Submit a preprint

Latest recommendationsrsstwitter

IdTitleAuthorsAbstractPictureThematic fields▲RecommenderReviewersSubmission date
31 Jan 2020
article picture

OneARK: Strengthening the links between animal production science and animal ecology

When scientific communities intertwine

Recommended by based on reviews by Rowland Raymond Kao, Arata Hidano and 1 anonymous reviewer

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

OneARK: Strengthening the links between animal production science and animal ecologyDelphine Destoumieux-Garzón, Pascal Bonnet, Céline Teplitsky, François Criscuolo, Pierre-Yves Henry, David Mazurais, Patrick Prunet, Gilles Salvat, Philippe Usseglio-Polatera, Etienne Verrier and Nicolas Friggens<p>1. Wild and farmed animals are key elements of natural and managed ecosystems that deliver functions such as pollination, pest control and nutrient cycling within the broader roles they play in contributing to biodiversity and to every category...Agricultural sustainability, Animal genetics, Animal welfare, Ecology, Precision livestock farming, Veterinary epidemiology Pauline Ezanno2019-07-05 15:33:21 View
16 Apr 2021
article picture

Modelling the impact of the macroalgae Asparagopsis taxiformis on rumen microbial fermentation and methane production

Understanding the mechanisms behind natural bioactive compounds that can potentially reduce methane production in anaerobic conditions. A case study of Asparagopsis taxiformis

Recommended by based on reviews by Alberto Atzori, Henk van Lingen and 2 anonymous reviewers

Naturally occurring compounds that can reduce methane production in anaerobic conditions have been studied for quite some time as feasible approaches to mitigate methane production in ruminant animals, especially those of commercial importance. Asparagopsis taxiformis (red algae) and Dictyota bartayresii (brown algae) are effective inhibitors of methane synthesis under in vitro anaerobic fermentation systems (Machado et al., 2014) likely because of their high concentration of secondary metabolites that are toxic to the typical rumen microbiota, including protozoa. In addition to phytoplankton (Palmer and Reason, 2009), Asparagopsis contains a high concentration of haloform compounds (e.g., bromoform, chloroform) while Dictyota has a high concentration of isoprenoid terpenes. Despite the economic and biological impact of diverse phytochemicals on reducing methane production in ruminant animals (Tedeschi et al., 2021), haloform compounds’ environmental impact and safety, in particular, are still unclear. In the present study, Munõz-Tamayo and collaborators (2021) listed relevant literature about the impact of A. taxiformis on ruminal methane production.

Concurrent to the understanding of mechanisms and biology behind the reduction of ruminal methane, mathematical models can lead the way to enhance the effectiveness of feeding A. taxiformis under commercial applications. Thus, in the present study, Munõz-Tamayo and collaborators (2021) sought to develop a mathematical model to understand the rumen fermentation changes in vitro experimentation containing extract of A. taxiformis by adapting a previously documented model by Muñoz-Tamayo et al. (2016).

Modeling development, calibration, and evaluation steps should be independent of each other, requiring complete, distinct, and separate databases (Tedeschi, 2006). However, in rare circumstances where such requirements cannot be met because data availability is scarce, the cross-validation technique, when possible, should be considered to assess data dispersion’s effects on model adequacy. In other situations, clear reasoning for failing to do so must be addressed in the paper. In the present paper, Munõz-Tamayo and collaborators (2021) explained the limitations in their modeling efforts were primarily due to the lack of ideal data: “experiments with simultaneous dynamic data of bromoform, volatile fatty acids, hydrogen, and methane.” Regardless of the availability of ideal data, improvements in the conceptual model are warranted to include amino acids and branched-chain fatty acids fermentation dynamics in the rumen and the fluctuations in ruminal pH.

References

Machado L, Magnusson M, Paul NA, Nys R de, Tomkins N (2014) Effects of Marine and Freshwater Macroalgae on In Vitro Total Gas and Methane Production. PLOS ONE, 9, e85289. https://doi.org/10.1371/journal.pone.0085289

Muñoz-Tamayo R, Chagas JC, Ramin M, Krizsan SJ (2021) Modelling the impact of the macroalgae Asparagopsis taxiformis on rumen microbial fermentation and methane production. bioRxiv, 2020.11.09.374330, ver. 4 peer-reviewed and recommended by PCI Animal Science. https://doi.org/10.1101/2020.11.09.374330

Muñoz-Tamayo R, Giger-Reverdin S, Sauvant D (2016) Mechanistic modelling of in vitro fermentation and methane production by rumen microbiota. Animal Feed Science and Technology, 220, 1–21. https://doi.org/10.1016/j.anifeedsci.2016.07.005

