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15 Dec 2020
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Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Accurate predictions of chemical composition of pigs for a wide range of body weights: no longer a myth!

Recommended by based on reviews by Mathieu Monziols and 1 anonymous reviewer

Assessing body or carcass composition in growing pigs is essential to refine nutritional models, select for specific traits and evaluate pork products. The gold standard methods are dissection and chemical measurements, which are time-consuming and invasive ways to obtain the data. Different teams have tested dual-energy x-ray absorptiometry (DEXA), especially for determining total and regional body composition of fat, soft lean tissues and bone minerals [1-3]. The DEXA measurements are quick, non-invasive, precise, and operator independent. However, the instruments from different manufacturers are unique in implementation so that it is difficult to obtain and share generalized equations. In addition, the validity and accuracy of the measures when applied to pigs having very different composition have been scarcely addressed.


The present manuscript shows that carcass analysis by DEXA can be used to predict empty body chemical composition, and it provides accuracy values for the content in single nutrients (protein, lipids, Ca, P). The body weight range used to generate differences in body composition is very large (20 to 100 kg), which is important when studying pigs along growth. Moreover, regression equations within weight classes (20, 60 and 100 kg) show no important biases, with the exception for body fat especially at the earliest growth stages. Limitations of the technique are the needs of anesthesia when applied to living pigs, and of standardizing the positions of body, carcass and cuts when applied to living or dissected pigs. Another originality of the manuscript is the comparison of the obtained calibrations with previously published prediction models, showing that the differences do not preclude the possibility to use a single model when built from a meta-analysis of the different data. Taken together, this work offers good perspectives to refine nutritional models by inputs from rapidly analyzed body chemical composition and to monitor body and carcass composition in several pigs for genetics applications.



[1] Mitchell AD., Scholz AM., Pursel VG., and Evock-Clover CM. (1998). Composition analysis of pork carcasses by dual-energy x-ray absorptiometry. Journal of Animal Science. 76(8), 2104-14.

[2] Marcoux M., Bernier JF., and Pomar C. (2003). Estimation of Canadian and European lean yields and composition of pig carcasses by dual-energy X-ray absorptiometry. Meat Science. 63(3), 359-65.

[3] Kipper M., Marcoux M., Andretta I., and Pomar C. (2018). Repeatability and reproducibility of measurements obtained by dual-energy X-ray absorptiometry on pig carcasses. Journal of Animal Science, 96(5), 2027-2037. "

Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometryClaudia Kasper, Patrick Schlegel, Isabel Ruiz-Ascacibar, Peter Stoll, Giuseppe Bee<p>Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements necessitate gathering exact measurements of body composition or body nutrient contents. Wet chemical analysis methods or standardized dissec...Agricultural sustainability, Animal nutrition modelling, Monogastrics, Physiology, Pig nutritionFlorence Gondret2020-09-17 10:44:58 View
09 Feb 2024
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Pig herd management and infection transmission dynamics: a challenge for modellers.

Towards models of infection transmission dynamics

Recommended by based on reviews by Gustavo Machado and 1 anonymous reviewer

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.


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.






Pig herd management and infection transmission dynamics: a challenge for modellers.Vianney Sicard, Sébastien Picault, Mathieu Andraud<p>The control of epidemics requires a thorough understanding of the complex interactions between pathogen transmission, disease impact, and population dynamics and management. Mechanistic epidemiological modelling is an effective way to address t...Animal epidemiology modelling, Animal health, Bioinformatics, Farming systems, Infectious diseases, Mathematical modelling, Open science, Population dynamics, Veterinary epidemiology Marie-Pierre Letourneau Montminy2023-05-22 15:07:37 View
01 Sep 2022
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Detecting dairy cows' lying behaviour using noisy 3D ultrawide band positioning data

A novel method to monitor lying behaviour of dairy cows by combining noisy spatial positioning data, time-series segmentation based on statistical changepoints and machine learning classification algorithm

Recommended by based on reviews by Kareemah Chopra and John Fredy Ramirez Agudelo

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.



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. 



