The topic of feed efficiency is under discussion in the scientific community and several studies pointed out that lactation stage has to be accounted for when estimates of feed efficiency are carried out, especially for genetic ranking of animals and their performances, as highlighted by Li et al. (2017). Other researchers applied a latin square design to test dietary effects across lactation (Ipharraguerre et al. 2002) but this approach cannot be followed out of experimental conditions and particularly does not allow, nowadays, to valorize precision livestock farm data to get phenotypic information from individual animals at farm level. 

The current manuscript by Fischer, et al. (2022a) describes an experimental trial in which cows were first fed a high starch diet-low fibre then switched over to a low starch diet-high fibre and individually monitored over time. Data were analyzed with the objective to investigate effects within diets and across diets. Since all cows went through the same sequence at the same time it was not possible to completely separate the confounding effect of lactation stage and diet as stated by the authors. However, this manuscript adds methodological discussions and opens research questions especially to the matter of repeatability and reproducibility of feed efficiency of individual animals over the lactation stage. These variables are fundamental to evaluate nutritional traits and phenotypic performances of dairy cows at farm level, as highlighted by a paper of the same first author (Fischer, et al. 2022b) dealing to reproducibility and repeatability with a similar approach. My opinion is that this manuscript gives the opportunity to enlarge the scientific discussions on the calculation of repeatability and reproducibility of feed efficiency of individual animals over time. In particular, as in this study, specific mathematical approaches need to be carried out with the final goal to analyze and valorize precision livestock farm data for cow phenotyping and to propose new methods of feed efficiency evaluations. It also needs complete databases carried out under experimental conditions. In fact it has to be considered that this manuscript makes available to the scientific community all the data and the R code developed for data analysis giving the opportunity to replicate the calculations and propose new advancements in the feed efficiency evaluations of dairy cows.

References 

Fischer A, Gasnier P, Faverdin P (2022a) Feed efficiency of lactating Holstein cows was not as repeatable across diets as within diet over subsequent lactation stages. bioRxiv, 2021.02.10.430560, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2021.02.10.430560

Fischer A, Dai X, Kalscheur KF (2022b) Feed efficiency of lactating Holstein cows is repeatable within diet but less reproducible when changing dietary starch and forage concentrations. animal, 16, 100599. https://doi.org/10.1016/J.ANIMAL.2022.100599

Ipharraguerre IR, Ipharraguerre RR, Clark JH (2002) Performance of Lactating Dairy Cows Fed Varying Amounts of Soyhulls as a Replacement for Corn Grain. Journal of Dairy Science, 85, 2905–2912. https://doi.org/10.3168/JDS.S0022-0302(02)74378-6

Li B, Berglund B, Fikse WF, Lassen J, Lidauer MH, Mäntysaari P, Løvendahl P (2017) Neglect of lactation stage leads to naive assessment of residual feed intake in dairy cattle. Journal of Dairy Science, 100, 9076–9084. https://doi.org/10.3168/JDS.2017-12775

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 ability to adequately characterize the lactation curve of livestock is important not only to ensure proper nutrition of the lactating animal but, among many other benefits, it can assist in diagnosing the incidence of diseases, predicting the quantity of milk production, and comparing animals within the herd for managerial strategies such as culling. Eventually, such smart fitting algorithms can lead to improved genetic selection of more productive animals after genetic-unrelated noises are removed from the data, systematically.
The manuscript by Ben Abdelkrim et al. developed and explained an algorithm to detect perturbations in lactation curves of dairy goats. Researchers have been interested in accurately describing lactation curves since the early-1960s. Johansson [1] proposed a nonlinear decay function, Nelder [2] described an inverse polynomial, and Wood [3] proposed the incomplete gamma function to describe milk production of dairy cows. Unfortunately, many of the lactating animals that yielded lactation curves that did not comply with the typical, expected pattern of milk production were usually discarded and, until then, efforts to address this lack of adherence were not conducted. The recommended manuscript provides a different perspective in which rather than discarding the lactation profile, one can model the perturbations of the lactation curve as an attempt to identify possible problems (e.g., mastitis) and minimize their occurrence. Such an algorithm is important to separate females that show resilient attributes from those females that show sustainable attributes, as per existing definitions proposed by Tedeschi et al. [4].
The recommended manuscript proposes the Perturbed Lactation Model to explicitly account for multiple perturbations in the time-series milk production in dairy goats. When perturbations occur in biological processes, a typical negative impact is observed in the animal’s response, but on rare occasions, positive impacts can occur. In this case, the animal responds positively to the perturbation (i.e., responsive), and as a result, there is an increase in their output when compared to unperturbed animals. The recommended manuscript only considered negative impacts due to perturbations in the lactation curve of dairy goats. Future modifications should include positive responses due to perturbations. In this case, animals would be “positively responsive” to perturbations, and examples of such behavior include feed intake and growth curves.

References

[1] Johansson, I. (1961). Genetic Aspects of Dairy Cattle Breeding. University of Illinois Press, Urbana, IL.

[2] Nelder, J. A. (1966). Inverse polynomials, a useful group of multi-factor response functions. Biometrics. 22 (1):128-141. doi: 10.2307/2528220
[3] Wood, P. D. P. (1967). Algebraic model of the lactation curve in cattle. Nature. 216 (5111):164-165. doi: 10.1038/216164a0
[4] Tedeschi, L. O., J. P. Muir, D. G. Riley, and D. G. Fox. (2015). The role of ruminant animals in sustainable livestock intensification programs. Int. J. Sustainable Dev. World Ecol. 22 (5):452-465. doi: 10.1080/13504509.2015.1075441

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 

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.

References

[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