Naturally occurring compounds that can reduce methane production in anaerobic conditions have been studied for quite some time as feasible approaches to mitigate methane production in ruminant animals, especially those of commercial importance. Asparagopsis taxiformis (red algae) and Dictyota bartayresii (brown algae) are effective inhibitors of methane synthesis under in vitro anaerobic fermentation systems (Machado et al., 2014) likely because of their high concentration of secondary metabolites that are toxic to the typical rumen microbiota, including protozoa. In addition to phytoplankton (Palmer and Reason, 2009), Asparagopsis contains a high concentration of haloform compounds (e.g., bromoform, chloroform) while Dictyota has a high concentration of isoprenoid terpenes. Despite the economic and biological impact of diverse phytochemicals on reducing methane production in ruminant animals (Tedeschi et al., 2021), haloform compounds’ environmental impact and safety, in particular, are still unclear. In the present study, Munõz-Tamayo and collaborators (2021) listed relevant literature about the impact of A. taxiformis on ruminal methane production.

Concurrent to the understanding of mechanisms and biology behind the reduction of ruminal methane, mathematical models can lead the way to enhance the effectiveness of feeding A. taxiformis under commercial applications. Thus, in the present study, Munõz-Tamayo and collaborators (2021) sought to develop a mathematical model to understand the rumen fermentation changes in vitro experimentation containing extract of A. taxiformis by adapting a previously documented model by Muñoz-Tamayo et al. (2016).

Modeling development, calibration, and evaluation steps should be independent of each other, requiring complete, distinct, and separate databases (Tedeschi, 2006). However, in rare circumstances where such requirements cannot be met because data availability is scarce, the cross-validation technique, when possible, should be considered to assess data dispersion’s effects on model adequacy. In other situations, clear reasoning for failing to do so must be addressed in the paper. In the present paper, Munõz-Tamayo and collaborators (2021) explained the limitations in their modeling efforts were primarily due to the lack of ideal data: “experiments with simultaneous dynamic data of bromoform, volatile fatty acids, hydrogen, and methane.” Regardless of the availability of ideal data, improvements in the conceptual model are warranted to include amino acids and branched-chain fatty acids fermentation dynamics in the rumen and the fluctuations in ruminal pH.

References

Machado L, Magnusson M, Paul NA, Nys R de, Tomkins N (2014) Effects of Marine and Freshwater Macroalgae on In Vitro Total Gas and Methane Production. PLOS ONE, 9, e85289. https://doi.org/10.1371/journal.pone.0085289

Muñoz-Tamayo R, Chagas JC, Ramin M, Krizsan SJ (2021) Modelling the impact of the macroalgae Asparagopsis taxiformis on rumen microbial fermentation and methane production. bioRxiv, 2020.11.09.374330, ver. 4 peer-reviewed and recommended by PCI Animal Science. https://doi.org/10.1101/2020.11.09.374330

Muñoz-Tamayo R, Giger-Reverdin S, Sauvant D (2016) Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota. Animal Feed Science and Technology, 220, 1–21. https://doi.org/10.1016/j.anifeedsci.2016.07.005

Palmer CJ, Reason CJ (2009) Relationships of surface bromoform concentrations with mixed layer depth and salinity in the tropical oceans. Global Biogeochemical Cycles, 23. https://doi.org/10.1029/2008GB003338

Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems, 89, 225–247. https://doi.org/10.1016/j.agsy.2005.11.004

Tedeschi LO, Muir JP, Naumann HD, Norris AB, Ramírez-Restrepo CA, Mertens-Talcott SU (2021) Nutritional Aspects of Ecologically Relevant Phytochemicals in Ruminant Production. Frontiers in Veterinary Science, 8. https://doi.org/10.3389/fvets.2021.628445

The predominant form of agriculture in sub-Saharan Africa is mixed crop and livestock production, typically in extensive systems characterised by low productivity and little input (Tui et al., 2021). Such extensive systems experience many challenges, including access to rangeland and other sources of livestock feed, loss of soil quality, increasingly unpredictable climate but also operational constraints such as access to markets and veterinary care.

The work by Gobvu and collaborators (2025) addresses livestock farming sustainability specifically in the context of a Transfrontier Conservation Area (TFCA). A TFCA is defined as part of an ecological region reaching across boundaries of two or more countries and in which there are protected areas as well as resource use areas. TFCAs are founded to facilitate collective management of resources for the benefit of biodiversity as well as socio-economic development (SADC, 2025).

Livestock farming in TFCAs face additional challenges arising from the tension between biodiversity protection and resource use, such as wildlife predation on livestock, competition between livestock and wildlife for feed and water as well as spread of infections between livestock and wild animals (Matseketsa et al., 2019; Caron et al., 2013; Cumming, 2011).

The paper reports a study that was part of a larger project (EU-ProSuLi project, Caron et al.,  2022) aiming at promoting local development and well-being of residents in the Great Limpopo TFCA, with focus on the Sengwe Communal Area in Zimbabwe. The specific aim was to test a methodology for identifying interventions that are aligned with the needs of the local stakeholders (demand-driven interventions. Anticipatory scenario building in the context of workshops was used in combination with individual questionnaires.

Community representatives (n=31) were purposefully selected for their expected ability to act as knowledge brokers. A team of 10 facilitators supported the discussion through plenary and group work sessions. The participants were asked to identify factors of change with impact on local community livelihood, and subsequently to vote to select the most influential factors. Different future states for the year 2038, resulting from the impact of the factors of change, were discussed. In a follow-up workshop, participants were asked to propose activities that would support the development towards the most desirable scenario. In addition, a questionnaire was distributed through semi-structured/structured interviews to 126 households in nine villages.

