The reduction of antibiotic use in livestock production, for the different species, has become a critical focus in national action plans across many countries, underscoring the urgent need to tackle this issue to preserve public health and combat antibiotic resistance (Xu et al., 2022; Jacobsen et al., 2023; Bava et al., 2024). Among these efforts, Vietnam's ambitious 2017 livestock plan is notable for its comprehensive regulatory framework aimed at controlling antibiotic use. This framework includes a phased ban on prophylactic antibiotics in animal feed and requires mandatory prescriptions for antibiotic access.

Despite these promising regulations, their actual implementation poses significant challenges, with limited data available on their practical application. A recent study led by Batie and collaborators attempts to fill this knowledge gap by examining how these regulations are understood, accepted, and applied by stakeholders in the veterinary drug value chain in both northern and southern Vietnam (Batie et al., 2024).

The study employed an interesting iterative stakeholder mapping and analysis approach, organizing a focus group in Hanoi with 12 participants and conducting 39 in-depth semi-structured interviews with a diverse range of stakeholders. These included government authorities, national research bodies, international partners, and private sector representatives. The qualitative analysis aimed to map the veterinary drug value chain, assess stakeholders' technical and social capital regarding regulations, and identify key factors influencing regulatory compliance.

This research convincingly unveiled a complex network of 30 stakeholder categories and identified ten crucial factors that affect the implementation of regulations. These factors include stakeholders’ perceptions and understanding of the regulations, the availability of technical guidance, economic conflicts of interest, management inconsistencies, and hurdles such as technical and financial constraints, informal distribution channels, international influence, and consumer demand for safety. Additionally, the collective drive to reduce antibiotic resistance emerged as an influential factor.

The comprehensive analysis reveals a pressing insight: although Vietnam's regulatory measures are essential for reducing antibiotic usage, their effectiveness is compromised by barriers such as inadequate local stakeholder involvement and various resource limitations. The study emphasizes the necessity for deeper engagement of local stakeholders in developing and refining these regulations. Furthermore, incorporating innovations from small producers into mainstream practices could be vital in overcoming current challenges.

Nonetheless, the study acknowledges several limitations. Most interviews were conducted online owing to the health crisis—a much-needed format for time and budget constraints, albeit with some drawbacks such as reduced direct observations and potential information loss (Namey et al., 2019). The sensitivity of the subject may have led participants to withhold their true opinions, although the researchers attempted to mitigate this bias by interviewing multiple respondents from each category and gathering diverse perspectives. Notably, the study struggled to engage informal stakeholders, which could have enriched the description of the informal value chain. Constraints of time and resources meant that only a single representative from some stakeholder categories was interviewed, suggesting that interviewing additional parties, such as another veterinary district station, might have clarified roles within the drug value chain. The stakeholder identification was initially influenced by the researchers’ familiarity with the Vietnamese context; however, the iterative process helped address this limitation by recruiting new participants based on existing participants' knowledge. Additionally, translation issues may have introduced misunderstandings, potentially leading to an incomplete representation of the veterinary drug value chain, which reflects the situation as of data collection in 2021.

For Vietnam to meet its policy objectives and contribute to the global endeavor against antibiotic resistance, it is crucial to reconcile stakeholder discrepancies and promote collaborative innovation. By fostering an inclusive environment for all parties, Vietnam can not only enhance regulatory adherence but also strengthen its commitment to sustainable and responsible livestock farming practices.

The study is thoughtfully designed and skillfully executed. Additionally, the authors have made further improvements based on feedback from the journal. Readers will find the article both informative and engaging, providing valuable insights. I highly recommend this original article on the regulatory framework for controlling antibiotic use in Vietnam's livestock production systems.