Detecting dairy cows' lying behaviour using noisy 3D ultrawide band positioning dataI. Adriaens, W. Ouweltjes, M. Pastell, E. Ellen, C. Kamphuis<p>In precision livestock farming, technology-based solutions are used to monitor and manage<br>livestock and support decisions based on on-farm available data. In this study, we developed<br>a methodology to monitor the lying behaviour of dairy c...Animal behaviour , Mathematical modelling, Precision livestock farmingEliel Gonzalez-Garcia2022-02-28 18:19:37 View
24 Mar 2023
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The use of pigs vocalisation structure to assess the quality of human-pig relationship

Qualitative aspects of grunts vary with pigs' mental states

Recommended by based on reviews by Matteo Chincarini and 1 anonymous reviewer

Villain et al., (2023) investigated the structure of vocalisations in piglets in relation to human-animal-relationship. They first established a positive relationship by habituating piglets to be positively handled at weaning or later on after weaning. They then compared the reactions of piglets previously positively handled at weaning to that of non-handled piglets during tests in presence of a human (interacting or not), and also before and after the conditioning period when all piglets received positive contacts. They showed that the duration and frequency of grunts emitted in the presence of the human depends on previous contacts. More specifically, grunts are shorter and higher pitched in pigs that have been positively handled, in line with a positive human-animal relationship which is also observed through proximity of the piglets with the human. The authors concluded that the structure of pig vocalisation can reflect the quality of their relationship with humans. 

The authors also showed that not only the response to humans is modified by positive contacts but also the general mood of piglets, with piglets positively handled at weaning emitting shorter grunts than non-handled piglets, whatever the context. 

Another interesting finding is the temporality of behaviour of pigs habituated to positive contacts: during the first tests, they stay close to the human, probably being reassured by the proximity of the human. Then, when tests are repeated, they explore more the test room, using the human as an exploratory basis as already reported in the literature. 

The hypotheses of the study are clear. The methods are reported in details so that the work is reproducible. The interpretation of results is sound. The manuscript is clearly written. 

This paper brings new and original knowledge in the field of animals’ emotional responses and human-animal relationship: on the structure of grunts in relation to positive affects (positive emotion, positive mood) and on the temporality of the responses to human presence.

I recommend this manuscript for its originality and quality.

Isabelle Veissier

Villain, A.S., Guérin, C., Tallet, C., 2023. The use of pigs vocalisation structure to assess the quality of human-pig relationship. bioRxiv 2022.03.15.484457, ver. 5 peer-reviewed and recommended by Peer Community in Animal Science.

The use of pigs vocalisation structure to assess the quality of human-pig relationshipAvelyne S Villain, Carole Guérin, Céline Tallet<p>Studying human-animal interactions in domestic species and how they affect the establishment of a positive Human-Animal Relationship (HAR) may help us improve animal welfare and better understand the evolution of interspecific interactions asso...Animal behaviour , Animal cognition, Animal welfareIsabelle Veissier2022-03-23 09:34:45 View
05 Dec 2019
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Effects of feeding treatment on growth rate and performance of primiparous Holstein dairy heifers

Optimizing growth rate of dairy heifers through nutrition to maximize reproduction and production

Recommended by based on reviews by Emilio Mauricio Ungerfeld and 2 anonymous reviewers

The idea of altering the growth rate of replacement heifers to improve reproductive and productive indicators of dairy cattle is not new. In the late 1970s, Gill and Allaire [1] indicated that the first parturition between 22.5 to 23.5 months of age yielded the optimum lifetime performance as long as the heifers had adequate body size [2]. Since 1980s, many studies have been conducted to understand the partitioning of energy between growth and lactation, including the impact of growth rates on the heifer puberty [3] as well as growth and development of the mammary gland [4,5]. The senior author of the recommended study has written previously about this research topic [6].

In the present manuscript, Le Cozler et al. studied the effect of feeding programs to increase the growth rate of late-born heifers to catch up with the growth of those born earlier in the calving season on their reproductive and productive performance. The authors analyzed 217 heifers for three consecutive years, split into three dietary treatments: control (C), accelerated growth rate from birth to 6 months of age (ID1), or accelerated growth rate from birth to 12 months of age (ID2). In this study, the late-born heifers receiving the ID2 treatment were able to partially reach the bodyweight of the early-born heifers at 24 months of age. In part, the incomplete understanding of the prioritization of the use of energy (and other nutrients) for different physiological stages (e.g., maintenance, growth, lactation, and pregnancy) of the dairy animal [7] undercuts the development of more robust feeding strategies to improve the reproductive and productive performance of the animal. In the recommended study by Le Cozler et al., although there was no impact on reproductive performance among groups, heifers in the group ID2 produced less milk (about 400 kg for the whole first lactation) than heifers in the groups C and ID1, apparently suggesting that energy allocation for growth had priority over that needed for lactation. The question then becomes what would have happened with energy partitioning if energy intake was restricted. Studies like this one are important to shed some light on the prioritization of the use of energy and other nutrients in support of growth, pregnancy, and lactation of dairy animals, and how compensatory growth differs between meat versus dairy growing animals, both physiologically and energetically.


