In this paper, Madouasse et al. (2021) present a generalisable Bayesian method for calculating the probability that a herd is free from disease, based on its prior disease status, and using data (herd status over time over a sufficient number of herds to inform the model) and reasonable prior estimates of the sensitivity and specificity of tests being used to determine animal infection status.  Where available, the modelling approach can also include relevant additional risk factors. 

By bringing all these factors together, it allows for most countries to use the same analytical approach on their data, with differences across datasets expressed in terms of the uncertainty around the central estimates. 

Having a single methodology that generates both a central estimate of disease freedom, and uncertainty thus provides the opportunity (given typically available data) to compare the probability of freedom across different systems. This is relevant in terms of the context of trade (since international trade of livestock in many cases depends on disease freedom). It is also important when evaluating, for example, transnational burdens of disease - and with different regulations in place in different countries, this is invaluable and can be used, for example, to assess risks of zoonotic infection including for zoonotic infection emergence. In the BVD example provided, the point is made that, since regular testing would probably pick up infection rapidly, the addition of risk factors is most valuable where testing is infrequent. This emphasizes the advantages of direct incorporation of risk factors into a single modelling framework. 

From a technical point of view, the analysis compares two different packages for the Markov Chain Monte Carlo (MCMC) implementation necesary to run the model. They show that, while there are some slight systematic differences, the estimates provided by the two methods are similar to each other; as one method is approximate but substantially more stable and generally much more computationally efficient, this is an important outcome. Both implementations are freely available and  with relevant additional software made similarly available by the authors. This is extremely welcome and should encourage its general adoption across different countries. 

No single model can of course account for everything. In particular, the reliance on past data means that there is an implicit assumption common to all purely statistical methods that the underlying risks have not changed. Thus projections to altered circumstances (changing underlying risk factors or systematic changes in testing or test performance) cannot so easily be incorporated, since these factors are complicated by the dynamics of infection that lie outside the modelling approach. Of course the well known quote from George Box that "all models are wrong" applies here - the generality of approach, statistical robustness and open source philosophy adopted make this model very useful indeed.  

Madouasse A, Mercat M, van Roon A, Graham D, Guelbenzu M, Santman Berends I, van Schaik G, Nielen M, Frössling J, Ågren E, Humphry RW, Eze J, Gunn GJ, Henry MK, Gethmann J, More SJ, Toft N, Fourichon C (2021) A modelling framework for the prediction of the herd-level probability of infection from longitudinal data. bioRxiv, 2020.07.10.197426, ver. 6 peer-reviewed and recommended by PCI Animal Science. https://doi.org/10.1101/2020.07.10.197426

The study by Jacques et al. (2024) addresses a critical concern in the context of genetic diversity erosion in domesticated animal populations.  The research uses data from the cryopreserved resources from the French National Cryobank to manage genetic diversity in livestock species. The authors employ a comprehensive methodology to propose novel biodiversity metrics to characterize the status of genetic diversity of cryopreserved collections including cattle, sheep, goat, horse, donkey, and pig livestock species.  The findings reveal significant variations of genetic diversity at species and breed levels. Breeds with a large commercial distribution had more donors in the collection than local breeds. The authors propose a practical framework for assessing germplasm collections, providing a valuable tool for planning and managing collections at both national and international levels.  The study also highlights the usefulness of the Gini-Simpson and effective donor numbers indices to plan a more efficient sampling, whereas the index of diversity impact can be employed in the selection of the most suitable donors for immediate use, based on pedigree but also using genetic markers. 

In resume, this study makes a significant contribution to the field by offering a framework for the assessment of germplasm collections. Its innovative metrics provide insights that could guide strategic decision-making in planning, managing, and utilizing cryopreserved resources. This research is relevant and can benefit conservationists, and population genetics working towards the preservation and sustainable use of genetic resources in livestock species.

