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The abundance and cross-linking of intramuscular connective tissue contributes to the background toughness of meat, and is thus undesirable. Connective tissue is mainly synthesized by intramuscular fibroblasts. Myocytes, adipocytes and fibroblasts are derived from a common pool of progenitor cells during the early embryonic development. It appears that multipotent mesenchymal stem cells first diverge into either myogenic or non-myogenic lineages; non-myogenic mesenchymal progenitors then develop into the stromal-vascular fraction of skeletal muscle wherein adipocytes, fibroblasts and derived mesenchymal progenitors reside. Because non-myogenic mesenchymal progenitors mainly undergo adipogenic or fibrogenic differentiation during muscle development, strengthening progenitor proliferation enhances the potential for both intramuscular adipogenesis and fibrogenesis, leading to the elevation of both marbling and connective tissue content in the resulting meat product. Furthermore, given the bipotent developmental potential of progenitor cells, enhancing their conversion to adipogenesis reduces fibrogenesis, which likely results in the overall improvement of marbling (more intramuscular adipocytes) and tenderness (less connective tissue) of meat. Fibrogenesis is mainly regulated by the transforming growth factor (TGF) β signaling pathway and its regulatory cascade. In addition, extracellular matrix, a part of the intramuscular connective tissue, provides a niche environment for regulating myogenic differentiation of satellite cells and muscle growth. Despite rapid progress, many questions remain in the role of extracellular matrix on muscle development, and factors determining the early differentiation of myogenic, adipogenic and fibrogenic cells, which warrant further studies.
Crossbreeding, considering either terminal or rotational crossing, synthetic breed creation or breed replacement, is often promoted as an efficient strategy to increase farmers’ income through the improvement of productivity of local livestock in developing countries. Sustainability of crossbreeding is however frequently challenged by constraints such as poor adaptation to the local environment or lack of logistic support. In this review, we investigate factors that may influence the long-term success or the failure of crossbreeding programs, based on the scientific literature and country reports submitted for The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture. Crossbreeding activities vary widely across species and countries. Its sustainability is dependent on different prerequisites such as continual access to adequate breeding stock (especially after the end of externally funded crossbreeding projects), the opportunity of improved livestock to express their genetic potential (e.g. through providing proper inputs) and integration within a reliable market chain. As formal crossbreeding programs are often associated with adoption of other technologies, they can be a catalyst for innovation and development for smallholders. Given the increasing global demand for animal products, as well as the potential environmental consequences of climate change, there is a need for practical research to improve the implementation of long-term crossbreeding programs in developing countries.
Neonatal mortality in small ruminant livestock has remained stubbornly unchanging over the past 40 years, and represents a significant loss of farm income, contributes to wastage and affects animal welfare. Scientific knowledge about the biology of neonatal adaptation after birth has been accumulating but does not appear to have had an impact in improving survival. In this paper, we ask what might be the reasons for the lack of impact of the scientific studies of lamb and kid mortality, and suggest strategies to move forward. Biologically, it is clear that achieving a good intake of colostrum, as soon as possible after birth, is crucial for neonatal survival. This provides fuel for thermoregulation, passive immunological protection and is involved in the development of attachment between the ewe and lamb. The behaviour of the lamb in finding the udder and sucking rapidly after birth is a key component in ensuring sufficient colostrum is ingested. In experimental studies, the main risk factors for lamb mortality are low birthweight, particularly owing to poor maternal nutrition during gestation, birth difficulty, litter size and genetics, which can all be partly attributed to their effect on the speed with which the lamb reaches the udder and sucks. Similarly, on commercial farms, low birthweight and issues with sucking were identified as important contributors to mortality. In epidemiological studies, management factors such as providing assistance with difficult births, were found to be more important than risk factors associated with housing. Social science studies suggest that farmers generally have a positive attitude to improving neonatal mortality but may differ in beliefs about how this can be achieved, with some farmers believing they had no control over early lamb mortality. Facilitative approaches, where farmers and advisors work together to develop neonatal survival strategies, have been shown to be effective in achieving management goals, such as optimising ewe nutrition, that lead to reductions in lamb mortality. We conclude that scientific research is providing useful information on the biology underpinning neonatal survival, such as optimal birthweights, lamb vigour and understanding the importance of sufficient colostrum intake, but the transfer of that knowledge would benefit from an improved understanding of the psychology of management change on farm. Developing tailored solutions, on the basis of adequate farm records, that make use of the now substantial body of scientific literature on neonatal mortality will help to achieve lower neonatal mortality.
