more genetic gain in the production environment as compared to only using nucleus reference population that were fed high-quality feed sources when the production animals were offspring of the nucleus animals. When the production animals were grand offspring of the nucleus animals, 43-104% more genetic gain was generated. Similarly, a higher genetic gain generated in the production environment when mixed reference population was used as compared to only using production animals. This was up to 19 and 14% when the production animals were offspring and grand offspring of nucleus animals, respectively. Therefore, in genomic selection pig breeding programmes, feed efficiency traits could be improved by properly designing the reference population. [ABSTRACT FROM AUTHOR]

Genetically heterogeneous materials of inbred crops, such as evolutionary populations (EPs) and line mixtures, could reduce the genetic erosion and increase the yield stability of crops. EPs may also be exploited for bulk-based selection of inbred lines as an alternative to single-seed descent (SSD)-derived lines. This study focused on pea to compare: (a) biparental EPs from three connected crosses and their combination, and their bulk-derived inbred lines and 6-line and 12-line static mixtures, for grain yield, yield stability, and reliability (which combines mean yield and yield stability); (b) bulk-based vs. SSD-based selection of inbred lines. Bulk-derived lines were mass-selected after four-year natural selection and underwent further multi-environment selection of elite lines and components of mixtures. EPs, selected bulk-derived lines, and static mixtures were evaluated across eight environments of Northern and Central Italy encompassing organic or conventional management and pure stand or intercropping with cereals. Bulk-based vs. SSD-based selection of inbred lines were compared across three environments. Farmers' acceptability and various adaptive traits were also recorded. We also assessed shifts for adaptive traits between EPs or derived lines for the target region and material selected for a non-target region featuring severe terminal drought. In a comparison based on best-performing material, a 6-line mixture exhibited 4–7 % greater yield or yield reliability, and EP material showed a modest penalty (actually nil in its area of initial development), compared with bulk-derived lines. Most of this material tended towards greater yield, yield stability and reliability (in a context of large genotype × environment interaction), competitive ability in intercropping and farmers' appreciation than the best-performing commercial cultivar. Bulk-derived lines displayed 24 % higher mean yield, 2.3–2.5 greater yield gain per selection cycle and greater farmers' acceptability than SSD-derived lines, and were the only lines that allowed for genetic progress over commercial cultivars. Their advantage was associated with greater tolerance to low winter temperatures and taller plant stature that favored their adaptation to intercropping or organic farming. The EPs selected in contrasting environments or their derived lines displayed rapid shifts in adaptation pattern and associated traits. Evolutionary breeding issued highly valuable populations, inbred lines and line mixtures. EPs displayed quick adaption to specific environments for a low selection cost. Our results have implications for small and large breeding programs, to increase the efficiency of phenotypic selection and favor the optimal integration of genomic selection. • Evolutionary breeding issued populations, inbred lines and line mixtures with high agronomic value. • This material tended to show high cold tolerance, yield stability and competitive ability. • Best-performing line mixtures, bulk-derived lines and evolutionary populations ranked in this order. • Evolutionary populations displayed quick adaption to specific environments for a low selection cost. • Bulk-derived, mass-selected lines allowed for much greater yield gain than single-seed descent lines. [ABSTRACT FROM AUTHOR]

Alternative Norwegian sheep breeding schemes were evaluated by stochastic simulation of a breeding population with about 120 000 ewes, considering the gain for an aggregate genotype including nine traits and also the rate of inbreeding. The schemes were: a scheme where both young unproven rams (test rams) and proven rams (elite rams) are used in artificial insemination (AI scheme), a scheme with test rams in natural mating in ram circles and elite rams (from one and a half years of age) in AI across all flocks in the country (NMAI2 scheme), a scheme where, in addition to testing rams, the youngest elite rams (one and a half years of age) are also used in natural mating in ram circles, while older elite rams are used in AI (NMAI1 scheme), and a scheme, acting as a control, where both test and elite rams are used in natural mating (NM scheme). Within the NMAI- and AI-schemes, experimentation was performed for percent ewes inseminated to elite rams v. test rams (EM%), numbers of ewes inseminated per elite ram (EAIn), and numbers of ewes mated per test ram by natural service (TNMn) or by AI (TAIn), respectively. With a restriction on the rate of inbreeding (⩽0.8% per generation), the AI scheme gave similar gain to the NMAI2 scheme (and about 40% more than did the NM scheme). Less gain was generated by the NMAI1 scheme, but it was still considerably more than for the NM scheme (about 25%). In the AI scheme, relatively few ewes (200/300) should be inseminated to each test/elite ram, and a low EM% should be chosen (10%). In the NMAI schemes, TNMn should be relatively high (40 to 50), combined with average and somewhat larger than average EAIn (NMAI2: 700 ewes, NMAI1: 900 ewes), and EM% medium (30%).

