In genetic selection, simplistic model of single-trait selection is usually considered, and the response to such approach is estimated using simple models. In practice, however, plant and animal breeders always deal with the selection of several traits, hence making the selection process very complex. Therefore, the simultaneous genetic improvement of several traits has always been one of the goals of livestock, including poultry breeding (Falconer, 1972). Studies that examine the indirect effects of selection on economic traits are eagerly awaited. In this context, the results of the study by Bédère et al., (2023) gives new insights about phenotypic and genotypic relationships between body reserves traits in laying hens. The authors aimed to propose novel data about the genetic architecture of traits related to body fat by measuring a series of phenotypic traits with relatively an easy approach. The authors further aimed to test and validate the phenotyping of backfat thickness as an indicator of the overall fatness of laying hens. Thus, the study allowed providing new evidence regarding the genetic determination of the backfat trait in chicken breeds.

The authors first estimated the effect of selection on the residual feed intake (trait x) on the trait of body reserves (trait y). In fact, divergent selection experiments are a fundamental research tool that allow revealing significant amount of data related to the possible span of genetic improvement for traits of interest. Consequently, by analyzing data from a divergent selection experiment, associations have been estimated between a number of feed-dependent traits that have practical use for chicken breeders. Estimation of the correlations between traits is under question in terms of the theory of genetics and their application in multi-trait selection. As a major finding of the study, the observation of a bimodal distribution of backfat in both lines and the heterogeneity of the variances between families allowed suggesting a possible major gene, which could be investigated in future studies using for instance quantitative genetics. Body composition is continually studied in broilers chicken, but this aspect of chicken genetic is more detailed in laying hens.

The current findings are worthy to validate using several approaches. In fact, one of the limitations of the study can be related to other statistical models that can be built. For example, the study revealed high correlations between egg production and body weight, thus body weight could be considered as a covariate in regression models. Moreover, the principal trait of selection (based on the residual feed intake) could be considered. 

References:

Falconer, D. S. (1972). Introduction to Quantitative Genetics. Publisher: Ronald Press Company. pp 365.

Bédère, N., Dupont, J., Baumard, Y., Staub, C., Gourichon, D., Elleboudt, F., Le Roy, P., Zerjal, T. (2023).  Genetic background of body reserves in laying hens through backfat thickness phenotyping. HAL ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://hal.inrae.fr/hal-04172576 

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

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 

Identifying individuals with a more remarkable feed efficiency may positively affect the profitability and sustainability of the beef industry (Cruz et al., 2010; Basarab et al., 2013). However, although most international nutrient requirements systems predict animal feed efficiency, intake data is usually unavailable at the farm level, and ranking animals based on efficiency might be challenging. In this sense, using differences in the occurrence of isotopic N between animal and diet (Δ15Nanimal-diet) might become a natural biomarker to determine feed efficiency at the farm level. This methodology was firstly demonstrated by Guarnido-Lopez et al. (2021). In the present study by Cantalapiedra-Hijar et al. (2022), the authors evaluated the extent to which Δ15Nanimal-diet can be used as a marker for feed efficiency in beef animals. For this, a meta-analysis was conducted using a database including 759 individual records for performance and N isotopic discrimination measured in plasma or muscle (Δ15Nanimal-diet; n = 749) and plasma urea concentration (n = 659). The database was composed of 37% Charolais, 15% Simmental, and 40% of beef crossbreds. The results confirmed that Δ15Nanimal-diet could discriminate animals with at least 0.10 kg/kg difference in feed efficiency. Furthermore, the Δ15Nanimal-diet marker also successfully discriminated the feed efficiency of two animals from the same contemporary group if they differ by at least 0.06 kg/kg of FCE. However, when trying to predict feed efficiency, using the two candidate biomarkers did not improve estimates. Lastly, when data from biomarkers were combined with performance data, improvement in the predictions was observed. Nonetheless, the present results warrant more studies to evaluate the use of Δ15Nanimal-diet as a biomarker for feed efficiency since it could be used not only for feed efficiency discrimination but also in genetic selections.

References

Cantalapiedra-Hijar G, Morel I, Sepchat B, Chantelauze C, Miller GA, Duthie CA, Ortigues-Marty I, Dewhurst RJ (2022). Identifying cattle with superior growth feed efficiency through their natural 15N abundance and plasma urea concentration: A meta-analysis. Zenodo, 5783960, ver. 3 peer-reviewed and recommended by Peer community in Animal Science. https://doi.org/10.5281/zenodo.5783960.

Cruz GD, Rodríguez-Sánchez JA, Oltjen JW, Sainz RD (2010). Performance, residual feed intake, digestibility, carcass traits, and profitability of Angus-Hereford steers housed in individual or group pens. J. Anim. Sci. 88:324-329. https://doi.org/10.2527/jas.2009-1932​.

​Basarab JA, Beauchemin  KA, Baron VS, Ominski KH, Guan LL, Miller SP, Crowley JJ  (2013). Reducing GHG emissions through genetic improvement for feed efficiency: effects on economically important traits and enteric methane production. Animal 7:303-315.  https://doi.org/10.1017/S1751731113000888​.

​Guarnido-Lopez P, Ortigues-Marty I, Taussat S, Fossaert C, Renand G, Cantalapiedra-Hijar G  (2021). Plasma proteins Δ​15N vs. plasma urea as candidate biomarkers of between-animal variations of feed efficiency in beef cattle: Phenotypic and genetic evaluation. Animal 15:100318. https://doi.org/10.1016/j.animal.2021.100318.​​​​​​​​​​

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