https://doi.org/10.4081/jae.2021.1323
Kernel density estimation analyses based on a low power-global positioning system for monitoring environmental issues of grazing cattle
Published: 25 March 2022
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The use of wearable sensors that record animal activity in intensive livestock systems has become more and more frequent for both early detection of diseases and improving production quality. Their application may also be significant in extensive livestock systems, with infrequent farmer-to-animal contact. The present study aimed to prove the feasibility of a novel automatic system for locating and tracking cows in extensive livestock systems based on space-time data provided by a low-power global positioning system (LP-GPS). The information was used to study the pasture exploitation by the herd for modelling the environmental impacts of extensive livestock systems through geographical information systems (GIS). A customised device, placed within a rectangular PVC case compatible with the collar usually worn by animals, was equipped with an LP-GPS omnidirectional system, an integrated SigFox communication system, and a power supply. The experimental trial was conducted in an existing semi-natural pasture characterised by good pasture allowance and cultivated grazing areas. Ten cows were embedded with LP-GPS collars, and the data, i.e., geographical coordinates and the time intervals related to each cow detection, were recorded every 20 minutes. Data were collected through a specifically developed AppWeb to be further imported and elaborated by using a GIS software tool. In the GIS environment, the daily distances travelled by each cow were linked with heatmaps obtained by applying Kernel density estimation models from the points obtained from the LP-GPS collars. The study results made it possible to obtain information on some relevant aspects of livestock’s environmental issues. In detail, it was possible to acquire information on herd behaviour related to the use of the pasture, e.g., the area of the pasture most frequently used during the day, individual use of the pasture, and possible animal interactions. These results represent the first step towards further insights and research activities because monitoring of animal locations could reduce several environmental issues such as soil degradation and greenhouse emissions.
Arcidiacono C., Porto S.M.C., Mancino M., Cascone, G. 2017a. A threshold-based algorithm for the development of inertial sensor-based systems to perform real-time cow step counting in free-stall barns. Biosyst. Eng. 153:99-109.
Arcidiacono C., Porto S.M.C., Mancino M., Cascone G. 2017b. Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Comput. Electron. Agric. 134:124-34.
Arcidiacono C., Barbari M., Benni S., Carfagna E., Cascone G., Conti L., di Stefano L., Guarino M., Leso L., Lovarelli D., Mancino M., Mattoccia S., Minozzi G., Porto S.M.C., Provolo G., Rossi G., Sandrucci A., Tamburini A., Tassinari P., Tomasello N., Torreggiani D., Valenti F. 2020a. Smart Dairy Farming: Innovative Solutions to Improve Herd Productivity. Lect. Notes Civ. Eng. 67:265-70.
Arcidiacono C., Mancino M., Porto S.M.C. 2020b. Moving mean-based algorithm for dairy cow’s oestrus detection from uniaxial-accelerometer data acquired in a free-stall barn. Comput. Electron. Agric. 175:105498.
Agouridis C.T, Koostra B.K, Edwards D.R, Stombaugh T.S., Workman S.R. 2003. Examination of GPS collar capabilities and limitations for tracking animal movement, grazed watershed studies. ASAE Paper No. 03-2001, St. Joseph, MI, USA.
Agouridis C.T., Koostra B.K., Edwards D.R., Stombaugh T.S., Vanzant E.S., Workman S.R. 2004. Suitability of a GPS collar for grazing studies. Trans ASAE 47:1321-9.
Bailey D.W., Trotter M.G., Knight C.W., Thomas M.G. 2018. Use of GPS tracking collars and accelerometers for rangeland livestock production research. Transl. Anim. Sci. 2:81-8.
Barbari M., Conti L., Koostra B. K., Masi G., Sorbetti Guerri F., Workman S.R. 2006. The use of global positioning and geographical information system in the management of extensive cattle grazing. Biosyst. Engine. 95:271-80.
Batalla I., Knudsen M.T., Mogensen L., del Hierro Ó., Pinto M., Hermansen J.E. 2015. Carbon footprint of milk from sheep farming systems in Northern Spain including soil carbon sequestration in grasslands. J. Clean. Prod. 104:121-9.
Baudry J., Thenail C. 2004. Interaction between farming systems, riparian zones, and landscape patterns: a case study in western France. Landsc. Urban Plann. 67:121-9.
