Precision livestock farming technologies: Novel direction of information flow

Abstract

Precision livestock farming (PLF) is a digital management system that continuously measures the production, reproduction, health and welfare of animals and environmental impacts of the herd by using information and communication technologies (ICT) and controls all stages of the production process. In conventional livestock management, decisions are mostly based on the appraisal, judgment, and experience of the farmer, veterinarian, and workers. The increasing demand for production and the number of animals makes it difficult for humans to keep track of animals. It is clear that a person is not able to continuously watch the animals 24 hours a day to receive reliable audio-visual data for management. Recent technologies already changed the information flow from animal to human, which helps people to collect reliable information and transform it into an operational decision-making process (eg reproduction management or calving surveillance). Today, livestock farming must combine requirements for a transparent food supply chain, animal welfare, health, and ethics as a traceable-sustainable model by obtaining and processing reliable data using novel technologies. This review provides preliminary information on the advances in ICT for livestock management.

Keywords

• Internet of things
• livestock data management monitoring tools
• Precision livestock farming
• health
• welfare

Hassas hayvancılık teknolojileri: Bilgi akışının yeni yönü

Abstract

Hassas hayvancılık (PLF), bilgi ve iletişim teknolojilerini (ICT) kullanarak hayvanların üretimini, üremesini, sağlığını ve refahını ve sürünün çevresel etkilerini sürekli olarak ölçen ve üretim sürecinin tüm aşamalarını kontrol eden dijital bir yönetim sistemidir. Geleneksel hayvancılık yönetiminde kararlar çoğunlukla çiftçinin, veterinerin ve işçilerin değerlendirmesine, muhakemesine ve deneyimine dayanmaktadır. Üretime yönelik artan talep ve hayvan sayısı, insanların hayvanları takip etmesini giderek zorlaştırmaktadır. Bir kişinin, yönetim için güvenilir görsel-işitsel veriler almak için günde 24 saat sürekli olarak hayvanları izleyemeyeceği ise açıktır. Son teknolojilerle bu bilgi akışı hayvandan insana olarak değişmiş ve bu da toplanılan güvenilir bilgilerin, operasyonel ve efektif olarak bir karar alma sürecine dönüştürmesine (örn. Üreme yönetimi veya buzağılama takibi) yardımcı olmuştur. Günümüzde hayvancılık, yeni teknolojileri kullanarak güvenilir verileri elde ederek ve işleyerek izlenebilir ve sürdürülebilir bir model olarak şeffaf bir gıda tedarik zinciri, hayvan refahı, sağlık ve etik gerekliliklerini birleştirmelidir. Bu yayında, hayvancılık veri yönetiminde kullanılan bilgi ve iletişim teknolojileri alanındaki gelişmeler hakkında güncel bilgiler derlenmiştir. 

Keywords

• nesnelerin interneti
• Hassas hayvancılık
• hayvancılık veri yönetimi
• izleme araçları

References

1. Aaltola E (2005): Animal ethics and interest conflicts. Environ Ethics, 10, 19-48.
2. Adenuga AH, Jack C, Olagunju KO, et al (2020): Economic viability of adoption of automated oestrus detection technologies on dairy farms: A review. Animals, 10, 1241.
3. Ahlberg CM, Allwardt K, Broocks A, et al (2018): Environmental effects on water intake and water intake prediction in growing beef cattle. J Anim Sci, 96, 4368-4384.
4. Alonso RS, Sittón-Candanedo I, García Ó, et al (2020): An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Netw, 98, 102047.
5. Andonovic I, Michie C, Cousin P, et al (2018): Precision livestock farming technologies. In 2018 Global Internet of Things Summit (GIoTS) (pp. 1-6). IEEE.
6. Anglart D, Hallén-Sandgren C, Emanuelson U, et al (2020): Comparison of methods for predicting cow composite somatic cell counts. J Dairy Sci, 103, 8433-8442.
7. Atherton BC, Morgan M, Shearer SA, et al (1999): Site-specific farming: A perspective on information needs, benefits and limitations. J Soil Water Conserv, 54, 455-461.
8. Augustine DJ, Derner JD (2013): Assessing herbivore foraging behavior with GPS collars in a semiarid grassland. Sensors, 13, 3711-3723.

9. Aungier SPM, Roche JF, Duffy P, et al (2015): The relationship between activity clusters detected by an automatic activity monitor and endocrine changes during the periestrous period in lactating dairy cows. J Dairy Sci, 98, 1666-1684.
