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Труды Института системного программирования РАН

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Интернет вещей для оценки поведения крупного рогатого скота при поиске корма и кормлении в пастбищных системах земледелия: концепции и обзор сенсорных технологий

https://doi.org/10.15514/ISPRAS-2019-31(2)-10

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Аннотация

В этой статье приводится обзор экологических аспектов перемещения, кормодобывания и кормления крупного рогатого скота, а также технологий датчиков, которые могут быть встроены в основанную на Интернете вещей платформу для поддержки точного животноводства. Всего были проанализированы 43 рецензированных журнальных статьи, проиндексированных Web of Science. Во-первых, были идентифицированы сенсорные технологии (например, RFID, GPS или акселерометр), используемые авторами каждой статьи. Затем документы были классифицированы в соответствии с их применимостью к экологическим исследованиям в области кормодобывания и кормления скота

Об авторах

Годофредо Рамон Гарай Альварес
Университет Камагуэй
Куба
Доцент факультета информатики


Хосе Альберто Бертоm Вальдес
Университет Камагуэй
Куба
Профессор кафедры репродукции животных


Карина Перес-Теруэль
Открытый университет для взрослых
Доминиканская Республика
Директор по инновациям


Список литературы

1. . Whitmore, A., Agarwal, A., Xu, L.D. The Internet of Things – A survey of topics and trends. I Information Systems Frontiers, vol. 17, no. 2, 2015, pp. 261–274.

2. . Halachmi, I., Guarino, M. Editorial: Precision livestock farming: a ‘per animal’ approach using advanced monitoring technologies. Animal, vol. 10, no. 9, 2016, pp. 1482–1483.

3. . Guo S., Qiang M., Luan X., Xu P., He G., Yin X., Xi L., Jin X, Shao J, Chen X, Fang D., Li B. The application of the Internet of Things to animal ecology. Integrative zoology, vol. 10, no. 6, 2015, pp. 572-578.

4. . Ingrand S. Opinion paper: ‘monitoring te salutant:’ combining digital sciences and agro-ecology to design multi-performant livestock farming systems. Animal, vol. 12, no. 1, 2018, pp. 2–3.

5. . Nathan R., Getz W.M., Revilla E., Holyoak M., Kadmon R., Saltz D., Smouse P. E. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, vol. 105, no. 49, 2008, pp. 19052-19059.

6. . Getz W.M., & Saltz D.A framework for generating and analyzing movement paths on ecological landscapes. Proceedings of the National Academy of Sciences, vol. 105, no. 49, 2008, pp.19066-19071.

7. . Lallo C. H., Cohen J., Rankine D., Taylor M., Cambell J., & Stephenson T. Characterizing heat stress on livestock using the temperature humidity index (THI) – prospects for a warmer Caribbean. Regional Environmental Change, vol. 18, no. 8, 2018, pp.1-12.

8. . Polsky L., & von Keyserlingk M.A. Invited review: Effects of heat stress on dairy cattle welfare. Journal of dairy science, vol. 100, no. 11, 2017, pp. 8645-8657.

9. . Nathan R., Getz W.M., Revilla E., Holyoak M., Kadmon R., Saltz D., & Smouse P.E. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, vol. 105, no. 49, 2008, pp. 19052-19059.

10. . Smouse P.E., Focardi S., Moorcroft P.R., Kie J.G., Forester J.D., & Morales J.M. Stochastic modelling of animal movement. Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 365, no. 1550, 2010, pp. 2201-2211.

11. . Seidel D.P., Dougherty E., Carlson C., Getz W.M. Ecological metrics and methods for GPS movement data. International Journal of Geographical Information Science, vol. 32, no. 11, 2018, pp. 2272-2293.

12. . Dougherty E.R., Seidel D.P., Carlson C. J., Spiegel O., & Getz W.M. Going through the motions: incorporating movement analyses into disease research. Ecology letters, vol. 21, no. 4, 2018, pp. 588-604.

