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This study was conducted to identify the selection criteria and husbandry practices of chicken producers in different agro-ecological zones of Northwest Ethiopia as input for designing a breeding program. The study employed a purposive selection of districts and peasant associations with high indigenous chicken potential. The study areas were stratified based on the major agro-ecologies (highland, midland, and lowland). A total of 360 households were included in the study, and data on chicken breeding practices, selection criteria, and reproductive performance were collected and analyzed using SPSS software. In all agro-ecologies, egg production was prioritized by chicken owners when choosing female chickens. For male chickens, plumage color (index = 0.27), appearance (index = 0.24), and growth rate (index = 0.23) were the main selection factors. Farmers kept chickens primarily to generate cash through the sale of eggs and live animals (male chickens). There was a significant difference (p < 0.01) among agro-ecologies in nutritional management and housing of chickens. Chicken flock composition showed a highly significant difference (p < 0.001) among agro-ecologies, except layers. Most of the farmers had their own cock born in the flock. Chicken owners found in all agro-ecologies were practicing culling unwanted chickens. All the reproductive performance traits have shown a highly significant (p < 0.001) difference among agro-ecologies. A relatively higher inbreeding coefficient (0.18) was obtained in the highland agro-ecology compared to midland (0.16) and lowland (0.12). The study highlighted the importance of designing breeding programs that align with farmers' production objectives and trait preferences based on specific agro-ecologies for sustainable increases in chicken productivity.

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The premise was tested that the additional genetic gain was achieved in the overall breeding objective in a pig breeding program using genomic selection (GS) compared to a conventional breeding program, however, some traits achieved larger gain than other traits. GS scenarios based on different reference population sizes were evaluated. The scenarios were compared using a deterministic simulation model to predict genetic gain in scenarios with and without using genomic information as an additional information source. All scenarios were compared based on selection accuracy and predicted genetic gain per round of selection for objective traits in both sire and dam lines. The results showed that GS scenarios increased overall response in the breeding objectives by 9% to 56% and 3.5% to 27% in the dam and sire lines, respectively. The difference in response resulted from differences in the size of the reference population. Although all traits achieved higher selection accuracy in GS, traits with limited phenotypic information at the time of selection or with low heritability, such as sow longevity, number of piglets born alive, pre- and post-weaning survival, as well as meat and carcass quality traits achieved the largest additional response. This additional response came at the expense of smaller responses for traits that are easy to measure, such as back fat and average daily gain in GS compared to the conventional breeding program. Sow longevity and drip loss percentage did not change in a favourable direction in GS with a reference population of 500 pigs. With a reference population of 1000 pigs or onwards, sow longevity and drip loss percentage began to change in a favourable direction. Despite the smaller responses for average daily gain and back fat thickness in GS, the overall breeding objective achieved additional gain in GS.

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The study aims at assessing the production systems and identifying the traits preferred by farmers in the study areas to establish community-based breeding and sustainable utilization programs. The study was conducted in the four purposively selected districts of North Shoa and South Wello zones of Amhara region (Merehabete, Wereillu, Moretena Jiru and Mojaena Wedera). A total of 120(30 farmers from each district) purposively selected farmers participated in their own flock ranking experiment. Furthermore, 216 (i.e., 54 farmers from each district) were interviewed about their goat production systems and trait preferences to be improved. The chi-square test was used to analyze qualitative data, and analysis of variance was used to analyze quantitative data generated by the survey. For the rank data, indexes were calculated using Microsoft Excel 2010. Income was the main objective of goat rearing in all the study areas. Doe has a larger proportion than other flock structures in all the study areas. Mostly, the age of castration was above one year in all the study areas. The breeding buck selection criteria were growth, shape, size, and color. Body size, litter size, and mothering ability were identified as important traits in all study areas. Early selling of fast-buck kids is practiced in all study areas for their immediate cash. This results in the use of poor-performing bucks for breeding and may result in negative selection. To address such issues, it is critical to create and implement a community-based breeding program that takes into account body size, litter size, mothering capacity, puberty age, and longevity.