[1] Gill, G. S., & Allaire, F. R. (1976). Relationship of Age at First Calving, Days Open, Days Dry, and Herdlife to a Profit function for Dairy Cattle1. Journal of Dairy Science, 59(6), 1131–1139. doi: 10.3168/jds.S0022-0302(76)84333-0
[2] Hoffman, P. C. (1997). Optimum body size of Holstein replacement heifers. Journal of Animal Science, 75(3), 836–845. doi: 10.2527/1997.753836x
[3] Cardoso, R. C., Alves, B. R. C., Prezotto, L. D., Thorson, J. F., Tedeschi, L. O., Keisler, D. H., … Williams, G. L. (2014). Use of a stair-step compensatory gain nutritional regimen to program the onset of puberty in beef heifers. Journal of Animal Science, 92(7), 2942–2949. doi: 10.2527/jas.2014-7713
[4] Sejrsen, K., Huber, J. T., Tucker, H. A., & Akers, R. M. (1982). Influence of Nutrition on Mammary Development in Pre- and Postpubertal Heifers1. Journal of Dairy Science, 65(5), 793–800. doi: 10.3168/jds.S0022-0302(82)82268-6
[5] Sejrsen, K., & Purup, S. (1997). Influence of prepubertal feeding level on milk yield potential of dairy heifers: a review. Journal of Animal Science, 75(3), 828–835. doi: 10.2527/1997.753828x
[6] Le Cozler, Y. L., Lollivier, V., Lacasse, P., & Disenhaus, C. (2008). Rearing strategy and optimizing first-calving targets in dairy heifers: a review. Animal, 2(9), 1393–1404. doi: 10.1017/S1751731108002498
[7] Tedeschi, L. O., and D. G. Fox. 2018. The Ruminant Nutrition System: An Applied Model for Predicting Nutrient Requirements and Feed Utilization in Ruminants. (2nd ed.). XanEdu, Acton, MA."

Effects of feeding treatment on growth rate and performance of primiparous Holstein dairy heifersYannick Le Cozler, Julien Jurquet, Nicolas Bedere<p>The objective of this study was to investigate effects of feeding-rearing programs that aim for first calving at 20-27 months (mo) of age on growth, reproduction and production performance of Holstein cows at nulliparous and primiparous stages....Cattle production, Reproduction, Ruminant nutritionLuis Tedeschi2019-09-09 09:22:36 View
02 Sep 2021
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A modelling framework for the prediction of the herd-level probability of infection from longitudinal data

Modelling freedom from disease - how do we compare between countries?

Recommended by based on reviews by Arata Hidano and 1 anonymous reviewer

In this paper, Madouasse et al. (2021) present a generalisable Bayesian method for calculating the probability that a herd is free from disease, based on its prior disease status, and using data (herd status over time over a sufficient number of herds to inform the model) and reasonable prior estimates of the sensitivity and specificity of tests being used to determine animal infection status.  Where available, the modelling approach can also include relevant additional risk factors. 

By bringing all these factors together, it allows for most countries to use the same analytical approach on their data, with differences across datasets expressed in terms of the uncertainty around the central estimates. 

Having a single methodology that generates both a central estimate of disease freedom, and uncertainty thus provides the opportunity (given typically available data) to compare the probability of freedom across different systems. This is relevant in terms of the context of trade (since international trade of livestock in many cases depends on disease freedom). It is also important when evaluating, for example, transnational burdens of disease - and with different regulations in place in different countries, this is invaluable and can be used, for example, to assess risks of zoonotic infection including for zoonotic infection emergence. In the BVD example provided, the point is made that, since regular testing would probably pick up infection rapidly, the addition of risk factors is most valuable where testing is infrequent. This emphasizes the advantages of direct incorporation of risk factors into a single modelling framework. 

From a technical point of view, the analysis compares two different packages for the Markov Chain Monte Carlo (MCMC) implementation necesary to run the model. They show that, while there are some slight systematic differences, the estimates provided by the two methods are similar to each other; as one method is approximate but substantially more stable and generally much more computationally efficient, this is an important outcome. Both implementations are freely available and  with relevant additional software made similarly available by the authors. This is extremely welcome and should encourage its general adoption across different countries. 