13. . Marcus Rowcliffe J., Carbone C., Kays R., Kranstauber B., & Jansen P. A. Bias in estimating animal travel distance: the effect of sampling frequency. Methods in Ecology and Evolution, vol. 3, no. 4, 2012, pp. 653-662.

14. . Perry J.N., Liebhold A.M., Rosenberg M.S., Dungan J., Miriti M., Jakomulska A., & Citron‐Pousty S. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography, vol. 25, no. 5, 2002, pp. 578-600.

15. . DelCurto T., Porath M., Parsons C.T., & Morrison J.A. Management strategies for sustainable beef cattle grazing on forested rangelands in the Pacific Northwest. Rangeland Ecology & Management, vol. 58, no. 2, 2005, pp.119-127.

16. . Sanon H.O., Kaboré-Zoungrana C., & Ledin I. Behaviour of goats, sheep and cattle and their selection of browse species on natural pasture in a Sahelian area. Small Ruminant Research, vol. 67, no. 1, 2007, pp .64-74.

17. . Kilgour R.J. In pursuit of “normal”: A review of the behaviour of cattle at pasture. Applied Animal Behaviour Science, vol. 138, no. 1-2, 2012, pp. 1-11.

18. . Guo Y., Corke P., Poulton G., Wark T., Bishop-Hurley G., & Swain D. Animal behaviour understanding using wireless sensor networks. In Proc. of the 31st IEEE Conference on Local Computer Networks, 2006, pp. 607-614.

19. . Polsky L., & von Keyserlingk M.A. Invited review: Effects of heat stress on dairy cattle welfare. Journal of dairy science, vol. 100, no. 11, 2017, pp. 8645-8657.

20. . Beyer H.L., Haydon D.T., Morales J.M., Frair J.L., Hebblewhite M., Mitchell M., & Matthiopoulos J. The interpretation of habitat preference metrics under use–availability designs. Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 365, no. 1550, 2010, pp. 2245-2254.

21. . Johnson D.H. The comparison of usage and availability measurements for evaluating resource preference. Ecology, vol. 61, no. 1, 1980, pp. 65-71.

22. . Rutter S.M. Diet preference for grass and legumes in free-ranging domestic sheep and cattle: current theory and future application. Applied Animal Behaviour Science, vol. 97, no. 1, 2006, pp.17-35.

23. . Johnson C.J., & Seip D.R. Relationship between resource selection, distribution, and abundance: a test with implications to theory and conservation. Population Ecology, vol. 50, no. 2, 2008, pp.145-157.

24. . Awad A.I. From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, vol. 123, 2016, pp. 423-435.

25. . Huhtala A., Suhonen K., Mäkelä P., Hakojärvi M., & Ahokas J. Evaluation of instrumentation for cow positioning and tracking indoors. Biosystems Engineering, vol. 96, no. 3, 2007, pp. 399-405.

26. . Vanhelst J., Béghin L., Duhamel A., Bergman P., Sjöström M., & Gottrand F. Comparison of uniaxial and triaxial accelerometry in the assessment of physical activity among adolescents under free-living conditions: the HELENA study. BMC medical research methodology, vol. 12, no. 1, 2012.

27. . Grundy E., Jones M.W., Laramee R. S., Wilson R.P., & Shepard E.L. Visualisation of sensor data from animal movement. In Computer Graphics Forum, vol. 28, no. 3, pp. 815-822.

28. . Frair J.L., Fieberg J., Hebblewhite M., Cagnacci F., DeCesare N.J., & Pedrotti L. Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data. Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 365, no. 1550, 2010, pp. 2187-2200.

29. . Tani Y., Yokota Y., Yayota M., & Ohtani S. Automatic recognition and classification of cattle chewing activity by an acoustic monitoring method with a single-axis acceleration sensor. Computers and Electronics in Agriculture, vol. 92, 2013, pp. 54-65.