Behr A. 2018. Best uses of wireless IoT communication technology. Behr Technologies Inc. Available from: https://industrytoday.com/best-uses-of-wireless-iot-communication-technology/
Crovetto G.M., Sandrucci A. 2010. Allevamento animale e riflessi ambientali. Fondazione Iniziative Zooprofilattiche e Zootecniche, Brescia.
de Weerd N., van Langevelde F., van Oeveren H., Nolet B.A., Kölzsch A., Prins H.H.T. 2015. Deriving animal behaviour from high-frequency GPS: tracking cows in open and forested habitat. PLoS One 10:e0129030.
D’Eon R.G., Serrouya R., Smith G., Kochanny C. 2000. GPS radiotelemetry error and bias in mountainous terrain. Wildlife Soc. B 30:430-9.
Eriksson C. 2011. What is traditional pastoral farming? The politics of heritage and ‘real values’ in Swedish summer farms (fäbodbruk). Pastoralism. 1:1-18.
Evans J.C., Dall S.R.X., Bolton M., Owen E., Votier S.C. 2016. Social foraging European shags: GPS tracking reveals birds from neighbouring colonies have shared foraging grounds. J. Ornithol. 157:23-32.
Feldt T., Schlecht E. 2016. Analysis of GPS trajectories to assess spatio-temporal differences in grazing patterns and land use preferences of domestic livestock in southwestern Madagascar. Pastoralism 6:5.
Fogarty E.S., Swain D.L., Cronin G., Trotter M. 2018. Autonomous on-animal sensors in sheep research: A systematic review. Comput. Electron. Agric. 150:245-56.
Frair J.L., Nielsen S.E., Merrill E.H., Lele S.R., Boyce M., Munro R.H.M., Stenhouse G.B., Beyer H.L. 2004. Removing GPS collar bias in habitat selection studies. J. Appl. Ecol. 41:201-12.
Frost A.R., Schofield C.P., Beaulah S.A., Mottram T.T., Lines J.A., Wathes C.M. 1977. A review of livestock monitoring and the need for integrated systems. Comput. Electron. Agric. 17:139-59.
Gomez C., Veras J.C., Vidal R., Casals L., Paradells J. 2019. A Sigfox energy consumption model. Sensors 19:681.
Gordon I.J. 2001. Tracking animals with GPS: an international conference held at the Macaulay Land Use Research Institute. Macaulay Land Use Research Institute, Aberdeen, Scotland, p. III.
Jiang Z., Sugita M., Kitahara M., Takatsuki S., Goto T. 2008. Effects of habitat feature, antenna position, movement, and fix interval on GPS radio collar performance in Mount Fuji, central Japan. Ecol Res. 23:581-8.
Leinonen I., Williams A.G., Wiseman J., Guy J., Kyriazakis I. 2012. Predicting the environmental impacts of chicken systems in the United Kingdom through a life cycle assessment: broiler production systems. Poult Sci. 91:8-25.
Liu T., Rodríguez L.F., Green A.R., Shike D.W., Segers J.R., Maia G.D.N., Norris H.D. 2012. Assessment of cattle impacts on soil characteristics in integrated crop-livestock systems. In Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting, 29 July-1 August, Dallas, TX, USA.
Meier M.S., Stoessel F., Jungbluth N., Juraske R., Schader C., Stolze M. 2015. Environmental impacts of organic and conventional agricultural products - Are the differences captured by life cycle assessment?. J. Environ Manage. 149:193-208.
Mekki K., Bajic E., Chaxel F., Meyer F. 2019. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express 5:1-7.
Nóbrega L., Tavares A., Cardoso A., Gonçalves P. 2018. Animal monitoring based on IoT technologies. In Proceedings of the IoT Vertical and Topical Summit for Agriculture, 8-9 May 2018, Tuscany, Italy.
Porto S.M.C., Arcidiacono C., Anguzza U., Cascone G. 2015. The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system. Biosyst. Eng. 133:46-55.
Qadir Q.M., Rashid T.A., Al-Salihi N.K., Ismael B., Kist A.A., Zhang Z. 2018. Low power wide area networks: a survey of enabling technologies, applications and interoperability needs. IEEE Access 6:77454-73.
