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The present study was applied to evaluate the effects of alternate feeding and feed restriction on gene expression, growth, proximate composition and biochemical indices in European seabass, Dicentrarchus labrax. Fish were randomly divided into six indoor tanks with 90 fish per tank in a recirculating aquaculture system. Two feeding strategies were applied, in which the first group was fed daily to satiation and the second was intermittently fed (8 days feeding to satiation–2 days starvation) for 40 days. At the end of the experiment, outlier fish were sorted as fast growers (FG) and slow growers (SG) according to their final body weight. The differential gene expression tested was related to glycolysis (pk, ldha, hk, g3pdh, eno1 and alda), fatty acid metabolism (lpl and acc) and cholesterol synthesis (7dhcr and sqle). In addition, muscle ldha and gpi expressions were positively correlated with fish weight. The concentrations of glucose, triglycerides, cholesterol and non-esterified fatty acids (NEFA) were not affected by the dietary treatments. Glucose and NEFA differed significantly between SG and FG fed groups. Overall, the physiological responses of glucose and fatty acid metabolism in fish, as recorded by gene expression assays, were triggered by minor interventions in feeding rather than the different growth rates. Expression of specific genes and biochemical parameters could be used as potential biomarkers to improve aquaculture practices and benefit fish husbandry through selective breeding, feeding strategies and farm management. The study provides new insights on the impact of intermittent feeding of European seabass, with gene markers and their potential effects, for European seabass aquaculture.

Insects are highly nutritious and rich in proteins and could help fulfil the worldwide increasing demand for proteins. Selection can provide lasting genetic improvements. Therefore, the aim of this study was to investigate the effect of genetic parameters in four different selection designs using a simulation study. Simulated were a mixed, half sib and full sib selection design and a combination of phenotypic selection and/or breeding values as selection method. The results show that in ten generations an improvement in body mass and development time is possible in all four selection designs and only minor differences between these designs were observed. Using estimated genetic parameters from literature, an improvement of 146-150% in biomass yield per year was observed. Thus, selection can provide lasting genetic improvements in insect populations, however inbreeding should be monitored and estimation of genetic parameters is necessary to prevent unfavourable correlated responses.

In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements., In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.

Gilthead seabream (Sparus aurata) is a key aquaculture species in the Mediterranean and surrounding regions. While many traits are of interest in seabream breeding programs, phenotypes of them cannot always be easily or accurately measured. Therefore, the objectives are to predict phenotypes of production traits by using automated measurements of body features from digital images and to obtain genetic correlations between measured and predicted phenotypes. The production traits analysed were harvest weight (HW), fillet weight (FW), and fillet percentage (F%). Each image feature was tested for prediction of phenotypes by using 10-fold cross-validation. The phenotypic and genetic correlations of measured and predicted phenotypes were 0.98 and 0.996 for HW, 0.93 and 0.99 for FW, 0.27 and 0.70 for F%, respectively. The relative efficiency of mass selection was higher for predicted phenotypes, except for HW.

Highly fecund aquaculture species provide more opportunities to optimize genotyping effort in genomic selection programs through selective genotyping and pre-selection than other livestock species. The aim of this study is to optimize pre-selection in a simulated fish breeding program to achieve the highest genetic gain per genotyped fish under restricted inbreeding. We simulated breeding programs in R with preselection among all selection candidates, between full sib families and within full sib families and several pre-selection intensities within each of the strategies. Pre-selection always reduces genetic gain. However, when genotyping costs are taken into account, genetic gain per genotyped fish can be increased with preselection. Pre-selecting 40% of the candidates from all available selection candidates produced the highest additional genetic gain per genotyped fish.

Livestock populations can have different genetic backgrounds and may vary in their capacity to respond to environmental changes. Our findings suggest that improved chicken breeds differ in growth performance and phenotypic plasticity (yield stability) when they are introduced into new tropical environments. Dual consideration of productivity and phenotypic plasticity gives opportunities to select or recommend genotypes with optimal performance and wider adaptability for smallholder farmers raising livestock in different agroecologies.

In aquaculture, high accuracy in trait measurements benefits the genetic progress from a breeding program. Breeding traits like fat percentage and visceral weight are related to feed/cost efficiency of growth and product quality, and important metabolism and health indicators. Problems concentrate on finding the proper methods to accurately measure or predict these traits, as most current approaches are invasive, labour-intensive or may disturb or damage the fish. Interior trait prediction from image analysis would allow a real-time, large-scale and non-invasive alternative for such traits. This study investigates using whole-fish images in combination with exterior traits to improve the prediction of fillet fat percentage and visceral weight. The result of including images as extra input shows improvement on the accuracy of fat percentage prediction. The neural network extracted contour-based features and brings into view several biological indicators that appear to be informative for prediction.

