eprintid: 4648
rev_number: 9
eprint_status: archive
userid: 408
dir: disk0/00/00/46/48
datestamp: 2021-11-16 04:38:13
lastmod: 2021-11-16 04:38:13
status_changed: 2021-11-16 04:38:13
type: conference_item
metadata_visibility: show
creators_name: Pham, Manh Linh
creators_name: Nguyen, Ba Hieu
creators_name: Nguyen, Hoai Son
creators_name: Lê, Huy Hàm
creators_id: linhmp@vnu.edu.vn
creators_id: nbhieu@vnua.edu.vn
creators_id: sonnh@vnu.edu.vn
creators_id: lhham@agi.ac.vn
title: Simulation of precision feeding systems for swine
ispublished: inpress
subjects: IT
subjects: at
divisions: FIMO
divisions: fac_fit
keywords: precision feeding system, simulation, agentbased modelling, swine, efficiency
abstract: Precision livestock farming has become an inevitable trend for agricultural industry in the world. In that field, precision feeding is widely acknowledged because of its potential to reduce feed costs, environmental footprint and to improve animal health and welfare. Precision feeding involves modern multidisciplinary technologies such as information technology, mechanics, electronics, automation, etc. Such a system consists of automatic troughs linked to a computer system to exploit data collected from the individual animals (e.g. body weight, feed intake and feeding behaviour), and/or from ambient sensors. Data is processed and analysed based on mathematical models to make predictions, warnings for farmers or to formulate diets that fit requirements of each individual animal at each production period. However, implementing such a system often requires high investment, which may go beyond the capabilities of smallholders and small/medium laboratories. Furthermore, the risk of implementing by design but not conforming to reality is very high. To avoid this problem, we introduce an agent-based modelling approach to simulate precision feeding systems for swine. Two simulation experiments were conducted to provide predictions about the growth of individual pigs and the usefulness of precision feeding systems over classic feeding models.
date: 2021-11-11
date_type: completed
contact_email: linhmp@vnu.edu.vn
full_text_status: public
pres_type: paper
event_title: The 13th International Conference on KNOWLEDGE AND SYSTEMS ENGINEERING (KSE 2021)
event_location: Bangkok, Thailand
event_dates: 10-12 Nov 2021
event_type: conference
refereed: TRUE
referencetext: [1] FAO 2009. How to feed the world in 2050. Retrieved on 16 July
2021, from http://www.fao.org/fileadmin/templates/wsfs/docs/expert_
paper/How_to_Feed_the_World_in_2050.pdf
[2] Brossard, L., Dourmad, J.Y., Garcia-Launay, F., Van Milgen, J., 2017.
Chapter 10 – modelling nutrient requirements for pigs to optimize feed
efficiency. Achieving sustainable production of pig meat Volume 2.
Burleigh Dodds Science Publishing, 2018. 207-230.
[3] Pomar, C., Hauschild, L., Zhang, G. H., Pomar, J. and Lovatto, P. A.
(2009), ‘Applying precision feeding techniques in growing-finishing pig
operations. L’utilisation d’une alimentation de precision chez les porcs
à l’engrais’, In Proceedings of 45th Eastern Nutrition Conference of
Canada, pp. 103–16. Animal Nutrition Association of Canada, Quebec,
Canada.
[4] Andretta, I., Pomar, C., Rivest, J., Pomar, J. and Radünz, J. (2016),
‘Precision feeding can significantly reduce lysine intake and nitrogen
excretion without compromising the performance of growing pigs’,
Animal, 10(7), 1137–47.
[5] Maselyne J, Saeys W and Van Nuffel A 2015. Quantifying animal
feeding behaviour with a focus on pigs. Physiology & Behavior 138,
37–51.
[6] Law on Veterinary Medicine 2015, No. 79/2015/QH13, National Assembly of Vietnam.
[7] Casti, J.L.: Would-be worlds: how simulation is changing the frontiers
of science. J. Wiley, New York (1997).
[8] Pomar, C., J. Pomar, J. Rivest, L. Cloutier, M.-P. Letourneau- Montminy,
I. Andretta, and L. Hauschild. 2014. Estimating real-time individual
amino acid requirements in growing-finishing pigs: Towards a new
definition of nutrient requirements in growing-finishing pigs?
