Animal Science

Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait

10.24072/pcjournal.136 - Peer Community Journal, Volume 2 (2022), article no. e38.

Get full text PDF Peer reviewed and recommended by PCI

Climate change, with its increasing frequency of environmental disturbances puts pressures on the livestock sector. To deal with these pressures, more complex traits such as resilience must be considered in our management strategies and in our breeding programs. Resilient animals respond well to environmental challenges, and have a decreased probability of needing assistance to overcome them. This paper discusses the need for operational measures of resilience that can be deployed at large scale across different farm types and livestock species. Such measures are needed to provide more precise phenotypes of resilience for use in farm management, but also for use in animal breeding. Any measure of response and recovery reflects both the animals resilience and the perceived size of the environmental disturbance, which can vary over time, depending on multiple animal and farm-related contexts. Therefore, and because universal definitions of resilience are too broad to be operational, we argue that resilience should be seen as a latent construct that cannot be directly measured and selected for. This leads to the following two points: (1) any postulated operational measure of resilience to a disturbance should be constructed from a sufficient number of indicators that each individually capture different facets of the resilience, such that when combined they better reflect the full resilience response; and (2) any postulated operational measure of resilience will have to be validated against reference measures that are the accumulated consequences of good resilience (e.g. productive lifespan or ability to re-calve). In a dairy cow case study, a practical resilience definition for dairy cattle was proposed and tested based on a scoring system containing several categories. In general terms and within a given parity, a cow receives plus points for each calving, and for a shorter calving interval, fewer inseminations and a higher milk production compared to her herd peers. She will receive minus points in case the number of inseminations increases, for each curative treatment day, and if her milk production is lower compared to her herd peers. By using readily available farm data, we were able to assess a practical lifetime resilience score, based on which cows can then be ranked within the herd. Cows that reach a next parity were shown to have a higher rank than cows that are culled before the next parity. To examine the usefulness of such a score, this resilience ranking was linked to two precision livestock technology-derived measures, related to milk yield deviations and accelerometer-derived deviations. Higher resilience ranking cows had fewer drops in milk yield and a more stable activity pattern during the lactation. This case study, taking the operational approach to quantifying and defining resilience, shows the promise of a data-driven approach for identifying resilience measures when applied within a biologically logical framework.

Published online:
DOI: 10.24072/pcjournal.136
Friggens, N.C. 1; Adriaens, I. 2, 3; Boré, R. 4; Cozzi, G. 5; Jurquet, J. 4; Kamphuis, C. 3; Leiber, F. 6; Lora, I. 5; Sakowski, T. 7; Statham, J. 8, 9; De Haas, Y. 3

1 Université Paris Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 75005, Paris, France
2 KU Leuven, Department of Biosystems, Biosystems Technology Cluster, Campus Geel, 2440 Geel, Belgium;
3 Wageningen University and Research, Animal Breeding and Genomics, P.O. Box 338, 6700 AH Wageningen, the Netherlands
4 Institut de l'Elevage, 42 rue Georges Morel, CS 60057- 49071 Beaucouzé, France
5 Department of Animal Medicine, Production and Health, University of Padova, Viale dell'Università 16, Legnaro (Padova) 35020, Italy
6 Research Institute of Organic Agriculture (FiBL), Ackerstrasse, 5070 Frick, Switzerland
7 Fundacja im. Stanislawa Karlowskiego, Juchowo 54a, 78-446 Silnowo, Poland
8 Harper & Keele Veterinary School, Keele University, Staffordshire ST5 5BG, United Kingdom
9 RAFT Solutions Ltd., Sunley-Raynes Farm, Ripon HG4 3AJ, United Kingdom;
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
     author = {Friggens, N.C. and Adriaens, I. and Bor\'e, R. and Cozzi, G. and Jurquet, J. and Kamphuis, C. and Leiber, F. and Lora, I. and Sakowski, T. and Statham, J. and De Haas, Y.},
     title = {Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait},
     journal = {Peer Community Journal},
     eid = {e38},
     publisher = {Peer Community In},
     volume = {2},
     year = {2022},
     doi = {10.24072/pcjournal.136},
     url = {}
TI  - Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait
JO  - Peer Community Journal
PY  - 2022
DA  - 2022///
VL  - 2
PB  - Peer Community In
UR  -
UR  -
DO  - 10.24072/pcjournal.136
ID  - 10_24072_pcjournal_136
ER  - 
%0 Journal Article
%T Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait
%J Peer Community Journal
%D 2022
%V 2
%I Peer Community In
%R 10.24072/pcjournal.136
%F 10_24072_pcjournal_136
Friggens, N.C.; Adriaens, I.; Boré, R.; Cozzi, G.; Jurquet, J.; Kamphuis, C.; Leiber, F.; Lora, I.; Sakowski, T.; Statham, J.; De Haas, Y. Resilience: reference measures based on longer-term consequences are needed to unlock the potential of precision livestock farming technologies for quantifying this trait. Peer Community Journal, Volume 2 (2022), article  no. e38. doi : 10.24072/pcjournal.136.

