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Genomic relationships can be computed with dense genome-wide genotypes through different methods, either based on identity-by-state (IBS) or identity-by-descent (IBD). The latter has been shown to increase the accuracy of both estimated relationships and predicted breeding values. However, it is not clear whether an IBD approach would achieve greater heritability ((Formula presented.)) and predictive ability ((Formula presented.)) than its IBS counterpart for data with low-depth pedigrees. Here, we compare both approaches in terms of the estimated of (Formula presented.) and (Formula presented.), using data on meat quality and carcass traits recorded in experimental crossbred pigs, with a pedigree constrained to only three generations. Three animal models were fitted which differed on the relationship matrix: an IBS model ((Formula presented.)), an IBD (defined within the known pedigree) model ((Formula presented.)), and a pedigree model ((Formula presented.)). In 9 of 20 traits, the range of increase for the estimates of (Formula presented.) and (Formula presented.) was 1.2–2.9 times greater with (Formula presented.) and (Formula presented.) models than with (Formula presented.). Whereas for all traits, both parameters were similar between genomic models. The (Formula presented.) of the genomic models was higher compared to (Formula presented.). A scarce increment in (Formula presented.) was found with (Formula presented.) when compared to (Formula presented.), most likely due to the former recovering sizeable relationships among founder F0 animals.

Dam construction and streamflow regulation are increasing throughout the world, with impacts in impounded aquatic ecosystems. Hydropower dams, some of them causing a phenomenon called “hydropeaking” during their operation, are known for having a variety of impacts on downstream aquatic biota, particularly fish, and respective habitat. This can result in significant changes, from the community (e.g., fish assemblage structure) to the individual level (e.g., physiological and behavioural adjustments). Researchers and managers involved in the assessment of hydropeaking impacts must be resourceful and use methods that allow their precise evaluation, from large to fine-scale habitat and biological responses. In the last decades, technological advances allowed for the development of techniques and instrumentations that are increasingly being used in hydropeaking impact and mitigation assessments. This paper aims to provide a review, to researchers and managers interested in this field, of some of the most innovative methods and techniques, involving technology, that are available to study hydropeaking effects on downstream ecosystem, particularly from a fish perspective. We discuss the fundamentals behind such techniques, their advantages, and disadvantages, while also providing practical examples of their application and of the type of results that can be obtained. We finish by discussing some of the shortcomings of these methods and how related technology can evolve to solve current limitations.

Technical dairy innovations (TDI), such as improved cow feeding, health management and genetic improvement, could boost milk production. At the same time, organisational and institutional dairy innovations (OIDI), including group milk sales, feed and credit access, could boost dairy supply chain efficiency. This study examined the TDI adoption determinants and the OIDI adoption intensity. Data were collected from 1146 farmers (410, 382 and 354 in the milksheds of Mukurweini Wakulima Dairy Limited [MWDL], Happy Cow Limited [HCL] and New Kenya Co-operative Creameries [NKCC], respectively) and analysed using a double hurdle model. Access to credit positively influenced the TDI adoption in the three milksheds. Adoption of TDI was influenced by hired employees, dairy records, total dairy cows and household head education. The empirical evidence from the study supports the observation that OIDI adoption intensity is influenced by income, farm size, dairy records, and dairy information access. To boost TDI adoption, the dairy development partners should link cooperative society members with agricultural credit lenders. Additionally, the dissemination of dairy information to farmers by the dairy stakeholders could spur TDI adoption, while providing dairy information and training farmers on dairy record keeping should be promoted to boost TDI and OIDI adoption.

Background Structural variants (SVs) are chromosomal segments that differ between genomes, such as deletions, duplications, insertions, inversions and translocations. The genomics revolution enabled the discovery of sub-microscopic SVs via array and whole-genome sequencing (WGS) data, paving the way to unravel the functional impact of SVs. Recent human expression QTL mapping studies demonstrated that SVs play a disproportionally large role in altering gene expression, underlining the importance of including SVs in genetic analyses. Therefore, this study aimed to generate and explore a high-quality bovine SV catalogue exploiting a unique cattle family cohort data (total 266 samples, forming 127 trios). Results We curated 13,731 SVs segregating in the population, consisting of 12,201 deletions, 1,509 duplications, and 21 multi-allelic CNVs (> 50-bp). Of these, we validated a subset of copy number variants (CNVs) utilising a direct genotyping approach in an independent cohort, indicating that at least 62% of the CNVs are true variants, segregating in the population. Among gene-disrupting SVs, we prioritised two likely high impact duplications, encompassing ORM1 and POPDC3 genes, respectively. Liver expression QTL mapping results revealed that these duplications are likely causing altered gene expression, confirming the functional importance of SVs. Although most of the accurately genotyped CNVs are tagged by single nucleotide polymorphisms (SNPs) ascertained in WGS data, most CNVs were not captured by individual SNPs obtained from a 50K genotyping array. Conclusion We generated a high-quality SV catalogue exploiting unique whole genome sequenced bovine family cohort data. Two high impact duplications upregulating the ORM1 and POPDC3 are putative candidates for postpartum feed intake and hoof health traits, thus warranting further investigation. Generally, CNVs were in low LD with SNPs on the 50K array. Hence, it remains crucial to incorporate CNVs via means other than tagging SNPs, such as investigation of tagging haplotypes, direct imputation of CNVs, or direct genotyping as done in the current study. The SV catalogue and the custom genotyping array generated in the current study will serve as valuable resources accelerating utilisation of full spectrum of genetic variants in bovine genomes.