10. Bailey DW, Trotter MG, Knight CW, et al (2018): Use of GPS tracking collars and accelerometers for rangeland livestock production research. Transl Anim Sci, 2, 81-88.
11. Banhazi T, Harmes M (2018): Development of precision livestock farming technologies. In: Advances in Agricultural Machinery and Technologies. pp. 179-194.
12. Banhazi T, Vranken E, Berckmans D, et al (2015): 3.4. Word of caution for technology providers: practical problems associated with large scale deployment of PLF technologies on commercial farms. In: Precision livestock farming applications: Making sense of sensors to support farm management (pp. 2-10). Wageningen Academic Publishers.
13. Banhazi TM, Black JL (2009): Precision livestock farming: a suite of electronic systems to ensure the application of best practice management on livestock farms. Aust J Multi-Discip Eng, 7, 1-14.
14. Batte MT, Arnholt MW (2003): Precision farming adoption and use in Ohio: case studies of six leading-edge adopters. Comput Electron Agric, 38, 125-139.
15. Berckmans D (2017): General introduction to precision livestock farming. Anim Front, 7, 6-11.
16. Bewley JM, Gray AW, Hogeveen H, et al (2010): Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation. Agric Finance Rev, 70, 126-150.
17. Bono C, Cornou C, Kristensen AR (2012): Dynamic production monitoring in pig herds I: Modeling and monitoring litter size at herd and sow level. Livestock Science, 149, 289-300.
18. Borchers MR, Chang YM, Proudfoot KL (2017): Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. J Dairy Sci, 100, 5664-5674.
19. Bos JM, Bovenkerk B, Feindt PH, et al (2018): The quantified animal: precision livestock farming and the ethical implications of objectification. Food Ethics, 2, 77-92.
20. Brena RF, Garcia-Vazquez JP, Galvan-Tejada CE, et al (2017): JF Jr.,“. Evolution of indoor positioning technologies: A survey,”. Hindawi Journal of Sensors, 2017, 1-21.
21. Buller H, Blokhuis H, Lokhorst K, et al (2020): Animal welfare management in a digital world. Animals, 10, 1779.
22. Burfeind O, Schirmann K, Von Keyserlingk MAG, et al (2011): Evaluation of a system for monitoring rumination in heifers and calves. J Dairy Sci, 94, 426-430.
23. Burfeind O, Suthar VS, Voigtsberger R, et al (2011): Validity of prepartum changes in vaginal and rectal temperature to predict calving in dairy cows. J Dairy Sci, 94, 5053-5061.
24. Cairo FC, Pereira LGR, Campos MM, et al (2020): Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers. Comput Electron Agric, 179, 105855.
25. Calvary G, Coutaz J, Thevenin D, et al (2003): A unifying reference framework for multi-target user interfaces. Interact Comput, 15, 289-308.
26. Cantor MC, Costa JH, Bewley JM (2018): Impact of observed and controlled water intake on reticulorumen temperature in lactating dairy cattle. Animals, 8, 194.
27. Carillo F, Abeni F (2020): An Estimate of the Effects from Precision Livestock Farming on a Productivity Index at Farm Level. Some Evidences from a Dairy Farms’ Sample of Lombardy. Animals, 10, 1781.
28. Carolan M (2018): ‘Smart’farming techniques as political ontology: Access, sovereignty and the performance of neoliberal and not‐so‐neoliberal worlds. Sociologia Ruralis, 58, 745-764.
29. Cen L, Zhang FL (2010): Study on information technology adoption in rural areas based on network externality. In: Applied Mechanics and Materials (Vol. 33, pp. 509-512). Trans Tech Publications Ltd.
30. Chacon E, Stobbs TH, Sandland RL (1976): Estimation of herbage consumption by grazing cattle using measurements of eating behaviour. Grass Forage Sci, 31, 81-87.
31. Chalmers PA (2003): The role of cognitive theory in human–computer interface. Comput Hum Behav, 19, 593-607.
32. Chamberlain-Ward SL (1998): Continuous ambient air monitoring systems. In: 14th International Clean Air & Environment Conference. Melbourne, Australia.
33. Chen Z, Xia F, Huang T, et al (2013): A localization method for the Internet of Things. J Supercomput, 63, 657-674.
34. Chewning Jr EG, Harrell AM (1990): The effect of information load on decision makers' cue utilization levels and decision quality in a financial distress decision task. Account Organ Soc, 15, 527-542.
35. Clark PE, Johnson DE, Kniep MA, et al (2006): An advanced, low-cost, GPS-based animal tracking system. Rangel Ecol Manag, 59, 334-340.