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Including resilience in the breeding objective of dairy cattle is gaining increasing attention, primarily as anticipated challenges to production systems, such as climate change, may make some perturbations more difficult to moderate at the farm level. Consequently, the underlying biological mechanisms by which resilience is achieved are likely to become an important part of the system itself, increasing value on the animal's ability to be unperturbed by variable production circumstances, or to quickly return to pre-perturbed levels of productivity and health. However, because the value of improving genetic traits to a system is usually based on known profit functions or bioeconomic models linked to current production conditions, it can be difficult to define longer-term value, especially under uncertain future production circumstances and where nonmonetary values may be progressively more important. We present the novel application of a discrete choice experiment, used to investigate potential antagonisms in the values of genetic improvements for 8 traits to dairy cattle system stakeholders in Europe when the production goal was either efficiency or resilience. A latent class model was used to identify heterogeneous preferences within each production goal, and postestimation was used to identify associations between these preferences and sociodemographic characteristics of respondents. Results suggested 3 distinct latent preference classes for each production goal. For the efficiency goal, yield and feed efficiency traits were generally highly valued, whereas for the resilience goal, health and robustness traits were generally highly valued. In both cases, these traits generally carried a low value in the other production scenario. Overall, in both scenarios, longevity was highly valued; however, the value of this trait in terms of resilience will depend on phenotyping across diverse environments to sufficiently capture performance under various anticipated system challenges. Additionally, results showed significant associations between membership of latent preference classes with education level and profession. In conclusion, as resilience becomes increasingly important, it is likely that a continued reliance on the short-term economic value of traits alone will lead decision makers to misrepresent the importance of some traits, including those with substantial contextual values in terms of resilience.

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With an increased implementation of chain speed measures of lean weight in lamb processing plants there is the potential for increased feedback of measures for carcase composition to the breeder. The following studies used computed tomography scans of 3167 Merino and Merino cross lamb carcases to determine the importance of capturing variation in carcase, fore, saddle and hind section lean to the processor and the breeder. Sectional measures of lean weight will provide processors with improved precision around the decision making associated with carcase processing and endpoint usage. Genetic correlations between lean (adjusted for carcase weight) within carcase sections ranged from 0.47 to 0.63. Whilst the correlations are moderate, current differences in the value of lean across the carcase does not make it viable for sheep breeders to target disproportional gains in lean across carcase sections. Instead, sheep breeders should continue to target overall carcase lean as part of their breeding objective.

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Infestation with Varroa destructor is a serious cause of bee colony (Apis mellifera) losses on a global level. However, the presence of untreated survivor populations in many different regions supports the idea that selection for resistance can be successful. As colony survival is difficult or impossible to measure, differences in mite infestation levels and tests for specific behavioral traits are used for selective breeding for Varroa resistance. In this paper we looked into different definitions of mite infestation and linked these with brood hygiene (pin test), brood recapping and suppressed mite reproduction. We based our analyses on datasets of Apis mellifera carnica from three countries: Austria (147 records), Croatia (135) and Germany (207). We concluded that bee infestation in summer, adjusted for the level of natural mite fall in spring, is a suitable trait in the breeding objective, and also suggested including brood infestation rate and the increase rate of bee infestation in summer. Repeatability for bee infestation rate was about 0.55, for cells opened in pin test about 0.33, for recapping 0.35 and for suppressed mite reproduction (SMR) virtually zero. Although in most cases we observed correlations with the expected sign between infestation parameters and behavioral traits, the values were generally low (<0.2) and often not significantly different from zero.

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Large-scale cultivation of medicinal plants is the most rapid and effective means of addressing the disparity between the supply and demand of medicinal plants. To achieve this scale of production, breeding studies are necessary for further development of medicinal plant cultivation. Although advances have been made in the breeding of some medicinal plants, a number of challenges remain, owing to the particularity and complexity in determining the breeding target. Additionally, there are limitations associated with research on traditional and modern breeding methods for medicinal plants. In this review, we summarize and analyze the selection strategies for breeding direction and breeding models, and emphasize the importance of breeding research in promoting the breeding of medicinal plants.