Background In genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a ‘J-factor’, which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors. Results For body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV. Conclusions Based on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH4) concentrations, using the largest data set from long-term repeatedly recorded CH4 on cows to date, and to evaluate (1) the accuracy of breeding values for different CH4 traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH4 and carbon dioxide (CO2), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH4 and CO2 concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH4/CO2 ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH4 per visit (0.13) but lower for logCH4 and CH4/CO2 (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH4 traits, apart from the genetic correlations with the CH4/CO2 trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH4, measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH4 (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations.

Background: The European Commission (EC) has launched the European Green Deal communication, setting out the path for a fundamental transformation of Europe. Key element in this policy is a fully sustainable food system outlined in the farm-to-fork strategy. Such strategy requires a systems approach in which all aspects related to the production and consumption of sufficient and healthy food are considered, including economic, environmental (climate, ecosystems) and social aspects. Scope and approach: Here, we present the systems approach concept for food production, following the farm-to-fork principle as embraced by the EC, and elaborate on how digitalisation and Artificial Intelligence (AI) can solve the challenges that a sustainable food system imposes. Key findings and conclusions: We present a number of research and innovation challenges and illustrate these by some specific examples. It is concluded that AI and digitalisation show great potential to support the transition towards a sustainable food system. This development will impact the roles and interactions of the actors in the entire value chain from farmers to consumers. Policy recommendations are made for a successful future implementation of AI in sustainable food production.

Improved understanding of the microbial populations during intensive feeding of feedlot cattle holds potential for optimizing production efficiency. Ionophores are used to increase the production and efficiency of ruminants and are commonly used in South African feedlots. Bonsmara bull calves (n=24) were subject to a four-phase feedlot diet in a growth trial commencing with backgrounding, followed by starter, grower and finisher diets. Animals were randomly divided into two groups: control and a group provided the in-feed ionophore monensin. Four animals from each group were randomly selected for rumen content collection using an oesophageal tube during the phases in the trial. Samples were analysed using 16S rRNA and internal transcribed spacers amplicon sequencing. Totals of 42 008 and 35 442 amplicon sequence variants were identified from 16S rRNA and internal transcribed spacers amplicon sequencing. The rumen microbiome composition and alpha diversity differed significantly between the phases, whereas no significant difference was observed between the control and monensin groups. Backgrounding had the highest bacterial richness, whereas the grower phase had the highest fungal richness. Bacteroidetes, Firmicutes and Proteobacteria were the most abundant phyla, with Bacteroidetes being most abundant in the backgrounding and starter phases, whereas Proteobacteria was the most abundant in the grower and finisher phases. Ascomycota, Basidiomycota and Neocallistigomycota were the most abundant fungal phyla. Improved knowledge of the shift in microbiome population during the growth period could assist in adapting feeding strategies to improve the efficiency of beef production.

Monitoring traits of cattle with an Unmanned Aerial Vehicle (UAV) could assist farmers in monitoring the status and health of their herd without actually visiting them. This feasibility study presents 3D methods to manually estimate body dimensions, such as height and weight, of individual Holstein Friesian cows with stereo RGB imagery, video and LiDAR data from UAVs. In total, 25 different cows over 3 years were monitored and 4,611 images, ∼10 videos, and a LiDAR dataset were analysed. The methods used were estimating weight by analysing 3D models made with RGB imagery and video from UAV, and estimating height and weight from LiDAR data. Software used in this study to process the UAV data and to extract 3D models are Agisoft Metashape Professional, CloudCompare, RiPROCESS 1.8.4 Riegl Software and Potree Desktop. LiDAR showed accurate results for estimating withers height (mean error 6cm) and weight (mean error 38 kg). Estimating weight with 3D models extracted with Structure for Motion (SfM) from overlapping RGB imagery resulted in less accurate results (mean error 62kg). The latter result improved to a mean error of 31 kg when three outliers were removed which exceeded twice the standard deviation. These results suggest that the use of UAVs is promising for estimating body dimensions in extensive beef production systems. The main challenge remains to record individual cows from all sides without too much movement or motion from the cow to enable 3D modelling. Another challenge was to decrease the mean error in weight estimations so that changes in body weight over time can be monitored. Future research should focus on improving the overall mean error of cattle weight estimation, finding solutions for moving cattle and shifting from manual to automated processing of UAV imagery into 3D models.

Aneuploidy is the loss or gain of one or more chromosomes. Although it is a rare phenomenon in liveborn individuals, it is observed in livestock breeding populations. These breeding populations are often routinely genotyped and the genotype intensity data from single nucleotide polymorphism (SNP) arrays can be exploited to identify aneuploidy cases. This identification is a time-consuming and costly task, because it is often performed by visual inspection of the data per chromosome, usually done in plots of the intensity data by an expert. Therefore, we wanted to explore the feasibility of automated image classification to replace (part of) the visual detection procedure for any diploid species. The aim of this study was to develop a deep learning Convolutional Neural Network (CNN) classification model based on chromosome level plots of SNP array intensity data that can classify the images into disomic, monosomic and trisomic cases. A multispecies dataset enriched for aneuploidy cases was collected containing genotype intensity data of 3321 disomic, 1759 monosomic and 164 trisomic chromosomes. The final CNN model had an accuracy of 99.9%, overall precision was 1, recall was 0.98 and the F1 score was 0.99 for classifying images from intensity data. The high precision assures that cases detected are most likely true cases, however, some trisomy cases may be missed (the recall of the class trisomic was 0.94). This supervised CNN model performed much better than an unsupervised k-means clustering, which reached an accuracy of 0.73 and had especially difficult to classify trisomic cases correctly. The developed CNN classification model provides high accuracy to classify aneuploidy cases based on images of plotted X and Y genotype intensity values. The classification model can be used as a tool for routine screening in large diploid populations that are genotyped to get a better understanding of the incidence and inheritance, and in addition, avoid anomalies in breeding candidates.