30. . Deniz N.N., Chelotti J.O., Galli J.R., Planisich A.M., Larripa M.J., Rufiner H.L., & Giovanin L.L. Embedded system for real-time monitoring of foraging behavior of grazing cattle using acoustic signals. Computers and Electronics in Agriculture, vol. 138, 2017, pp.167-174.

31. . Schirmann K., von Keyserlingk M.A., Weary D.M., Veira D.M., & Heuwieser W. Validation of a system for monitoring rumination in dairy cows. Journal of Dairy Science, vol. 92, no. 12, 2009, pp. 6052-6055.

32. . Wilson R.P., & McMahon C.R. Measuring devices on wild animals: what constitutes acceptable practice? Frontiers in Ecology and the Environment, vol. 4, no. 3, 2006, pp.147-154.

33. . Ungar E.D., Henkin Z., Gutman M., Dolev A., Genizi A., & Ganskopp D. Inference of animal activity from GPS collar data on free-ranging cattle. Rangeland Ecology & Management, vol. 58, no. 3, 2005, pp. 256-266.

34. . McGavin S.L., Bishop-Hurley G.J., Charmley E., Greenwood P.L., & Callaghan M.J. Effect of GPS sample interval and paddock size on estimates of distance travelled by grazing cattle in rangeland, The Rangeland Journal, vol. 40, no. 1, 2018, pp. 55-64.

35. . Liu T., Green A.R., Rodríguez L.F., Ramirez B.C., & Shike D.W. Effects of number of animals monitored on representations of cattle group movement characteristics and spatial occupancy. PloS one, vol. 10, no. 2, 2015.

36. . McGranahan D.A., Geaumont B., & Spiess, J.W. Assessment of a livestock GPS collar based on an open‐source datalogger informs best practices for logging intensity. Ecology and Evolution, vol. 8, no. 11, 2018, pp. 5649-5660.

37. . de Weerd N., van Langevelde F., van Oeveren H., Nolet B.A., Kölzsch A., Prins H.H., & de Boer W.F. Deriving animal behaviour from high-frequency GPS: tracking cows in open and forested habitat. PloS one, vol. 10, no. 6, 2015.

38. . Tofastrud M., Hegnes H., Devineau O., & Zimmermann B. Activity patterns of free-ranging beef cattle in Norway. Acta Agriculturae Scandinavica, Section A — Animal Science, vol. 68, issue 1, 2018, pp. 39-47.

39. . Meunier B., Pradel P., Sloth K.H., Cirié C., Delval E., Mialon M.M., & Veissier I. Image analysis to refine measurements of dairy cow behaviour from a real-time location system. Biosystems Engineering, vol. 173, 2018, pp. 32-44.

40. . Larson-Praplan S., George M.R., Buckhouse J.C., & Laca E.A. Spatial and temporal domains of scale of grazing cattle. Animal Production Science, vol. 55, no. 3, 2015, pp. 284-297.

41. . Zhao K., & Jurdak R. Understanding the spatiotemporal pattern of grazing cattle movement. Scientific reports, vol. 6, 2016.

42. . Liao C., Clark P.E., Shibia M., & DeGloria S.D. Spatiotemporal dynamics of cattle behavior and resource selection patterns on East African rangelands: evidence from GPS-tracking. International Journal of Geographical Information Science, vol. 32, no. 7, 2018, pp. 1523-1540.

43. . Saint-Dizier M., & Chastant-Maillard S. Methods and on-farm devices to predict calving time in cattle. The Veterinary Journal, vol. 205, no. 3, 2015, pp. 349-356.

44. . Williams M.L., James W.P., & Rose M.T. Fixed-time data segmentation and behavior classification of pasture-based cattle: Enhancing performance using a hidden Markov model. Computers and Electronics in Agriculture, vol. 142, 2017, pp.585-596.

45. . Sawalhah M.N., Cibils A.F., Hu C., Cao H., & Holechek J.L. Animal-driven rotational grazing patterns on seasonally grazed New Mexico rangeland. Rangeland ecology & management, vol. 67, no. 6, 2014, pp. 710-714.