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A method is described for deriving the genetic matrix G needed for breeders to select across breeds for any breeding objective. The matrix comprises partitions among selection criteria (G11 ), between selection criteria and the breeding objective traits (G12 ), and among the breeding objective traits (G22 ). A weighting procedure is used to combine the G matrix of individual breeds with known breed differences. Between-breed variances are added to the diagonals of the matrix. Selection across many breeds can be quite different from that across a subset of the breeds. The particular breeds, and the numbers of each breed, that the breeder is prepared to consider in selection changes the genetic matrix and the selection index that should be used.

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BACKGROUND: Sheep production is a major component of the livestock sector in Ethiopia. The country owing to the large population of 30.70 million estimated numbers of sheep in the country and out of which about 72.14% are females, and 27.86% are males with diverse genetic resources. The real value of indigenous breeds was often under-estimated mostly due to their poor appearance and relatively low productivity. Developing countries in most cases opt for exotic breeds to increase animal productivity through crossbreeding or breed substitution without properly investigating the production potential of the indigenous breeds. The main objective of the research was to identify sheep flock composition and structure, farmers' traits of interest and breeding objective of Wollo highland sheep, and their F1 crossbreed progenies. RESULTS: Smallholder farmers' flock synthesized from breeding ewes, breeding rams, pre-weaned ewe lambs, pre-weaned ram lambs, unproductive ewes, castrated and fattened rams, with the percentage coverage of 29.2, 13.3, 15.5, 16.5, 12.4, and 12.5%, respectively. The maximum number of flock size was 289.0 sheep per flock and higher in the third stratum. The off-take rate percentage of the three strata presented as 21.9% in 1st stratum, 12% in the 2nd stratum, and 16.4% in the 3rd stratum and higher off-take rate recorded in the first stratum. Sheep producer's traits of interest ranked by growth rate (first), body size (second) and marketing value was third rank. Communal breeding (random mating), village based controlled breeding, mixed type and private ram controlled breeding practice were comprised of 39.7, 61.7, 52 and 71.3%, respectively. The percentages of ewes per flock composition were presented as 36.5, 27.1 and 25.5%, respectively in the 3rd stratum, 2nd stratum and 3rd stratum in the order of their importance's. CONCLUSION: Genetic improvement practices at smallholder sheep producers situation was showing promising outcome with indigenous Washera F1 crossbred lambs and which designated for weaning rate, body size, marketing age, age at first lambing, good temperament and large litter size in the order of their rank. The contemporary breeding practice tendency indicated that, reduced flock size to improve flock productivity via crossbreeding practices.

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Genomic selection has led to opportunities for developing new breeding values that rely on phenotypes in dedicated reference populations of genotyped cows. In Australia, it has been applied to 2 novel traits: feed efficiency, which was released in 2015 as feed saved breeding values, and heat tolerance genomic breeding values, released for the first time in 2017. Feed saved is already included in the national breeding objective, which is focused on profitability and designed to be in line with farmer preferences. Our future focus is on traits associated with animal health, either directly or in combination with predictor traits, such as mid-infrared spectral data and, into the future, automated data capture. Although it is common for many evaluated traits to have genomic reliabilities ranging between 60 and 75%, many new, genomic information-only traits are likely to have reliabilities of less than 50%. Pooling of phenotype data internationally and investing in maintenance of reference populations is one option to increase the reliability of these traits; the other is to apply improved genomic prediction methods. For example, advances in the use of sequence data, in addition to gene expression studies, can lead to improved persistence of genomic breeding values across breeds and generations and potentially lead to greater reliabilities. Lower genomic reliabilities of novel traits could reduce the overall index reliability. However, provided these traits contribute to the overall breeding objective (e.g., profit), they are worth including. Bull selection tools and personalized genetic trends are already available, but increased access to economic and automatic capture farm data may see even better use of data to improve farm management and selection decisions.