Reference

Jacques, A., Duclos, D., Danchin-Burge, C., Mercat, M. J.,  Tixier-Boichard M., Restoux, G. (2024). Assessing the potential of germplasm collections for the management of genetic diversity: the case of the French National Cryobank. bioRxiv 2023.07.19.549644. ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2023.07.19.549644

One of the achievements of animal genetics is that it finds solutions along with the emergence of new needs of the animal husbandry community or the adoption of new laws. Muir and Cheng (2013) research serves as a classic example of using the innovations of animal genetics to meet new legal challenges, such as the restriction of beak cutting in laying hens. Muir and Cheng (2013) investigated the genetic diversity to deal with the cannibalism of intact chickens. In 2021, the European Citizens' Initiative urged the European Commission to legislate against the use of cages for farm animals in the livestock industry. Chapuis et al., (2024) presented a successful solution to the poultry industry about this (future) law by presenting a cost-efficient assignment SNP panel.

In animal breeding, access to pedigree information is necessary for genetic progress. Since the 1970s, the development of genomic science and  molecular techniques has shown their ability in this field. Despite the substantial reduction of genotyping costs in the last 20 years, the practical use of genome-wide genotyping for thousands of SNPs remains challenging. Therefore, the search for a small, cost-effective SNP panel is ongoing, with objectives including genetic diversity (Viale et al., 2017), product traceability (Dominik et al., 2021), species and hybrid identification (Harmoinen et al., 2021) and pedigree construction in wild populations (Ekblom et al., 2021). Furthermore, especially in recent decades, small panels of markers have been proposed for parentage assignment in different animals. For example, Domínguez-Viveros et al., (2020) developed panels with 42 to 63 markers for different sheep breeds in Mexico. Similar panels for parentage assignment have been proposed for salmon (May et al., 2020), rainbow trout (Liu et al., 2016), French sheep (Tortereau et al., 2017), Spanish sheep (Calvo et al., 2021), and European bison (Wehrenberg et al., 2024), with marker numbers of 142, 95, 180, 173, and 96, respectively. Massault et al., (2021) showed by simulation that a panel with at least 50 markers is sufficient for progeny assignment in pearl oysters. These examples highlight that extracting a small panel of markers (usually less than 200) from the total genotyping introduced in different species, can open new horizons for applying genomic information in animal breeding.

Chapuis et al. (2024) addressed the challenge of finding an efficient set of markers that can be used in the hybridization of two species of the Pekin duck and the Muscovy duck. They used KASPar technology to setup a panel, with SNPs existing in both species and their hybrids. This panel has sufficient polymorphism to use in practice. Thus, it can be considered as a step forward compared to previous work done on microsatellites. A final list of SNPs was constructed from a reference set comprising 600 K genotyping of Anas platyrhynchos, Cairina moschata and mule duck. 

In addition to developing of a cost-efficient assignment panel, the work of Chapuis et al. (2024) presented a factorial design to maintain genetic diversity while considering specificities of duck production. The use of factorial design in avian pedigreed populations is relatively novel, making this research particularly innovative. The study's approach to factorial design in populations with limited size may be generalized to similar poultry species. Furthermore, sufficient effective size of population is selected. So, the panel can be used in other populations outside the tested populations. A notable feature of this panel is the use of neutral SNPs, which ensures that markers will not be lost due to future selection pressures over time.

The paper of Chapuis et al. (2024) exemplifies the application of molecular genetics to address challenges in the poultry industry.  The use of kinship matrix instead of relationship matrix, taking into account the unique characteristics of duck production, could be another novelty of the paper. According to the reviewers' comments, the results can be beneficial in the future, particularly with the introduction of the specific factorial design.