To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.

Commonly, female European eels are injected weekly with pituitary extract (PE) from carp (CPE) or salmon (SPE) to induce sexual maturation. However, a PE is a mixture of gonadotropins and other hormones that are not specific for eel and rapidly cleared from circulation. The aim of this study was therefore to test the effects of highly stable eel-specific recombinant gonadotropins (rGTHs) on oocyte development in vitro and in vivo in European eels. For the in vitro trial, the dose–effect responses of maturing eel oocytes on CPE and recombinant luteinizing hormone (rLH) were studied before and after 12 and 18 h of incubation. For the in vivo experiment, sexual maturation was stimulated by treatment with (i) CPE, (ii) recombinant follicle-stimulating hormone (rFSH) followed by CPE and (iii) rFSH followed by rLH. For the in vitro experiment, the expression of the nuclear progestin receptor 2 (pgr2) was induced by rLH, implying that rLH was preparing the oocyte for ovulation. For the in vivo experiment, the females treated with rGTHs had high gonadosomatic index (GSI) values (rFSH-CPE: 75, 77; rFSH-rLH: 80) in comparison with the females injected with CPE (50–60), suggesting that rFSH strongly induced vitellogenic growth. Larvae were produced for all treatment groups and for the first time by rGTH treatment alone but dose and timing still need optimization.

Accurate measurements of breeding traits on individuals are critical in aquaculture for obtaining breeding values and tracking the progress of the breeding program. Modern breeding programs prioritize not only production traits but also complex traits related to production, product quality, body composition, disease resistance, and fish health, such as slaughter traits. Slaughter traits can be selected indirectly and incorporated into breeding programs. Indirect selection is cost-effective, but there is often little genetic correlation between measured and target traits. Accurate phenotypic prediction of the target traits using modern phenotyping technology can be game-changing in indirect selection. This paper proposes an analytical framework for predicting slaughter traits using images. The framework demonstrated that using images in addition to body weight improved fat percentage prediction accuracy from 0.4 to 0.7 when compared to a model that only used body weight and its numerical derivations. The framework also allowed for the interpretation of the prediction by providing imaginal features. In the case study, the dorsal side, the upper edge of the pectoral fin, and operculum edge were discovered to be the three regions on seabream that have properties that are negatively correlated with fillet fat percentage. The framework showed that both body weight and visceral weight are highly correlated with total fish body area. The framework also revealed that the lower edge of the pectoral fin, operculum edge, and anal fin are the regions with properties that explain variation in the visceral percentage. Future research will be required to segment and quantify each predictive imaginal feature to calculate its heritability. The framework can potentially predict other harvest, post-slaughter, and metabolic traits for aquacultural study.

This report describes the results of a review of five aquaculture projects that have been implemented in the past twelve years. The projects were located in Kenya (4) and Ethiopia (1). The aim of the review was to draw lessons and recommendations for design and implementation of future aquaculture projects. All projects had been funded completely or partly by the Government of the Kingdom of the Netherlands with the aim to support aquaculture sector development. Beside project documents the main sources of information were thirty two interviews with a range of project stakeholders. Four projects had capacity building as a major or even main component. The experiences and lessons learnt by Dutch and African project stakeholders as well as impacts on the aquaculture sector that lasted after project closure are described. Recommendations for design and implementation of future aquaculture projects are provided.

Global warming is expected to result in larger temperature fluctuations by which heat stress may become an important stressor for animals, affecting health and productivity. Animals can cope with and adapt to heat stress by changing their physiology. To investigate general physiological reactions to heat stress in muscle and heart tissues of chickens we combined results from three independent experiments. Two experiments studied the transcriptome profiles of heart and muscle tissues of mature chickens using heat stress adapted and nonadapted chickens. One experiment studied the epigenome changes of heat stress during chicken egg incubation. In all three datasets epigenome changes were important biological response mechanisms, which may underlie genome-wide regulation of the affected biological mechanisms. Pre and postnatal heat stress reaction showed changed expression of genes related to metabolic rate, energy, and protein metabolism. Furthermore, tissue integrity may be affected due to changed cell−cell contacts, vascularization, and growth reduction.

We have previously shown that single-step genomic best linear unbiased prediction (ssGBLUP) estimates breeding values of genomically preselected animals without preselection bias for widely recorded traits, that is traits recorded for the majority of animals in the breeding population. This study investigated the impact of genomic preselection (GPS) on accuracy and bias in ssGBLUP evaluation of genomically preselected animals for a scarcely recorded trait, that is a trait recorded for only a small proportion of the animals, which generally has a lower prediction accuracy than widely recorded traits, mainly due to having a much smaller number of phenotypes available. We used data from a commercial pig breeding program, considering feed intake as a scarcely recorded target trait, being available for ~30% of the animals with phenotypes for any trait, and average daily gain, backfat thickness and loin depth as widely recorded predictor traits, being available for >95% of the animals with phenotypes for any trait. The data contained the routine GPS implemented by commercial animal breeding programs, and we retrospectively implemented two scenarios with additional layers of GPS by discarding pedigree, genotypes and phenotypes of animals without progeny. The ssGBLUP evaluation following GPS used records only from the target trait, only from the predictor traits, or both. Accuracy for feed intake did not differ statistically across GPS scenarios, although it tended to decrease with more intense GPS. The accuracy had average values of 0.37, 0.44, and 0.45 across all GPS scenarios when, respectively, records from only the target trait, only the predictor traits, or both were used in the ssGBLUP evaluation. Considerable deflation of the genomic breeding values for feed intake was observed in the most stringent GPS scenario, due to the variance components being underestimated as a result of the limited amount of strongly preselected data. As long as (co)variance components were unbiased, no or only marginal bias was observed. These results for accuracy and bias were observed whether records of the scarcely recorded target trait, of the predictor traits, or both were used in the ssGBLUP evaluation. Our results show that for the scarcely recorded feed intake in pigs, ssGBLUP is able to estimate breeding values of preselected animals without preselection bias, similarly as previously observed for widely recorded traits.