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To optimize breeding objectives of Fleckvieh and Brown Swiss cattle, economic values were re-estimated using updated prices, costs, and population parameters. Subsequently, the expected selection responses for the total merit index (TMI) were calculated using previous and newly derived economic values. The responses were compared for alternative scenarios that consider breeders' preferences. A dairy herd with milk production, bull fattening, and rearing of replacement stock was modeled. The economic value of a trait was derived by calculating the difference in herd profit before and after genetic improvement. Economic values for each trait were derived while keeping all other traits constant. The traits considered were dairy, beef, and fitness traits, the latter including direct health traits. The calculation of the TMI and the expected selection responses was done using selection index methodology with estimated breeding values instead of phenotypic deviations. For the scenario representing the situation up to 2016, all traits included in the TMI were considered with their respective economic values before the update. Selection response was also calculated for newly derived economic values and some alternative scenarios, including the new trait vitality index (subindex comprising stillbirth and rearing losses). For Fleckvieh, the relative economic value for the trait groups milk, beef, and fitness were 38, 16, and 46%, respectively, up to 2016, and 39, 13, and 48%, respectively, for the newly derived economic values. Approximately the same selection response may be expected for the milk trait group, whereas the new weightings resulted in a substantially decreased response in beef traits. Within the fitness block, all traits, with the exception of fertility, showed a positive selection response. For Brown Swiss, the relative economic values for the main trait groups milk, beef, and fitness were 48, 5, and 47% before 2016, respectively, whereas for the newly derived scenario they were 40, 14, and 39%. For both Brown Swiss and Fleckvieh, the fertility complex was expected to further deteriorate, whereas all other expected selection responses for fitness traits were positive. Several additional and alternative scenarios were calculated as a basis for discussion with breeders. A decision was made to implement TMI with relative economic values for milk, beef, and fitness with 38, 18, and 44% for Fleckvieh and 50, 5, and 45% for Brown Swiss, respectively. In both breeds, no positive expected selection response was predicted for fertility, although this trait complex received a markedly higher weight than that derived economically. An even higher weight for fertility could not be agreed on due to the effect on selection response of other traits. Hence, breeders decided to direct more attention toward the preselection of bulls with regard to fertility.

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Giving consideration to farmers' preferences for improvements in animal traits when designing genetic selection tools such as selection indexes might increase the uptake of these tools. The increase in use of genetic selection tools will, in turn, assist in the realization of genetic gain in breeding programs. However, the determination of farmers' preferences is not trivial because of its large heterogeneity. The aim of this study was to quantify Australian dairy farmers' preferences for cow trait improvements to inform and ultimately direct the choice of traits and selection indexes in the 2014 review of the National Breeding Objective. A specific aim was to analyze the heterogeneity of preferences for cow trait improvements by determining whether there are farmer types that can be identified with specific patterns of preferences. We analyzed whether farmer types differed in farming system, socioeconomic profile, and attitudes toward breeding and genetic evaluation tools. An online survey was developed to explore farmers' preferences for improvement in 13 cow traits. The pairwise comparisons method was used to derive a ranking of the traits for each respondent. A total of 551 farmers fully completed the survey. A principal component analysis followed by a Ward hierarchical cluster analysis was used to group farmers according to their preferences. Three types of farmers were determined: (1) production-focused farmers, who gave the highest preference of all for improvements in protein yield, lactation persistency, feed efficiency, cow live weight, and milking speed; (2) functionality-focused farmers with the highest preferences of all for improvements in mastitis, lameness, and calving difficulty; and (3) type-focused farmers with the highest preferences of all for mammary system and type. Farmer types differed in their age, their attitudes toward genetic selection, and in the selection criteria they use. Surprisingly, farmer types did not differ for herd size, calving, feeding system, or breed. These results support the idea that preferences for cow trait improvements are intrinsic to farmers and not to production systems or breeds. As a result of this study, and some bioeconomic modeling (not included in this study), the Australian dairy industry has implemented a main index and 2 alternative indexes targeting the different farmer types described here.

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Breeding programmes described as community-based (CBBP) typically relate to low-input systems with farmers having a common interest to improve and share their genetic resources. CBBPs are more frequent with keepers of small ruminants, in particular smallholders of local breeds, than with cattle, pigs or chickens with which farmers may have easier access to alternative programmes. Constraints that limit the adoption of conventional breeding technologies in low-input systems cover a range of organizational and technical aspects. The analysis of 8 CBBPs located in countries of Latin-America, Africa and Asia highlights the importance of bottom-up approaches and involvement of local institutions in the planning and implementation stages. The analysis also reveals a high dependence of these programmes on organizational, technical and financial support. Completely self-sustained CBBPs seem to be difficult to realize. There is a need to implement and document formal socio-economic evaluations of CBBPs to provide governments and other development agencies with the information necessary for creating sustainable CBBPs at larger scales.

