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

Araar, Abdelkrim; Puillet, Laurence; Gomes, Paloma Gabriela Batista; Martin, O. C.

doi:10.1016/j.animal.2020.100074

Cited by 32 publications

(30 citation statements)

References 31 publications

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“…The vectors representing the underlying mechanisms of growth may provide new metrics to help our understanding of complex phenotypes such as resilience. Ben Abdelkrim et al (2020) developed a perturbed lactation model that defines a theoretical unperturbed lactation curve and calculates actual deviations from this unperturbed lactation model. The same approach was proposed for BW by Martin and Ben Abdelkrim (2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies

Araar

Tribout

Martin

et al. 2021

Journal of Dairy Science

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Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.

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Section: Introductionmentioning

confidence: 99%

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies

Araar

Tribout

Martin

et al. 2021

Journal of Dairy Science

Self Cite

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“…In the context of our study, pigs can be subjected to different perturbations at different scales depending on the groups: temperature, social hierarchy, health situation. Our model approach does not include an explicit representation of the perturbations and thus differ from other approaches in which the number of perturbations and its duration are either fixed and know (11, 24) or are to be estimated (9, 10). In this study, we described a combined model approach to extract, in a two-step mathematical model approach, perturbed and unperturbed individual growth curves over the pig-fattening period.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have explored continuous recording of pig performance to study the impact of perturbations, including novel phenotypes related to disease resilience using daily FI (7, 8), and modelling approaches to detect potential perturbations as deviations of FI (9). Modelling efforts to characterize the animal response to perturbations in dairy cattle have also been developed (10). Our group has recently developed a modelling approach, for facilitating the quantification of piglet resilience to weaning (11).…”

Section: Introductionmentioning

confidence: 99%

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

Revilla

Lenoir

Flatres-Grall³

et al. 2020

Preprint

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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 informative indicators for general resilience and therefore incorporate this concept in breeding goals. In the modern swine breeding industry, new technologies such as automatic feeding system are increasingly common and can be used to capture useful data to monitor animal phenotypes such as feed efficiency. This automatic and longitudinal data collection integrated with mathematical modelling has a great potential to determine accurate resilience indicators, for example by measuring the deviation from expected production levels over a period of time. Results: This work aimed at developing a modelling approach for facilitating the quantification of pig resilience during the fattening period. A total of 13 093 pigs, belonging to three different genetic lines were monitored, and body weight measures registered with automatic feeding systems. We used the Gompertz model and linear interpolation on body weight data to quantify individual deviations from expected production, thereby creating a resilience index. The approach was able to quantify different degrees of perturbation. Further, we evaluated the heritability of the resilience index in the different lines analyzed. Conclusions: Our model-based approach can be useful to quantify pig responses to perturbations using exclusively the growth curves and should contribute to the improvement of swine productive performance. Keywords: modelling, perturbation, resilience, robustness, body weight, big data, pig, precision livestock farming

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“…Gilbert et al (2017) reported that a pig line selected for low RFI had significantly less body fat and a decreased fasting heat production relative to the high RFI line, although there were no clear differences in the growth between lines when exposed to a sanitary challenge (Chatelet et al, 2018). More recently, studies are emerging that seek to characterize resilience to environmental perturbations (Ben Abdelkrim et al, 2019;Adriaens et al, 2020) and to relate variation in these resilience measures to frequency of health events and productive longevity (Berghof et al, 2019). Poppe et al (2020), building on work of Elgersma et al (2018) and using the residual variance in milk yield from daily milk records as a resilience measure, found favorable genetic correlations between resilience and health, fertility, and longevity.…”

Section: Toward Sustainable Efficiency: Short-versus Long-term Efficiencymentioning

confidence: 99%

Invited review: Disentangling residual feed intake—Insights and approaches to make it more fit for purpose in the modern context

Martin

Ducrocq

Faverdin

et al. 2021

Journal of Dairy Science

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Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

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

Cited by 32 publications

References 31 publications

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies

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

Invited review: Disentangling residual feed intake—Insights and approaches to make it more fit for purpose in the modern context

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