Biology subjects

Friggens, N. C.

Publications and source records attributed to Friggens, N. C..

5 recordsLinked to original sources

bioRxiv2022

Disentangling the dynamics of energy allocation to provide a proxy of robustness in fattening pigs

BackgroundThere is a growing need to improve robustness characteristics in fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a robustness proxy on the basis of modelling of longitudinal energetic allocation coefficient to growth for fattening pigs. Consequently, the environmental variance of this allocation coefficient was considered as a proxy of robustness. The second objective was to estimate its genetic parameters and correlation with traits under selection as well with phenotypes routinely collected on farms. A total of 5848 pigs, from Pietrain NN paternal line, were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm was equipped with automatic feeding system, recording individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between cumulative net energy available, estimated from feed intake, and cumulative weight gain during fattening period. Longitudinal energetic allocation coefficients were analysed using a two-step approach, to estimate both its genetic variance and the genetic variance in the residual variance, trait LSR. ResultsThe LSR trait, that could be interpreted as an indicator of the response of the animal to perturbations/stress, showed low heritability (0.05{+/-}0.01). The trait LSR had high favourable genetic correlations with average daily growth (-0.71{+/-}0.06) and unfavourable with feed conversion ratio (-0.76{+/-}0.06) and residual feed intake (-0.83{+/-}0.06). The analysis of the relationship between estimated breeding values (EBV) LSR quartiles and phenotypes routinely collected on farms shows the most favourable situation for animals from quartile with the weakest EBV LSR, i.e., the most robust. ConclusionsThese results show that selection for robustness based on deviation from energetic allocation coefficient to growth can be considered in breeding programs for fattening pigs.

genetics↗

bioRxiv2020

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

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSResilience 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. ResultsThis work aimed at developing a modelling approach for facilitating the quantification of pig resilience during the fattening period, from approximately 34 kg to 105 kg of body weight. A total of 13 093 pigs, belonging to three different genetic lines were monitored (Pietrain, Pietrain NN and Duroc) since 2015, and body weight measures registered (approximately 11.1 million of weightings) 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 (ABC). The estimated heritabilities of ABC are low but not zero from 0.03 to 0.04 ({+/-} 0.01) depending on the breed. ConclusionsOur model-based approach can be useful to quantify pig responses to perturbations using exclusively the growth curves and should contribute to the genetic improvement of resilience of fattening pigs by providing a resilience index.

systems biology↗

bioRxiv2019

Productive lifespan and resilience rank can be predicted from on-farm first parity sensor data but not using a common equation across farms

A dairy cows lifetime resilience and her ability to re-calve gain importance on dairy farms as they affect all aspects of the sustainability of the dairy industry. Many modern farms today have milk meters and activity sensors that accurately measure yield and activity at a high frequency for monitoring purposes. We hypothesized that these same sensors can be used for precision phenotyping of complex traits such as lifetime resilience or productive lifespan. The objective of this study was to investigate if lifetime resilience and productive lifespan of dairy cows can be predicted using sensor-derived proxies of first parity sensor data. We used a data set from 27 Belgian and British dairy farms with an automated milking system containing at least 5 years of successive measurements. All of these farms had milk meter data available, and 13 of these farms were also equipped with activity sensors. This subset was used to investigate the added value of activity meters to improve the models prediction accuracy. To rank cows for lifetime resilience, a score was attributed to each cow based on her number of calvings, her 305-day milk yield, her age at first calving, her calving intervals and the days in milk at the moment of culling, taking her entire lifetime into account. Next, this lifetime resilience score was used to rank the cows within their herd resulting in a lifetime resilience ranking. Based on this ranking, the cows were classified in a low (last third), moderate (middle third) or high (first third) resilience category. In total 45 biologically-sound sensor features were defined from the time-series data, including measures of variability, lactation curve shape, milk yield perturbations, activity spikes indicating estrous events and activity dynamics representing health events. These features, calculated on first lactation data, were used to predict the lifetime resilience rank and thus, the classification within the herd (low/moderate/high). Using a specific linear regression model progressively including features stepwise selected at farm level (cut-off P-value of 0.2), classification performances were between 35.9% and 70.0% (46.7 {+/-} 8.0, mean {+/-} standard deviation) for milk yield features only and between 46.7% and 84.0% (55.5 {+/-} 12.1, mean {+/-} standard deviation) for lactation and activity features together. This is respectively 13.7 and 22.2% higher than what random classification would give. Moreover, using these individual farm models, only 3.5% and 2.3% of the cows were classified high while being low and vice versa, while respectively 91.8% and 94.1% of the wrongly classified animals were predicted in an adjacent category. A common equation across farms to predict this rank could not be found, which demonstrates the variability in culling and management strategies across farms and within farms over time. The lack of a common model structure across farms suggests the need to consider local (and evidence based) culling management rules when developing decision support tools for dairy farms. With this study we showed the potential of precision phenotyping of complex traits based on biologically meaningful features derived from readily available sensor data. We conclude that first lactation milk and activity sensor data have the potential to predict cows lifetime resilience rankings within farms but that consistency over farms is currently lacking.