CONTEXT: In Ethiopia, many agricultural interventions have been introduced for mixed farm types in different biophysical and socioeconomic conditions. The contribution of such interventions to multi-objectives at farm level and beyond remains unclear. OBJECTIVE: To derive insights into interaction of multi-objectives on economic profit-, nutrition security-, and environmental performance that are relevant for improvement of farm household living income in Amhara, Ethiopia. METHODS: FarmDESIGN model evaluated performance of four stylized medium- and small-scale farm types in a low (Lay Gayint)- and high (South Achefer)- potential district: LG-M, LG-S, SA-M and SA-S. Pareto-based multi-objective optimization was performed to maximize farm profit, livestock density, dietary energy and vitamin A yield, and minimize GHG and soil N losses. Further analysis resulted in 3–4 clusters per farm type with varied farm configurations. RESULTS AND CONCLUSIONS: Higher dietary energy- and vitamin A- yield that meets household and Ethiopian society requirements increased synergistically with profit in three of the four farm type clusters, while LG-S clusters could only increase vitamin A yield substantially with profit. Only LG-M clusters intensified livestock by addition of 4–8 crossbred cows and 1–10 chickens, supported by imported feed and off-farm grazing but face strong environmental trade-offs of increased GHG and soil N losses. Chicken production (eggs and chicken meat) is an option to reduce environmental impact, increase profit and contribute to both human nutrition requirements. In LG-S, SA-M and SA-S farm type clusters, profit was generated mainly from sale of eucalyptus, maize, potato, carrot, finger millet, garlic, beetroot and banana, with eucalyptus accounting at least 35% of the crop profit. Livestock diversification in these clusters also contributed to farm revenue, but always to a smaller extent, due to relatively high management costs. A living income from farming activities was only achieved on SA-M3 as 3.96 USD/capita/day (+0.36 above the threshold). This cluster had a 2.90 ha land area, farm income almost equally generated from crop and livestock production, and successfully met all set objectives except maximizing livestock density. SIGNIFICANCE: Our study infers the need to increase land area, expand poultry and rear crossbred cows, and cultivate high value crops to realize a living income solely from agricultural activities. For implementation, current land-use policies should support farm area expansion. The suggested agricultural options, which already align with current Ethiopian development plans, must utilize sustainable measures that will not lead to short- or long-term challenges as higher GHG and soil nutrient mining.

Background: This paper highlights the relationships between economic weights, genetic progress, and phenotypic progress in genomic breeding programs that aim at generating genetic progress in complex, i.e., multi-trait, breeding objectives via a combination of estimated breeding values for different trait complexes. Results: Based on classical selection index theory in combination with quantitative genetic models, we provide a methodological framework for calculating expected genetic and phenotypic progress for all components of a complex breeding objective. We further provide an approach to study the sensitivity of the system to modifications, e.g. to changes in the economic weights. We propose a novel approach to derive the covariance structure of the stochastic errors of estimated breeding values from the observed correlations of estimated breeding values. We define ‘realized economic weights’ as those weights that would coincide with the observed composition of the genetic trend and show, how they can be calculated. The suggested methodology is illustrated with an index that aims at achieving a breeding goal composed of six trait complexes, that was applied in German Holstein cattle breeding until 2021. Conclusions: Based on the presented results, the main conclusions are (i) the composition of the observed genetic progress matches the expectations well, with predictions being slightly better when the covariance of estimation errors is taken into account; (ii) the composition of the expected phenotypic trend deviates significantly from the expected genetic trend due to the differences in trait heritabilities; and (iii) the realized economic weights derived from the observed genetic trend deviate substantially from the predefined ones, in one case even with a reversed sign. Further results highlight the implications of the change to a modified breeding goal based on the example of a new index comprising eight, partly new, trait complexes, which is used since 2021 in the German Holstein breeding program. The proposed framework and the analytical tools and software provided will be useful to define more rational and generally accepted breeding objectives in the future.

Walking ability of broilers can be improved by selective breeding, but large-scale phenotypic records are required. Currently, gait of individual broilers is scored by trained experts, however, precision phenotyping tools could offer a more objective and high-throughput alternative. We studied whether specific walking characteristics determined through pose estimation are linked to gait in broilers. We filmed male broilers from behind, walking through a 3 m × 0.4 m (length × width) corridor one by one, at 3 time points during their lifetime (at 14, 21, and 33 d of age). We used a deep learning model, developed in DeepLabCut, to detect and track 8 keypoints (head, neck, left and right knees, hocks, and feet) of broilers in the recorded videos. Using the keypoints of the legs, 6 pose features were quantified during the double support phase of walking, and 1 pose feature was quantified during steps, at maximum leg lift. Gait was scored on a scale from 0 to 5 by 4 experts, using the videos recorded on d 33, and the broilers were further classified as having either good gait (mean gait score ≤2) or suboptimal gait (mean gait score >2). The relationship of pose features on d 33 with gait was analyzed using the data of 84 broilers (good gait: 57.1%, suboptimal gait: 42.9%). Birds with suboptimal gait had sharper hock joint lateral angles and lower hock-feet distance ratios during double support on d 33, on average. During steps, relative step height was lower in birds with suboptimal gait. Step height and hock-feet distance ratio showed the largest mean deviations in broilers with suboptimal gait compared to those with good gait. We demonstrate that pose estimation can be used to assess walking characteristics during a large part of the productive life of broilers, and to phenotype and monitor broiler gait. These insights can be used to understand differences in the walking patterns of lame broilers, and to build more sophisticated gait prediction models.