Raizman E.A., Barner Rasmussen H., King L.E., Ihwagi F.W. Douglas-Hamilton I. 2013. Feasibility study on the spatial and temporal movement of Samburu’s cattle and wildlife in Kenya using GPS radio-tracking, remote sensing and GIS, Prev. Vet. Med. 111:76-80.
Riaboff L., Couvreur S., Madouasse A., Roig-Pons M., Aubin S., Massabie P., Chauvin A., Bédère N., Plantier G. 2020. Use of predicted behavior from accelerometer data combined with GPS data to explore the relationship between dairy cow behavior and pasture characteristics. Sensors 20:4741.
Rivero M.J., Grau-Campanario P., Mullan S. 2021. Factors affecting site use preference of grazing cattle studied from 2000 to 2020 through GPS tracking: a review. Sensors 21:2696.
Rodgers A.R., Rempel R.S., Abraham K.F. 1996. A GPS-based telemetry system. Wildlife Soc. B, 24:559-66.
Schieltz J.M., Okanga S., Allan B.F., Rubenstein D.I. 2017. GPS tracking cattle as a monitoring tool for conservation and management. Afr. J. Range For. Sci. 34:173-7.
Seaman D.E., Powell R.A. 1996. An evaluation of the accuracy of kernel density estimators for home-range analysis. Ecol. 77:2075-85.
Senneke S.L., Macneil M.D., Van Vleck L.D. 2004. Effects of sire misidentification on estimates of genetic parameters for birth and weaning weights in Hereford cattle. J. Anim. Sci. 82:2307-12.
Stache A., Löttker P., Heurich M. 2012. Red deer telemetry: Dependency of the position acquisition rate and accuracy of GPS collars on the structure of a temperate forest dominated by European beech and Norway spruce. Silva Gabreta 18:45-8.
Steinfeld H., Gerber P., Wassenaar T., Castel V., Rosales M., de Haan C. 2006. Livestock’s long shadow. Environmental issues and options. FAO, Rome, Italy. Available from: ftp://ftp.fao.org/docrep/fao/010/ a0701e/A0701E00.pdf
Sturaro E., Marchiori E., Cocca G., Penasa M., Ramanzin M., Bittante G. 2013. Dairy systems in mountainous areas: farm animal biodiversity, milk production and destination, and land use. Livest Sci. 58:15768.
Tobin C., Bailey D.W., Trotter M.G. 2021. Tracking and sensor-based detection of livestock water system failure: A case study simulation. Rangel Ecol Manag. 77:9-16.
Tomkiewicz S.M., Fuller M.R., Kie J.G., Bates K.K. 2010. Global positioning system and associated technologies in animal behaviour and ecological research. Philos. Trans. Roy. Soc. B 365:2163-76.
Turner L.W., Udal M.C., Larson B.T., Shearer S.A. 2000. Monitoring cattle behavior and pasture use with GPS and GIS. Can. J. Anim. Sci. 80:405-13.
Van Beest F.M., Loe L.E., Mysterud A., Milner J.M. 2010.Comparative space use and habitat selection of moose around feeding stations. J. Wildl. Manag. 74:219-27.
Vanslembrouck I., Van Huylenbroeck G. 2006. Landscape amenities from agriculture: economic assessment of agricultural landscapes. Springer, Netherlands.
Veissier I., Boissy A. Nowak R., Orgeur P., Poindron P. 1998. Ontogeny of social awareness in domestic herbivores. Appl. Anim. Behav. Sci. 57:233-45.
Zendri F., Ramanzin M., Bittante G., Sturaro E. 2016. Transhumance of dairy cows to highland summer pastures interacts with breed to influence body condition, milk yield and quality, Ital. J. Anim. Sci. 15:481-91.
Zendri F., Sturaro E., Ramanzin M. 2013. Highland summer pastures play a fundamental role for dairy systems in an Italian Alpine region. Agric Conspec Sci. 78: 295-29.
How to Cite
Porto, S. M. (2022) “Kernel density estimation analyses based on a low power-global positioning system for monitoring environmental issues of grazing cattle”, Journal of Agricultural Engineering, 53(2). doi: 10.4081/jae.2021.1323.
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