There is a global imperative to reduce phosphorous (P) excretion from pig systems. In this study, a previously validated deterministic model was modified to be stochastic, in order to investigate the consequences of different management strategies on P excretion by a group of growing pigs. The model predicts P digestion, retention and excretion from feed composition and growth parameters that describe a specified pig phenotype. Stochasticity was achieved by introducing random variation in the latter. The strategies investigated were: (1) changing feed composition frequently in order to match more closely pig digestible P (digP) requirements to feed composition (phase feeding) and (2) grouping pigs into light and heavy groups and feeding each group according to the requirements of their group average BW (sorting). Phase feeding reduced P excretion as the number of feeding phases increased. The effect was most pronounced as feeding phases increased from 1 to 2, with a 7.5% decrease achieved; the increase in phases from 2 to 3 was associated with a further 2.0% reduction. Similarly, the effect was more pronounced when the feed targeted the population requirements for digP at the average BW of the first third, rather than the average requirements at the mid-point BW of each feeding sequence plan. Increasing the number of feeding phases increased the percentage of pigs that met their digP requirements during the early stages of growth and reduced the percentage of pigs that were supplied <85% of their digP requirements at any stage of their growth; the latter may have welfare implications. Sorting of pigs reduced P excretion to a lesser extent; the reduction was greater as the percentage of pigs in the light group increased from 10% to 30% (from 1.5% to 3.0% reduction, respectively). This resulted from an increase in the P excreted by the light group, accompanied by a decrease in the P excreted by the remaining pigs. Sorting increased the percentage of light pigs that met their dig P requirements, but only slightly decreased the percentage of heavy pigs that met these requirements at any point of their growth. Exactly the converse was the case as far as the percentage of pigs that were supplied <85% of their digP requirements were concerned. The developed model is flexible and can be used to investigate the effectiveness of other management strategies in reducing P excretion from groups of pigs, including precision livestock feeding.
The evolution of feeding systems for ruminants towards evaluation of diets in terms of multiple responses requires the updating of the calculation of nutrient supply to the animals to make it more accurate on aggregated units (feed unit, or UF, for energy and protein digestible in the intestine, or PDI, for metabolizable protein) and to allow prediction of absorbed nutrients. The present update of the French system is based on the building and interpretation through meta-analysis of large databases on digestion and nutrition of ruminants. Equations involved in the calculation of UF and PDI have been updated, allowing: (1) prediction of the out flow rate of particles and liquid depending on the level of intake and the proportion of concentrate, and the use of this in the calculation of ruminal digestion of protein and starch from in situ data; (2) the system to take into account the effects of the main factors of digestive interactions (level of intake, proportion of concentrate, rumen protein balance) on organic matter digestibility, energy losses in methane and in urine; (3) more accurate calculation of the energy available in the rumen and the efficiency of its use for the microbial protein synthesis. In this renewed model UF and PDI values of feedstuffs vary depending on diet composition, and intake level. Consequently, standard feed table values can be considered as being only indicative. It is thus possible to predict the nutrient supply on a wider range of diets more accurately and in particular to better integrate energy×protein interactions occurring in the gut.