Peer reviewed and recommended by PCI : 10.24072/pci.animsci.100010

[1] Adriaens, I.; Friggens, N.; Ouweltjes, W.; Scott, H.; Aernouts, B.; Statham, J. 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, Volume 103 (2020) no. 8, pp. 7155-7171 | DOI

[2] Adriaens, I.; van den Brulle, I.; D'Anvers, L.; Statham, J.; Geerinckx, K.; De Vliegher, S.; Piepers, S.; Aernouts, B. Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage, Journal of Dairy Science, Volume 104 (2021) no. 1, pp. 405-418 | DOI

[3] Adriaens, I.; Van den Brulle, I.; Geerinckx, K.; D’Anvers, L.; De Vliegher, S.; Aernouts, B. Milk losses linked to mastitis treatments at dairy farms with automatic milking systems, Preventive Veterinary Medicine (In press) (2022)

[4] Ahlman, T.; Berglund, B.; Rydhmer, L.; Strandberg, E. Culling reasons in organic and conventional dairy herds and genotype by environment interaction for longevity, Journal of Dairy Science, Volume 94 (2011) no. 3, pp. 1568-1575 | DOI

[5] ATF (Eds), 2018 (

[6] Bauman, D. E.; Bruce Currie, W. Partitioning of Nutrients During Pregnancy and Lactation: A Review of Mechanisms Involving Homeostasis and Homeorhesis, Journal of Dairy Science, Volume 63 (1980) no. 9, pp. 1514-1529 | DOI

[7] Ben Abdelkrim, A.; Puillet, L.; Gomes, P.; Martin, O. Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming, Animal, Volume 15 (2021) no. 1 | DOI

[8] Ben Abdelkrim, A.; Tribout, T.; Martin, O.; Boichard, D.; Ducrocq, V.; Friggens, N. Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies, Journal of Dairy Science, Volume 104 (2021) no. 1, pp. 459-470 | DOI

[9] Berghof, T. V. L.; Poppe, M.; Mulder, H. A. Opportunities to Improve Resilience in Animal Breeding Programs, Frontiers in Genetics, Volume 9 (2019) | DOI

[10] Boulton, K.; Nolan, M. J.; Wu, Z.; Psifidi, A.; Riggio, V.; Harman, K.; Bishop, S. C.; Kaiser, P.; Abrahamsen, M. S.; Hawken, R.; Watson, K. A.; Tomley, F. M.; Blake, D. P.; Hume, D. A. Phenotypic and genetic variation in the response of chickens to Eimeria tenella induced coccidiosis, Genetics Selection Evolution, Volume 50 (2018) no. 1 | DOI

[11] Codrea, M. C.; Højsgaard, S.; Friggens, N. C. Differential smoothing of time-series measurements to identify disturbances in performance and quantify animal response characteristics: An example using milk yield profiles in dairy cows1, Journal of Animal Science, Volume 89 (2011) no. 10, pp. 3089-3098 | DOI

[12] Colditz, I. G.; Hine, B. C. Resilience in farm animals: biology, management, breeding and implications for animal welfare, Animal Production Science, Volume 56 (2016) no. 12 | DOI

[13] de Mol, R.; André, G.; Bleumer, E.; van der Werf, J.; de Haas, Y.; van Reenen, C. Applicability of day-to-day variation in behavior for the automated detection of lameness in dairy cows, Journal of Dairy Science, Volume 96 (2013) no. 6, pp. 3703-3712 | DOI

[14] De Vries, A.; Marcondes, M. Review: Overview of factors affecting productive lifespan of dairy cows, Animal, Volume 14 (2020) | DOI

[15] Doeschl-Wilson, A. B.; Villanueva, B.; Kyriazakis, I. The first step toward genetic selection for host tolerance to infectious pathogens: obtaining the tolerance phenotype through group estimates, Frontiers in Genetics, Volume 3 (2012) | DOI

[16] Dunne, F.; Kelleher, M.; Walsh, S.; Berry, D. Characterization of best linear unbiased estimates generated from national genetic evaluations of reproductive performance, survival, and milk yield in dairy cows, Journal of Dairy Science, Volume 101 (2018) no. 8, pp. 7625-7637 | DOI

[17] Elgersma, G.; de Jong, G.; van der Linde, R.; Mulder, H. Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows, Journal of Dairy Science, Volume 101 (2018) no. 2, pp. 1240-1250 | DOI