Palmer CJ, Reason CJ (2009) Relationships of surface bromoform concentrations with mixed layer depth and salinity in the tropical oceans. Global Biogeochemical Cycles, 23. https://doi.org/10.1029/2008GB003338

Tedeschi LO (2006) Assessment of the adequacy of mathematical models. Agricultural Systems, 89, 225–247. https://doi.org/10.1016/j.agsy.2005.11.004

Tedeschi LO, Muir JP, Naumann HD, Norris AB, Ramírez-Restrepo CA, Mertens-Talcott SU (2021) Nutritional Aspects of Ecologically Relevant Phytochemicals in Ruminant Production. Frontiers in Veterinary Science, 8. https://doi.org/10.3389/fvets.2021.628445

Modelling the impact of the macroalgae Asparagopsis taxiformis on rumen microbial fermentation and methane productionRafael Muñoz-Tamayo , Juana C. Chagas, Mohammad Ramin, Sophie J. Krizsan<p>Background: The red macroalgae Asparagopsis taxiformis is a potent natural supplement for reducing methane production from cattle. A. taxiformis contains several anti-methanogenic compounds including bromoform that inhibits directly methanogene...Agricultural sustainability, Animal nutrition modelling, Emissions , Mathematical modelling, Microbial fermentation, Rumen microbiology, Rumen microbiome Luis Tedeschi2020-11-17 06:28:29 View
15 Dec 2020
article picture

Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometry

Accurate predictions of chemical composition of pigs for a wide range of body weights: no longer a myth!

Recommended by based on reviews by Mathieu Monziols and 1 anonymous reviewer

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 "

Accuracy of predicting chemical body composition of growing pigs using dual-energy X-ray absorptiometryClaudia Kasper, Patrick Schlegel, Isabel Ruiz-Ascacibar, Peter Stoll, Giuseppe Bee<p>Studies in animal science assessing nutrient and energy efficiency or determining nutrient requirements necessitate gathering exact measurements of body composition or body nutrient contents. Wet chemical analysis methods or standardized dissec...Agricultural sustainability, Animal nutrition modelling, Monogastrics, Physiology, Pig nutritionFlorence Gondret2020-09-17 10:44:58 View
01 Sep 2022
article picture

Detecting dairy cows' lying behaviour using noisy 3D ultrawide band positioning data

A novel method to monitor lying behaviour of dairy cows by combining noisy spatial positioning data, time-series segmentation based on statistical changepoints and machine learning classification algorithm

Recommended by based on reviews by Kareemah Chopra and John Fredy Ramirez Agudelo

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 

 

 

Detecting dairy cows' lying behaviour using noisy 3D ultrawide band positioning dataI. Adriaens, W. Ouweltjes, M. Pastell, E. Ellen, C. Kamphuis<p>In precision livestock farming, technology-based solutions are used to monitor and manage<br>livestock and support decisions based on on-farm available data. In this study, we developed<br>a methodology to monitor the lying behaviour of dairy c...Animal behaviour , Mathematical modelling, Precision livestock farmingEliel Gonzalez-Garcia2022-02-28 18:19:37 View
20 Dec 2021
article picture

Quantifying growth perturbations over the fattening period in swine via mathematical modelling

An innovative modelling approach to enhance the quality of the quantification of pig resilience during the entire fattening period: Towards an individual pig resilience index

Recommended by based on reviews by Arata Hidano, Ludovic Brossard and 2 anonymous reviewers

The identification of reliable estimates of growth potential and resilience over the fattening period in large populations is a challenge in actual swine breeding conditions. To overcome this drawback, the study by Revilla et al. 2021 in the frame of precision livestock farming aimed to propose an innovative modelling approach, in addition to previous studies from the same group (Revilla et al. 2019), to enhance the quality of the quantification of pig resilience during the entire fattening period. 

The authors developed a model that quantifies an “individual pig resilience indicator” based on longitudinal data, for instance body weight, recorded routinely by a commercially available automatic feeding system. Revilla and co-workers considered in their study two mainly commercialised pure pig breeds these being Piétrain including Piétrain Français NN Axiom line (Pie NN) free from halothane-sensitivity (ryanodine receptor gene, RYR1) and Piétrain Français Axiom line positive to this gene and Duroc. Therefore, the authors investigated the potential of improving resilience of swine livestock through inclusion for the first time of an “individual pig resilience indicator” in breeding objectives. A database of 13 093 boars (approximately 11.1 million of weightings) belonging to Pie (n= 5 841), Pie NN (n = 5 032) and Duroc (n= 2 220) finished under ad libitum feeding, high sanitary level and controlled temperature was used to develop robust models.