46. . Yoshitoshi R., Watanabe N., Kawamura K., Sakanoue S., Mizoguchi R., Lee H. J., & Kurokawa Y. Distinguishing cattle foraging activities using an accelerometry-based activity monitor. Rangeland ecology & management, vol. 66, no. 3, 2013, pp. 382-386.

47. . Thorup V.M., Munksgaard L., Robert P.E., Erhard H.W., Thomsen P.T., & Friggens N.C. Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms. Animal, vol. 9, no. 10, 2015, pp.1704-1712.

48. . Silper B.F., Madureira A.M.L., Kaur M., Burnett T.A., & Cerri R.L.A. Comparison of estrus characteristics in Holstein heifers by 2 activity monitoring systems. Journal of dairy science, vol. 98, no. 5, 2015, pp. 3158-3165.

49. . Bonk S., Burfeind O., Suthar V.S., & Heuwieser W. Evaluation of data loggers for measuring lying behavior in dairy calves. Journal of Dairy Science, vol. 96, no. 5, 2013, pp. 3265-3271.

50. . Kienitz M.J., Heins B.J., & Chester-Jones H. Growth, behavior, and economics of group-fed dairy calves fed once or twice daily in an organic production system. Journal of dairy science, vol. 100, no. 4, 2017, pp. 3318-3325.

51. . Minnaert B., Thoen B., Plets D., Joseph W., & Stevens N. Wireless energy transfer by means of inductive coupling for dairy cow health monitoring. Computers and Electronics in Agriculture, vol. 152, 2018, pp. 101-108.

52. . Nadimi E.S., Søgaard H.T., & Bak T. ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosystems engineering, vol. 100, no. 2, 2008, pp. 167-176.

53. . Reiter S., Sattlecker G., Lidauer L., Kickinger F., Öhlschuster M., Auer W., Schweinzer V., Klein-Jöbstl D., Drillich M., & Iwersen M. Evaluation of an ear-tag-based accelerometer for monitoring rumination in dairy cows. Journal of dairy science, vol. 101, no, 4, 2018, pp. 3398-3411.

54. . Rutten C.J., Kamphuis C., Hogeveen H., Huijps K., Nielen M., & Steeneveld W. Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows. Computers and Electronics in Agriculture, vol. 132, 2017, pp. 108-118.

55. . Wolfger B., Timsit E., Pajor E. A., Cook N., Barkema H.W., & Orsel K. Accuracy of an ear tag-attached accelerometer to monitor rumination and feeding behavior in feedlot cattle. Journal of animal science, vol. 93, no. 6, 2015, pp. 3164-3168.

56. . Dutta R., Smith D., Rawnsley R., Bishop-Hurley G., Hills J., Timms G., & Henry D. Dynamic cattle behavioural classification using supervised ensemble classifiers. Computers and Electronics in Agriculture, vol. 111, 2015, pp. 18-28.

57. . Oudshoorn F.W., Cornou C., Hellwing A.L.F., Hansen H.H., Munksgaard L., Lund P., & Kristensen T. Estimation of grass intake on pasture for dairy cows using tightly and loosely mounted di-and tri-axial accelerometers combined with bite count. Computers and Electronics in Agriculture, vol. 99, 2013, pp. 227-235.

58. . Stone A.E., Jones B.W., Becker C.A., & Bewley J.M. Influence of breed, milk yield, and temperature-humidity index on dairy cow lying time, neck activity, reticulorumen temperature, and rumination behavior. Journal of dairy science, vol. 100, no. 3) 2017, pp. 2395-2403.

59. . Nogueira G., Ajmone-Marsan P., Milanesi M., Zavarez L., Aguiar T.S., Sandre D., Maioli M.A., Ferreira G., Bispo G., Stabile S., Caputo R., Toyama C., Garcia J.F., & Caputo, R. 1283 Understanding behavior patterns of cattle adaptation to heat stress. Journal of Animal Science, vol. 94, issue suppl_5, 2016, pp. 619-619.