References

Calvo JH, Serrano M, Tortereau F, Sarto P, Iguacel LP, Jiménez MA, Folch J, Alabart JL, Fabre S and Lahoz B (2021). Development of a SNP parentage assignment panel in some North-Eastern Spanish meat sheep breeds. Spanish Journal of Agricultural Research 18, e0406. https://doi.org/10.5424/sjar/2020184-16805    

Chapuis H, Brard-Fudulea S, Hazard A, Vignal A, Demars J, Rouger R, Teissier M, Gilbert H (2024). Cost-efficient assignment panel for ducks. Setup of a cost-efficient assignment panel for duck populations.: An illustration with experimental data. HAL, hal-04542880, ver. 2 peer-reviewed and recommended by Peer Community in Animal Science. https://hal.inrae.fr/hal-04542880 

Domínguez-Viveros J, Rodríguez-Almeida FA, Jahuey-Martínez FJ, Martínez-Quintana JA, Aguilar-Palma GN, Ordoñez-Baquera P (2020). Definition of a SNP panel for paternity testing in ten sheep populations in Mexico, Small Ruminant Research ,193,106262. https://doi.org/10.1016/j.smallrumres.2020.106262

Dominik S, Duff CJ, Byrne AI, Daetwyler H, Reverter A (2021). Ultra-small SNP panels to uniquely identify individuals in thousands of samples. Animal Production Science 61, 1796–1800. https://doi.org/10.1071/AN21123

Ekblom R, Aronsson M, Elsner-Gearing F, Johansson M, Fountain T, Persson J (2021). Sample identification and pedigree reconstruction in Wolverine (Gulo gulo) using SNP genotyping of non-invasive samples. Conservation Genetics Resources 13, 261–274. https://doi.org/10.1007/s12686-021-01208-5

Harmoinen J, von Thaden A, Aspi J, Kvist L, Cocchiararo B, Jarausch A, Gazzola A, Sin T, Lohi H, Hytönen MK, Kojola I, Stronen AV, Caniglia R, Mattucci F, Galaverni M, Godinho R, Ruiz-González A, Randi E, Muñoz-Fuentes V, Nowak C (2021). Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples. BMC Genomics 22, 473. https://doi.org/10.1186/s12864-021-07761-5

Liu S, Palti Y, Gao G, Rexroad CE (2016). Development and validation of a SNP panel for parentage assignment in rainbow trout. Aquaculture 452, 178–182. https://doi.org/10.1016/j.aquaculture.2015.11.001

Massault C, Jones DB, Zenger KR, Strugnell JM, Barnard R, Jerry DR (2021). A SNP parentage assignment panel for the silver lipped pearl oyster (Pinctada maxima). Aquaculture Reports 20, 100687. https://doi.org/10.1016/j.aqrep.2021.100687

May SA, McKinney GJ, Hilborn R, Hauser L, Naish KA (2020). Power of a dual-use SNP panel for pedigree reconstruction and population assignment. Ecology and Evolution 10, 9522–9531. https://doi.org/10.1002/ece3.6645

Muir WM, Cheng HW(2013). Genetics and the Behaviour of Chickens: Welfare and Productivity. In Genetics and the Behaviour of Domestic Animals. Vol. 2 (2nd ed.). pp. 1–30.ISBN: 9780128100165

Tortereau F, Moreno CR, Tosser-Klopp G, Servin B, Raoul J (2017). Development of a SNP panel dedicated to parentage assignment in French sheep populations. BMC Genetics 18, 50. https://doi.org/10.1186/s12863-017-0518-2

Viale E, Zanetti E, Özdemir D, Broccanello C, Dalmasso A, De Marchi M, Cassandro M (2017). Development and validation of a novel SNP panel for the genetic characterization of Italian chicken breeds by next-generation sequencing discovery and array genotyping. Poultry Science 96, 3858–3866. https://doi.org/10.3382/ps/pex238

Wehrenberg G, Tokarska M, Cocchiararo B, Nowak C (2024). A reduced SNP panel optimised for non-invasive genetic assessment of a genetically impoverished conservation icon, the European bison. Scientific Reports 14, 1875. https://doi.org/10.1038/s41598-024-51495-9

Using on-farm sensors in dairy farming is known to help decision makings and farmer objectives in the monitoring and potential improvement of animal behaviour, health and production performance. However, in indoor positioning systems, data interpretation is complicated by the inaccuracy and noise in the time series, missing data caused not only by sensor failure or the harsh and changing farm environments in which they operate, but also by the animals' specific physiology itself. Thus, working with spatial data has proven challenging mainly due to their enormous heteroscedasticity, which depends on multiple factors such as the cow, the time of the day, the behaviour, factors interfering with the sensor system, etc., for which we cannot account mathematically. Applying purely black-box approaches generally results in insufficient robustness, interpretability and generalisability. 

