New ways of measuring intake, efficiency and behaviour of grazing livestock
Paul L. Greenwood A B D , Philip Valencia C , Leslie Overs C , David R. Paull B and Ian W. Purvis BA NSW Department of Primary Industries Beef Industry Centre, University of New England, Armidale, NSW 2351, Australia.
B CSIRO Animal Food and Health Sciences, Armidale, NSW 2350, Australia.
C CSIRO Computational Informatics, Pullenvale, Qld 4069, Australia.
D Corresponding author. Email: paul.greenwood@dpi.nsw.gov.au
Animal Production Science 54(10) 1796-1804 https://doi.org/10.1071/AN14409
Submitted: 16 March 2014 Accepted: 28 June 2014 Published: 19 August 2014
Abstract
Wireless sensor networks (WSN) offer a novel method for measuring important livestock phenotypes in commercial grazing environments. This information can then be used to inform genetic parameter estimation and improve precision livestock management. Arguably, these technologies are well suited for such tasks due to their small, non-intrusive form, which does not constrain the animals from expressing the genetic drivers for traits of interest. There are many technical challenges to be met in developing WSN technologies that can function on animals in commercial grazing environments. This paper discusses the challenges of the software development required for the collection of data from multiple types of sensors, the management and analyses of the very large volumes of data, determination of which sensing modalities are sufficient and/or necessary, and the management of the constrained power source. Assuming such challenges can be met however, validation of the sensor accuracy against benchmark data for specific traits must be performed before such a sensor can be confidently adopted. To achieve this, a pasture intake research platform is being established to provide detailed estimates of pasture intake by individual animals through chemical markers and biomass disappearance, augmented with highly annotated video recordings of animal behaviours. This provides a benchmark against which any novel sensor can be validated, with a high degree of flexibility to allow experiments to be designed and conducted under continually differing environmental conditions. This paper also discusses issues underlying the need for new and novel phenotyping methods and in the establishment of the WSN and pasture intake research platforms to enable prediction of feed intake and feed efficiency of individual grazing animals.
Additional keywords: alkanes, cattle, chromic oxide, goats, phenomics, sheep.
References
Anthony D, Bennett WP, Vuran MC, Dwyer MB, Elbaum S, Lacy A, Engels M, Wehtje W (2012) Sensing through the continent: towards monitoring migratory birds using cellular sensor networks. In ‘Proceedings of the 11th international conference on information processing in sensor networks’. pp. 329–340. (Association for Computing Machinery: New York)Arthur PF, Herd RM (2005) Efficiency of feed utilisation by livestock – implications and benefits of genetic improvement. Canadian Journal of Animal Science 85, 281–290.
| Efficiency of feed utilisation by livestock – implications and benefits of genetic improvement.Crossref | GoogleScholarGoogle Scholar |
Barlow R, Ellis KJ, Williamson PJ, Costigan P, Stephenson PD, Rose G, Mears PT (1988) Dry-matter intake of Hereford and first-cross cows measured by controlled release of chromic oxide on three pasture systems. The Journal of Agricultural Science 110, 217–231.
| Dry-matter intake of Hereford and first-cross cows measured by controlled release of chromic oxide on three pasture systems.Crossref | GoogleScholarGoogle Scholar |
Bell AW, Greenwood PL (2013) Optimising maternal cow, grower and finisher performance in beef production systems. In ‘Optimisation of feed use efficiency in ruminant production systems’. (Eds HPS Makkar, D Beever) pp. 51–72. (FAO: Rome)
Cottle DJ (2013) The trials and tribulations of estimating the pasture intake of grazing animals. Animal Production Science 53, 1209–1220.
| The trials and tribulations of estimating the pasture intake of grazing animals.Crossref | GoogleScholarGoogle Scholar |
Dove H, Mayes R (2006) Protocol for the analysis of n-alkanes and other plant wax compounds and for their use as markers for quantifying the nutrient supply of large mammalian herbivores. Nature Protocols 1, 1680–1697.
| Protocol for the analysis of n-alkanes and other plant wax compounds and for their use as markers for quantifying the nutrient supply of large mammalian herbivores.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtFagtrfE&md5=7939c00ced7d742305ce85d2db287bf1CAS | 17487151PubMed |
Galli JR, Cangiano CA, Milone DH, Laca EA (2011) Acoustic monitoring of short-term ingestive behavior and intake in grazing sheep. Livestock Science 140, 32–41.
| Acoustic monitoring of short-term ingestive behavior and intake in grazing sheep.Crossref | GoogleScholarGoogle Scholar |
Greenwood PL, Bell AW (2014) Consequences of nutrition during gestation, and the challenge to better understand and enhance livestock productivity and efficiency in pastoral ecosystems. Animal Production Science
| Consequences of nutrition during gestation, and the challenge to better understand and enhance livestock productivity and efficiency in pastoral ecosystems.Crossref | GoogleScholarGoogle Scholar |
Handcock RN, Swain DL, Bishop-Hurley GJ, Patison KP, Wark T, Valencia P, Corke P, O’Neill CJ (2009) Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing. Sensors 9, 3586–3603.
| Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing.Crossref | GoogleScholarGoogle Scholar | 22412327PubMed |
Hocquette JF, Capel C, David V, Guemene D, Didanel J, Ponsart C, Gastnel PL, Le Bail PY, Monget P, Mormede P, Barbezant M, Guillou F, Peyraud JL (2012) Objectives and applications of phenotyping network set-up for livestock. Animal Science Journal 83, 517–528.