Results were largely similar and complementary between the two approaches. Preferred interventions were: restocking herds with locally adapted breeds, training in livestock management, marketing support, feed development and value addition, loan schemes for investment in livestock production and support for animal health interventions to reduce the heavy disease burden. Whereas the participatory process is time consuming and requires considerable human resources, it is important to ensure locally relevant interventions are chosen, and it is in itself part of the process towards successful implementation of these interventions.

The study is thoughtfully designed based on previous work by the authors. The paper is well written and has been further improved through the reviewer feedback and revision process.

References

Caron, A., Miguel, E., Gomo, C., Makaya, P., Pfukenyi, D.M., Foggin, C., Hove, T. and de Garine-Wichatitsky, M., 2013. Relationship between burden of infection in ungulate populations and wildlife/livestock interfaces. Epidemiology & Infection, 141 (7), pp.1522-1535. https://doi.org/10.1017/S0950268813000204

Caron, A., Mugabe, P., Bourgeois, R., Delay, E., Bitu, F., Ducrot, R., Fafetine, J., Fynn, R., Guerbois, C., Motsholapheko, M. and Daré, W., 2022. Social-ecological System Health in  Transfrontier Conservation Areas to Promote the Coexistence Between People and Nature. One Health Cases. https://doi.org/10.1079/onehealthcases.2022.0005 

Cumming, D. H. M. 2011. Constraints to conservation and development success at the wildlife-livestock-human interface in southern African transfrontier conservation areas: a preliminary review. Wildlife Conservation Society, New York.  http://www.wcs-ahead.org/workinggrps_kaza.html

Gobvu, V. Ncube, S. Imbayarwo-Chikosi, V.E., Bourgeois, R. Mugabe, P.H. and Caron A. 2025. Preferred livestock interventions for small-scale farmers in the Great Limpopo Transfrontier Conservation Area: a demand-driven and participatory approach. HAL, ver.5 peer-reviewed and recommended by PCI Animal Science. https://hal.science/hal-04060712

Matseketsa, G., Muboko, N., Gandiwa, E., Kombora, D.M. and Chibememe, G., 2019. An assessment of human-wildlife conflicts in local communities bordering the western part of Save Valley Conservancy, Zimbabwe. Global Ecology and Conservation, 20, p.e00737. https://doi.org/10.1016/j.gecco.2019.e00737 

Tui, S.H.K., Descheemaeker, K., Valdivia, R.O., Masikati, P., Sisito, G., Moyo, E.N., Crespo, O., Ruane, A.C. and Rosenzweig, C., 2021. Climate change impacts and adaptation for dryland farming systems in Zimbabwe: a stakeholder-driven integrated multi-model assessment. Climatic Change, 168 (1-2), p.10. https://doi.org/10.1007/s10584-021-03151-8 

South African Development Community. 2025. Transfrontier Conservation Areas.  . Consulted 7 January 2025. https://www.sadc.int/pillars/transfrontier-conservation-areas

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 

As the world slowly (or not so slowly) warms, the ability to regulate heat becomes even more pertinent for livestock kept in enclosed areas. Trees are not always present on land grazed by sheep, and when the pasture has some forestation, the coverage will provide varying degrees of shade. In this study by Ginane et al. (2025), ewes kept in enclosures with different levels of tree cover were observed at different times over a period of three years to investigate the extent to which the animals chose to spend time in the shade. By using fields with very different provision of shade (approximately 1, 40, and 81% shade, respectively), the authors wanted to test the hypothesis that ewes would actively seek out tree shade when the combined temperature and humidity increased – especially if the conditions reached levels associated with heat stress. Even at the lowest provision of shade, which consisted of a single tree in the paddock, all ewes could fit within the shade cast by the tree; but if the distribution of ewes or groups of ewes were random, i.e. independent of shade, the likelihood of these ewes being in the shade by chance was effectively 1%. By factoring in the element of chance, the authors found that mean shade use was greater than the tree canopy cover for the low and medium shade treatments, whereas it didn’t differ from chance for the densely forested treatment. Across treatments, all ewes spent just under 60% of the observation time grazing, and the ewes with the low and medium level of shade actively selected shade for foraging activity, whereas the ewes with over 80% canopy cover avoided it. Across treatments, shade was used primarily for resting and ruminating.

Tree cover affected the availability of forage in a negative manner, with more biomass available for ewes in the low shade treatment and significantly less in the high treatment, although this did not translate into significant differences in live weight or body condition score. Using this information, Ginane et al. (2025) calculated the optimal level of tree cover to be somewhere between the low and medium cover, at roughly 30 trees per hectare – preferably spread out over the area to offer different locations of shade and to encourage a natural spread of manure.

This longitudinal study of shade use by ewes provides novel and useful information on the positive and negative effects of tree cover in paddocks used to rear ewes with lambs. The authors raise the limitation of the study themselves, and they would have liked to also include observations on non-sunny days, to be able to eliminate place preferences independently of shade availability. But the clever calculation of active shade-selection makes this study easily applicable for use in the assessment of paddock suitability for pregnant ewes.

References

Cécile Ginane, Mickaël Bernard, Véronique Deiss, Donato Andueza, Camille Béral (2025) Shade use, welfare and performance of ewes grazing in temperate silvopastures differing in tree density. Zenodo, ver.4 peer-reviewed and recommended by PCI Animal Science https://doi.org/10.5281/zenodo.15001481

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