The authors checked the three datasets (for each pig breed)​ independently to explore the variation and gaps (a data pre-treatment procedure) to ensure high quality data for the modelling approach. Then, they applied the Gompertz model and linear interpolation on body weight data to quantify individual deviations from the expected production, allowing the creation of the ABC index. For the modelling, the authors applied a two-step mathematical model approach by first establishing a theoretical growth curve of each animal, while the second step aimed to build the actual perturbed growth curve. The heritability of the index ranged from 0.03 to 0.04, with similar heritability between Piétrain and Duroc breeds. Moreover, moderate genetic relationships were computed between the proposed index and important phenotypic traits in swine production likely BF100: backfat thickness at 100kg; LD100: longissimus dorsi thickness at 100kg; ADG: average daily gain during control and FCR: feed conversion ratio.

Developing models able to capture perturbations during the fattening period is a challenge in swine breeding industry. The model and methodology proposed by the authors in this innovative work (although preliminary and with low heritabilities) would help overcome such limit and facilitate a real implementation at large scale in pig breeding system. The modelling approach further offers an opportunity to develop a selection criterion to improve resilience in swine breeding conditions. 

To explore the full potential of this modelling approach, a larger database and other factors such as breed, behaviour and feeding behaviour of the animals, rearing practices, management and environment conditions, age… etc. are worthy to consider. In the future, more in depth measurements of behaviour that can be computed for example using computer vision should be desirable to increase the robustness of the proposed model.

References

Revilla, M., Friggens, N.C., Broudiscou, L.P., Lemonnier, G., Blanc, F., Ravon, L., Mercat, M.J., Billon, Y., Rogel-Gaillard, C., Le Floch, N. and Estellé, J. (2019). Towards the quantitative characterisation of piglets’ robustness to weaning: a modelling approach. Animal, 13(11), 2536-2546. https://doi.org/10.1017/S1751731119000843 

Revilla M, Lenoir G, Flatres-Grall L, Muñoz-Tamayo R, Friggens NC (2021). Quantifying growth perturbations over the fattening period in swine via mathematical modelling. bioRxiv, 2020.10.22.349985, ver. 5 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2020.10.22.349985 

Quantifying growth perturbations over the fattening period in swine via mathematical modellingManuel Revilla, Guillaume Lenoir, Loïc Flatres-Grall, Rafael Muñoz-Tamayo, Nicolas C Friggens<p>Background: Resilience can be defined as the capacity of animals to cope with short-term perturbations in their environment and return rapidly to their pre-challenge status. In a perspective of precision livestock farming, it is key to create i...Animal genetics, Animal health, Farming systems, Mathematical modelling, Precision livestock farmingMohammed Gagaoua 2020-10-26 11:47:08 View
05 Jul 2022
article picture

Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy.

Managing the feeding of rabbits to improve metabolic efficiency

Recommended by based on reviews by Marion Boutinaud, Davi Savietto and 1 anonymous reviewer

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 

Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy.Cathy Hue-Beauvais, Karine Bebin, Raphael Robert, Delphine Gardan-Salmon, Mickael Maupin, Nicolas Brun, Etienne Aujean, Florence Jaffrezic, Steve Simon, Madia Charlier, Fabienne Le Provost<p>Optimizing rabbit does preparation during early life to improve reproductive potential is a major challenge for breeders. Does selected for reproduction have specific nutritional needs, which may not be supplied with the common practice of feed...Animal nutrition modellingGiuseppe Conte2022-01-19 14:44:30 View
28 Jan 2022
article picture

Microbial colonization of tannin-rich tropical plants: interplay between degradability, methane production and tannin disappearance in the rumen

Ruminal microbial degradation of tannin-rich tropical plants and methane production