[18] Friggens, N.; Duvaux-Ponter, C.; Etienne, M.; Mary-Huard, T.; Schmidely, P. Characterizing individual differences in animal responses to a nutritional challenge: Toward improved robustness measures, Journal of Dairy Science, Volume 99 (2016) no. 4, pp. 2704-2718 | DOI

[19] Friggens, N.; Ingvartsen, K.; Korsgaard, I.; Larsen, T.; Ridder, C.; Nielsen, N., 2010

[20] Friggens, N.; Ridder, C.; Løvendahl, P. On the Use of Milk Composition Measures to Predict the Energy Balance of Dairy Cows, Journal of Dairy Science, Volume 90 (2007) no. 12, pp. 5453-5467 | DOI

[21] Friggens, N.; Blanc, F.; Berry, D.; Puillet, L. Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management, Animal, Volume 11 (2017) no. 12, pp. 2237-2251 | DOI

[22] Garcia-Baccino, C. A.; Marie-Etancelin, C.; Tortereau, F.; Marcon, D.; Weisbecker, J.-L.; Legarra, A. Detection of unrecorded environmental challenges in high-frequency recorded traits, and genetic determinism of resilience to challenge, with an application on feed intake in lambs, Genetics Selection Evolution, Volume 53 (2021) no. 1 | DOI

[23] Grodkowski, G.; Sakowski, T.; Puppel, K.; Baars, T. Comparison of different applications of automatic herd control systems on dairy farms - a review, Journal of the Science of Food and Agriculture, Volume 98 (2018) no. 14, pp. 5181-5188 | DOI

[24] Gross, J.; Bruckmaier, R. Invited review: Metabolic challenges and adaptation during different functional stages of the mammary gland in dairy cows: Perspectives for sustainable milk production, Journal of Dairy Science, Volume 102 (2019) no. 4, pp. 2828-2843 | DOI

[25] Hansen, J.; Sato, M.; Ruedy, R. Perception of climate change, Proceedings of the National Academy of Sciences, Volume 109 (2012) no. 37 | DOI

[26] Herd Navigator

[27] Hogeveen, H.; Kamphuis, C.; Steeneveld, W.; Mollenhorst, H. Sensors and Clinical Mastitis—The Quest for the Perfect Alert, Sensors, Volume 10 (2010) no. 9, pp. 7991-8009 | DOI

[28] Højsgaard, S.; Friggens, N. Quantifying degree of mastitis from common trends in a panel of indicators for mastitis in dairy cows, Journal of Dairy Science, Volume 93 (2010) no. 2, pp. 582-592 | DOI

[29] Kamphuis, C.; Dela Rue, B.; Mein, G.; Jago, J. Development of protocols to evaluate in-line mastitis-detection systems, Journal of Dairy Science, Volume 96 (2013) no. 6, pp. 4047-4058 | DOI

[30] Kamphuis, C.; Mollenhorst, H.; Heesterbeek, J.; Hogeveen, H. Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction, Journal of Dairy Science, Volume 93 (2010) no. 8, pp. 3616-3627 | DOI

[31] Knap, P. W.; Doeschl-Wilson, A. Why breed disease-resilient livestock, and how?, Genetics Selection Evolution, Volume 52 (2020) no. 1 | DOI

[32] La Fontaine, J., 1668 (

[33] Lora, I.; Gottardo, F.; Contiero, B.; Zidi, A.; Magrin, L.; Cassandro, M.; Cozzi, G. A survey on sensor systems used in Italian dairy farms and comparison between performances of similar herds equipped or not equipped with sensors, Journal of Dairy Science, Volume 103 (2020) no. 11, pp. 10264-10272 | DOI

[34] Mendes, L.; Coppa, M.; Rouel, J.; Martin, B.; Dumont, B.; Ferlay, A.; Espinasse, C.; Blanc, F. Profiles of dairy cows with different productive lifespan emerge from multiple traits assessed at first lactation: the case of a grassland-based dairy system, Livestock Science, Volume 246 (2021) | DOI

[35] Muñoz-Ulecia, E.; Bernués, A.; Casasús, I.; Olaizola, A.; Lobón, S.; Martín-Collado, D. Drivers of change in mountain agriculture: A thirty-year analysis of trajectories of evolution of cattle farming systems in the Spanish Pyrenees, Agricultural Systems, Volume 186 (2021) | DOI

[36] Nguyen-Ba, H.; van Milgen, J.; Taghipoor, M. A procedure to quantify the feed intake response of growing pigs to perturbations, Animal, Volume 14 (2020) no. 2, pp. 253-260 | DOI

[37] Ollion E.; Blanc, F.; Chassaing, C. How livestock farmers define robust dairy cows, Fourrages, Volume 235 (2018) (

[38] Ouweltjes, W.; de Haas, Y.; Kamphuis, C. At-market sensor technologies to develop proxies for resilience and efficiency in dairy cows In: 9th European Conference on Precision Livestock Farming, Cork, Ireland (2019), pp. 246-252