Background: Single-step genomic best linear unbiased prediction (ssGBLUP) models allow the combination of genomic, pedigree, and phenotypic data into a single model, which is computationally challenging for large genotyped populations. In practice, genotypes of animals without their own phenotype and progeny, so-called genotyped selection candidates, can become available after genomic breeding values have been estimated by ssGBLUP. In some breeding programmes, genomic estimated breeding values (GEBV) for these animals should be known shortly after obtaining genotype information but recomputing GEBV using the full ssGBLUP takes too much time. In this study, first we compare two equivalent formulations of ssGBLUP models, i.e. one that is based on the Woodbury matrix identity applied to the inverse of the genomic relationship matrix, and one that is based on marker equations. Second, we present computationally-fast approaches to indirectly compute GEBV for genotyped selection candidates, without the need to do the full ssGBLUP evaluation. Results: The indirect approaches use information from the latest ssGBLUP evaluation and rely on the decomposition of GEBV into its components. The two equivalent ssGBLUP models and indirect approaches were tested on a six-trait calving difficulty model using Irish dairy and beef cattle data that include 2.6 million genotyped animals of which about 500,000 were considered as genotyped selection candidates. When using the same computational approaches, the solving phase of the two equivalent ssGBLUP models showed similar requirements for memory and time per iteration. The computational differences between them were due to the preprocessing phase of the genomic information. Regarding the indirect approaches, compared to GEBV obtained from single-step evaluations including all genotypes, indirect GEBV had correlations higher than 0.99 for all traits while showing little dispersion and level bias. Conclusions: In conclusion, ssGBLUP predictions for the genotyped selection candidates were accurately approximated using the presented indirect approaches, which are more memory efficient and computationally fast, compared to solving a full ssGBLUP evaluation. Thus, indirect approaches can be used even on a weekly basis to estimate GEBV for newly genotyped animals, while the full single-step evaluation is done only a few times within a year.

Currently, enhancing the collaboration between related breeds is of main importance to increase the competitivity and the sustainability of local breeds. One type of collaboration is the development of an across-breed reference population that will allow a better management of local breeds. For this purpose, the genomic relatedness between the local target breed and possible breeds to be included in the reference population should be estimated. In Europe, there are several local red-pied cattle breeds that would benefit from this kind of collaboration. However, how different red-pied cattle breeds from the Benelux are related to each other and can collaborate is still unclear. The objectives of this study were therefore: (1) to estimate the level of inbreeding of the East Belgian Red and White (EBRW), the Red-Pied of the Ösling (RPO) and Dutch red-pied cattle breeds; (2) to determine the genomic relatedness of several red-pied cattle breeds, with a special focus on two endangered breeds: the EBRW and the RPO, and (3) based on the second objective, to detect animals from other breeds that were genomically close enough to be considered as advantageous in the creation of an across-breed reference population of EBRW or RPO. The estimated inbreeding levels based on runs of homozygosity were relatively low for almost all the studied breeds and especially for the EBRW and RPO. This would imply that inbreeding is currently not an issue in these two endangered breeds and that their sustainability is not threatened by their level of inbreeding. The results from the principal component analysis, the phylogenetic tree and the clustering all highlighted that the EBRW and RPO breeds were included in the genomic continuum of the studied red-pied cattle breeds and can be therefore considered as genomically close to Dutch red-pied cattle breeds, highlighting the possibility of a collaboration between these breeds. Especially, EBRW animals were closely related to Deep Red and Improved Red animals while, to a lesser extent, the RPO animals were closely related to the Meuse-Rhine-Yssel breed. Based on these results, we could use distance measures, based either on the principal component analysis or clustering, to detect animals from Dutch breeds that were genomically closest to the EBRW or RPO breeds. This will finally allow the building of an across-breed reference population for EBRW or RPO for further genomic evaluations, considering these genomically closest animals from other breeds.

It is uncertain whether Sustainable Development Goal 2 (SDG2), a healthy diet for all, can be achieved in East Africa given its strong population growth, low agricultural yields, and the high perishability of nutrient-dense foods. We examine the consequences of a locally produced healthy diet on land use in a case study of the Arua district in Uganda. This type of analysis can alert policy makers to looming nutrition gaps and support the selection of alternative solution strategies. Using a linear programming (LP) model and three population growth projections, we estimate the minimum agricultural area needed in 2040 to produce a healthy diet that follows EAT-Lancet dietary diversity guidelines and supplies the average requirements of calories, proteins, Iron, and vitamin A. We also compare in scenarios to what extent i) production intensification, ii) food loss reduction, iii) by-product consumption, and iv) vitamin supplementation could reduce the required agricultural area. Results show that the necessary area to produce a healthy diet in 2040 is 160% larger than Arua’s current crop area and would greatly exceed the district’s total area. We also show that none of the changes proposed in our scenarios allows a sufficient increase in food production, suggesting that a mix of even more drastic changes across sectors will be necessary. The results underline the challenge for rural areas in East Africa like Arua to provide a healthy diet to its fast growing population, requiring integrated food system changes and policy coordination to orchestrate the increased availability of diverse and nutritious foods.