No single model can of course account for everything. In particular, the reliance on past data means that there is an implicit assumption common to all purely statistical methods that the underlying risks have not changed. Thus projections to altered circumstances (changing underlying risk factors or systematic changes in testing or test performance) cannot so easily be incorporated, since these factors are complicated by the dynamics of infection that lie outside the modelling approach. Of course the well known quote from George Box that "all models are wrong" applies here - the generality of approach, statistical robustness and open source philosophy adopted make this model very useful indeed.  

Madouasse A, Mercat M, van Roon A, Graham D, Guelbenzu M, Santman Berends I, van Schaik G, Nielen M, Frössling J, Ågren E, Humphry RW, Eze J, Gunn GJ, Henry MK, Gethmann J, More SJ, Toft N, Fourichon C (2021) A modelling framework for the prediction of the herd-level probability of infection from longitudinal data. bioRxiv, 2020.07.10.197426, ver. 6 peer-reviewed and recommended by PCI Animal Science.


A modelling framework for the prediction of the herd-level probability of infection from longitudinal dataAurélien Madouasse, Mathilde Mercat, Annika van Roon, David Graham, Maria Guelbenzu, Inge Santman Berends, Gerdien van Schaik, Mirjam Nielen, Jenny Frössling, Estelle Ågren, Roger Humphry, Jude Eze, George Gunn, Madeleine Henry, Jörn Gethmann, Sim...<p>The collective control programmes (CPs) that exist for many infectious diseases of farm animals rely on the application of diagnostic testing at regular time intervals for the identification of infected animals or herds. The diversity of these ...TEST, Veterinary epidemiology Rowland Raymond Kao2020-07-23 08:13:18 View
14 Oct 2020
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Determining insulin sensitivity from glucose tolerance tests in Iberian and Landrace pigs

Iberian pigs: more than excellent ham!

Recommended by based on reviews by 2 anonymous reviewers

Iberian pigs represent a treasured resource that allows the maintenance of their “montanera” traditional breeding system and, thus, contributes to the socioeconomic sustainability of the rural areas in the south-western regions of Iberian Peninsula. While the excellence of Iberian meat products is widely recognized, the idea of using Iberian pigs as biomedical models is currently emerging. Interestingly, due to the particular fatty acid metabolism of this porcine breed, Iberian pigs have been proposed as models for type 2 diabetes (Torres-Rovira et al. 2012) or obesity-related renal disease (Rodríguez et a. 2020).

In the present manuscript, Rodríguez-López et al. provide further insights on the particularities of “obese” Iberian pigs by comparing their insulin sensitivity in a glucose tolerance test with that of commercial “lean” Landrace pigs. The authors compared four Iberian pigs with five Landrace pigs in an intense time-series following an intra-arterial glucose tolerance test and measuring insulin, glucose, lactate, triglycerides, cholesterol, creatinine, albumin and urea plasma levels. Several of these parameters showed significant differences between both breeds, with some of them being compatible with an early stage of insulin resistance in Iberian pigs. These results are relevant from an animal production perspective, but provide also further evidence for considering the Iberian pigs as a suitable biomedical model for obesity-related disorders.


[1] Torres-Rovira, L., Astiz, S., Caro, A., Lopez-Bote, C., Ovilo, C., Pallares, P., Perez-Solana, M. L., Sanchez-Sanchez, R., & Gonzalez-Bulnes, A. (2012). Diet-induced swine model with obesity/leptin resistance for the study of metabolic syndrome and type 2 diabetes. The Scientific World Journal, 510149.
[2] Rodríguez, R. R., González-Bulnes, A., Garcia-Contreras, C., Elena Rodriguez-Rodriguez, A., Astiz, S., Vazquez-Gomez, M., Luis Pesantez, J., Isabel, B., Salido-Ruiz, E., González, J., Donate Correa, J., Luis-Lima, S., & Porrini, E. (2020). The Iberian pig fed with high-fat diet: a model of renal disease in obesity and metabolic syndrome. International journal of obesity, 44(2), 457–465. "

Determining insulin sensitivity from glucose tolerance tests in Iberian and Landrace pigsJ. M. Rodríguez-López, M. Lachica, L. González-Valero, I. Fernández-Fígares<p>As insulin sensitivity may help to explain divergences in growth and body composition between native and modern breeds, metabolic responses to glucose infusion were measured using an intra-arterial glucose tolerance test (IAGTT). Iberian (n = 4...Monogastrics, Physiology, Pig nutritionJordi Estellé2019-12-28 10:51:03 View
09 Apr 2022
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The impact of housing conditions on porcine mesenchymal stromal/stem cell populations differ between adipose tissue and skeletal muscle

Housing conditions affect cell populations in adipose and muscle tissues of pigs

Recommended by based on reviews by 2 anonymous reviewers

The adaptability of livestock to changing environments is based in particular on their genetic characteristics but also on the farming conditions to which they are subjected. However, this last point is poorly documented and little is known about its contribution to environmental challenges. The study by Quéméner and colleagues [1] addresses this question by assessing the effect of two hygiene conditions (good vs poor) on the distribution of cell populations present in adipose and muscle tissues of pigs divergently selected for feed efficiency [2].