36. Costa Jr JBG, Ahola JK, Weller ZD, et al (2016): Reticulo-rumen temperature as a predictor of calving time in primiparous and parous Holstein females. J Dairy Sci, 99, 4839-4850.
37. Crowe MA, Hostens M, Opsomer G (2018): Reproductive management in dairy cows-the future. Ir Vet J, 71, 1-13.
38. Cumby TR, Phillips VR (2001): Environmental impacts of livestock production. BSAP Occas Publ, 28, 13-22.
39. Cyples JA, Fitzpatrick CE, Leslie KE, et al (2012): The effects of experimentally induced Escherichia coli clinical mastitis on lying behavior of dairy cows. J Dairy Sci, 95, 2571-2575.
40. Davenport TH, Beck JC (2001): The attention economy. Ubiquity, 2001 (May), 1-es.
41. Dawood FS, Dong L, Liu F, et al (2011): A pre-pandemic outbreak of triple-reassortant swine influenza virus infection among university students, South Dakota, 2008. J Infect Dis, 204, 1165-1171.
42. De Wet L, Vranken E, Chedad A, et al (2003): Computer-assisted image analysis to quantify daily growth rates of broiler chickens. Br Poult Sci, 44, 524-532.
43. Deloitte (2021): Smart Livestock Farming Potential of Digitalization for Global Meat Supply. Available at https://www2.deloitte.com/content/dam/Deloitte/de/Documents/operations/Smart-livestock-farming_Deloitte.pdf. (Accessed March 01, 2021)..
44. Devitt S (2018): Cognitive factors that affect the adoption of autonomous agriculture. Farm Policy Journal, 15, 49-60.
45. Diskin MG (2008): Reproductive management of dairy cows: a review (part I). Ir Vet J, 61, 326.
46. Diskin MG, Sreenan JM (2000): Expression and detection of oestrus in cattle. Reprod Nutr Dev, 40, 481-491.
47. Dolecheck KA, Silvia WJ, Heersche Jr G, et al (2015): Behavioral and physiological changes around estrus events identified using multiple automated monitoring technologies. J Dairy Sci, 98, 8723-8731.
48. Drewe JA, Hoinville LJ, Cook AJC, et al (2012): Evaluation of animal and public health surveillance systems: a systematic review. Epidemiol Infect, 140, 575-590.
49. Driessen C, Heutinck LF (2015): Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms. Agr Hum Val, 32, 3-20.
50. EJ F (1954): Activity of dairy cows during estrus. J Am Vet Med Assoc, 125, 117-120.
51. Elischer MF, Arceo ME, Karcher EL, et al (2013): Validating the accuracy of activity and rumination monitor data from dairy cows housed in a pasture-based automatic milking system. J Dairy Sci, 96, 6412-6422.
52. Felder RM, Spurlin J (2005): Applications, reliability and validity of the index of learning styles. Int J Eng Educ, 21, 103-112.
53. EU-PLF (2021): Final Report Summary-EU-PLF (Bright Farm by Precision Livestock Farming) | Report Summary | EU-PLF | FP7 | CORDIS | European Commission. Available at https://cordis.europa.eu/project/id/311825/ reporting. (Accessed Feb 15, 2021).
54. Finger R, Swinton SM, El Benni N, et al (2019): Precision farming at the nexus of agricultural production and the environment. Annu Rev Resour Econ, 11, 313-335.
55. Fogsgaard KK, Bennedsgaard TW, Herskin MS (2015): Behavioral changes in freestall-housed dairy cows with naturally occurring clinical mastitis. J Dairy Sci, 98, 1730-1738.
56. Fontana I, Tullo E, Butterworth A (2015): The use of vocalisation sounds to assess responses of broiler chicken to environmental variables. Precision Livestock Farming Applications. Wageningen Academic Publishers, Wageningen, The Netherlands, 187-198.
57. Fountas S, Blackmore S, Ess D, et al (2005): Farmer experience with precision agriculture in Denmark and the US Eastern Corn Belt. Precis Agric, 6, 121-141.
58. Fourichon C, Seegers H, Malher X (2000): Effect of disease on reproduction in the dairy cow: a meta-analysis. Theriogenology, 53, 1729-1759.
59. Fricke PM, Carvalho PD, Giordano JO, et al (2014): Expression and detection of estrus in dairy cows: the role of new technologies. Animal, 8, 134-143.