Striga hermonthica (Del.) Benth is a parasitic weed that is damaging major cereal crops in sub-Saharan Africa (SSA). Although Striga is recognised as an agricultural scourge, there is limited information available indicating the extent of its growth and spread as impacted by the changing climate in Kenya. This study investigated the impact of current climate conditions and projected future (2050) climate change on the infestation of Striga hermonthica in the western Kenya region. Specifically, the study aimed to predict Striga hermonthica habitat suitability in five counties in the western Kenya region through using the maximum entropy (MaxEnt) model and bioclimatic, soil, topographic and land use, and land cover (LULC) variables. Striga hermonthica geolocations were collected and collated and ecological niche models were developed to determine the habitat suitability. The results showed that approximately 1767 km2 (10% of the total study area) is currently highly suitable for Striga hermonthica occurrence. The future projections showed a range between 2106 km2 (19% of the total study area) and 2712 km2 (53% of the total study area) at the minimum carbon (RCP 2.6) and the maximum carbon emission scenarios (RCP 8.5) respectively. Elevation, annual precipitation, LULC, temperature seasonality and soil type were determined to be the most influential ecological predictor variables for Striga hermonthica establishment. The study revealed the importance of using climate, soil, topographic and LULC variables when evaluating agricultural production constraints such as Striga's prevalence. The methodology used in this study should be tested in other Striga affected areas.

Ketosis is a metabolic disorder associated with high production and low energy intake in dairy cows. Fat reserves are mobilized, and gluconeogenesis occurs. Traditionally, rapid tests for blood, milk or urine are used to detect increased ketone body levels in case of ketosis. Breath analysis is relatively new and relevant for the development of automatic sensor systems for early warning. This study aims to determine whether and when a postpartum rise in ketone bodies occurs in breath with elevated blood β-hydroxybutyrate (BHB) in cows at risk of ketosis. Postpartum breath, blood, urine and milk samples were taken daily until day 7, with one prepartum sample excluding milk, and ketone body concentrations were determined. Concentrations of blood BHB were 0.4–2.6 mmol/L (lab) and 0.3–3.1 mmol/L (rapid test), breath acetone was 2.3–20.0 ppm, urine acetoacetate 0–8 mmol/L and milk BHB 0–500 µmol/L. A rise in blood BHB was related to that in urine acetoacetate and milk BHB and followed by a rise in breath acetone. However, breath acetone levels of ketotic and non-ketotic cows were similar. We conclude that rising ketone bodies can be detected in blood, urine, milk and breath, but to use breath analysis as an alternative to rapid tests, longitudinal sampling is needed.

This report describes the results of a review of five aquaculture projects that have been implemented in the past twelve years. The projects were located in Kenya (4) and Ethiopia (1). The aim of the review was to draw lessons and recommendations for design and implementation of future aquaculture projects. All projects had been funded completely or partly by the Government of the Kingdom of the Netherlands with the aim to support aquaculture sector development. Beside project documents the main sources of information were thirty two interviews with a range of project stakeholders. Four projects had capacity building as a major or even main component. The experiences and lessons learnt by Dutch and African project stakeholders as well as impacts on the aquaculture sector that lasted after project closure are described. Recommendations for design and implementation of future aquaculture projects are provided.

Phenotypes on individual animals are required for breeding programs to be able to select for traits. However, phenotyping individual animals can be difficult and time-consuming, especially for traits related to health, welfare, and performance. Individual broiler behavior could serve as a proxy for these traits when recorded automatically and reliably on many animals. Sensors could record individual broiler behavior, yet different sensors can differ in their assessment. In this study a comparison was made between a passive radio frequency identification (RFID) system (grid of antennas underneath the pen) and video tracking for the determination of location and movement of 3 color-marked broilers at d 18. Furthermore, a systems comparison of derived behavioral metrics such as space usage, locomotion activity and apparent feeding and drinking behavior was made. Color-marked broilers simplified the computer vision task for YOLOv5 to detect, track, and identify the animals. Animal locations derived from the RFID-system and based on video were largely in agreement. Most location differences (77.5%) were within the mean radius of the antennas’ enclosing circle (≤128 px, 28.15 cm), and 95.3% of the differences were within a one antenna difference (≤256 px, 56.30 cm). Animal movement was not always registered by the RFID-system whereas video was sensitive to detection noise and the animal's behavior (e.g., pecking). The method used to determine location and the systems’ sensitivities to movement led to differences in behavioral metrics. Behavioral metrics derived from video are likely more accurate than RFID-system derived behavioral metrics. However, at present, only the RFID-system can provide individual identification for non-color marked broilers. A combination of verifiable and detailed video with the unique identification of RFID could make it possible to identify, describe, and quantify a wide range of individual broiler behaviors.

One of the key challenges for aquaculture is to reduce “fishing-for-feed”. Alternative fish feeds need to be environmentally assessed to ensure they are sustainable. The present research consisted of an attributional LCA to (i) estimate the impact on salmon farming of a partially algal–insect-based diet vs a conventional fish meal/fish oil-based diet, (ii) identify the contribution of each process to the environmental impacts of the whole fish farming system, and (iii) identify potential improvements in the algal–insect value chain through sensitivity analysis of various algal–insect production pathways. The study shows that use of algal–insect-based feed resulted in a higher impact for most of the environmental impact categories due to fish feed production, particularly for soybean, insect, and algal meal. This points to the need to optimise production chains for new fish feed ingredients. Algal meal production using sugarcane sugar and optimised technology and insect meal using exhaust heat and renewable electricity would improve the environmental performance of salmon farming systems using insect- and algal-based fish feed. Methodological improvements with regard to system C and N cycle, biodiversity, and plastic use should be explored to inform policy making and support the implementation of sustainable future salmon farming innovations.

