In the livestock industry, the concerns of society and livestock farmers have changed in recent decades, moving from a focus solely on production traits to sustainable production while respecting animal welfare (Ducrot et al., 2024). Animal behavior parameters can serve as a reliable proxy for animal welfare, but collecting large data sets of behavioral data on farm is very time-consuming when not automatized.  Accelerometers are promising devices to detect animal behavior. A key element of the efficiency of the prediction of animal behavior from accelerometer data is the adequacy of pre-processing methods (Riaboff et al., 2019,Riaboff et al., 2022, Vidal et al., 2023).  

The article of Mauny et al., (2025) aims to find a solution for using huge automatized data. So, livestock farmers can effectively use new technologies to monitor animal behavior and then correct poor husbandry routines. Authors used the pipeline ACT4Behav - (Accelerometer-based Classification Tool for identifying Behaviours) - (Mauny et al., 2024) for the pre-processing of accelerometer data with the aim of selecting the most important features of dairy goats related to rumination, head in the feeder, lying and standing (using  data based on video recordings for the validation of behavior activity). Mauny et al., (2025) established a clear methodology that systematically obtain the best features and processing techniques to predict each targeted variable from raw accelerometer data. The work provides valuable information for both animal husbandry specialists and data mining scientists. The main limitation of the work is the small number of animals for both training and model testing. 

References 

Ducrot C, Barrio MB, Boissy A, Charrier F, Even S, Mormède P, Petit S, Pinard-van der laan M-H, Schelcher F, Casabianca F, Ducos A, Foucras G, Guatteo R, Peyraud J-L, Vayssier-Taussat M, Veysset P, Friggens NC and Fernandez X. 2024. Animal board invited review: Improving animal health and welfare in the transition of livestock farming systems: Towards social acceptability and sustainability. animal 18, 101100. https://doi.org/10.1016/j.animal.2024.101100

Mauny, S, Kwon J, Friggens NC, Duvaux-Ponter C, Taghipoor M. 2024. ACT4Behav (Accelerometer-based Classification Tool for identifying Behaviours): a Machine Learning pipeline with extensive pre-processing and feature creation options. Zenodo. https://doi.org/10.5281/zenodo.12624796

Mauny S, Kwon J, Friggens NC, Duvaux-Ponter C, Taghipoor M. 2025. A pipeline with pre-processing options to detect behaviour from accelerometer data using Machine Learning tested on dairy goats.. Zenodo, ver. 6 peer-reviewed and recommended by PCI Animal Science. https://doi.org/10.5281/zenodo.12627197

Riaboff L, Aubin S, Bédère N, Couvreur S, Madouasse A, Goumand E, Chauvin A, Plantier G. 2019. Evaluation of pre-processing methods for the prediction of cattle behaviour from accelerometer data. Computers and Electronics in Agriculture 165, 104961. https://doi.org/10.1016/j.compag.2019.104961

Riaboff L, Shalloo L, Smeaton AF, Couvreur S, Madouasse A, Keane MT. 2022. Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Computers and Electronics in Agriculture 192, 106610. https://doi.org/10.1016/j.compag.2021.106610

Vidal G, Sharpnack J, Pinedo P, Tsai IC, Lee AR, Martínez-López B. 2023. Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle. Preventive Veterinary Medicine 215, 105903. https://doi.org/10.1016/j.prevetmed.2023.105903

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