60. Frost AR, Schofield CP, Beaulah SA, et al (1997): A review of livestock monitoring and the need for integrated systems. Comput Electron Agric, 17, 139-159.
61. Giordano JO, Stangaferro ML, Wijma R, et al (2015): Reproductive performance of dairy cows managed with a program aimed at increasing insemination of cows in estrus based on increased physical activity and fertility of timed artificial inseminations. J Dairy Sci, 98, 2488-2501.
62. González LA, Bishop-Hurley G, Henry D, et al (2014): Wireless sensor networks to study, monitor and manage cattle in grazing systems. Anim Prod Sci, 54, 1687-1693.
63. González LA, Tolkamp BJ, Coffey MP, et al (2008): Changes in feeding behavior as possible indicators for the automatic monitoring of health disorders in dairy cows. J Dairy Sci, 91, 1017-1028.
64. Guarino M, Jans P, Costa A, et al (2008): Field test of algorithm for automatic cough detection in pig houses. Comput Electron Agric, 62, 22-28.
65. Halachmi I, Guarino M, Bewley J, et al (2019): Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annu Rev Anim Biosci, 7, 403-425.
66. Hamilton AW, Davison C, Tachtatzis C, et al (2019): Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensors. Sensors, 19, 1165.
67. Hansen MF, Smith M, Smith L, et al (2015): Non-intrusive automated measurement of dairy cow body condition using 3D video. In Proceedings of the Machine Vision of Animals and their Behaviour (MVAB), pp: 1.1-1.8. BMVA Press, September 2015.
68. Hasan B, Ahmed MU (2007): Effects of interface style on user perceptions and behavioral intention to use computer systems. Comput Hum Behav, 23, 3025-3037.
69. Hernando A, Lazaro J, Gil E, et al (2016): Inclusion of respiratory frequency information in heart rate variability analysis for stress assessment. IEEE J Biomed Health Inform, 20, 1016-1025.
70. Higaki S, Koyama K, Sasaki Y, et al (2020): Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning. J Dairy Sci, 103, 8535-8540.
71. Hockey CD, Morton JM, Norman ST, et al (2010): Evaluation of a neck mounted 2‐hourly activity meter system for detecting cows about to ovulate in two paddock‐based Australian dairy herds. Reprod Domest Anim, 45, e107-e117.
72. Hogeveen H, Kamphuis C, Steeneveld W, et al (2010): Sensors and clinical mastitis—The quest for the perfect alert. Sensors, 10, 7991-8009.
73. Hogeveen H, Steeneveld W (2013): Integrating It All: Making It Work And Pay At The Farm. Proceedings of the Precision Dairy Conference and Expo a Conference on Precision Dairy Technologies.
74. Højsgaard S, Friggens NC (2010): Quantifying degree of mastitis from common trends in a panel of indicators for mastitis in dairy cows. J Dairy Sci, 93, 582-592.
75. Hostiou N, Fagon J, Chauvat S, et al (2017): Impact of precision livestock farming on work and human-animal interactions on dairy farms. A review. Biotechnol Agron Soc Environ, 21, 268-275.
76. Hostiou N, Kling-Eveillard F, Ganis E (2017): The effects of PLF on human-animal relationships on farms. In: 8. European Conference on Precision Livestock Farming (ECPLF) (pp. 9-p).Nantes, France.
77. Hovinen M, Pyörälä S (2011): Invited review: Udder health of dairy cows in automatic milking. J Dairy Sci, 94, 547-562.
78. Huang C, Wang Y, Li X, et al (2020): Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395, 497-506.
79. Huybrechts T, Mertens K, De Baerdemaeker J, et al (2014): Early warnings from automatic milk yield monitoring with online synergistic control. J Dairy Sci, 97, 3371-3381.
80. Huzzey JM, Von Keyserlingk MAG, Weary DM (2005): Changes in feeding, drinking, and standing behavior of dairy cows during the transition period. J Dairy Sci, 88, 2454-2461.
81. Jensen MB (2012): Behaviour around the time of calving in dairy cows. Appl Anim Behav Sci, 139, 195–202.
82. Jónsson R, Blanke M, Poulsen NK, et al (2011): Oestrus detection in dairy cows from activity and lying data using on-line individual models. Comput Electron Agric, 76, 6-15.
83. Jorquera-Chavez M, Fuentes S, Dunshea FR, et al (2019): Modelling and validation of computer vision techniques to assess heart rate, eye temperature, ear-base temperature and respiration rate in cattle. Animals, 9, 1089.