11 Pág. Departamento de Reproducción animal, A classical chicken semen diluent (Lake's 7.1 diluent) was modified to have lowered osmolalities (ranging from 290 to 410 mOsm/kg). The modified medium with physiological osmolality of 325 mOsm/kg allowed cold storage of fresh semen for several days with very little loss of membrane integrity and motility, while high osmolalities inhibited motility. This modified medium was then used as base for freezing medium to test effects of the type and concentration of cryoprotective agent (CPA), and the cooling rate (CR). A number of CPAs (methylformamide, methylacetamide, dimethylformamide (DMF), dimethylacetamide (DMA), diethylformamide, and propylene glycol) were first compared by freezing semen with 0.6 mol/l of the respective CPA at a cooling rate of 250 °C/min. Post-thaw motility and membrane integrity were highest with DMA and DMF. Finally, in more detailed factorial experiments, semen from individual cocks or pooled semen was frozen using CRs of 4, 50, 250, and 440 °C/min and DMA concentrations ([DMA]) of 0.4, 0.6, 1.0, and 1.5 mol/l. Straws from each semen sample x treatment combination were divided for semen assessment at three different research groups for sperm motility, membrane integrity, kinked tails, and DNA fragmentation, using microscopy, computer assisted motility analysis, and flow cytometry. There were clear effects of both CR and [DMA] and their interaction. CRs 50 and 250 °C/min gave best post-thaw sperm performance. Higher DMA concentrations gave better post-thaw membrane integrity, but concentrations above 1.0 mol/l can decrease sperm velocity or even inhibit sperm motility. Therefore [DMA] may best be 0.6-1.0 mol/l at a CR of 50-250 °C/min., Funding from the Dutch Ministry of Agriculture, Nature and Food Quality (project KB-Dier, WOT-03-436-077) and from European Union's Horizon 2020 Research and Innovation Programme (project IMAGE, grant agreement no 677353) are gratefully acknowledged.

In this paper, we consider a block Jacobi preconditioner and various deflation techniques applied in the Deflated Preconditioned Conjugate Gradient (DPCG) method for solving a sparse system of linear equations derived from a statistical linear mixed model that analyses simultaneously phenotypic and pedigree information of genotyped and ungenotyped animals with Single Polymorphism Nucleotide genotypes of genotyped animals. In livestock production systems, evaluating the genetic merit of the animals through such a model is a key process to ensure an improvement of animals for some characteristics of interest at each generation. First, we propose to define the deflation vectors using a subdomain deflation approach that considers some biological properties of the genotypes. Using simulated data, this approach reduces the number of iterations by up to 87% in comparison to a Preconditioned Conjugate Gradient method with a Jacobi preconditioner. Furthermore, compared to a DPCG method with same number of subdomains but defined randomly, this approach reduces the number of iterations by up to 20% for the same computational costs of one DPCG iteration. The properties of the resulting systems show that this approach annihilates the largest eigenvalues of the preconditioned coefficient matrix. Second, we propose the use of solution vectors of 12 systems of equations that include between 0.25% and 3% less data, as deflation vectors. For reducing the computational costs, we also consider a Proper Orthogonal Decomposition-reduced set of these 12 vectors. The properties of the resulting systems show that this recycling information approach annihilates the smallest eigenvalues of the preconditioned coefficient matrix, and results in a reduction of up to 39% in comparison to the PCG method. Finally, based on our experiment, the combination of the subdomain deflation approach relying on biological properties and of the POD-based approach to recycle previous solution vectors, for defining the deflation vectors, results in annihilating both the smallest and largest eigenvalues, and in a reduction of up to 88 % of the number of iterations in comparison to the PCG method.

Current breeding tools aiming to improve feed efficiency use definitions based on total dry matter intake (DMI); for example, residual feed intake or feed saved. This research aimed to define alternative traits using existing data that differentiate between feed intake capacity and roughage or concentrate intake, and to investigate the phenotypic and genetic relationships among these traits. The data set contained 39,017 weekly milk yield, live weight, and DMI records of 3,164 cows. The 4 defined traits were as follows: (1) Feed intake capacity (FIC), defined as the difference between how much a cow ate and how much she was expected to eat based on diet satiety value and status of the cow (parity and lactation stage); (2) feed saved (FS), defined as the difference between the measured and the predicted DMI, based on the regression of DMI on milk components within experiment; (3) residual roughage intake (RRI), defined as the difference between the measured and the predicted roughage intake, based on the regression of roughage intake on milk components and concentrate intake within experiment; and (4) residual concentrate intake (RCI), defined as the difference between the measured and the predicted concentrate intake, based on the regression of concentrate intake on milk components and roughage intake within experiment. The phenotypic correlations were -0.72 between FIC and FS, -0.84 between FS and RRI, and -0.53 between FS and RCI. Heritability of FIC, FS, RRI, and RCI were estimated to be 0.21, 0.12, 0.15, and 0.03, respectively. The genetic correlations were -0.81 between FS and FIC, -0.96 between FS and RRI, and -0.25 between FS and RCI. Concentrate intake and RCI had low heritability. Genetic correlation between DMI and FIC was 0.98. Although the defined traits had moderate phenotypic correlations, the genetic correlations between DMI, FS, FIC, and RRI were above 0.79 (in absolute terms), suggesting that these traits are genetically similar. Therefore, selecting for FIC is expected to simply increase DMI and RRI, and there seems to be little advantage in separating concentrate and roughage intake in the genetic evaluation, because measured concentrate intake was determined by the feeding system in our data and not by the genetics of the cow.