bioengineering↗

bioRxiv2019

Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows. Timing of the alerts and robustness against missing values

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize the fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test dataset of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient dataset of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modelling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, two progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control PMASC, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using two different sampling schemes: one sample per cow every eight hours versus one sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 hours earlier compared to the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when one sample per day was used, the Kalman filter gave its alerts on average 24 hours later, and the variability in timing of the alerts compared to simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared to the current state of the art.\n\nInterpretative SummaryValidation of luteolysis monitoring tool for dairy cows.\n\nAdriaensIn this study, the performance of two monitoring algorithms to detect luteolysis using milk progesterone measurements was validated on a simulated dataset of realistic milk progesterone profiles. The synergistic control-based algorithm, PMASC, was able to identify luteolysis almost simultaneously with its occurrence. It was found to be more robust against missing samples and less dependent on the absolute milk progesterone values compared to a multiprocess Kalman filter combined with a fixed threshold. This research showed that implementation of PMASC could improve progesterone-based fertility monitoring on farm.

bioengineering↗

bioRxiv2018

Towards the quantitative characterization of piglets’ robustness to weaning: A modelling approach

Weaning is a critical transition phase in swine production in which piglets must cope with different stressors that may affect their health. During this period, the prophylactic use of antibiotics is still frequent to limit piglet morbidity, which raises both economic and public health concerns such as the appearance of antimicrobial-resistant microbes. With the interest of developing tools for assisting health and management decisions around weaning, it is key to provide robustness indexes that inform on the animals capacity to endure the challenges associated to weaning. This work aimed at developing a modelling approach for facilitating the quantification of piglet resilience to weaning. We monitored 325 Large White pigs weaned at 28 days of age and further housed and fed conventionally during the post-weaning period without antibiotic administration. Body weight and diarrhoea scores were recorded before and after weaning, and blood was sampled at weaning and one week later for collecting haematological data. We constructed a dynamic model based on the Gompertz-Makeham law to describe live weight trajectories during the first 75 days after weaning following the rationale that the animal response is partitioned in two time windows (a perturbation and a recovery window). Model calibration was performed for each animal. Our results show that the transition time between the two time windows, as well as the weight trajectories are characteristic for each individual. The model captured the weight dynamics of animals at different degrees of perturbation, with an average coefficient of determination of 0.99, and a concordance correlation coefficient of 0.99. The utility of the model is that it provides biological parameters that inform on the amplitude and length of perturbation, and the rate of animal recovery. Our rationale is that the dynamics of weight inform on the capability of the animal to cope with the weaning disturbance. Indeed, there were significant correlations between model parameters and individual diarrhoea scores and haematological traits. Overall, the parameters of our model can be useful for constructing weaning robustness indexes by using exclusively the growth curves. We foresee that this modelling approach will provide a step forward in the quantitative characterization of robustness.\n\nImplicationsThe quantitative characterization of animal robustness at weaning is a key step for management strategies to improve health and welfare. This characterization is also instrumental for the further design of selection strategies for productivity and robustness. Within a precision livestock farming optic, this study develops a mathematical modelling approach to describe the body weight of piglets from weaning with the rationale that weight trajectories provide central information to quantify the capability of the animal to cope with the weaning disturbance.

systems biology↗