| Objectives and applications of phenotyping network set-up for livestock.Crossref | GoogleScholarGoogle Scholar | 22776789PubMed |
Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nature Reviews. Genetics 11, 855–866.
| Phenomics: the next challenge.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsVejs7jK&md5=966ade0a015bf83fbaf02c296203bdc6CAS | 21085204PubMed |
Jurdak R, Sommer P, Kusy B, Kottege N, Crossman C, McKeown A, Westcott D (2013) Camazotz: multimodal activity-based GPS sampling. In ‘Proceedings of the 12th international conference on information processing in sensor networks’. pp. 67–78. (Association for Computing Machinery: New York)
Kwong KH, Yu TT, Goh HG, Sasloglou K, Stephen B, Glover I, Shen C, Du W, Michie C, Andonovic I (2012) Practical considerations for wireless sensor networks in cattle monitoring applications. Computers and Electronics in Agriculture 81, 33–44.
| Practical considerations for wireless sensor networks in cattle monitoring applications.Crossref | GoogleScholarGoogle Scholar |
Lukuyu M, Paull DR, Johns WH, Niemeyer D, McLeod J, McCorkell B, Savage D, Purvis IW, Greenwood PL (2014) Precision of estimating individual feed intake of grazing animals offered low, declining pasture availability. Animal Production Science
| Precision of estimating individual feed intake of grazing animals offered low, declining pasture availability.Crossref | GoogleScholarGoogle Scholar |
McSweeney C (2008) ‘Novel individual enteric methane measuring system for multiple ruminants.’ Project No. B.CCH.1003. (Meat and Livestock Australia: Sydney)
Navon S, Mizrach A, Hetzroni A, Ungar ED (2013) Automatic recognition of jaw movements in free-ranging cattle, goats and sheep, using acoustic monitoring. Biosystems Engineering 114, 474–483.
| Automatic recognition of jaw movements in free-ranging cattle, goats and sheep, using acoustic monitoring.Crossref | GoogleScholarGoogle Scholar |
Oudshoorn FW, Cornou C, Hellwing ALF, Lund P, Kristensen T, Munksgaard L (2012) ‘Estimation of grazing time and grass intake on pasture for dairy cows using tightly and loosely mounted di- and tri-axial accelerometers.’ Report 644. (Innovations in Grazing Livestock Research: Wageningen UR, Netherlands)
Penning PD (Ed.) (2004) ‘Herbage intake handbook.’ (British Grassland Society: Reading, UK)
Pollak EJ, Bennett GL, Snelling WM, Thallman RM, Kuehn LA (2012) Genomics and the global beef industry. Animal Production Science 52, 92–99.
| Genomics and the global beef industry.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XjtlSjtbk%3D&md5=ec79edaf99cf665c61a1912b9ffe5d4cCAS |
Poppi DP (2011) Nutritional constraints for grazing animals and the importance of selective grazing behaviour. In ‘Grassland productivity and ecosystems services’. (Eds G Lemaire, J Hodgson, A Chabbi) pp. 19–26. (CABI: Wallingford, UK)
Purvis IW, Valencia P, Overs L, Greenwood PL (2013) Novel phenotyping techniques for enhancing genetic and genomic predictions of traits that are difficult to measure in grazing livestock. In ‘Proceedings of the 20th conference of the Association for the Advancement of Animal Breeding and Genetics’. pp. 282–285. (AAABG: Armidale)
Reynolds CK, Crompton LA, Mills AN (2011) Improving the efficiency of energy utilization in cattle. Animal Production Science 51, 6–12.
| Improving the efficiency of energy utilization in cattle.Crossref | GoogleScholarGoogle Scholar |
Rushen J, Chapinal N, Passille AM (2012) Automated monitoring of behavioural-based animal welfare indicators. Animal Welfare (South Mimms, England) 21, 339–350.
| Automated monitoring of behavioural-based animal welfare indicators.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtFOntL7F&md5=35bd61c383bced698fab64f292210035CAS |
Rutter SM (2013) Can precision farming technologies be applied to grazing management? In ‘Proceedings of the 22nd international grassland congress’. (Eds DL Michalk, GD Millar, WB Badgery, KM Broadfoot) pp. 617–620. (NSW Department of Primary Industries: Orange)
Scollan ND, Greenwood PL, Newbold CJ, Yanez Ruiz DR, Shingfield KJ, Wallace RJ, Hocquette JF (2011) Future research priorities for animal production in a changing world. Animal Production Science 51, 1–5.
| Future research priorities for animal production in a changing world.Crossref | GoogleScholarGoogle Scholar |
Swain DL, Hancock RN, Bishop-Hurley GJ, Menzies D (2013) Opportunities for improving livestock production with e-management systems. In ‘Proceedings of the 22nd international grassland congress’. (Eds DL Michalk, GD Millar, WB Badgery, KM Broadfoot) pp. 603–609. (NSW Department of Primary Industries: Orange)
Tani Y, Yokota Y, Yayota M, Ohtani S (2013) Automatic recognition and classification of cattle chewing activity by an acoustic monitoring method with a single-axis acceleration sensor. Computers and Electronics in Agriculture 92, 54–65.
| Automatic recognition and classification of cattle chewing activity by an acoustic monitoring method with a single-axis acceleration sensor.Crossref | GoogleScholarGoogle Scholar |
