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

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

Rira et al. (2022) evaluated ruminal degradation of tropical tannins-rich plants and the relationship between condensed tannins disappearance and microbial communities. I found this study relevant because a major limitation for tropical plants utilization by ruminants is their potential reduced nutrient digestion. In this study, authors used leaves from Calliandra calothyrsus, Gliricidia sepium, and Leucaena leucocephala, pods from Acacia nilotica and the leaves of Manihot esculenta and Musa spp., which were incubated in situ in the rumen of dairy cows. An in vitro approach was also used to assess the effects of these plants on ruminal fermentation. They observed that hydrolysable and free condensed tannins from all plants completely disappeared after 24 h incubation in the rumen. Disappearance of protein-bound condensed tannins was variable with values ranging from 93% for Gliricidia sepium to 21% for Acacia nilolitica. This demonstrated some potential for selection and improvements in protein digestion. In contrast, fibre-bound condensed tannins disappearance averaged ~82% and did not vary between plants, which was remarkable. The authors noted that disappearance of bound fractions of condensed tannins was not associated with degradability of plant fractions and that the presence of tannins interfered with the microbial colonisation of plants. Each plant had distinct bacterial and archaeal communities after 3 and 12 h of incubation in the rumen and distinct protozoal communities at 3 h. This suggests a great deal of specificity for microbial-plant interactions, which warrants further evaluation to consider also animal contributions to such specificity. Adherent communities in tannin-rich plants had a lower relative abundance of fibrolytic microbes, notably Fibrobacter spp. Whereas, archaea diversity was reduced in high tannin-containing Calliandra calothyrsus and Acacia nilotica at 12 h of incubation. Concurrently, in vitro methane production was lower for Calliandra calothyrsus, Acacia nilotica and Leucaena leucocephala although for the latter total volatile fatty acids production was not affected and was similar to control. Finally, the study demonstrated that the total amount of hydrolysable and condensed tannins contained in a plant play a role governing the interaction with rumen microbes affecting degradability and fermentation. The effect of protein- and fibre-bound condensed tannins on degradability is less important. The major limitation of the study is the lack of animal validation at this stage; therefore, further studies are warranted, especially studies evaluating these plants in vivo. Furthermore, mechanisms associated with plant-microbial specificity, the role played by the host, and more data on nutrient utilization and gas production should be investigated. Nonetheless, this work show interesting microbial colonization and specific plant-microbial relationships that are novel in the ruminal environment.

Reference:

Rira M, Morgavi DP, Popova M, Maxin G, Doreau M (2022). Microbial colonization of tannin-rich tropical plants: interplay between degradability, methane production and tannin disappearance in the rumen. bioRxiv, 2021.08.12.456105, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2021.08.12.456105

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 

To limit the use of antibiotics in the few days after hatching, it is necessary to improve the robustness of chicks during the early post-hatch period. This can be achieved by ensuring immediate access to feeds, optimizing the implantation and maturation of the microbiota and immune system of each chick, and minimizing exposure of stressors such as transportation. The study conducted by Guilloteau and colleagues (2024) compared the performance and health of chicks raised in conventional hatching systems with those raised in on-farm hatching systems. The authors showed that both systems yielded similar hatching percentage of eggs. Chicks from on-farm hatching systems exhibited higher body weights during the post-hatch period compared to those from conventional hatching, whereas health parameters were not affected by the system. An originality of the study was the examination of the benefits of the presence of an adult hen in hatching systems. The effects on chick traits were interpreted in relation to the hen behavior at hatching and a classification according to maternal or agonistic activities towards the chicks. However, the experimental design did not allow to make statistical correlations between hen behavior pattern and chick traits. Importantly, the presence of a hen decreased the hatching percentage, and this was likely associated with hen aggressiveness in the pen. The presence of the hen deteriorated the quality scores of the chicks in the on-farm hatching system, and increased mortality of chicks at hatching, negatively impacting chick weight gain and feed efficiency during the few days after hatching in both conventional and on-farm hatching systems. Thereafter, the effect of the presence of a hen on chick body weight was different according to the sex of the chicks and the type of hatching system. The presence of a hen did not reduce the parasitic load of the chicks nor improved clinical signs. No specific characterization of the fecal microbiota of the chicks was conducted, preventing the testing whether or not the presence of the hen affected the early implantation and maturation of the chick microbiome. Altogether, the data indicate that on-farm hatching systems are at least equivalent (in terms of health traits, feed efficiency) or even favorable (for faster growth in the early period after hatching) for chicks. Training the hens (considered as foster adults) to the presence of eggs and chicks or selecting hens according to specific activity behavioral patterns could be ways to establish better interactions between hens and chicks. Although the number and type of environmental stressors tested in the experiment differ from those in commercial farms, the article opens new perspectives for alternative hatching and farming practices.

Reference

Guilloteau LA, Bertin A, Crochet S, Bagnard C, Hondelatte A, Ravon L, Schouler C, Germain K, Collin A (2024) On-farm hatching and contact with adult hen post hatch induce sex-dependent effects on performance, health and robustness in broiler chickens. bioRxiv, 2023.05.17.541117. ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2023.05.17.541117.