Recommended by based on reviews by Todd Callaway and Srinivasan Mahalingam

Rira et al. (2022) evaluated ruminal degradation of tropical tannins-rich plants and the relationship between condensed tannins disappearance and microbial communities. I found this study relevant because a major limitation for tropical plants utilization by ruminants is their potential reduced nutrient digestion. In this study, authors used leaves from Calliandra calothyrsus, Gliricidia sepium, and Leucaena leucocephala, pods from Acacia nilotica and the leaves of Manihot esculenta and Musa spp., which were incubated in situ in the rumen of dairy cows. An in vitro approach was also used to assess the effects of these plants on ruminal fermentation. They observed that hydrolysable and free condensed tannins from all plants completely disappeared after 24 h incubation in the rumen. Disappearance of protein-bound condensed tannins was variable with values ranging from 93% for Gliricidia sepium to 21% for Acacia nilolitica. This demonstrated some potential for selection and improvements in protein digestion. In contrast, fibre-bound condensed tannins disappearance averaged ~82% and did not vary between plants, which was remarkable. The authors noted that disappearance of bound fractions of condensed tannins was not associated with degradability of plant fractions and that the presence of tannins interfered with the microbial colonisation of plants. Each plant had distinct bacterial and archaeal communities after 3 and 12 h of incubation in the rumen and distinct protozoal communities at 3 h. This suggests a great deal of specificity for microbial-plant interactions, which warrants further evaluation to consider also animal contributions to such specificity. Adherent communities in tannin-rich plants had a lower relative abundance of fibrolytic microbes, notably Fibrobacter spp. Whereas, archaea diversity was reduced in high tannin-containing Calliandra calothyrsus and Acacia nilotica at 12 h of incubation. Concurrently, in vitro methane production was lower for Calliandra calothyrsus, Acacia nilotica and Leucaena leucocephala although for the latter total volatile fatty acids production was not affected and was similar to control. Finally, the study demonstrated that the total amount of hydrolysable and condensed tannins contained in a plant play a role governing the interaction with rumen microbes affecting degradability and fermentation. The effect of protein- and fibre-bound condensed tannins on degradability is less important. The major limitation of the study is the lack of animal validation at this stage; therefore, further studies are warranted, especially studies evaluating these plants in vivo. Furthermore, mechanisms associated with plant-microbial specificity, the role played by the host, and more data on nutrient utilization and gas production should be investigated. Nonetheless, this work show interesting microbial colonization and specific plant-microbial relationships that are novel in the ruminal environment.

Reference:

Rira M, Morgavi DP, Popova M, Maxin G, Doreau M (2022). Microbial colonization of tannin-rich tropical plants: interplay between degradability, methane production and tannin disappearance in the rumen. bioRxiv, 2021.08.12.456105, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. https://doi.org/10.1101/2021.08.12.456105

 

Microbial colonization of tannin-rich tropical plants: interplay between degradability, methane production and tannin disappearance in the rumenMoufida Rira, Diego P Morgavi, Milka Popova, Gaelle Maxin, Michel Doreau<p>Condensed tannins in plants are found free and attached to protein and fibre but it is not<br>known whether these fractions influence rumen degradation and microbial colonization.<br>This study explored the rumen degradation of tropical tannins...Animal nutrition modelling, Cattle production, Emissions , Farming systems, Gut microbiology, Microbial ecology, Microbial fermentation, Rumen microbiology, Rumen microbiome , Ruminant nutritionAntonio Faciola2021-08-16 08:56:45 View
05 Dec 2019
article picture

Effects of feeding treatment on growth rate and performance of primiparous Holstein dairy heifers

Optimizing growth rate of dairy heifers through nutrition to maximize reproduction and production

Recommended by based on reviews by Emilio Mauricio Ungerfeld and 2 anonymous reviewers

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."

Effects of feeding treatment on growth rate and performance of primiparous Holstein dairy heifersYannick Le Cozler, Julien Jurquet, Nicolas Bedere<p>The objective of this study was to investigate effects of feeding-rearing programs that aim for first calving at 20-27 months (mo) of age on growth, reproduction and production performance of Holstein cows at nulliparous and primiparous stages....Cattle production, Reproduction, Ruminant nutritionLuis Tedeschi2019-09-09 09:22:36 View
14 Dec 2022
article picture

Feed efficiency of lactating Holstein cows was not as repeatable across diets as within diet over subsequent lactation stages

A focus on feed efficiency reproducibility and repeatability of dairy cows fed different diets over the lactation stage.

Recommended by based on reviews by Ioannis Kaimakamis, Angela Schwarm and 2 anonymous reviewers

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

 

 

Feed efficiency of lactating Holstein cows was not as repeatable across diets as within diet over subsequent lactation stagesAmelie Fischer, Philippe Gasnier, philippe faverdin<p>&nbsp;Background: Improving feed efficiency has become a common target for dairy farmers to<br>meet the requirement of producing more milk with fewer resources. To improve feed<br>efficiency, a prerequisite is to ensure that the cows identified...Cattle production, Ruminant nutritionAlberto AtzoriAnonymous, Ioannis Kaimakamis, Giuseppe Conte, Angela Schwarm2021-02-11 08:43:59 View
06 Sep 2019
article picture

Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming.

Developing smart fitting algorithms to identify random perturbations in time-series data

Recommended by based on reviews by Alberto Atzori, Jennifer Spencer and 1 anonymous reviewer

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

Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming.Ben Abdelkrim Ahmed, Puillet Laurence, Gomes Pierre, Martin Olivier<p>Background Understanding the effects of environment on livestock provides valuable information on how farm animals express their production potential, and on their welfare. Ruminants are often confronted with perturbations that affect their per...Lactation biology , Mathematical modelling, Precision livestock farmingLuis Tedeschi2019-06-07 09:38:26 View