Special Topic: Genomic selection with numerically small reference populations
The reliability of genomic breeding values (DGV) decays over generations. To keep the DGV reliability at a constant level, the reference population (RP) has to be continuously updated with animals from new generations. Updating RP may be challenging due to economic reasons, especially for novel traits involving expensive phenotyping. Therefore, the goal of this study was to investigate a minimal RP update size to keep the reliability at a constant level across generations. We used a simulated dataset resembling a dairy cattle population. The trait of interest was not included itself in the selection index, but it was affected by selection pressure by being correlated with an index trait that represented the overall breeding goal. The heritability of the index trait was assumed to be 0.25 and for the novel trait the heritability equalled 0.2. The genetic correlation between the two traits was 0.25. The initial RP (n=2000) was composed of cows only with a single observation per animal. Reliability of DGV using the initial RP was computed by evaluating contemporary animals. Thereafter, the RP was used to evaluate animals which were one generation younger from the reference individuals. The drop in the reliability when evaluating younger animals was then assessed and the RP was updated to re-gain the initial reliability. The update animals were contemporaries of evaluated animals (EVA). The RP was updated in batches of 100 animals/update. First, the animals most closely related to the EVA were chosen to update RP. The results showed that, approximately, 600 animals were needed every generation to maintain the DGV reliability at a constant level across generations. The sum of squared relationships between RP and EVA and the sum of off-diagonal coefficients of the inverse of the genomic relationship matrix for RP, separately explained 31% and 34%, respectively, of the variation in the reliability across generations. Combined, these parameters explained 53% of the variation in the reliability across generations. Thus, for an optimal RP update an algorithm considering both relationships between reference and evaluated animals, as well as relationships among reference animals, is required.
The aim of this study was to test how genetic gain for a trait not measured on the nucleus animals could be obtained within a genomic selection pig breeding scheme. Stochastic simulation of a pig breeding program including a breeding nucleus, a multiplier to produce and disseminate semen and a production tier where phenotypes were recorded was performed to test (1) the effect of obtaining phenotypic records from offspring of nucleus animals, (2) the effect of genotyping production animals with records for the purpose of including them in a genomic selection reference population or (3) to combine the two approaches. None of the tested strategies affected genetic gain if the trait under investigation had a low economic value of only 10% of the total breeding goal. When the relative economic weight was increased to 30%, a combination of the methods was most effective. Obtaining records from offspring of already genotyped nucleus animals had more impact on genetic gain than to genotype more distant relatives with phenotypes to update the reference population. When records cannot be obtained from offspring of nucleus animals, genotyping of production animals could be considered for traits with high economic importance.
Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.
This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.
In modern dairy cattle breeding, genomic breeding programs have the potential to increase efficiency and genetic gain. At the same time, the requirements and the availability of genotypes and phenotypes present a challenge. The set-up of a large enough reference population for genomic prediction is problematic for numerically small breeds but also for hard to measure traits. The first part of this study is a review of the current literature on strategies to overcome the lack of reference data. One solution is the use of combined reference populations from different breeds, different countries, or different research populations. Results reveal that the level of relationship between the merged populations is the most important factor. Compiling closely related populations facilitates the accurate estimation of marker effects and thus results in high accuracies of genomic prediction. Consequently, mixed reference populations of the same breed, but from different countries are more promising than combining different breeds, especially if those are more distantly related. The use of female reference information has the potential to enlarge the reference population size. Including females is advisable for small populations and difficult traits, and maybe combined with genotyping females and imputing those that are un-genotyped.
The efficient use of imputation for un-genotyped individuals requires a set of genotyped related animals and well-considered selection strategies which animals to choose for genotyping and phenotyping. Small populations have to find ways to derive additional advantages from the cost-intensive establishment of genomic breeding schemes. Possible solutions may be the use of genomic information for inbreeding control, parentage verification, within-herd selection, adjusted mating plans or conservation strategies.
The second part of the paper deals with the issue of high-quality phenotypes against the background of new, difficult and hard to measure traits. The use of contracted herds for phenotyping is recommended, as additional traits, when compared to standard traits used in dairy cattle breeding can be measured at set moments in time. This can be undertaken even for the recording of health traits, thus resulting in complete contemporary groups for health traits. Future traits to be recorded and used in genomic breeding programs, at least partly will be traits for which traditional selection based on widespread phenotyping is not possible. Enabling phenotyping of sufficient numbers to enable genomic selection will rely on cooperation between scientists from different disciplines and may require multidisciplinary approaches.
