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

The dairy sector is facing an historical period of high milk demand. However, increasing feed prices continually reduces the economic margins for farms. Managerial strategies to increase economical and technical awareness of animal performance, support the decision chain and optimize the use of production inputs are increasingly necessary, especially in goat farms with intensive production systems. Among the scientific goals, there is a particular emphasis on increasing knowledge about nutrition partitioning between milk production and body reserves — a topic that not easily addressed by nutritional models, limiting the attempts at production forecasting. 

The paper by Gafsi et al (2024) presents an interesting approach to studying phenotypic traits and trajectories of goat performance. It assesses the diversity of phenotypic trajectories reflecting functions such as milk production, body weight and condition score. This approaches aims to describe, understand and explore the interactions among biological functions and potential trade-offs of phenotypic trajectories across current and successive lactations. The work significantly contributes to the literature, particularly because previous descriptions of lactation curves relied primarily on mathematical outputs lacking information about the relationship among physiologically related variables. 

The analysis retrieved data from about 1500 goats over more than 20 years and was conducted with a multiscale approach. Data were fitted considering different types of models, including description of perturbations for lactation curves and with multiphasic models for the body weight and body condition score. Synthetic indicators were then estimated with a multivariate approach to define fitted trajectories and changes in performance. 

The association among performance directly refers to nutrient partitioning, and the adaptive response of individual animals refers to nutrient availability and their aptitude to direct the metabolic effort toward milk production. In fact, the research shows that trajectories from the first lactation were often maintained in sequent ones. Positive associations among milk curve perturbations and the individual aptitude to drive nutrients to body reserves were also observed.

The multiscale approach developed in the paper may provide original insights both for describing  animal performance and for ranking individual animals within farm groups. Practically, the method could inform the development of algorithms to support culling policies and individual animal assessments. Additionally, the data are available to the scientific community  for further research and applications. 

I recommend this paper as a methodological example with high replicable approaches for both goats and other dairy species, with significant applicative opportunities at the farm level.

Reference

Gafsi N, Martin O, Bidan F, Grimard B, Puillet L (2024) Diversity of performance patterns in dairy goats: multi-scale analysis of the lactation curves of milk yield, body condition score and body weight. Zenodo. 10101318. ver.3 peer-reviewed and recommended by Peer Community In Animal Science. https://doi.org/10.5281/zenodo.10101318

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. https://doi.org/10.1101/2020.07.10.197426

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.

References

[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."

One of the achievements of animal genetics is that it finds solutions along with the emergence of new needs of the animal husbandry community or the adoption of new laws. Muir and Cheng (2013) research serves as a classic example of using the innovations of animal genetics to meet new legal challenges, such as the restriction of beak cutting in laying hens. Muir and Cheng (2013) investigated the genetic diversity to deal with the cannibalism of intact chickens. In 2021, the European Citizens' Initiative urged the European Commission to legislate against the use of cages for farm animals in the livestock industry. Chapuis et al., (2024) presented a successful solution to the poultry industry about this (future) law by presenting a cost-efficient assignment SNP panel.

In animal breeding, access to pedigree information is necessary for genetic progress. Since the 1970s, the development of genomic science and  molecular techniques has shown their ability in this field. Despite the substantial reduction of genotyping costs in the last 20 years, the practical use of genome-wide genotyping for thousands of SNPs remains challenging. Therefore, the search for a small, cost-effective SNP panel is ongoing, with objectives including genetic diversity (Viale et al., 2017), product traceability (Dominik et al., 2021), species and hybrid identification (Harmoinen et al., 2021) and pedigree construction in wild populations (Ekblom et al., 2021). Furthermore, especially in recent decades, small panels of markers have been proposed for parentage assignment in different animals. For example, Domínguez-Viveros et al., (2020) developed panels with 42 to 63 markers for different sheep breeds in Mexico. Similar panels for parentage assignment have been proposed for salmon (May et al., 2020), rainbow trout (Liu et al., 2016), French sheep (Tortereau et al., 2017), Spanish sheep (Calvo et al., 2021), and European bison (Wehrenberg et al., 2024), with marker numbers of 142, 95, 180, 173, and 96, respectively. Massault et al., (2021) showed by simulation that a panel with at least 50 markers is sufficient for progeny assignment in pearl oysters. These examples highlight that extracting a small panel of markers (usually less than 200) from the total genotyping introduced in different species, can open new horizons for applying genomic information in animal breeding.

Chapuis et al. (2024) addressed the challenge of finding an efficient set of markers that can be used in the hybridization of two species of the Pekin duck and the Muscovy duck. They used KASPar technology to setup a panel, with SNPs existing in both species and their hybrids. This panel has sufficient polymorphism to use in practice. Thus, it can be considered as a step forward compared to previous work done on microsatellites. A final list of SNPs was constructed from a reference set comprising 600 K genotyping of Anas platyrhynchos, Cairina moschata and mule duck. 

