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

With the increasing use of cage-free housing systems for laying hens comes the challenge of monitoring the behaviour of individual hens in large enclosures where they can be not only on the floors but on different levels. The aim of the present study by Gebhardt-Henrich et al., (2023) was to validate a Radio Frequency Identification (RFID) system with the capacity to track a large number of hens for different research and applied purposes where behaviour monitoring is relevant, such as heritability estimates for breeding programs.

In a housing system with 225 birds per pens, 26 antennae were placed at different locations. All birds in 5 pens were equipped with a glass tag in a custom-developed leg band. For validation purposes, the behaviour of three hens who could move between two pens was also monitored on video. Equipping these hens with colour-coded backpacks made them identifiable on video.

Matching the antennae detection of the focal birds with the behaviour observation showed that the antennae were able to detect a hen on the right tier in > 90% of cases, but that match on antenna level was lower.

The limitations of the system are also discussed in this concise methods paper that will be helpful to many researchers interested in tracking laying hens in loose housing systems.

Gebhardt-Henrich, S.G., Kashev, A., Petelle, M.B., Toscano, M.J., 2023. Validation of a Radio frequency identification system for tracking location of laying hens in a quasi-commercial aviary system. bioRxiv 2023.02.16.528820. ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2023.02.16.528820

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

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.

References

[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. https://doi.org/10.2527/1998.7682104x

[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. https://doi.org/10.1016/S0309-1740(02)00094-3

[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. https://doi.org/10.1093/jas/skx046 "

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.