60. . Marchesini G., Mottaran D., Contiero B., Schiavon E., Segato S., Garbin E., Tenti S., & Andrighetto I. Use of rumination and activity data as health status and performance indicators in beef cattle during the early fattening period. The Veterinary Journal, vol. 231, 2018, pp. 41-47.

61. . Greenwood P.L., Paull D.R., McNally J., Kalinowski T., Ebert D., Little B., Smith D.V., Rahman A., Valencia P., Ingham A.B., & Bishop-Hurley G.J. Use of sensor-determined behaviours to develop algorithms for pasture intake by individual grazing cattle. Crop and Pasture Science, vol. 68, no. 12, 2017, pp.1091-1099.

62. . Schirmann K., Chapinal N., Weary D.M., Heuwieser W., & Von Keyserlingk M.A. Rumination and its relationship to feeding and lying behavior in Holstein dairy cows. Journal of dairy science, vol. 95, no. 6, 2012, pp. 3212-3217.

63. . Knight C.W., Bailey D.W., & Faulkner D. Low-Cost Global Positioning System Tracking Collars for Use on Cattle. Rangeland Ecology & Management, vol. 71, no. 4, 2018, pp. 506-508.

64. . Curtis A.K., Scharf B., Eichen P.A., & Spiers D.E. Relationships between ambient conditions, thermal status, and feed intake of cattle during summer heat stress with access to shade. Journal of thermal biology, vol. 63, 2017, pp. 104-111.

65. . Odadi W.O., Riginos C., & Rubenstein D.I. Tightly Bunched Herding Improves Cattle Performance in African Savanna Rangeland. Rangeland Ecology & Management, vol. 71, no. 4, 2018, pp. 481-491.

66. . Samuels I., Cupido C., Swarts M.B., Palmer A.R., & Paulse J.W. Feeding ecology of four livestock species under different management in a semi-arid pastoral system in South Africa. African Journal of Range & Forage Science, vol. 33, no. 1, 2016, pp.1-9.

67. . Arcidiacono C., Porto S.M.C., Mancino M., & Cascone G. Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Computers and electronics in agriculture, vol. 134, 2017, pp.124-134.

68. . Barker Z.E., Diosdado J.V., Codling E.A., Bell N.J., Hodges H.R., Croft D.P., & Amory J.R. Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. Journal of dairy science, vol. 101, no. 7, 2018, p. 6310–6321.

69. . Sprecher D.J., Hostetler D.E., & Kaneene J.B. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology, vol. 47, no. 6, 1997, pp.1179-1187.

70. . Brown-Brandl T.M., & Eigenberg R.A. Determination of minimum meal interval and analysis of feeding behavior in shaded and open-lot feedlot heifers. Transactions of the ASABE, vol. 58, no. 6, 2015, pp. 1833-1839.

71. . Schieltz J.M., Okanga S., Allan B.F., & Rubenstein D.I. GPS tracking cattle as a monitoring tool for conservation and management. African Journal of Range & Forage Science, vol. 34, no. 3, 2017, pp.173-177.

72. . Valls‐Fox H., Chamaillé‐Jammes S., de Garine‐Wichatitsky M., Perrotton A., Courbin N., Miguel E., Guerbois C., Caron A., Loveridge A., Stapelkamp B., & Muzamba M. Water and cattle shape habitat selection by wild herbivores at the edge of a protected area. Animal Conservation, vol. 21, no. 5, 2018, pp. 365–375.

73. . Mahoney P.J., & Young, J.K. Uncovering behavioural states from animal activity and site fidelity patterns. Methods in Ecology and Evolution, vol. 8, no, 2, 2017, pp. 174-183.

74. . Mendes E.D.M., Carstens G.E., Tedeschi L.O., Pinchak W.E., & Friend T.H. Validation of a system for monitoring feeding behavior in beef cattle. Journal of animal science, vol. 89, no. 9, 2011, pp.2904-2910.