The working hypothesis is that degraded housing conditions would be at the origin of an hyper stimulation of the immune system that can influence the homeostasis of adipose tissue and skeletal muscle and consequently modulate the cellular content of these tissues. Cellular compositions are thus interesting intermediate phenotypes for quantifying complex traits. The study uses pigs divergently selected for residual feed intake (RFI+ and RFI-) to assess whether there is a genetic effect associated with the observed phenotypes. 

The study characterized different stromal cell populations based on the expression of surface markers: CD45 to separate hematopoietic lineages and markers associated with the stem properties of mesenchymal cells: CD56, CD34, CD38 and CD140a. The authors observed that certain subpopulations are differentially enriched according to the hygiene condition (good vs poor) in adipose and skeletal tissue (CD45-CD56-) sometimes with an associated (genetic) lineage effect. This pioneering study validates a number of tools for characterizing cell subpopulations present in porcine adipose and muscle tissue. It confirms that housing conditions can have an effect on intermediate phenotypes such as intra-tissue cell populations. This pioneering work will pave the way to better understand the effects of livestock systems on tissue biology and animal phenotypes and to characterize the nature and function of progenitor cells present in muscle and adipose tissue.

[1] Quéméner A, Dessauge F, Perruchot MH, Le Floc’h N, Louveau I. 2022. The impact of housing conditions on porcine mesenchymal stromal/stem cell populations differ between adipose tissue and skeletal muscle. bioRxiv 2021.06.08.447546, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. 

[2] Gilbert H, Bidanel J-P, Gruand J, Caritez J-C, Billon Y, Guillouet P, Lagant H, Noblet J, Sellier P. 2007. Genetic parameters for residual feed intake in growing pigs, with emphasis on genetic relationships with carcass and meat quality traits. Journal of Animal Science 85:3182–3188.

The impact of housing conditions on porcine mesenchymal stromal/stem cell populations differ between adipose tissue and skeletal muscleAudrey Quéméner, Frédéric Dessauge, Marie-Hélène Perruchot, Nathalie Le Floc’h, Isabelle Louveau<p><strong>Background.</strong> In pigs, the ratio between lean mass and fat mass in the carcass determines production efficiency and is strongly influenced by the number and size of cells in tissues. During growth, the increase in the number of c...Monogastrics, Physiology, Veterinary scienceHervé Acloque2021-06-08 17:34:54 View
31 Jan 2020
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OneARK: Strengthening the links between animal production science and animal ecology

When scientific communities intertwine

Recommended by based on reviews by Rowland Raymond Kao, Arata Hidano and 1 anonymous reviewer

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.


[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

OneARK: Strengthening the links between animal production science and animal ecologyDelphine Destoumieux-Garzón, Pascal Bonnet, Céline Teplitsky, François Criscuolo, Pierre-Yves Henry, David Mazurais, Patrick Prunet, Gilles Salvat, Philippe Usseglio-Polatera, Etienne Verrier and Nicolas Friggens<p>1. Wild and farmed animals are key elements of natural and managed ecosystems that deliver functions such as pollination, pest control and nutrient cycling within the broader roles they play in contributing to biodiversity and to every category...Agricultural sustainability, Animal genetics, Animal welfare, Ecology, Precision livestock farming, Veterinary epidemiology Pauline Ezanno2019-07-05 15:33:21 View
07 Feb 2022
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Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait

Measuring resilience in farm animals: theoretical considerations and application to dairy cows

Recommended by ORCID_LOGO based on reviews by Ian Colditz and 2 anonymous reviewers

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.



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.

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.

Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this traitFriggens, N. C., Adriaens, I., Boré, R., Cozzi, G., Jurquet, J., Kamphuis, C., Leiber, F., Lora, I., Sakowski, T., Statham, J. and De Haas, Y.<p style="text-align: justify;">Climate change, with its increasing frequency of environmental disturbances puts pressures on the livestock sector. To deal with these pressures, more complex traits such as resilience must be considered in our mana...Precision livestock farmingAurélien Madouasse2021-08-20 15:34:13 View