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

A correct execution of feeding plan for growing rabbit decreases the possibility of post-weaning digestive disorders, thus enhancing the feed efficiency in the animals. However, a limitation of feed daily quantity is required between 10 weeks of age and the first artificial insemination. This limitation causes energy deficiency with a consequent reduction in fertility. Beauvais et al. (2022) studied the impact of feed restriction strategies in female rabbits. Four feed restriction strategies were applied in two distinct periods (post-weaning and puberty) and evaluated by different physiological parameters (growth rate, metabolic profiles, reproductive parameters and mammary gland development). In the first part of the paper, the authors evaluated the association between weight slopes and feeding strategies in the late post-weaning and peripartum period in the four groups. As revealed by the authors, a significant difference was observed during the late post-weaning period, which remained significant between the pubertal and fattening phases. Probably these differences are related to the restriction feeding pattern. The results indicated that restrictive feeding changes in the first step of post-weaning period is associated with a significant difference in body weight. This difference occurs from the third week of diet, highlighting the high sensitivity of growing rabbit to nutrition during the post-weaning period.

In the second part of the paper, the authors evaluated the expression of genes involved in the lipid metabolism. During the mid-pregnancy, was revealed a significant higher expression of lipogenic genes, which may be considered as useful markers for the mammary epithelial development in less restrictive strategies during early life period.

The results proposed by Beauvais et al. (2022) enlighten the important role played by the feeding conditions of young female rabbits in the early life rearing. In particular, this activity provides specific recommendations for optimizing lactation and thus preventing neonatal mortality of the offspring. Moreover, the authors provide indications about the effect of feeding strategies on the mammary development and gene expression with absolute consequences on the development of offspring. Mammary lipid metabolism affects the milk profile and therefore the growth performance of the young animals.

Reference

Hue-Beauvais C, Bebin K, Robert R, Gardan-Salmon D, Maupin M, Brun N, Aujean E, Jaffrezic F, Simon S, Charlier M, Le Provost F (2022). Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy. biorXiv, 2022.01.17.476562, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2022.01.17.476562 

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 

Using sequence data in livestock genomics has often been regarded as a solution to revolutionize livestock breeding (Meuwissen & Goddard, 2010). The main expected benefits were to enhance the accuracy of breeding values, achieve better persistence of the accuracy over generations, and enable across populations or breed predictions (Hickey, 2013). Despite the promised benefits, whole-genome sequencing has not yet been implemented in livestock breeding programs, replacing SNP arrays for routine evaluation.

In this work, Johnsson (2023) thoroughly reviewed the literature regarding the implications of whole-genome sequencing and functional genomics for livestock breeding practice. The author discusses the potential applications and reasons for difficulties in their implementation. The author speculates that the main challenge for making using the sequence data profitable is to overcome the problem of the small dimensionality of the genetic data and proposes three potential ways to achieve this goal. The first approach is better modeling of genomic segments, the second inclusion of undetected genetic variation, and the third use of functional genomic information.  

The paper presents an original and interesting perspective on the current status of the use of sequence data in livestock breeding programs and perspectives for the future. 

References

Hickey,J.M.,2013.Sequencing millions of animals for genomic selection 2.0. Journal of Animal Breeding and Genetics 130:331–332. https://doi.org/10.1111/jbg.12054 

Johnsson, M., 2023. The big challenge for livestock genomics is to make sequence data pay. arXiv, 2302.01140, ver. 4 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.48550/arXiv.2302.01140 

Meuwissen, T., Goddard, M.,2010. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631. https://doi.org/10.1534/genetics.110.116590 

Tailor-made health plans for farming animals, including pigs, are highly beneficial due to their customized nature, addressing the unique needs of each farm and promoting efficient husbandry practices. However, assessing the effectiveness of individualized approaches can be challenging. Levallois et al. (1) tackled this challenge by evaluating the effectiveness of tailor-made health plans of pig farms based on a systematic biosecurity and herd health audit. The study involved twenty farrow-to-finish pig farms, each receiving specific plans tailored to their specific needs. Compliance with the recommendations was monitored over an eight-month period. In the literature, various studies have delved into specific issues in detail, such as disease incidence (e.g., (2)). However, the authors of this research applied a comprehensive approach through an integrative analysis of multiple complementary indicators to provide an effective evaluation of the changes and health disorders.

The authors' holistic approach to measuring the effectiveness of tailor-made health plans is noteworthy. They employed up to seven methods to identify advantages and limitations, providing valuable insights for applied research and practitioners in the field of farm animals. Additionally, the study's inclusion of diverse farms, ranging from conventional to antibiotic-free and varying in sow breeding numbers (from 70 to 800), demonstrates the flexibility of the proposed approach, accommodating different farming systems.

The study revealed three crucial considerations for future evaluations of tailor-made health plans. Firstly, placing compliance as the primary assessment indicator is a priority. Secondly, it is essential to tailor outcome indicators and monitoring periods according to each farm's specific health disorder. Lastly, a comprehensive understanding of the health disorder's evolution can be achieved through the amalgamation of multiple indicators.

While the study does have limitations, such as the relatively short time window for assessment, the methodological framework and results are promising. Further, the discussion of the results raises several areas worthy of future investigation to improve compliance and address farmers' hesitations towards action (i.e., lack of willingness). More research in this context will be beneficial for veterinarians and practitioners, enhancing their understanding and positively impacting both farmers and animals.

In conclusion, the study underscores the significant impact of tailor-made health plans on promoting positive changes in farm management. Assessing the effectiveness of these plans enables the refinement of new strategies and enhances the overall quality of work in animal production. The study by Levallois et al (1) sheds valuable light on the challenges and potentials of such plans, providing essential insights for pig farming practices. While further research and improvements are necessary, the study strongly emphasizes the pivotal role of individualized approaches in attaining improved farm management and enhancing animal welfare.

 
References:

1.     Levallois P, Leblanc-Maridor M, Scollo A, Ferrari P, Belloc C, Fourichon C. (2023). Combining several indicators to assess the effectiveness of tailor-made health plans in pig farms. Zenodo, 7789634. ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.5281/zenodo.7789634 

2.   Collineau L, Rojo-Gimeno C, Léger A, Backhans A, Loesken S, Nielsen EO, Postma M, Emanuelson U, grosse Beilage E, Sjölund M, Wauters E, Stärk KDC, Dewulf J, Belloc C, Krebs S. (2017). Herd-specific interventions to reduce antimicrobial usage in pig production without jeopardising technical and economic performance. Preventive veterinary medicine, 144:167-78. https://doi.org/10.1016/j.prevetmed.2017.05.023