ObjectiveUnprecedented SARS-CoV-2 infections in farmed minks raised immediate concerns regarding transmission to humans and initiated intensive environmental investigations to assess occupational and environmental exposure.MethodsAir sampling was performed at infected Dutch mink farms, at farm premises and at nearby residential sites. A range of other environmental samples were collected from minks’ housing units, including bedding materials. SARS-CoV-2 RNA was analysed in all samples by quantitative PCR.ResultsInside the farms, considerable levels of SARS-CoV-2 RNA were found in airborne dust, especially in personal inhalable dust samples (approximately 1000–10 000 copies/m3). Most of the settling dust samples tested positive for SARS-CoV-2 RNA (82%, 75 of 92). SARS-CoV-2 RNA was not detected in outdoor air samples, except for those collected near the entrance of the most recently infected farm. Many samples of minks’ housing units and surfaces contained SARS-CoV-2 RNA.ConclusionsInfected mink farms can be highly contaminated with SARS-CoV-2 RNA. This warns of occupational exposure, which was substantiated by considerable SARS-CoV-2 RNA concentrations in personal air samples. Dispersion of SARS-CoV-2 to outdoor air was found to be limited and SARS-CoV-2 RNA was not detected in air samples collected beyond farm premises, implying a negligible risk of environmental exposure to nearby communities. Our occupational and environmental risk assessment is in line with whole genome sequencing analyses showing mink-to-human transmission among farm workers, but no indications of direct zoonotic transmission events to nearby communities.

Assisted propagation of the European eel will lead to a closed production cycle supplying the aquaculture industry with juvenile glass eels. Females require long-term weekly treatment with pituitary extract (PE), which is stressful and causes abnormalities in oogenesis. We tested the effects of 17α-methyltestosterone (17 MT), as potent androgen activating the androgen receptor, and 17β-estradiol (E2), as an inducer of vitellogenesis, to shorten the duration of PE treatment.Four groups of feminized eels were subjected to a simulated migration and subsequent injection with implants containing 17 MT (17 MT-group), E2 (E2-group) or 17 MT plus E2 (17 MT + E2-group) to test for synergistic effects, or without any steroids as controls (C-group). The effects of a 2-months treatment were investigated by determining the eye index (EI), hepatosomatic and gonadosomatic index (HSI and GSI, respectively), plasma steroid concentrations by liquid chromatography mass spectrometry (LCMS), gonadal histology, expression of androgen receptors a and b (ara, arb); estrogen receptor 1 (esr1); FSH receptor (fshr); vitellogenin receptor (vtgr) and aromatase (cyp19), and the required number of weekly PE injections to fully mature. For many parameters, both the 17 MT and E2 groups showed an increase vs. controls, with the 17 MT + E2 group showing a synergistic effect, as seen for EI, GSI (3.4 for 17 MT and for E2, 6.6 for 17 MT + E2), oocyte diameter and ara, arb and esr1 expression. Concentrations of almost all focal steroids decreased with simulated migration and steroid treatment. Only eels of the 17 MT-group showed increased expression of cyp19 and of fshr, while fshr expression increased 44-fold in the 17 MT + E2 group, highlighting that co-implantation is most effective in raising fshr mRNA levels. Specific for eels of the E2 groups were vitellogenesis-associated changes such as an increase of HSI, plasma E2, and presence of yolk in the oocytes. Steroid treatments reduced the duration of PE treatment, again synergistically for co-implantation. In conclusion, E2 is necessary to start vitellogenesis, but 17 MT has specific effects on cyp19 and fshr expression. The combination is necessary for synergistic effects and as such, steroid implants could be applied in assisted reproduction protocols for European eel to improve oocyte quality leading to the production of more vital larvae.

The calculation of exact reliabilities involving the inversion of mixed model equations poses a heavy computational challenge when the system of equations is large. This has prompted the development of different approximation methods. We give an overview of the various methods and computational approaches in calculating reliability from the era before the animal model to the era of single-step genomic models. The different methods are discussed in terms of modeling, development, and applicability in large dairy cattle populations. The paper also describes the problems faced in reliability computation. Many details dispersed throughout the literature are presented in this paper. It is clear that a universal solution applicable to every model and input data may not be possible, but we point out several efficient and accurate algorithms developed recently for a variety of very large genomic evaluations.

In precision livestock farming, technology-based solutions are used to monitor and manage livestock and support decisions based on on-farm available data. In this study, we developed a methodology to monitor the lying behaviour of dairy cows using noisy spatial positioning data, thereby combining time-series segmentation based on statistical changepoints and a machine learning classification algorithm using bagged decision trees. Position data (x, y, z -coordinates) collected with an ultra-wide band positioning system from 30 dairy cows housed in a freestall barn were used. After the data pre-processing and selection, statistical changepoints were detected per cow-day (no. included = 331) in normalized 'distance from the centre of the barn' and (z) time series. Accelerometer-based lying bout data were used as a practical ground truth. For the segmentation, changepoint detection was compared with getting-up or lying-down events as indicated by the accelerometers. For the classification of segments into lying or non-lying behaviour, two data splitting techniques resulting in 2 different training and test sets were implemented to train and evaluate performance: one based on the data collection day and one based on cow identity. In 85.5% of the lying-down or getting-up events a changepoint was detected in a window of 5 minutes. Of the events where no detection had taken place, 86.2% could be associated with either missing data (large gaps) or a very short lying or non-lying bout. Overall classification and lying behaviour prediction performance was above 91% in both independent test sets, with a very high consistency across cow-days. Per cow-day, the average error in the estimation of the lying durations were 7.1% and 7.8% for the cow-identity and time-based data splits respectively. This resulted in sufficient accuracy for automated quantification of lying behaviour in dairy cows, for example for health or welfare monitoring purposes. ispartof: Peer Community Journal vol:2 status: published