[9] Thornley, J.H.M., France, J., 2007. Mathematical Models in Agriculture,
second ed. CABI Publishing, Wallingford, UK, 923 pp.
[10] H. Nguyen-Ba, J. van Milgen, and M. Taghipoor, “A procedure to
quantify the feed intake response of growing pigs to perturbations,”
Animal, vol. 14, no. 2, pp. 253–260, 2020.
[11] F Labroue, R Guéblez, P Sellier, M.C Meunier-Salaün, Feeding behaviour of group-housed large white and landrace pigs in french central
test stations, Livestock Production Science, Volume 40, Issue 3, 1994,
Pages 303-312, ISSN 0301-6226.
[12] GAMA platform, urlhttps://gama-platform.github.io/wiki/Tutorials
[13] Bruijnis, M. R. N., H. Hogeveen, and E. N. Stassen. 2010. "Assessing
Economic Consequences of Foot Disorders in Dairy Cattle Using
a Dynamic Stochastic Simulation Model". Journal of Dairy Science
93(6):2419-2432.
[14] Sørensen, A. I. V., N. Toft, A. Boklund, C. Espinosa-Gongora, K.
Græsbøll, J. Larsen, and T. Hisham Beshara Halasa. 2017. "A Mechanistic Model for Spread of Livestock-Associated Methicillin-Resistant
Staphylococcus Aureus (LA-MRSA) within a Pig Herd". PLoSOne
12(11):1-18.
[15] Tedeschi, L. O., C. F. Nicholson, and E. Rich. 2011. "Using System
Dynamics Modelling Approach to Develop Management Tools for Animal Production with Emphasis on Small Ruminants". Small Ruminant
Research 98(1):102-110.
[16] Guimarães, V. P., L. O. Tedeschi, and M. T. Rodrigues. 2009. "Development of a Mathematical Model to Study the Impacts of Production and
Management Policies on the Herd Dynamics and Profitability of Dairy
Goats". Agricultural Systems 101(3):186-196.
[17] Parlavantzas, N.; Pham, L.M.; Morin, C.; Arnoux, S.; Beaunée, G.; Qi,
L.; Gontier, P.; Ezanno, P. A Service-based Framework for Building
and Executing Epidemic Simulation Applications in the Cloud. Concurr.
Comput. Pract. Exp. 2020, 32, e5554.
[18] Pham, M.L.; Parlavantzas, N.; Morin, C.; Arnoux, S.; Qi, L.; Gontier, P.;
Ezanno, P. DiFFuSE, a distributed framework for cloud-based epidemic
simulations: A case study in modelling the spread of bovine viral diarrhea virus. In Proceedings of the 2017 IEEE International Conference
on Cloud Computing Technology and Science (CloudCom), Hong Kong,
China, 11–14 December 2017.
[19] Al-Mamun, M. A., and Y. T. Grohn. 2017. "Mabsdairy: A Multiscale
Agent Based Simulation of a Dairy Herd". In Proceedings of the
50th Annual Simulation Symposium, April 23rd-26th, Virginia Beach,
Virginia, 1-12.
[20] Matthews, R., and I. Bakam. 2007. "A Combined Agent-Based and
Biophysical Modelling Approach to Address GHG Mitigation Policy
Issues." In Proceedings of the MODSIM International Congress on
Modelling and Simulation, edited by L. Oxley and D. Kulasiri, 10-
13. Christchurch, New Zealand: Modelling and Simulation Society of
Australia and New Zealand Inc.
[21] Pomar, J., V. López, and C. Pomar. 2011. "Agent-Based Simulation
Framework for Virtual Prototyping of Advanced Livestock Precision
Feeding Systems". Computers Electronics in Agriculture 78(1):88-97.
funders: CHEY Institute for Advanced Studies
funders: Asia Research Center, Vietnam National University, Hanoi
projects: CA.20.9A
citation:   Pham, Manh Linh and Nguyen, Ba Hieu and Nguyen, Hoai Son and Lê, Huy Hàm  (2021) Simulation of precision feeding systems for swine.  In: The 13th International Conference on KNOWLEDGE AND SYSTEMS ENGINEERING (KSE 2021), 10-12 Nov 2021, Bangkok, Thailand.    (In Press)  
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4648/1/2021218273.pdf