With this work, Adriaens et al. (2022) developed a relatively simple and new methodology to monitor the lying behaviour of dairy cows by using noisy spatial positioning data, while combining time-series segmentation based on statistical changepoints and a machine learning classification algorithm. The two-step methodology identifies lying behaviour using an ultra-wide band indoor positioning system. Getting-up or lying-down events were indicated by the accelerometers. Overall classification and lying behaviour prediction performance was above 91% in independent test sets, with a very high consistency across cow-days. The robustness of the algorithm was demonstrated by the fact that both the cow identity-based split and the time-based split performed equally well. 

The article represents an original contribution for advancing the state of the art in the automated quantification of lying behaviour in dairy cows, aiming to monitor health or animal welfare issues. Future research must be considered however to validate the performance of the model when using different position-measuring technologies, in other farm settings and over a longer period of time.

Reference

Adriaens I, Ouweltjes W, Pastell M, Ellen E, Kamphuis C. 2022. Detecting dairy cows' lying behaviour using noisy 3D ultra-wide band positioning data. Zenodo, 6627251, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.5281/zenodo.6627251 

The dairy sector is facing an historical period of high milk demand. However, increasing feed prices continually reduces the economic margins for farms. Managerial strategies to increase economical and technical awareness of animal performance, support the decision chain and optimize the use of production inputs are increasingly necessary, especially in goat farms with intensive production systems. Among the scientific goals, there is a particular emphasis on increasing knowledge about nutrition partitioning between milk production and body reserves — a topic that not easily addressed by nutritional models, limiting the attempts at production forecasting. 

The paper by Gafsi et al (2024) presents an interesting approach to studying phenotypic traits and trajectories of goat performance. It assesses the diversity of phenotypic trajectories reflecting functions such as milk production, body weight and condition score. This approaches aims to describe, understand and explore the interactions among biological functions and potential trade-offs of phenotypic trajectories across current and successive lactations. The work significantly contributes to the literature, particularly because previous descriptions of lactation curves relied primarily on mathematical outputs lacking information about the relationship among physiologically related variables. 

The analysis retrieved data from about 1500 goats over more than 20 years and was conducted with a multiscale approach. Data were fitted considering different types of models, including description of perturbations for lactation curves and with multiphasic models for the body weight and body condition score. Synthetic indicators were then estimated with a multivariate approach to define fitted trajectories and changes in performance. 

The association among performance directly refers to nutrient partitioning, and the adaptive response of individual animals refers to nutrient availability and their aptitude to direct the metabolic effort toward milk production. In fact, the research shows that trajectories from the first lactation were often maintained in sequent ones. Positive associations among milk curve perturbations and the individual aptitude to drive nutrients to body reserves were also observed.

The multiscale approach developed in the paper may provide original insights both for describing  animal performance and for ranking individual animals within farm groups. Practically, the method could inform the development of algorithms to support culling policies and individual animal assessments. Additionally, the data are available to the scientific community  for further research and applications. 

I recommend this paper as a methodological example with high replicable approaches for both goats and other dairy species, with significant applicative opportunities at the farm level.

Reference

Gafsi N, Martin O, Bidan F, Grimard B, Puillet L (2024) Diversity of performance patterns in dairy goats: multi-scale analysis of the lactation curves of milk yield, body condition score and body weight. Zenodo. 10101318. ver.3 peer-reviewed and recommended by Peer Community In Animal Science. https://doi.org/10.5281/zenodo.10101318