84. Kamphuis C, DelaRue B, Burke CR, et al (2012): Field evaluation of 2 collar-mounted activity meters for detecting cows in estrus on a large pasture-grazed dairy farm. J Dairy Sci, 95, 3045-3056.
85. Kamphuis C, Mollenhorst H, Heesterbeek JAP, et al (2010): Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. J Dairy Sci, 93, 3616-3627.
86. Kamphuis C, Steeneveld W, Hogeveen H (2015): 2. Economic modelling to evaluate the benefits of precision livestock farming technologies. In: Precision livestock farming applications: Making sense of sensors to support farm management (pp. 163-171). Wageningen Academic Publishers.
87. Kashiha M, Pluk A, Bahr C, et al (2013): Development of an early warning system for a broiler house using computer vision. Biosyst Eng, 116, 36-45.
88. Kaske M, Beyerbach M, Hailu Y, et al (2002): The assessment of the frequency of chews during rumination enables an estimation of rumination activity in hay fed sheep. J Anim Physiol Anim Nutr, 86, 83-89.
89. Kester HJ, Sorter DE, Hogan JS (2015): Activity and milk compositional changes following experimentally induced Streptococcus uberis bovine mastitis. J Dairy Sci, 98, 999-1004.
90. Khanna M, Epouhe OF, Hornbaker R (1999): Site specific crop management: adoption patterns and incentives. Appl Econ Perspect Policy, 21, 455-472.
91. Khatun M, Clark CE, Lyons NA, et al (2017): Early detection of clinical mastitis from electrical conductivity data in an automatic milking system. Anim Prod Sci, 57, 1226-1232.
92. Khatun M, Thomson PC, Kerrisk KL, et al (2018): Development of a new clinical mastitis detection method for automatic milking systems. J Dairy Sci, 101, 9385-9395.
93. Klerkx L, Jakku E, Labarthe P (2019): A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wagen J Life Sc, 90, 100315.
94. Kling-Eveillard F, Allain C, Boivin X, et al (2020): Farmers’ representations of the effects of precision livestock farming on human-animal relationships. Livest Sci, 238, 104057.
95. Kling-Eveillard F, Hostiou N, Ganis E, et al (2017): The effects of PLF on human animal relationship on farm. In: 8th European Conference on Precision Livestock Farming, Nantes, FR.
96. Knight B, Malcolm B (2009): A whole-farm investment analysis of some precision agriculture technologies. Australian Farm Business Management Journal, 6, 41-54.
97. Laca EA, WallisDeVries MF (2000): Acoustic measurement of intake and grazing behaviour of cattle. Grass Forage Sci, 55, 97-104.
98. Lee JI, Kim IH (2007): Pregnancy loss in dairy cows: the contributing factors, the effects on reproductive performance and the economic impact. J Vet Sci, 8, 283.
99. LeRoy CNS, Walton JS, LeBlanc SJ (2018): Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows. J Dairy Sci, 101, 1638-1647.
100. Levit H, Pinto S, Amon T, et al (2020): Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows. Animal, 15, 100093.
101. Lhermie G, Toutain PL, El Garch F, et al (2017): Implementing precision antimicrobial therapy for the treatment of bovine respiratory disease: current limitations and perspectives. Front Vet Sci, 4, 143.
102. Li Y, Zhuang Y, Hu X, et al (2020): Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation. IEEE Internet Things J, 8, 4035-4062.
103. Lokhorst C, De Mol RM, Kamphuis C (2019): Invited review: Big Data in precision dairy farming. Animal, 13, 1519-1528.
104. Lopes SI, Bexiga R, Araújo JP, et al (2016): Precision livestock farming for reproductive performance optimization: a survey. In: Food futures: ethics, science and culture (pp. 3197-3211). Wageningen Academic Publishers.
105. Lucy MC (2001): Reproductive loss in high-producing dairy cattle: where will it end? J Dairy Sci, 84, 1277-1293.
106. Luo W, Chen D, Wu M, et al (2019): Pharmacokinetics/Pharmacodynamics models of veterinary antimicrobial agents. J Vet Sci, 20, e40.
107. Maatje K, De Mol RM, Rossing W (1997): Cow status monitoring (health and oestrus) using detection sensors. Comput Electron Agric, 16, 245-254.
108. Madouasse A, Marceau A, Lehébel A, et al (2013): Evaluation of a continuous indicator for syndromic surveillance through simulation. application to vector borne disease emergence detection in cattle using milk yield. PloS one, 8, e73726.
109. Madureira AML, Silper BF, Burnett TA, et al (2015). Factors affecting expression of estrus measured by activity monitors and conception risk of lactating dairy cows. J Dairy Sci, 98, 7003-7014.
110. McCown RL (2002): Changing systems for supporting farmers' decisions: problems, paradigms, and prospects. Agric Syst, 74, 179-220.
111. McCown RL, Carberry PS, Hochman Z, et al (2009): Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action research. Crop Pasture Sci, 60, 1017-1030.
112. Medrano-Galarza C, Gibbons J, Wagner S, et al (2012): Behavioral changes in dairy cows with mastitis. J Dairy Sci, 95, 6994-7002.
113. Mee JF (2004): Managing the dairy cow at calving time. Vet Clin N Am-Food A, 20, 521-546.
114. Michaelis I, Hasenpusch E, Heuwieser W (2013): Estrus detection in dairy cattle: Changes after the introduction of an automated activity monitoring system? Tierarztl Prax Ausg G, 41, 159-165.
115. Miedema HM, Cockram MS, Dwyer CM, et al (2011): Changes in the behaviour of dairy cows during the 24h before normal calving compared to behaviour during late pregnancy. Appl Anim Behav Sci, 131, 8-14.
116. Miller GA, Mitchell M, Barker ZE, et al (2020): Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows. Animal, 14, 1304-1312.
117. Mollenhorst H, Rijkaart LJ, Hogeveen H (2012): Mastitis alert preferences of farmers milking with automatic milking systems. J Dairy Sci, 95, 2523-2530.
118. Mottram T (2016): Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection. Animal, 10, 1575-1584.
119. Neves RC, Leslie KE, Walton JS, et al (2012): Reproductive performance with an automated activity monitoring system versus a synchronized breeding program. J Dairy Sci, 95, 5683-5693.
120. Niu M, Ying Y, Bartell PA, et al (2017): The effects of feeding rations that differ in fiber and fermentable starch within a day on milk production and the daily rhythm of feed intake and plasma hormones and metabolites in dairy cows. J Dairy Sci, 100, 187-198.
121. Norton T, Chen C, Larsen MLV, et al (2019): Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer. Animal, 13, 3009-3017.
122. O'Grady MJ, O'Hare GM (2017): Modelling the smart farm. Inf Process Agric, 4, 179-187.
123. OECD-FAO (2007): OECD-FAO Agricultural Outlook, 2007.
124. Ouellet V, Vasseur E, Heuwieser W, et al (2016): Evaluation of calving indicators measured by automated monitoring devices to predict the onset of calving in Holstein dairy cows. J Dairy Sci, 99, 1539-1548.
125. Palombi C, Paolucci M, Stradaioli G, et al (2013): Evaluation of remote monitoring of parturition in dairy cattle as a new tool for calving management. BMC Vet Res, 9, 1-9.
126. Pastell M, Frondelius L, Järvinen M, et al (2018): Filtering methods to improve the accuracy of indoor positioning data for dairy cows. Biosyst Eng, 169, 22-31.
127. Penning PD, Steel GL, Johnson RH (1984): Further development and use of an automatic recording system in sheep grazing studies. Grass Forage Sci, 39, 345-351.
128. Penry JF, Crump PM, Ruegg PL, et al (2017): Cow-and quarter-level milking indicators and their associations with clinical mastitis in an automatic milking system. J Dairy Sci, 100, 9267-9272.
129. Pereira GM, Heins BJ, Endres MI (2018): Validation of an ear-tag accelerometer sensor to determine rumination, eating, and activity behaviors of grazing dairy cattle. J Dairy Sci, 101, 2492-2495.
130. Pfeiffer J, Gandorfer M, Ettema JF (2020): Evaluation of activity meters for estrus detection: A stochastic bioeconomic modeling approach. J Dairy Sci, 103, 492-506.
131. Pinto S, Hoffmann G, Ammon C, et al (2019): Influence of barn climate, body postures and milk yield on the respiration rate of dairy cows. Ann Anim Sci, 19, 469-481.
132. Pluk A, Cangar O, Bahr C, et al (2010): Impact of process related problems on water intake pattern of broiler chicken. In: International Conference on Agricultural Engineering-AgEng 2010: towards environmental technologies, Clermont-Ferrand, France, 6-8 September 2010. Cemagref.
133. Porto SM, Arcidiacono C, Anguzza U, et al (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.
134. FARMS (2021): Principles of RMS | FARMS Initiative. Available at http://www. farms-initiative.com/responsible-minimum-standards/principles-of-rms/#. (Accessed Feb 15, 2021).
135. Proudfoot KL, Jensen MB, Weary DM, et al (2014): Dairy cows seek isolation at calving and when ill. J Dairy Sci, 97, 2731-2739.
136. Rahman A, Smith DV, Little B, et al (2018): Cattle behaviour classification from collar, halter, and ear tag sensors. Inf Process Agric, 5, 124-133.
137. Reith S, Brandt H, Hoy S (2014): Simultaneous analysis of activity and rumination time, based on collar-mounted sensor technology, of dairy cows over the peri-estrus period. Livest Sci, 170, 219-227.
138. Reith S, Hoy S (2018): Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle. Animal, 12, 398-407.
139. Roelofs JB (2008): Prediction of ovulation and optimal insemination interval. Vet Quart, 30, 58-77.
140. Roelofs JB, Van Eerdenburg FJCM, Soede NM, et al (2005): Various behavioral signs of estrous and their relationship with time of ovulation in dairy cattle. Theriogenology, 63, 1366-1377.
141. Rojo-Gimeno C, Fievez V, Wauters E (2018): The economic value of information provided by milk biomarkers under different scenarios: case-study of an ex-ante analysis of fat-to-protein ratio and fatty acid profile to detect subacute ruminal acidosis in dairy cows. Livest Sci, 211, 30-41.
142. Rojo-Gimeno C, van der Voort M, Niemi JK, et al (2019): Assessment of the value of information of precision livestock farming: A conceptual framework. NJAS-Wagen J Life Sc, 90, 100311.
143. Rotz S, Duncan E, Small M, et al (2019): The politics of digital agricultural technologies: a preliminary review. Sociol Ruralis, 59, 203-229.
144. Rutten CJ, Kamphuis C, Hogeveen H, et al (2017): Sensor data on cow activity, rumination, and ear temperature improve prediction of the start of calving in dairy cows. Comput Electron Agric, 132, 108-118.
145. Rutten CJ, Steeneveld W, Inchaisri C, et al (2014): An ex ante analysis on the use of activity meters for automated estrus detection: To invest or not to invest? J Dairy Sci, 97, 6869-6887.
146. Rutten CJ, Velthuis AGJ, Steeneveld W, et al (2013): Invited review: Sensors to support health management on dairy farms. J Dairy Sci, 96, 1928-1952.
147. Saint-Dizier M, Chastant-Maillard S (2015): Methods and on-farm devices to predict calving time in cattle. The Vet J, 205, 349-356.
148. Saint Dizier M, Chastant Maillard S (2012): Towards an automated detection of oestrus in dairy cattle. Reprod Domest Anim, 47, 1056-1061.
149. Salau J, Haas JH, Junge W, et al (2014): Feasibility of automated body trait determination using the SR4K time-of-flight camera in cow barns. SpringerPlus, 3, 1-16.
150. Salman MD (2013): Surveillance tools and strategies for animal diseases in a shifting climate context. Anim Health Res Rev, 14, 147.
151. Santegoeds OJ (2016): Predicting dairy cow parturition using real-time behavior data from accelerometers: A study in commercial setting. Available at file:///C:/Users/ EnstMdYrd/Downloads/Thesis%20Oscar%20Santegoeds%20-% 20Public%20version.pdf. (Accessed Feb 15, 2021).
152. Schilkowsky EM, Granados GE, Sitko EM, et al (2021): Evaluation and characterization of estrus alerts and behavioral parameters generated by an ear-attached accelerometer-based system for automated detection of estrus. J Dairy Sci, In press.
153. Schirmann K, von Keyserlingk MA, Weary DM, et al (2009): Validation of a system for monitoring rumination in dairy cows. J Dairy Sci, 92, 6052-6055.
154. Schneirla TC (1950): The relationship between observation and experimentation in the field study of behavior. Ann N Y Acad Sci, 51, 1022-1044.
155. Schuenemann GM, Nieto I, Bas S, et al (2011): Assessment of calving progress and reference times for obstetric intervention during dystocia in Holstein dairy cows. J Dairy Sci, 94, 5494-5501.
156. Senger PL (1994): The estrus detection problem: new concepts, technologies, and possibilities. J Dairy Sci, 77, 2745-2753.
157. Skerratt S (2010): Hot spots and not spots: addressing infrastructure and service provision through combined approaches in rural Scotland. Sustainability, 2, 1719-1741.
158. Sonka S (2014): Big data and the ag sector: More than lots of numbers. Int Food Agribus Man, 17, 1-20.
159. Stangaferro ML, Wijma R, Caixeta LS, et al (2016): Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part III. Metritis. J Dairy Sci, 99, 7422-7433.
160. Steeneveld W, Amuta P, van Soest FJ, et al (2020): Estimating the combined costs of clinical and subclinical ketosis in dairy cows. PloS one, 15, e0230448.
161. Steeneveld W, Hogeveen H, Lansink AO (2015): Economic consequences of investing in sensor systems on dairy farms. Comput Electron Agric, 119, 33-39.
162. Steeneveld W, van der Gaag LC, Barkema HW, et al (2010): Simplify the interpretation of alert lists for clinical mastitis in automatic milking systems. Comput Electron Agric, 71, 50-56.
163. Stephen C, Menzies D, Swain D, et al (2019): Telemetric monitoring of calving using a novel calf alert device. In 2019 Society for Theriogenoloy and American College of Theriogenologists Annual Conference.
164. Stygar AH, Kristensen AR (2016): Monitoring growth in finishers by weighing selected groups of pigs–a dynamic approach. J Anim Sci, 94, 1255-1266.
165. Stygar AH, Kristensen AR (2018): Detecting abnormalities in pigs’ growth–A dynamic linear model with diurnal growth pattern for identified and unidentified pigs. Comput Electron Agric, 155, 180-189.
166. Swain DL, Friend MA, Bishop-Hurley GJ, et al (2011): Tracking livestock using global positioning systems–are we still lost? Anim Prod Sci, 51, 167-175.
167. Theurer ME, Amrine DE, White BJ (2013): Remote noninvasive assessment of pain and health status in cattle. Vet Clin N Am-Food A, 29, 59-74.
168. Tory M, Moller T (2004): Human factors in visualization research. IEEE T Vis Comput Gr, 10, 72-84.
169. Tufte ER (1985): The visual display of quantitative information. J Healthc Qual, 7, 15.
170. Umstatter C, Morgan-Davies J, Waterhouse T (2015): Cattle responses to a type of virtual fence. Rangel Ecol Manag, 68, 100-107.
171. Uzmay C, Kaya İ, Tömek B (2010): Süt Sığırcılığında Hassas Sürü Yönetim Uygulamaları. Hayvansal Üretim, 51, 50-58.
172. Valros A, Munsterhjelm C, Hänninen L, et al (2016): Managing undocked pigs–on-farm prevention of tail biting and attitudes towards tail biting and docking. Porc Health Manag, 2, 1-11.
173. van Dam YK, Feindt PH, Bovenkerk B, et al (2018): The Quantified Animal: Precision Livestock Farming and the Ethical Implications of Objectification.
174. Van De Gucht T, Saeys W, Van Meensel J, et al (2018): Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations. J Dairy Sci, 101, 637-648.
175. Van Erp-van der Kooij E, Rutter SM (2020): Using precision farming to improve animal welfare. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 15.
176. Vasseur E (2017): Animal behavior and well-being symposium: Optimizing outcome measures of welfare in dairy cattle assessment. J Anim Sci, 95, 1365-1371.
177. Vessey I, Galletta D (1991): Cognitive fit: An empirical study of information acquisition. Inf Syst Res, 2, 63-84.
178. Voß AL, Fischer-Tenhagen C, Bartel A, et al (2020): Sensitivity and specificity of a tail-activity measuring device for calving prediction in dairy cattle. J Dairy Sci, 104, 3353-3363.
179. Wagner K, Brinkmann J, March S, et al (2018): Impact of daily grazing time on dairy cow welfare-results of the welfare quality® protocol. Animals, 8, 1.
180. Wang, J, Bell M, Liu X, et al (2020): Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data. Animals, 10, 1160.
181. Wathes CM (2009): Precision livestock farming for animal health, welfare and production. In: Sustainable animal production: The challenges and potential developments for professional farming.
182. White MD, Iivonen M (2001): Questions as a factor in Web search strategy. Inf Process Manag, 37, 721-740.
183. Wurtz K, Camerlink I, D’Eath RB, et al (2019): Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review. PloS one, 14, e0226669.
184. Yigitbasioglu OM, Velcu O (2012): A review of dashboards in performance management: Implications for design and research. Int J Account Inf Syst, 13, 41-59.
185. Zehner N, Umstätter C, Niederhauser JJ, et al (2017): System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Comput Electron Agric, 136, 31-41.
186. Zhang Y, Wang L, Duan Y (2016): Agricultural information dissemination using ICTs: A review and analysis of information dissemination models in China. Inf Process Agric, 3, 17-29.