In most of African countries, livestock production branches from an ancient tradition where nearly all rural and peri-urban families keep different indigenous breeds in scavenging systems. In sub-Saharan Africa, where these production systems are the most prominent, livestock mainly forages for resources that are highly dependent on the local environment and season. Even though these breeds are said to be adapted to the local conditions, their productivity is still low compared to commercial breeds. There have been several efforts from researchers, policy makers and livestock specialists to introduce commercial breeds to support the generation of food security and poverty alleviation. However, most of these attempts have failed because of the non-adaptability of introduced breeds to the local conditions. Typically there is no prior knowledge on the environmental sensitivity from these breeds to this new tropical environments. Throughout this thesis I use Geographic Information Systems (GIS) that describe the environment, and models used in ecology to investigate the match of animals with their environment. The aim of this thesis was to evaluate how the environment plays a role in shaping differences in breed performance across agro-ecological zones, and what implications this can have in genetic improvement of livestock.Several animal breeding studies tested breeds in different environments to evaluate whether genotypes respond differently to changes in the environment (i.e. G x E). To estimate if there is a re-ranking in breed/genotype performance between environments, a genetic correlation is estimated. To accurately estimate this correlation, an optimal mating design should be established. Breeding programs use full-sibs or half-sibs to perform testing in different environments. Within families, common environmental effects can be present generating a covariance between siblings, and should therefore be taken into account when estimating genetic correlations. In

Environmental differences between the breeding (B) and commercial production (C) environments may lead to genotype-by-environment interactions (GxE) i.e. re-ranking of breeding values of animals in the two environments. A substantial re-ranking implies genetic progress achieved in breeding programs is not realized in performance of production animals. The issues of GxE are not new and several solutions exist, however, there has not been much focus on solutions for breeding programs for poultry. This PhD-project investigated GxE interactions in breeding programs for poultry and solutions to improve genetic progress in these breeding programs. A strong GxE interaction for body weight (BW) traits was found in broilers that were raised in B and C environments. Indications of GxE were significant re-ranking of breeding values, heterogeneous variances and different heritability for BW under B and C conditions. The genetic correlations between BW traits measured in B and C environments were in the range 0.48-0.54. Genetic variances of C traits were more than 2 times higher than those of B traits. Heritability of C traits (0.31-0.37) were higher than those of B traits (0.27-0.30). In this thesis, several approaches to improve genetic gains of the poultry breeding programs in the presence of GxE have been investigated: phenotyping strategies, optimal modelling of traits, use of group records, and the use of genomic information. Different phenotyping strategies were compared in a breeding program for broilers that used genomic selection. It was found that when the genetic correlations between traits measured in B and C were 0.5 and 0.7, allocation of 70% and 30% hatched birds to B and C environments, respectively, for phenotype testing led to the highest genetic gains among the compared phenotyping strategies. When the genetic correlation was 0.9, moving birds to C did not improve genetic gains of the breeding scheme due to reduced selection intensity. Increasing proportion o

Livestock production systems of the developing world use indigenous breeds that locally adapted to specific agro-ecologies. Introducing commercial breeds usually results in lower productivity than expected, as a result of unfavourable genotype by environment interaction. It is difficult to predict of how these commercial breeds will perform in different conditions encountered in e.g. sub-Saharan Africa. Here, we present a novel methodology to model performance, by using growth data from different chicken breeds that were tested in Ethiopia. The suitability of these commercial breeds was tested by predicting the response of body weight as a function of the environment across Ethiopia. Phenotype distribution models were built using machine learning algorithms to make predictions of weight in the local environmental conditions based on the productivity for the breed. Based on the predicted body weight, breeds were assigned as being most suitable in a given agro-ecology or region. We identified the most important environmental variables that explained the variation in body weight across agro-ecologies for each of the breeds. Our results highlight the importance of acknowledging the role of environment in predicting productivity in scavenging chicken production systems. The use of phenotype distribution models in livestock breeding is recommended to develop breeds that will better fit in their intended production environment.

In pig breeding programs, purebred (PB) boars are selected in a nucleus, and mated with crossbred (CB) dams to produce CB growing-finishing pigs used for pork production in commercial farms. The majority of the cost of pork production comes from feeding CB pigs. Therefore, increasing attention is given to selection for feed efficiency and to include in the genetic evaluations the performance records of CB pigs in commercial production circumstances. In addition, sustainability should be at the top of the agenda for all livestock production systems, and thus, improving the feed efficiency of CB pigs farmed around the globe is necessary. Differences between the genetic background of PB and CB, as well as differences between the nucleus and the commercial farms environments will lower the genetic correlation of feed efficiency for PB performance in the nucleus level and CB performance in the commercial level (rpc). My main aim in this thesis was to investigate the possible causes of an rpc in growing-finishing pigs between the feed efficiency in CB pigs kept under Brazilian commercial production circumstances and PB pigs kept under Dutch circumstances being below 1. Another aim was to compare the properties of different traits to represent feed efficiency and the implications of their adoption by pig breeding programs. The results of this thesis show that the collection of feed intake data on CB at commercial farms is worthwhile to increase genetic progress in CB feed efficiency and that residual energy intake is an attractive trait for pig breeding programs. Depending on the definition of feed efficiency, this trait is variably sensitive to changes in the ingredients of the two most common pig commercial rations (corn/soy or wheat/barley/co-products). Breeding for feed efficiency under lower-input diets, such as wheat/barley/co-products, should be considered as feed efficiency will become more important, and lower-input diets will become more widespread in the near fu