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The objective of this study was to develop a transparent, comprehensive, and flexible model for each trait for the formulation of breeding objectives for sow traits in swine breeding programs. Economic values were derived from submodels considering a typical Australian pig production system. Differences in timing and expressions of traits were accounted for to derive economic weights that were compared on the basis of their relative size after multiplication by their corresponding genetic standard deviation to account for differences in scale and genetic variability present for each trait. The number of piglets born alive had the greatest contribution (27.1%) to a subindex containing only maternal traits, followed by daily gain (maternal; 22.0%) and sow mature weight (15.0%). Other traits considered in the maternal breeding objective were preweaning survival (11.8%), sow longevity (12.5%), gilt age at puberty (8.7%), and piglet survival at birth (3.1%). The economic weights for number of piglets born alive and preweaning piglet survival were found to be highly dependent on the definition of scale of enterprise, with each economic value increasing by approximately 100% when it was assumed that the value of extra output per sow could be captured, rather than assuming a consequent reduction in the number of sows to maintain a constant level of output from a farm enterprise. In the context of a full maternal line index that must account also for the expression of direct genetic traits by the growing piglet progeny of sows, the maternal traits contributed approximately half of the variation in the overall breeding objective. Deployment of more comprehensive maternal line indexes incorporating the new maternal traits described would lead to more balanced selection outcomes and improved survival of pigs. Future work could facilitate evaluation of the economic impacts of desired-gains indexes, which could further improve animal welfare through improved sow and piglet survival. The results justify further development of selection criteria and breeding value prediction systems for a wider range of maternal traits relevant to pig production systems.

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The objective of this paper was to derive economic weights for performance and survival traits of growing pigs including feed conversion ratio (FCR), daily feed intake (DFI), ADG, postweaning survival of the growing pig (SG), and carcass fat depth at the P2 site (CFD). An independent model was developed for each trait to derive economic values directly based on a typical Australian production system. This flexible approach may be used to customize economic values for different production systems and alternative trait combinations in breeding objectives. Discounted genetic expressions were used as a means of taking into account differences in frequency and timing of expression of traits to obtain economic weights. Economic values for SG were derived based on a cost-saving and a lost-revenue approach. The correct formulation of the economic value of ADG depends on how feed cost is included in the breeding objective. If FCR is defined as a breeding objective trait, then savings in feed costs through earlier slaughter should not be counted in the economic value of ADG. In contrast, if DFI is included in the breeding objective instead of FCR, then feed-cost savings through earlier slaughter need to be attributed to the economic value for ADG, as a benefit from faster ADG. The paper also demonstrates that economic weightings in indexes for FCR can potentially be overestimated by 70% when it is assumed that DFI or FCR records taken from a limited duration test period reflect the corresponding trait over the full lifetime of the growing pig destined for slaughter. Postweaning survival of the growing pig was the most important breeding objective trait of growing pigs. The relative importance of each breeding objective trait in a sire-line index based on the genetic SD of each trait was 44.5, 27.0, 17.4, and 11.1% for SG, FCR, ADG, and CFD, respectively. Further studies to better clarify the extent of genetic variation that exists in SG under nucleus-farm and commercial-farm conditions are warranted, given the high economic importance of this survival trait of growing pigs.

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Procedures are described for estimating selection index accuracies for individual animals and expected genetic change from selection for the general case where indexes of EBVs predict an aggregate breeding objective of traits that may or may not have been measured. Index accuracies for the breeding objective are shown to take an important general form, being able to be expressed as the product of the accuracy of the index function of true breeding values and the accuracy with which that function predicts the breeding objective. When the accuracies of the individual EBVs of the index are known, prediction error variances (PEVs) and covariances (PECs) for the EBVs within animal are able to be well approximated, and index accuracies and expected genetic change from selection estimated with high accuracy. The procedures are suited to routine use in estimating index accuracies in genetic evaluation, and for providing important information, without additional modelling, on the directions in which a population will move under selection.

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