75. . Curtis A.K., Scharf B., Eichen, P.A., & Spiers D.E. Relationships between ambient conditions, thermal status, and feed intake of cattle during summer heat stress with access to shade. Journal of thermal biology, vol. 63, 2017, pp. 104-111.

76. . Oliveira B.R., Ribas M.N., Machado F.S., Lima J. A.M., Cavalcanti L.F.L., Chizzotti M.L., & Coelho S.G. Validation of a system for monitoring individual feeding and drinking behaviour and intake in young cattle. Animal, vol. 12, no. 3, 2018, pp. 634-639.

77. . Williams L.R., Fox D.R., Bishop-Hurley G.J., & Swain D.L. Use of radio frequency identification (RFID) technology to record grazing beef cattle water point use. Computers and Electronics in Agriculture, vol. 156, 2019, pp. 193-202.

78. . Bonk S., Burfeind O., Suthar V.S., & Heuwieser W. Evaluation of data loggers for measuring lying behavior in dairy calves. Journal of Dairy Science, vol. 96, no. 5, 2013, pp. 3265-3271.

79. . Michelot T., Langrock R., & Patterson T.A. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods in Ecology and Evolution, vol. 7, no. 11, 2016, pp. 1308-1315.

80. . Calabrese J.M., Fleming C.H., & Gurarie E. ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process. Methods in Ecology and Evolution, vol. 7, no. 9, 2016, pp. 1124-1132.

81. . LaZerte S E., Reudink M.W., Otter K.A., Kusack J., Bailey J.M., Woolverton A., Paetkau M., de Jong A., & Hill D.J. feedr and animalnexus. ca: A paired R package and user‐friendly Web application for transforming and visualizing animal movement data from static stations. Ecology and evolution, vol. 7, no. 19, 2017, pp.7884-7896.

82. . McLean D.J., & Skowron Volponi M. A. Trajr: an R package for characterisation of animal trajectories. Ethology, vol. 124, no. 6, 2018, pp. 440-448.

83. . Kranstauber B., Cameron A., Weinzerl R., Fountain T., Tilak S., Wikelski M., & Kays R. The Movebank data model for animal tracking. Environmental Modelling & Software, vol. 26, no. 6, 2011, pp. 834-835.

84. . Gurarie E., Andrews R.D., & Laidre K.L. A novel method for identifying behavioural changes in animal movement data. Ecology letters, vol. 12, no. 5, 2009, pp. 395-408.

85. . Hunter J., Brooking C., Brimblecombe W., Dwyer R.G., Campbell H.A., Watts M.E., & Franklin C.E. OzTrack--E-Infrastructure to Support the Management, Analysis and Sharing of Animal Tracking Data. In Proc. of the IEEE 9th International Conference on eScience, 2013, pp. 140-147.

86. . Hirakawa T., Yamashita T., Tamaki T., Fujiyoshi H., Umezu Y., Takeuchi I., Matsumoto A., & Yoda K. Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning. Ecosphere, vol. 9, no. 10, 2018.

87. . Williams M.L., Mac Parthaláin N., Brewer P., James W.P. ., & Rose M.T.A novel behavioral model of the pasture-based dairy cow from GPS data using data mining and machine learning techniques. Journal of dairy science, vol. 99, no. 3, 2016, pp. 2063-2075.


Для цитирования:


Гарай Альварес Г., Бертоm Вальдес Х., Перес-Теруэль К. Интернет вещей для оценки поведения крупного рогатого скота при поиске корма и кормлении в пастбищных системах земледелия: концепции и обзор сенсорных технологий. Труды Института системного программирования РАН. 2019;31(2):137-152. https://doi.org/10.15514/ISPRAS-2019-31(2)-10

For citation:


Garay Alvarez G., Bertot Valdés Kh., Pérez-Teruel K. Internet of Things for evaluating foraging and feeding behavior of cattle on grassland-based farming systems: concepts and review of sensor technologies. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2019;31(2):137-152. https://doi.org/10.15514/ISPRAS-2019-31(2)-10

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