In addition to developing of a cost-efficient assignment panel, the work of Chapuis et al. (2024) presented a factorial design to maintain genetic diversity while considering specificities of duck production. The use of factorial design in avian pedigreed populations is relatively novel, making this research particularly innovative. The study's approach to factorial design in populations with limited size may be generalized to similar poultry species. Furthermore, sufficient effective size of population is selected. So, the panel can be used in other populations outside the tested populations. A notable feature of this panel is the use of neutral SNPs, which ensures that markers will not be lost due to future selection pressures over time.

The paper of Chapuis et al. (2024) exemplifies the application of molecular genetics to address challenges in the poultry industry.  The use of kinship matrix instead of relationship matrix, taking into account the unique characteristics of duck production, could be another novelty of the paper. According to the reviewers' comments, the results can be beneficial in the future, particularly with the introduction of the specific factorial design.

References

Calvo JH, Serrano M, Tortereau F, Sarto P, Iguacel LP, Jiménez MA, Folch J, Alabart JL, Fabre S and Lahoz B (2021). Development of a SNP parentage assignment panel in some North-Eastern Spanish meat sheep breeds. Spanish Journal of Agricultural Research 18, e0406. https://doi.org/10.5424/sjar/2020184-16805    

Chapuis H, Brard-Fudulea S, Hazard A, Vignal A, Demars J, Rouger R, Teissier M, Gilbert H (2024). Cost-efficient assignment panel for ducks. Setup of a cost-efficient assignment panel for duck populations.: An illustration with experimental data. HAL, hal-04542880, ver. 2 peer-reviewed and recommended by Peer Community in Animal Science. https://hal.inrae.fr/hal-04542880 

Domínguez-Viveros J, Rodríguez-Almeida FA, Jahuey-Martínez FJ, Martínez-Quintana JA, Aguilar-Palma GN, Ordoñez-Baquera P (2020). Definition of a SNP panel for paternity testing in ten sheep populations in Mexico, Small Ruminant Research ,193,106262. https://doi.org/10.1016/j.smallrumres.2020.106262

Dominik S, Duff CJ, Byrne AI, Daetwyler H, Reverter A (2021). Ultra-small SNP panels to uniquely identify individuals in thousands of samples. Animal Production Science 61, 1796–1800. https://doi.org/10.1071/AN21123

Ekblom R, Aronsson M, Elsner-Gearing F, Johansson M, Fountain T, Persson J (2021). Sample identification and pedigree reconstruction in Wolverine (Gulo gulo) using SNP genotyping of non-invasive samples. Conservation Genetics Resources 13, 261–274. https://doi.org/10.1007/s12686-021-01208-5

Harmoinen J, von Thaden A, Aspi J, Kvist L, Cocchiararo B, Jarausch A, Gazzola A, Sin T, Lohi H, Hytönen MK, Kojola I, Stronen AV, Caniglia R, Mattucci F, Galaverni M, Godinho R, Ruiz-González A, Randi E, Muñoz-Fuentes V, Nowak C (2021). Reliable wolf-dog hybrid detection in Europe using a reduced SNP panel developed for non-invasively collected samples. BMC Genomics 22, 473. https://doi.org/10.1186/s12864-021-07761-5

Liu S, Palti Y, Gao G, Rexroad CE (2016). Development and validation of a SNP panel for parentage assignment in rainbow trout. Aquaculture 452, 178–182. https://doi.org/10.1016/j.aquaculture.2015.11.001

Massault C, Jones DB, Zenger KR, Strugnell JM, Barnard R, Jerry DR (2021). A SNP parentage assignment panel for the silver lipped pearl oyster (Pinctada maxima). Aquaculture Reports 20, 100687. https://doi.org/10.1016/j.aqrep.2021.100687

May SA, McKinney GJ, Hilborn R, Hauser L, Naish KA (2020). Power of a dual-use SNP panel for pedigree reconstruction and population assignment. Ecology and Evolution 10, 9522–9531. https://doi.org/10.1002/ece3.6645

Muir WM, Cheng HW(2013). Genetics and the Behaviour of Chickens: Welfare and Productivity. In Genetics and the Behaviour of Domestic Animals. Vol. 2 (2nd ed.). pp. 1–30.ISBN: 9780128100165

Tortereau F, Moreno CR, Tosser-Klopp G, Servin B, Raoul J (2017). Development of a SNP panel dedicated to parentage assignment in French sheep populations. BMC Genetics 18, 50. https://doi.org/10.1186/s12863-017-0518-2

Viale E, Zanetti E, Özdemir D, Broccanello C, Dalmasso A, De Marchi M, Cassandro M (2017). Development and validation of a novel SNP panel for the genetic characterization of Italian chicken breeds by next-generation sequencing discovery and array genotyping. Poultry Science 96, 3858–3866. https://doi.org/10.3382/ps/pex238

Wehrenberg G, Tokarska M, Cocchiararo B, Nowak C (2024). A reduced SNP panel optimised for non-invasive genetic assessment of a genetically impoverished conservation icon, the European bison. Scientific Reports 14, 1875. https://doi.org/10.1038/s41598-024-51495-9