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 

Epidemics such as PRRSv-like virus in pig farms has tremendous impact on the competitiveness of swine production. However, its control requires an understanding of the complex interaction between pathogen transmission, disease impact, population dynamics and management. By using mechanistic epidemiological modelling, Sicard et al. (2023) open up a very interesting field of possibilities. This article describes work aimed at assessing the consequences of infections, taking into account the interaction between clinical outcomes and population dynamics. This study shows how this interaction can influence transmission dynamics at the herd level. It highlights the need to further explore this direction, integrating both disease impacts in breeding practices and structural changes in population dynamics, such as pig crossbreeding and grouping methodologies.
The provision of a new tool making it possible to model herd management practices and the transmission of a virus, such as PRRS, in time and space is a major contribution to understanding the dynamics of this category of diseases. It opens up the possibility of being able to represent specific herd structures and evaluate transmission dynamics using real data. This work improves our understanding of disease spread across herds, taking into account herd management.

Reference

Sicard V, Picault S, Andraud M (2023) Pig herd management and infection transmission dynamics: a challenge for modellers. bioRxiv, 2023.05.17.541128. ver. 2 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2023.05.17.541128

The identification of reliable estimates of growth potential and resilience over the fattening period in large populations is a challenge in actual swine breeding conditions. To overcome this drawback, the study by Revilla et al. 2021 in the frame of precision livestock farming aimed to propose an innovative modelling approach, in addition to previous studies from the same group (Revilla et al. 2019), to enhance the quality of the quantification of pig resilience during the entire fattening period. 

The authors developed a model that quantifies an “individual pig resilience indicator” based on longitudinal data, for instance body weight, recorded routinely by a commercially available automatic feeding system. Revilla and co-workers considered in their study two mainly commercialised pure pig breeds these being Piétrain including Piétrain Français NN Axiom line (Pie NN) free from halothane-sensitivity (ryanodine receptor gene, RYR1) and Piétrain Français Axiom line positive to this gene and Duroc. Therefore, the authors investigated the potential of improving resilience of swine livestock through inclusion for the first time of an “individual pig resilience indicator” in breeding objectives. A database of 13 093 boars (approximately 11.1 million of weightings) belonging to Pie (n= 5 841), Pie NN (n = 5 032) and Duroc (n= 2 220) finished under ad libitum feeding, high sanitary level and controlled temperature was used to develop robust models.

The authors checked the three datasets (for each pig breed)​ independently to explore the variation and gaps (a data pre-treatment procedure) to ensure high quality data for the modelling approach. Then, they applied the Gompertz model and linear interpolation on body weight data to quantify individual deviations from the expected production, allowing the creation of the ABC index. For the modelling, the authors applied a two-step mathematical model approach by first establishing a theoretical growth curve of each animal, while the second step aimed to build the actual perturbed growth curve. The heritability of the index ranged from 0.03 to 0.04, with similar heritability between Piétrain and Duroc breeds. Moreover, moderate genetic relationships were computed between the proposed index and important phenotypic traits in swine production likely BF100: backfat thickness at 100kg; LD100: longissimus dorsi thickness at 100kg; ADG: average daily gain during control and FCR: feed conversion ratio.

Developing models able to capture perturbations during the fattening period is a challenge in swine breeding industry. The model and methodology proposed by the authors in this innovative work (although preliminary and with low heritabilities) would help overcome such limit and facilitate a real implementation at large scale in pig breeding system. The modelling approach further offers an opportunity to develop a selection criterion to improve resilience in swine breeding conditions. 

To explore the full potential of this modelling approach, a larger database and other factors such as breed, behaviour and feeding behaviour of the animals, rearing practices, management and environment conditions, age… etc. are worthy to consider. In the future, more in depth measurements of behaviour that can be computed for example using computer vision should be desirable to increase the robustness of the proposed model.

References

Revilla, M., Friggens, N.C., Broudiscou, L.P., Lemonnier, G., Blanc, F., Ravon, L., Mercat, M.J., Billon, Y., Rogel-Gaillard, C., Le Floch, N. and Estellé, J. (2019). Towards the quantitative characterisation of piglets’ robustness to weaning: a modelling approach. Animal, 13(11), 2536-2546. https://doi.org/10.1017/S1751731119000843 

Revilla M, Lenoir G, Flatres-Grall L, Muñoz-Tamayo R, Friggens NC (2021). Quantifying growth perturbations over the fattening period in swine via mathematical modelling. bioRxiv, 2020.10.22.349985, ver. 5 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2020.10.22.349985 

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

Tailor-made health plans for farming animals, including pigs, are highly beneficial due to their customized nature, addressing the unique needs of each farm and promoting efficient husbandry practices. However, assessing the effectiveness of individualized approaches can be challenging. Levallois et al. (1) tackled this challenge by evaluating the effectiveness of tailor-made health plans of pig farms based on a systematic biosecurity and herd health audit. The study involved twenty farrow-to-finish pig farms, each receiving specific plans tailored to their specific needs. Compliance with the recommendations was monitored over an eight-month period. In the literature, various studies have delved into specific issues in detail, such as disease incidence (e.g., (2)). However, the authors of this research applied a comprehensive approach through an integrative analysis of multiple complementary indicators to provide an effective evaluation of the changes and health disorders.

The authors' holistic approach to measuring the effectiveness of tailor-made health plans is noteworthy. They employed up to seven methods to identify advantages and limitations, providing valuable insights for applied research and practitioners in the field of farm animals. Additionally, the study's inclusion of diverse farms, ranging from conventional to antibiotic-free and varying in sow breeding numbers (from 70 to 800), demonstrates the flexibility of the proposed approach, accommodating different farming systems.

The study revealed three crucial considerations for future evaluations of tailor-made health plans. Firstly, placing compliance as the primary assessment indicator is a priority. Secondly, it is essential to tailor outcome indicators and monitoring periods according to each farm's specific health disorder. Lastly, a comprehensive understanding of the health disorder's evolution can be achieved through the amalgamation of multiple indicators.

While the study does have limitations, such as the relatively short time window for assessment, the methodological framework and results are promising. Further, the discussion of the results raises several areas worthy of future investigation to improve compliance and address farmers' hesitations towards action (i.e., lack of willingness). More research in this context will be beneficial for veterinarians and practitioners, enhancing their understanding and positively impacting both farmers and animals.

In conclusion, the study underscores the significant impact of tailor-made health plans on promoting positive changes in farm management. Assessing the effectiveness of these plans enables the refinement of new strategies and enhances the overall quality of work in animal production. The study by Levallois et al (1) sheds valuable light on the challenges and potentials of such plans, providing essential insights for pig farming practices. While further research and improvements are necessary, the study strongly emphasizes the pivotal role of individualized approaches in attaining improved farm management and enhancing animal welfare.

 
References:

1.     Levallois P, Leblanc-Maridor M, Scollo A, Ferrari P, Belloc C, Fourichon C. (2023). Combining several indicators to assess the effectiveness of tailor-made health plans in pig farms. Zenodo, 7789634. ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.5281/zenodo.7789634 

2.   Collineau L, Rojo-Gimeno C, Léger A, Backhans A, Loesken S, Nielsen EO, Postma M, Emanuelson U, grosse Beilage E, Sjölund M, Wauters E, Stärk KDC, Dewulf J, Belloc C, Krebs S. (2017). Herd-specific interventions to reduce antimicrobial usage in pig production without jeopardising technical and economic performance. Preventive veterinary medicine, 144:167-78. https://doi.org/10.1016/j.prevetmed.2017.05.023