We studied the effect of including genomic data for cows in the reference population of single-step evaluations. Deregressed individual cow genetic evaluations (DRP) from milk production evaluations of Nordic Red Dairy cattle were used to estimate the single-step breeding values. Validation reliability and bias of the evaluations were calculated with four data sets including different amount of DRP record information from genotyped cows in the reference population. The gain in reliability was from 2% to 4% units for the production traits, depending on the used DRP data and the amount of genomic data. Moreover, inclusion of genotyped bull dams and their genotyped daughters seemed to create some bias in the single-step evaluation. Still, genotyping cows and their inclusion in the reference population is advantageous and should be encouraged.
Small reference populations limit the accuracy of genomic prediction in numerically small breeds, such like Danish Jersey. The objective of this study was to investigate two approaches to improve genomic prediction by increasing size of reference population in Danish Jersey. The first approach was to include North American Jersey bulls in Danish Jersey reference population. The second was to genotype cows and use them as reference animals. The validation of genomic prediction was carried out on bulls and cows, respectively. In validation on bulls, about 300 Danish bulls (depending on traits) born in 2005 and later were used as validation data, and the reference populations were: (1) about 1050 Danish bulls, (2) about 1050 Danish bulls and about 1150 US bulls. In validation on cows, about 3000 Danish cows from 87 young half-sib families were used as validation data, and the reference populations were: (1) about 1250 Danish bulls, (2) about 1250 Danish bulls and about 1150 US bulls, (3) about 1250 Danish bulls and about 4800 cows, (4) about 1250 Danish bulls, 1150 US bulls and 4800 Danish cows. Genomic best linear unbiased prediction model was used to predict breeding values. De-regressed proofs were used as response variables. In the validation on bulls for eight traits, the joint DK-US bull reference population led to higher reliability of genomic prediction than the DK bull reference population for six traits, but not for fertility and longevity. Averaged over the eight traits, the gain was 3 percentage points. In the validation on cows for six traits (fertility and longevity were not available), the gain from inclusion of US bull in reference population was 6.6 percentage points in average over the six traits, and the gain from inclusion of cows was 8.2 percentage points. However, the gains from cows and US bulls were not accumulative. The total gain of including both US bulls and Danish cows was 10.5 percentage points. The results indicate that sharing reference data and including cows in reference population are efficient approaches to increase reliability of genomic prediction. Therefore, genomic selection is promising for numerically small population.
The objective of this study was to investigate relationships between ovulation rate (OR) and embryonic and placental development in sows. Topigs Norsvin® sows (n=91, parity 2 to 17) from three different genetic backgrounds were slaughtered at 35 days of pregnancy and the reproductive tract was collected. The corpora lutea (CL) were counted and the number of vital and non-vital embryos, embryonic spacing (distance between two embryos), implantation length, placental length, placental weight and embryonic weight were assessed. The difference between number of CL and total number of embryos was considered as early embryonic mortality. The number of non-vital embryos was considered as late mortality. Relationships between OR and all other variables were investigated using two models: the first considered parity as class effect (n=91) and the second used a subset of sows with parities 4 to 10 (n=47) to analyse the genetic background as class effect. OR was significantly affected by parity (P<0.0001), but was not affected by the genetic background of the sows. Parity and genetic background did not affect embryonic and placental characteristics at 35 days of pregnancy. OR (varying from 17 to 38 CL) was positively related with early embryonic mortality (β=0.49±0.1 n/ovulations, P<0.0001), with late embryonic mortality or number of non-vital embryos (β=0.24±0.1 n/ovulations, P=0.001) and with the number of vital embryos (β=0.26±0.1 n/ovulations, P=0.01). However, dividing OR in four classes, showed that the number of vital embryos was lowest in OR class 1 (17 to 21 CL), but not different for the other OR classes, suggesting a plateau for number of vital embryos for OR above 22. There was a negative linear relationship between OR and vital embryonic spacing (β=−0.45±0.1 cm/ovulation, P=0.001), implantation length (β=−0.35±0.1 cm/ovulation, P=0.003), placental length (β=−0.38±0.2 cm/ovulation, P=0.05) and empty space around embryonic-placental unit (β=−0.4±0.2 cm/ovulation, P=0.02), indicating uterine crowding. Further analyses showed that effects of OR on embryonic and uterine parameters were related with the increase in late mortality and not early embryonic mortality. Therefore, we conclude that a high OR results in an moderate increase in the number of vital embryos at day 35 of pregnancy, but compromises development in the surviving embryonic/placental units, suggesting that the future growth and survival of the embryos might be further compromised.