[39] Poppe, M.; Veerkamp, R.; van Pelt, M.; Mulder, H. Exploration of variance, autocorrelation, and skewness of deviations from lactation curves as resilience indicators for breeding, Journal of Dairy Science, Volume 103 (2020) no. 2, pp. 1667-1684 | DOI

[40] Poppe, M.; Mulder, H.; Veerkamp, R. Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level, Journal of Dairy Science, Volume 104 (2021) no. 7, pp. 8094-8106 | DOI

[41] Putz, A. M.; Harding, J. C. S.; Dyck, M. K.; Fortin, F.; Plastow, G. S.; Dekkers, J. C. M. Novel Resilience Phenotypes Using Feed Intake Data From a Natural Disease Challenge Model in Wean-to-Finish Pigs, Frontiers in Genetics, Volume 9 (2019) | DOI

[42] Revilla, M.; Friggens, N.; Broudiscou, L.; Lemonnier, G.; Blanc, F.; Ravon, L.; Mercat, M.; Billon, Y.; Rogel-Gaillard, C.; Le Floch, N.; Estellé, J.; Muñoz-Tamayo, R. Towards the quantitative characterisation of piglets’ robustness to weaning: a modelling approach, Animal, Volume 13 (2019) no. 11, pp. 2536-2546 | DOI

[43] Roff, D.; Mostowy, S.; Fairburn, D. The evolution of trade-offs: testing predictions on response to selection and environmental variation, Evolution, Volume 56 (2002) (

[44] Rostellato, R.; Promp, J.; Leclerc, H.; Mattalia, S.; Friggens, N.; Boichard, D.; Ducrocq, V. Influence of production, reproduction, morphology, and health traits on true and functional longevity in French Holstein cows, Journal of Dairy Science, Volume 104 (2021) no. 12, pp. 12664-12678 | DOI

[45] Sadoul, B.; Martin, O.; Prunet, P.; Friggens, N. C. On the Use of a Simple Physical System Analogy to Study Robustness Features in Animal Sciences, PLOS ONE, Volume 10 (2015) no. 8 | DOI

[46] Sadoul, B.; Friggens, N.; Valotaire, C.; Labbé, L.; Colson, V.; Prunet, P.; Leguen, I. Physiological and behavioral flexibility to an acute CO 2 challenge, within and between genotypes in rainbow trout, Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, Volume 209 (2017), pp. 25-33 | DOI

[47] Savietto, D.; Friggens, N. C.; Pascual, J. Reproductive robustness differs between generalist and specialist maternal rabbit lines: the role of acquisition and allocation of resources, Genetics Selection Evolution, Volume 47 (2015) no. 1 | DOI

[48] Scheffer, M.; Bolhuis, J. E.; Borsboom, D.; Buchman, T. G.; Gijzel, S. M. W.; Goulson, D.; Kammenga, J. E.; Kemp, B.; van de Leemput, I. A.; Levin, S.; Martin, C. M.; Melis, R. J. F.; van Nes, E. H.; Romero, L. M.; Olde Rikkert, M. G. M. Quantifying resilience of humans and other animals, Proceedings of the National Academy of Sciences, Volume 115 (2018) no. 47, pp. 11883-11890 | DOI

[49] Schuster, J. C.; Barkema, H. W.; De Vries, A.; Kelton, D. F.; Orsel, K. Invited review: Academic and applied approach to evaluating longevity in dairy cows, Journal of Dairy Science, Volume 103 (2020) no. 12, pp. 11008-11024 | DOI

[50] Tarrés, J.; Tibau, J.; Piedrafita, J.; Fàbrega, E.; Reixach, J. Factors affecting longevity in maternal Duroc swine lines, Livestock Science, Volume 100 (2006) no. 2-3, pp. 121-131 | DOI

[51] van Dixhoorn, I.; de Mol, R.; van der Werf, J.; van Mourik, S.; van Reenen, C. Indicators of resilience during the transition period in dairy cows: A case study, Journal of Dairy Science, Volume 101 (2018) no. 11, pp. 10271-10282 | DOI

[52] Waiblinger, S.; Boivin, X.; Pedersen, V.; Tosi, M.-V.; Janczak, A. M.; Visser, E. K.; Jones, R. B. Assessing the human–animal relationship in farmed species: A critical review, Applied Animal Behaviour Science, Volume 101 (2006) no. 3-4, pp. 185-242 | DOI

[53] Yin, T.; Jaeger, M.; Scheper, C.; Grodkowski, G.; Sakowski, T.; Klopčič, M.; Bapst, B.; König, S. Multi-breed genome-wide association studies across countries for electronically recorded behavior traits in local dual-purpose cows, PLOS ONE, Volume 14 (2019) no. 10 | DOI

Cited by Sources: