Skip to main content Accessibility help
×
Home

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

  • J. L. Ellis (a1), M. Jacobs (a2), J. Dijkstra (a3), H. van Laar (a2), J. P. Cant (a1), D. Tulpan (a1) and N. Ferguson (a4)...

Abstract

Mechanistic models (MMs) have served as causal pathway analysis and ‘decision-support’ tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches – access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data
      Available formats
      ×

Copyright

Corresponding author

References

Hide All
Ackoff, R 1989. From data to wisdom. Journal of Applied Systems Analysis 16, 39.
Aitkin, M and Foxall, R 2003. Statistical modelling of artificial neural networks using the multi-layer perceptron. Statistics and Computing 13, 227239.
Alonso, J, Villa, A and Bahamonde, A 2015. Improved estimation of bovine weight trajectories using Support Vector Machine Classification. Computers and Electronics in Agriculture 110, 3641.
Astill, J, Dara, RA, Fraser, EDG and Sharif, S 2018. Detecting and predicting emerging disease in poultry with the implementation of new technologies and Big Data: a focus on Avian Influenza virus. Frontiers in Veterinary Science 5, 112.
Basheer, IA and Hajmeer, M 2000. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 43, 331.
Bongiovanni, R and Lowenberg-Deboer, J 2004. Precision agriculture and sustainability. Precision Agriculture 5, 359387.
Borchers, MR, Chang, YM, Tsai, IC, Wadsworth, BA and Bewley, JM 2016. A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors. Journal of Dairy Science 99, 74587466.
Cartwright, SJ, Bowgen, KM, Collop, C, Hyder, K, Nabe-Nielsen, J, Stafford, R, Stillman, RA, Thorpe, RB and Sibly, RM 2016. Communicating complex ecological models to non-scientist end users. Ecological Modelling 338, 5159.
Ching, T, Himmelstein, DS, Beaulieu-Jones, BK, Kalinin, AA, Do, BT, Way, GP, Ferrero, E, Agapow, P-M, Zietz, M, Hoffman, MM, Xie, W, Rosen, GL, Lengerich, BJ, Israeli, J, Lanchantin, J, Woloszynek, S, Carpenter, AE, Shrikumar, A, Xu, J, Cofer, EM, Lavender, CA, Turaga, SC, Alexandari, AM, Lu, Z, Harris, DJ, DeCaprio, D, Qi, Y, Kundaje, A, Peng, Y, Wiley, LK, Segler, MHS, Boca, SM, Swamidass, SJ, Huang, A, Gitter, A and Greene, CS 2018. Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface 15, 20170387.
Colles, FM, Cain, RJ, Nickson, T, Smith, AL, Roberts, SJ, Maiden, MCJ, Lunn, D and Dawkins, MS 2016. Monitoring chicken flock behaviour provides early warning of infection by human pathogen Campylobacter. Proceedings of the Royal Society B: Biological Sciences 283: 20152323.
Crow, M, Lim, N, Ballouz, S, Pavlidis, P and Gillis, J 2019. Predictability of human differential gene expression. Proceedings of the National Academy of Sciences 116, 64916500.
Dempster, AP, Laird, NM and Rubin, DB 1977. Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39, 138.
Dickinson, RA, Morton, JM, Beggs, DS, Anderson, GA, Pyman, MF, Mansell, PD and Blackwood, CB 2013. An automated walk-over weighing system as a tool for measuring liveweight change in lactating dairy cows. Journal of Dairy Science 96, 44774486.
Dijkstra, J, France, J, Dhanoa, MS, Maas, JA, Hanigan, MD, Rook, AJ and Beever, DE 1997. A model to describe growth patterns of the mammary gland during pregnancy and lactation. Journal of Dairy Science 80, 23402354.
Dijkstra, J, Neal, HDStC, Beever, DE and France, J 1992. Simulation of nutrient digestion, absorption and outflow in the rumen: model description. The Journal of Nutrition 122, 22392256.
Dumas, A, Dijkstra, J and France, J 2008. Mathematical modelling in animal nutrition: a centenary review. The Journal of Agricultural Science 146, 123142.
Dutta, R, Smith, D, Rawnsley, R, Bishop-Hurley, G, Hills, J, Timms, G and Henry, D 2015. Dynamic cattle behavioural classification using supervised ensemble classifiers. Computers and Electronics in Agriculture 111, 1828.
Ellis, J, Jacobs, M, Dijkstra, J, van Laar, H, Cant, J, Tulpan, D and Ferguson, N 2019. The role of mechanistic models in the era of big data and intelligent computing. Advances in Animal Biosciences 7, 285367.
Elshawi, R, Maher, M and Sakr, S 2019. Automated machine learning: state-of-the-art and open challenges. arXiv:1906.02287 [cs, stat].
Emmans, GC 1981. A model of the growth and feed intake of ad libitum fed animals, particularly poultry. British Society of Animal Production (BSAP) Occasional Publications 5, 103110.
Ferguson, NS 2014. Optimization: A paradigm change in nutrition and economic solutions. Advances in Pork Production 25, 121127.
Ferguson, NS 2015. Commercial application of integrated models to improve performance and profitability in finishing pigs. In Nutritional modelling for pigs and poultry (ed. Sakmoura, NK, Gous, R, Kyriazakis, L and Hauschild, L), pp. 141156. CABI, Wallingford, UK.
Foskolos, A, Calsamiglia, S, Chrenková, M, Weisbjerg, MR and Albanell, E 2015. Prediction of rumen degradability parameters of a wide range of forages and non-forages by NIRS. Animal 9, 11631171.
France, J 1988. Mathematical modelling in agricultural science. Weed Research 28, 419423.
France, J, Dijkstra, J, Dhanoa, MS, Lopez, S and Bannink, A 2000. Estimating the extent of degradation of ruminant feeds from a description of their gas production profiles observed in vitro: derivation of models and other mathematical considerations. British Journal of Nutrition 83, 143150.
France, J, Hanigan, MD, Reynolds, CK, Dijkstra, J, Crompton, LA, Maas, JA, Bequette, BJ, Metcalf, JA, Lobley, GE, MacRae, JC and Beever, DE 1999. An isotope dilution model for partitioning leucine uptake by the liver of the lactating dairy cow. Journal of Theoretical Biology 198, 121133.
Gatys, LA, Ecker, AS, Bethge, M 2016. Image style transfer using convolutional neural networks. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 26 June–1 July 2016, Las Vegas, NV, USA, pp. 24142423.
Halachmi, I, Edan, Y, Moallem, U and Maltz, E 2004. Predicting feed intake of the individual dairy cow. Journal of Dairy Science 87, 22542267.
Ilse, M, Tomczak, JM, Welling, M 2018. Attention-based deep multiple instance learning. Proceedings of the 35th International Conference on Machine Learning, PMLR, 10–15 July 2018, Stockholm, Sweden, pp. 21272136.
Jaddoa, M, Al-Jumeily, A, Gonzalez, L and Cuthbertson, H 2019. Automatic temperature measurement for hot spots in face region of cattle using infrared thermography. Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics, 29–31 July, 2019, Prague, Czech Republic, pp. 196201.
Johnson, SC 1967. Hierarchical clustering schemes. Psychometrika 32, 241254.
Kamilaris, A and Prenafeta-Boldu, FX 2018. Deep learning in agriculture: a survey. Computers and Electronics in Agriculture 147, 7090.
Knight, W 2017. There’s a big problem with AI: even its creators can’t explain how it works. MIT Technology Review. Retrieved on 27 June 2019 from https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
LeCun, Y, Bengio, Y and Hinton, G 2015. Deep learning. Nature 521, 436444.
Liakos, K, Busato, P, Moshou, D, Pearson, S and Bochtis, D 2018. Machine learning in agriculture: a review. Sensors 18, 2674.
Lloyd, S 1982. Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 129137.
Matthews, SG, Miller, AL, PlÖtz, T and Kyriazakis, I 2017. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Scientific Reports 7, 17582.
McCulloch, WS and Pitts, W 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5, 115133.
Misra, BB, Langefeld, C, Olivier, M and Cox, LA 2018. Integrated omics: tools, advances and future approaches. Journal of Molecular Endocrinology 62, R21R45.
Morota, G, Ventura, RV, Silva, FF, Koyama, M and Fernando, SC 2018. Big Data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science 96, 15401550.
Neethirajan, S, Tuteja, SK, Huang, S-T and Kelton, D 2017. Recent advancement in biosensors technology for animal and livestock health management. Biosensors and Bioelectronics 98, 398407.
Olah, C 2017. Feature Visualization. Retrieved on 8 July, 2019 from https://distill.pub/2017/feature-visualization/
Parsons, DJ, Green, DM, Schofield, CP and Whittemore, CT 2007. Real-time control of pig growth through an integrated management system. Biosystems Engineering 96, 257266.
Pearl, J and Mackenzie, D 2018. The book of why: the new science of cause and effect. Basic Books, New York, NY, USA.
Pegorini, V, Zen Karam, L, Pitta, C, Cardoso, R, da Silva, J, Kalinowski, H, Ribeiro, R, Bertotti, F and Assmann, T 2015. In vivo pattern classification of ingestive behavior in ruminants using FBG sensors and machine learning. Sensors 15, 2845628471.
Pomar, C, Pomar, J, Rivest, J, Cloutier, L, Létourneau-Montminy, M, Andretta, I, Hauschild, L, Sakomura, NK, Gous, RM and Kyriazakis, I 2015. Estimating real-time individual amino acid requirements in growing-finishing pigs: towards a new definition of nutrient requirements in growing-finishing pigs? In International Symposium: Modelling in Pig and Poultry Production, 18–20 June 2013, Sao Paulo, Brazil, pp. 1–19.
Pomar, C and Remus, A 2019. Precision pig feeding: a breakthrough toward sustainability. Animal Frontiers 9, 5259.
Rosenblatt, F 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 386408.
Sadeghi, M, Banakar, A, Khazaee, M and Soleimani, M 2015. An intelligent procedure for the detection and classification of chickens infected by clostridium perfringens based on their vocalization. Revista Brasileira de Ciência Avícola 17, 537544.
Sakomura, NK, Gous, R, Kyriazakis, I, Hauschild, L 2015. Nutritional modelling for pigs and poultry. CABI, Wallingford, UK.
Samek, W, Wiegand, T and Müller, K-R 2017. Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv:1708.08296 [cs, stat].
Sauvant, D and Nozière, P 2016. Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems. Animal 10, 755770.
Stefanovic, N, Milosevic, D 2017. Model for big data analytics in supply chain management. Proceedings of the 7th International Conference on Information Society and Technology ICIST, 12–15 March, 2017, Kopaonik, Serbia, pp. 163–168.
Tedeschi, LO 2006. Assessment of the adequacy of mathematical models. Agricultural Systems 89, 225247.
Tedeschi, LO 2019. ASN-ASAS symposium: Future of Data Analytics in Nutrition: mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics,. Journal of Animal Science 97, 19211944.
Tjørve, KMC and Tjørve, E 2017. The use of Gompertz models in growth analyses, and new Gompertz-model approach: an addition to the Unified-Richards family. PLoS ONE 12, 117.
Vasilieva, EV 2018. Developing the creative abilities and competencies of future digital professionals. Automatic Documentation and Mathematical Linguistics 52, 248256.
White, RP, Schofield, CP, Green, DM, Parsons, DJ and Whittemore, CT 2004. The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs. Animal Science 78, 409418.
Xu, M and Rhee, SY 2014. Becoming data-savvy in a big-data world. Trends in Plant Science 19, 619622.

Keywords

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

  • J. L. Ellis (a1), M. Jacobs (a2), J. Dijkstra (a3), H. van Laar (a2), J. P. Cant (a1), D. Tulpan (a1) and N. Ferguson (a4)...

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.