The developments in Norwegian sheep breeding since the early 1990s are reviewed. For the largest breeding population, the Norwegian White Sheep, results are presented for both genetic and phenotypic changes. Of the nine traits that make up the aggregate genotype, the largest gain per year, in per cent of the corresponding phenotypic average, was found for carcass grade (1.66%) and carcass weight (0.99%), number of lambs born at 1, 2 and 3 years of age (0.32% to 0.60%) and the maternal effect on weaning weight (0.26%). For fat grade, a genetic deterioration was estimated. This may be due to the too small weighting of this trait in the aggregate genotype and the true genetic parameters being somewhat different from the estimates in the prediction of breeding values. For lamb as well as ewe fleece weight, genetic change was close to zero – interpreted as mainly a correlated response to other traits in the aggregate genotype. Data for the two traits of fleece weight were, respectively, selected and few. Thus, phenotypic change was calculated for all traits except for fleece weight, and in addition for number of lambs at weaning, being indirectly selected for through number of lambs born. For all traits, with the exception of fat grade, advantageous phenotypic change was estimated. For weaning and carcass weight, the phenotypic change was less than the genetic change, while the opposite was observed for carcass and fat grade and number of lambs born. The latter traits can be more easily controlled by environmental actions, and the results thus exemplify the interdependency between environmental and genetic change.

Abstract According to the general approach developed in this paper, dynamic management of genetic variability in selected populations of dairy cattle is carried out for three simultaneous purposes: procreation of young bulls to be further progeny-tested, use of service bulls already selected and approval of recently progeny-tested bulls for use. At each step, the objective is to minimize the average pairwise relationship coefficient in the future population born from programmed matings and the existing population. As a common constraint, the average estimated breeding value of the new population, for a selection goal including many important traits, is set to a desired value. For the procreation of young bulls, breeding costs are additionally constrained. Optimization is fully analytical and directly considers matings. Corresponding algorithms are presented in detail. The efficiency of these procedures was tested on the current Norman population. Comparisons between optimized and real matings, clearly showed that optimization would have saved substantial genetic variability without reducing short-term genetic gains.

Stochastic simulation was used to compare the efficiency of 3 pig breeding schemes based on either traditional genetic evaluation or genomic evaluation. The simulated population contained 1,050 female and 50 male breeding animals. It was selected for 10 yr for a synthetic breeding goal that included 2 traits with equal economic weights and heritabilities of 0.2 or 0.4. The reference breeding scheme, named BLUP-AM, was based on the phenotyping of all candidates (13,770 animals/yr) for Trait 1 and of relatives from 10% of the litters (270 animals/yr) for Trait 2 and on BLUP-Animal Model genetic evaluations. Under the first alternative scenario, named GE-1TP, selection was based on genomic breeding values (GBV) estimated with one training population (TP) made up of candidate relatives phenotyped for both traits, with a size increasing from 1,000 to 3,430 over time. Under the second alternative scenario, named GE-2TP, the GBV for Trait 2 were estimated using a TP identical to that of GE-1TP, but the GBV for Trait 1 were estimated using a large TP made up of candidates that increased in number from 13,770 to 55,080 over time. Over the simulated period, both genomic breeding schemes generated 39 to 58% more accurate EBV for Trait 2 than the reference scheme, resulting in 78 to 128% (GE-1TP) and 63 to 84% (GE-2TP) greater average annual genetic trends for this trait. For Trait 1, GE-1TP was 18 to 24% less accurate than BLUP-AM, reducing average annual genetic trends by 27 to 44%. By contrast, GE-2TP generated 35 to 43% more accurate EBV and 8 to 22% greater average annual genetic trends for Trait 1 than the reference scheme. Consequently, GE-2TP was 27 to 33% more efficient in improving the global breeding goal than BLUP-AM whereas GE-1TP was globally as efficient as the reference scheme. Both genomic schemes reduced the inbreeding rate, the greatest decrease being observed for GE-2TP (-49 to -60% compared with BLUP-AM). In conclusion, genomic selection could substantially and durably improve the efficiency of pig breeding schemes in terms of reliability, genetic trends, and inbreeding rate without any need to modify their current structure. Even though it only generates a small TP, limited annual phenotyping capacity for traits currently only recorded on relatives would not be prohibitive. A large TP is, however, required to outperform the current schemes for traits recorded on the candidates in the latter. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare alternative designs for implementation of genomic selection to improve maternal traits in pigs, with a conventional breeding scheme and a progeny testing scheme. The comparison was done through stochastic simulation of a pig population. It was assumed that selection was performed based on a trait that could be measured on females after the first litter, with a heritability of 0.1. Genomic selection increased genetic gain and reduced the rate of inbreeding, compared with conventional selection without progeny testing. Progeny testing could also increase genetic gain and decrease the rate of inbreeding, but because of the increased generation interval, the increase in annual genetic gain was only 7%. When genomic selection was applied, genetic gain was increased by 23 to 91%, depending on which and how many animals were genotyped. Genotyping dams in addition to the male selection candidates gave increased accuracy of the genomic breeding values, increased genetic gain, and decreased rate of inbreeding. To genotype 2 or 3 males from each litter, in order to perform within-litter selection, increased genetic gain 8 to 12%, compared with schemes with the same number of genotyped females but only 1 male candidate per litter. Comparing schemes with the same total number of genotyped animals revealed that genotyping more females caused a greater increase in genetic gain than genotyping more males because greater accuracy of selection was more advantageous than increasing the number of male selection candidates. When more than 1 male per litter was genotyped, and thereby included as selection candidates, rate of inbreeding increased because of coselection of full sibs. The conclusion is that genomic selection can increase genetic gain for traits that are measured on females, which includes several traits with economic importance in maternal pig breeds. Genotyping females is essential to obtain a high accuracy of selection. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare genetic gain for a traditional aquaculture sib breeding scheme with breeding values based on phenotypic data (TBLUP) with a breeding scheme with genome-wide (GW) breeding values. Both breeding schemes were closed nuclei with discrete generations modeled by stochastic simulation. Optimum contribution selection was applied to restrict pedigree-based inbreeding to either 0.5 or 1% per generation. There were 1,000 selection candidates and a sib test group of either 4,000 or 8,000 fish. The number of selected dams and sires to create full sib families in each generation was determined from the optimum contribution selection method. True breeding values for a trait were simulated by summing the number of each QTL allele and the true effect of each of the 1,000 simulated QTL. Breeding values in TBLUP were predicted from phenotypic and pedigree information, whereas genomic breeding values were computed from genetic markers whose effects were estimated using a genomic BLUP model. In generation 5, genetic gain was 70 and 74% greater for the GW scheme than for the TBLUP scheme for inbreeding rates of 0.5 and 1%. The reduction in genetic variance was, however, greater for the GW scheme than for the TBLUP scheme due to fixation of some QTL. As expected, accuracy of selection increased with increasing heritability (e.g., from 0.77 with a heritability of 0.2 to 0.87 with a heritability of 0.6 for GW, and from 0.53 and 0.58 for TBLUP in generation 5 with sib information only). When the trait was measured on the selection candidate compared with only on sibs and the heritability was 0.4, accuracy increased from 0.55 to 0.69 for TBLUP and from 0.83 to 0.86 for GW. The number of selected sires to get the desired rate of inbreeding was in general less in GW than in TBLUP and was 33 for GW and 83 for TBLUP (rate of inbreeding 1% and heritability 0.4). With truncation selection, genetic gain for the scheme with GW breeding values was nearly twice as large as a scheme with traditional BLUP breeding values. The results indicate that the benefits of applying CW breeding values compared with TBLUP are reduced when contributions are optimized. In conclusion, genetic gain in aquaculture breeding schemes with optimized contributions can increase by as much as 81% by applying genome-wide breeding values compared with traditional BLUP breeding values. [ABSTRACT FROM AUTHOR]

Alternative Norwegian sheep breeding schemes were evaluated by stochastic simulation of a breeding population with about 120 000 ewes, considering the gain for an aggregate genotype including nine traits and also the rate of inbreeding. The schemes were: a scheme where both young unproven rams (test rams) and proven rams (elite rams) are used in artificial insemination (AI scheme), a scheme with test rams in natural mating in ram circles and elite rams (from one and a half years of age) in AI across all flocks in the country (NMAI2 scheme), a scheme where, in addition to testing rams, the youngest elite rams (one and a half years of age) are also used in natural mating in ram circles, while older elite rams are used in AI (NMAI1 scheme), and a scheme, acting as a control, where both test and elite rams are used in natural mating (NM scheme). Within the NMAI- and AI-schemes, experimentation was performed for percent ewes inseminated to elite rams v. test rams (EM%), numbers of ewes inseminated per elite ram (EAIn), and numbers of ewes mated per test ram by natural service (TNMn) or by AI (TAIn), respectively. With a restriction on the rate of inbreeding (⩽0.8% per generation), the AI scheme gave similar gain to the NMAI2 scheme (and about 40% more than did the NM scheme). Less gain was generated by the NMAI1 scheme, but it was still considerably more than for the NM scheme (about 25%). In the AI scheme, relatively few ewes (200/300) should be inseminated to each test/elite ram, and a low EM% should be chosen (10%). In the NMAI schemes, TNMn should be relatively high (40 to 50), combined with average and somewhat larger than average EAIn (NMAI2: 700 ewes, NMAI1: 900 ewes), and EM% medium (30%). [ABSTRACT FROM AUTHOR]

Abstract: A selection scheme for jumping sport horses is modelled with four stages of selection for males and one stage for females. The selection objective included three traits: conformation and gaits (CG, weighted 20%), competition jumping (CJ, weighted 60%) and a third trait (TT, weighted 20%) such as sperm quality or orthopaedic status. The first selection stage is based on knowledge of the pedigree with the aim of selecting horses suitable for CG test (at 3 years old) and CJ test (at 5 years old). The second stage includes the horse''s own performance with respect to CG and CJ with the aim of selecting horses suitable for the TT test. The third stage is the selection of a limited number of males who are allowed to reproduce. The fourth stage (at 12 years old) takes into account the results of the horse''s progeny. Females are selected in one step, whatever the number of performances measured at 5 years old. The annual genetic response was 9.4% genetic standard deviation of the objective, 2.6% for CG, 9.0% for CJ and 1.5% for TT. Results showed that selection by progeny testing did not contribute much to genetic response (12% of progeny issued from proven sires), the female pathway represented 26% of genetic response, TT was difficult to improve when the genetic correlation was unfavourable (−0.6% genetic standard deviation for −0.20 genetic correlation), and should consequently be directed towards the use of molecular markers. When compared with a selection scheme involving a station test, genetic response was the same if the breeding values used for selection before entering the station test took into account the results of the relatives for CJ and CG. This revealed the importance of an extensive performance test (like for competition performance) when designing breeding schemes for sport horses. [Copyright &y& Elsevier]

Abstract: The first In Vitro Produced (IVP) calf was born in 1981 and the non-surgical Ovum Pick Up (OPU) technique for the bovine was adapted from the human in 1987. Since then, considerable research has been aimed at improving both technologies in the bovine. Both OPU and IVP can now be seen as mature technologies. It can be estimated that more than 200,000 IVP calves have been born world wide to date, and when the two technologies are combined they are capable of producing over 50 calves per donor cow per year, albeit with a large variation between donors. Not many new breakthroughs are expected for OPU. For IVP however, automation and miniaturization as well as a greater understanding of the embryo through the application of gene based technologies such as micro-arrays, may provide an in vitro environment that is more in vivo-like than traditional micro drop/well systems. This improved environment should result in higher embryo developmental rates as well as improved quality and welfare of subsequent offspring. The application of OPU/IVP has progressed from treating infertile high genetic multiple ovulation and embryo transfer (MOET) cows in commercial situations to enhancing breeding scheme designs. With the bovine genome being rapidly sequenced and bovine genes for traits of economic interest becoming available in the coming years, OPU/IVP will prove invaluable in rapidly multiplying rare genes or Quantitative Trait Loci (QTL) of high value. In due course, it is anticipated that Marker Assisted Selection or Gene Assisted Selection (MAS/GAS) schemes will be more widely implemented. In addition, OPU, and particularly IVP, provide the basis for more advanced technologies such as cloning and transgenics. This paper is dedicated to celebrate and recognize the significant contributions made by Theo Kruip (1939–2003) to the wide area of bovine OPU and IVP. [Copyright &y& Elsevier]

The potential benefits of closed adult nucleus multiple ovulation and embryo transfer (MOET) and conventional progeny testing (CNS) schemes, and the logistics of their integration into large-scale continuous production of crossbred cattle were studied by deterministic simulation. The latter was based on F1 (Bos taurus x Bos indicus) production using AI or natural mating and MOET, and continuous F2 production by mating of F1 animals. The gene flow and the cumulative discounted expressions (CDES) were also calculated. Both schemes had 8, 16, 32, or 64 dams with 2, 4, 8, 16, or 32 sires selected. In the MOET nucleus scheme (MNS), the test capacity was 1, 2, 8, or 16 offspring, and the number of matings per dam per year was 1, 2, or 4. A scheme of 8 sires with 64 dams and a test capacity of 4 female offspring per dam per year resulted in an annual genetic gain (in phenotypic standard deviation) of 0.324 and 0.081 for MNS and CNS, respectively. In the MNS, there was substantial genetic gain with a relatively small number of animals compared with a CNS. The F1 had the highest, and the F2 scheme the lowest CDES. However, a very large number of B. indicus females would be required in the F1 scheme. This scheme may not be practical under conditions in developing countries. The F2 scheme was logistically attractive because it produces its own replacements, and the number of B. taurus females required would be easy to attain. Accompanying technical and financial constraints of nucleus schemes should be addressed before applying them. [ABSTRACT FROM AUTHOR]

Local breeding schemes for Holstein cattle of Costa Rica were compared with the current practice based on continuous semen importation (SI) by deterministic simulation. Comparison was made on the basis of genetic response and correlation between breeding goals. A local breeding goal was defined on the basis of prevailing production circumstances and compared against a typical breeding goal for an exporting country. Differences in genetic response were <3%, and the correlation between breeding goals was 0.99. Therefore, difference between breeding objectives proved negligible. For the evaluation of genetic response, the current scheme based on SI was evaluated against a progeny testing (PT) scheme and a closed nucleus (CN) breeding scheme, both local. Selection intensities and accuracy of selection were defined according to current population size and reproduction efficiency parameters. When genotype x environment interaction (G x E) was ignored, SI was the strategy with the highest genetic response: 5.0% above the CN breeding scheme and 33.2% above PT. A correlation between breeding values in both countries lower than one was assumed to assess the effect of G x E. This resulted in permanent effects on the relative efficiencies of breeding strategies because of the reduction in the rate of genetic response when SI was used. When the genetic correlation was assumed equal to 0.75, the genetic response achieved with SI was reduced at the same level as local PT. when an initial difference in average genetic merit of the populations was assumed, this only had a temporal effect on the relative ranking of strategies, which is reverted after some years of selection because the rate of change in genetic responses remains unchanged. Given that the actual levels of genetic correlation between countries may be around 0.60, it was concluded that a local breeding scheme based on a nucleus herd could provide better results than the current strategy based on SI. [ABSTRACT FROM AUTHOR]