Brazil is the fourth largest producer and exporter of pork in the world. Pig farming is raising environmental and economic concerns, mainly associated with the production and use of feed. It causes major environmental impacts due to its strong dependence on scarce resources (e.g. arable land, fossil fuel), and release of pollutants to the air, water and soil (e.g. greenhouse gases, nitrogen). Pig farming relies heavily on high quality food crops (i.e. cereals and oilseeds). In recent years, the growing competition for these high quality food crops with other sectors such as the energy and food sectors has resulted in rising feed costs. The problem of rising feed cost is worsened by price volatility of cereals and oilseeds. The use of alternative feed sources and the genetic improvement of pigs through selective breeding are expected to improve the environmental and economic sustainability of pig farming. The aim of this thesis was to assess the impacts of using co-products in the diets of pigs and of genetic improvement of pigs through selective breeding on both the environmental and economic sustainability of pig farming in Brazil. The results show that the use of co-products in the diets of pigs in Brazil raises feed costs, global warming potential, energy use, and excretions of nitrogen and phosphorus. However, it reduces land use. The use of co-products that can be produced on marginal land (e.g. macaúba cake) improves the efficiency of pork production when marginal land is not used to grow food crops. Breeders can use economic values that are derived by accounting for risk and risk preferences of farmers in order to produce breeding materials that increase the utility of risk averse farmers. Similarly, the mitigation of environmental impacts can be incorporated in breeding goals via using economic values that are derived by accounting for environmental costs. Genetic improvement of traits that raise farm productivity has the potential to reduce environmental im

Economic values (EVs) of traits, accounting for environmental impacts and risk preferences of farmers, are required to design breeding goals that contribute to both economic and environmental sustainability. The objective of this study was to assess the effects of incorporating environmental costs and the risk preferences of farmers on the EVs of pig breeding goal traits. A breeding goal consisting of both sow efficiency and production traits was defined for a typical Brazilian farrow‐to‐finish pig farm with 1,500 productive sows. A mean‐variance utility function was employed for deriving the EVs at finishing pig level assuming fixed slaughter weight. The inclusion of risk and risk aversion reduces the economic weights of sow efficiency traits (17%) while increasing the importance of production traits (7%). For a risk‐neutral producer, inclusion of environmental cost reduces the economic importance of sow efficiency traits (3%) while increasing the importance of production traits (1%). Genetic changes of breeding goal traits by their genetic standard deviations reduce emissions of greenhouse gases, and excretions of nitrogen and phosphorus per finished pig by up to 6% while increasing farm profit. The estimated EVs could be used to improve selection criteria and thereby contribute to the sustainability of pig production systems.

Background: Reproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks. Results: Comparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes. Conclusions: Our comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known new

Background Inbreeding and population bottlenecks in the ancestry of Friesian horses has led to health issues such as dwarfism. The limbs of dwarfs are short, ribs are dented, while the head looks adult-like at young age and the back appears as relatively normal. A striking feature of the condition is the flexor tendon laxity that leads to hyperextension of the fetlock joints. The growth plates of dwarfs display disorganized and thickened chondrocyte columns. The aim of this study was to identify the gene defect that causes the recessively inherited trait in Friesian horses thus to improve our understanding of the disease process and mechanisms behind also at the human molecular level (‘one health’). Results We have localized the genetic cause of the dwarfism phenotype by a genome wide approach to a 3 Mb region on the p-arm of equine chromosome 14. The DNA of four dwarfs and three control Friesian horse was sequenced completely and we identified the missense mutation ECA14:g.4535550C>T that cosegregated with the phenotype in all Friesians analyzed. The mutation leads to the amino acid substitution p.Arg17Lys of xylosylprotein beta 1,4-galactosyltransferase 7 encoded by B4GALT7. The protein is one of the enzymes that synthesize the tetrasaccharide linker between protein and glycosaminoglycan moieties of proteoglycans of the extracellular matrix. The mutation not only affects a conserved arginine codon but also the last nucleotide of the first exon of the gene. With that we showed that it impedes splicing of the primary transcript in cultured fibroblasts from a heterozygous horse. As a result, the level of B4GALT7 mRNA in fibroblasts from a dwarf is only 3% compared to normal levels. Mutations in B4GALT7 in humans are associated with Ehlers-Danlos syndrome progeroid type 1 and Larsen of Reunion Island syndrome. Growth retardation is a common manifestation in both of these syndromes. Conclusions We suggest that the identified mutation of equine B4GALT7 leads to the typi