The pivotal roles of regulatory jurisdictions in the feed additive sector cannot be over-emphasized. In the European Union (EU), antioxidant substances are authorized as feed additives for prolonging the shelf life of feedstuffs based on their effect for preventing lipid peroxidation. However, the efficacy of antioxidants transcends their functional use as technological additives in animal feeds. Promising research results have revealed the in vivo efficacy of dietary antioxidants for combating oxidative stress in production animals. The in vivo effect of antioxidants is significant for enhancing animal health and welfare. Similarly, postmortem effect of dietary antioxidants has been demonstrated to improve the nutritional, organoleptic and shelf-life qualities of animal products. In practice, dietary antioxidants have been traditionally used by farmers for these benefits in livestock production. However, some antioxidants particularly when supplemented in excess could act as prooxidants and exert detrimental effects on animal well-being and product quality. Presently, there is no exclusive legislation in the EU to justify the authorization of antioxidant products for these in vivo and postmortem efficacy claims. To indicate these efficacy claims and appropriate dosage on product labels, it is important to broaden the authorization status of antioxidants through the appraisal of existing EU legislations on feed additives. Such regulatory review will have major impact on the legislative categorization of antioxidants and the efficacy assessment in the technical dossier application. The present review harnesses the scientific investigations of these efficacy claims in production animals and, proposes potential categorization and appraisal of in vivo methodologies for efficacy assessment of antioxidants. This review further elucidates the implication of such regulatory review on the practical application of antioxidants as feed additives in livestock production. Effecting these regulatory changes will stimulate the innovation of more potent antioxidant products and create potential new markets that will have profound economic impacts on the feed additive industry. Based on the in vivo efficacy claims, antioxidants may have to contend with the legislative controversy of either to be considered as veterinary drugs or feed additives. In this scenario, antioxidants are not intended to diagnose or cure diseases as ascribed to veterinary products. This twisted distinction can be logically debated with reference to the stipulated status of feed additives in Commission Regulation (EC) No 1831/2003. Nonetheless, it is imperative for relevant stakeholders in the feed additive industry to lobby for the review of existing EU legislations for authorization of antioxidants for these efficacy claims.
Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.
The production of protein from animal sources is often criticized because of the low efficiency of converting plant protein from feeds into protein in the animal products. However, this critique does not consider the fact that large portions of the plant-based proteins fed to animals may be human-inedible and that the quality of animal proteins is usually superior as compared with plant proteins. The aim of the present study was therefore to assess changes in protein quality in the course of the transformation of potentially human-edible plant proteins into animal products via livestock production; data from 30 Austrian dairy farms were used as a case study. A second aim was to develop an approach for combining these changes with quantitative aspects (e.g. with the human-edible feed conversion efficiency (heFCE), defined as kilogram protein in the animal product divided by kilogram potentially human-edible protein in the feeds). Protein quality of potentially human-edible inputs and outputs was assessed using the protein digestibility-corrected amino acid score and the digestible indispensable amino acid score, two methods proposed by the Food and Agriculture Organization of the United Nations to describe the nutritional value of proteins for humans. Depending on the method used, protein scores were between 1.40 and 1.87 times higher for the animal products than for the potentially human-edible plant protein input on a barn-gate level (=protein quality ratio (PQR)). Combining the PQR of 1.87 with the heFCE for the same farms resulted in heFCE×PQR of 2.15. Thus, considering both quantity and quality, the value of the proteins in the animal products for human consumption (in this case in milk and beef) is 2.15 times higher than that of proteins in the potentially human-edible plant protein inputs. The results of this study emphasize the necessity of including protein quality changes resulting from the transformation of plant proteins to animal proteins when evaluating the net contribution of livestock to the human food supply. Furthermore, these differences in protein quality might also need to be considered when choosing a functional unit for the assessment of environmental impacts of the production of different proteins.