References

Batie, C., Duy, N. V., Khue, N. T. M., Peyre, M., Bordier, M., Dien, N. T., et al. (2024). Understanding the implementation of antimicrobial resistance policies in Vietnam: a multilayer analysis of the veterinary drug value chain. medRxiv, 2024.06.27.24309573, ver. 2 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2024.06.27.24309573   

Bava, R., Castagna, F., Lupia, C., Poerio, G., Liguori, G., Lombardi, R., et al. (2024). Antimicrobial Resistance in Livestock: A Serious Threat to Public Health. Antibiotics 13, 551. https://doi.org/10.3390/antibiotics13060551

Jacobsen, A. B. J. E., Ogden, J., and Ekiri, A. B. (2023). Antimicrobial resistance interventions in the animal sector: scoping review. Front. Antibiot. 2. https://doi.org/10.3389/frabi.2023.1233698 

Namey, E., Guest, G., O’Regan, A., Godwin, C. L., Taylor, J., and Martinez, A. (2019). How Does Mode of Qualitative Data Collection Affect Data and Cost? Findings from a Quasi-experimental Study. Field Methods. https://doi.org/10.1177/1525822X19886839 

Xu, C., Kong, L., Gao, H., Cheng, X., and Wang, X. (2022). A Review of Current Bacterial Resistance to Antibiotics in Food Animals. Front Microbiol 13, 822689. https://doi.org/10.3389/fmicb.2022.822689 

Scientific research can be seen by some as a competitive territory: competition of opinions, concepts, publications, competition for funding. Fortunately, it is above all a territory of sharing and cross-fertilization of ideas. It is gradually becoming a territory of productive interdisciplinary collaborations, despite persistent resistance to making borders more permeable [1]. At the crossroads of worlds, many challenges must be met for communities to understand each other, to be able to communicate with one another, and to benefit mutually from scientific interactions [2].

Delphine Destoumieux-Garzon and co-authors [3] propose to stimulate a single Animal Research Kinship (OneARK) to promote the crossing of the scientific communities in animal production and animal ecology. These two communities share many concepts and methods, which, while they are based on marked specificities (natural versus artificial systems), also and above all have common points that need to be explored more closely. Seven concepts of shared interest to improve the resilience and sustainability of animal population systems were explored by the authors: selection, system viability, system management, animal adaptability, inter-individual diversity in systems, agroecology, and animal monitoring.

This foundation stone paves the way for a finer integration between these two communities, which are close and yet distant, and which are slowly getting to know, understand, and recognize each other.

References

[1] Ledford, H. (2015). How to solve the world’s biggest problems. Nature, 525, 308–311. doi: 10.1038/525308a
[2] Knapp, B., Bardenet, R., Bernabeu, M. O., Bordas, R., Bruna, M., Calderhead, B., … Deane, C. M. (2015). Ten simple rules for a successful cross-disciplinary collaboration. PLoS Computational Biology, 11(4), e1004214. doi: 10.1371/journal.pcbi.1004214
[3] Destoumieux-Garzón, D., Bonnet, P., Teplitsky, C., Criscuolo, F., Henry, P.-Y., Mazurais, D., … Friggens, N. (2020). OneARK: Strengthening the links between animal production science and animal ecology. Ver 6 Peer-Reviewed and Recommended by PCI Animal Science. doi: 10.5281/zenodo.3632731

Farm animals differ in their ability to respond to the many environmental challenges they face. Such challenges include infectious diseases, metabolic diseases resulting from inadequate coverage of dietary needs, as well as the diverse consequences of climate change. Various concepts exist to characterise the responses of animals to different types of challenges. This article by Friggens et al. (2022) focuses on resilience, providing a conceptual definition and proposing a method to quantify resilience in dairy cows.

The first part of the paper provides a definition of resilience and highlights its differences and relations with the related concepts of robustness, and, to a lesser extent, resistance and tolerance. In essence, resilience is the ability of an animal to bounce back quickly after a challenge of limited duration. On the other hand, robustness is the ability of an animal to cope with conditions that are overall unfavourable. From these conceptual and intuitive definitions, there are several difficulties precluding the design of concrete methods to measure resilience. First, there is some degree of overlap between the concepts of resilience, robustness, resistance and tolerance. Secondly, resilience is a multidimensional concept whereby resilience to a given perturbation does not imply resilience to other types of perturbation, e.g. resilience to a challenge by a specific pathogen does not imply resilience to a nutritional challenge. A further difficulty in the measure of resilience is the fact that different animals may be exposed to challenges that are different in nature and in number. The authors argue that although resilience cannot be measured directly (it should be seen as a latent construct), it is possible to quantify it indirectly through its consequences.

In the second part of the paper, the authors propose a method to quantify resilience of individual dairy cows. The method is based on the premise that resilient animals should be kept longer in their herd than non-resilient animals. The main criterion in the evaluation is therefore the ability of cows to re-calve. Each cow that is calving receives a certain number of points, to  which, in each lactation, bonus points are added for higher milk production and penalty points are removed for each insemination after the first one, for each disease event and for each day of calving interval above some herd specific value. Therefore, cows have a resilience score in each lactation. They also have a lifetime resilience score obtained by summing the scores for all the lactations, that gets bigger as the cow has more calves, and that also takes the age at first calving into account. In a previous study, Adriaens et al. (2020) showed that higher resilience scores were associated with fewer drops in milk yield and more stable activity dynamics.

Starting from theoretical considerations on the notion of resilience, this paper describes a concrete method to quantify animal-level resilience on farm. Such quantification will be useful for breeding and culling decisions. Finally, the general framework to design resilience measures that is presented will be useful to researchers working on the quantification of farm animal resilience using new methods and data sources.

References

Adriaens I, Friggens NC, Ouweltjes W, Scott H, Aernouts B and Statham J 2020. Productive life span and resilience rank can be predicted from on-farm first-parity sensor time series but not using a common equation,  across farms. Journal of Dairy Science 103, 7155-7171.https://doi.org/10.3168/jds.2019-17826

Friggens, N.C. , Adriaens, I., Boré, R., Cozzi, G., Jurquet, J., Kamphuis, C., Leiber, F., Lora, I., Sakowski, T., Statham, J., De Haas, Y. (2022). Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait. Zenodo, 5215797, ver. 5 peer-reviewed and recommended by Peer community in Animal Science. https://dx.doi.org/10.5281/zenodo.5215797

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 

Many cognitive studies use paradigms based on active decision-making, that require that animals are motivated to participate and interested in the reward (e.g. Rivas-Blanco et al., 2023). By contrast, looking time paradigms, in which the visual attention of an animal to a stimulus is measured, requires little training and little action from the subject, and can be used without reinforcement (e.g. Wilson et al., 2023). 

In this methodological paper, Jana Deutsch and her collaborators investigated the possibility of using a looking time paradigm to study perception and cognition in goats. The advantage of such a paradigm would be that it requires little training and can be used with no reinforcement. Goats were observed in front of two video screens presenting pictures of goats (familiar or not), of humans (familiar or not), or remaining white. The authors hypothesised that goats would pay more attention to pictures than to a white screen, would pay more attention to goats than to humans, and would discriminate familiar vs. unfamiliar beings. The goats had received previous positive contacts with the familiar humans. The goats were extensively habituated to the experimental set-up so that stress did not interfere in responses to testing. The stimuli were presented on the screens in a pseudorandomized and counterbalanced order.

As hypothesised, goats looked longer at screen with pictures, and longer when the picture was that of another goat (familiar or not) than of a human being. Goats however did not seem to discriminate between familiar and unfamiliar being, or were equally motivated by the two types of beings. Ear postures were also recorded but did not show a relation with looking time and were not related to the type of picture shown on screens. Therefore, the authors argue that looking time but not ear posture is considered appropriate to test discrimination abilities or preferences in goats. More studies are needed to check if goats can differentiate familiar vs. unfamiliar beings.

The experimental design is sound. The statistical analyses are rigorous and very relevant.  The paper is clearly written.

I recommend the manuscript for publication for its originality and its quality; In addition, the paper bring findings – that looking time is an adequate paradigm in goats to analyse how they pay attention to stimuli – that have potential impacts on further studies in animal cognition.

References

Deutsch, J., Lebing, S., Eggert, A., Nawroth, C. (2024). Goats who stare at video screens – assessing behavioural responses of goats towards images of familiar and unfamiliar con- and heterospecifics. OSF, ver.4 peer-reviewed and recommended by Peer Community In Animal Science. https://doi.org/10.31219/osf.io/d4nzk

Rivas-Blanco, D., Monteiro, T., Virányi, Z., Range, F. (2024). Going back to “basics”: Harlow’s learning set task with wolves and dogs. Learning & Behavior. https://doi.org/10.3758/s13420-024-00631-6 

Wilson, V. A. D., Bethell, E. J., Nawroth, C. (2023). The use of gaze to study cognition: limitations, solutions, and applications to animal welfare. Frontiers in Psychology, 14:1147278. https://doi.org/10.3389/fpsyg.2023.1147278