Recognizing and Prioritizing Smart Solutions in the Poultry Industry based on Sustainability Criteria

Document Type : Research Article-en

Authors

A. Khadivar ¹

F. Mojibian ²

Z. Torkashvand ¹

¹ Department of Management, Alzahra University, Tehran, Iran

² Department of Management and Economics, Tarbiat Modares University, Tehran, Iran

https://doi.org/10.22067/jead.2022.76267.1130

Abstract

Livestock and poultry production and supply is one of the significant food sectors in which more production can lead to a decrease in dependence on exports and earning foreign exchange. Poultry farming is a vital industry for sustainable food supply in all countries. In this research, intelligent applications and solutions in the poultry industry are identified and prioritized using the simultaneous evaluation of criteria and alternatives (SECA) methodbased on criteria representing the sustainable development. Analysis showed that eighteenprincipalfieldsof intelligent solutions are identified in the poultry industry. The weights obtained for sustainable development criteria based on the SECA method are economic (0.351), social (0.3383), and environmental (0.3065) in order of value. Economic sustainability should be most importantinimplementing smart solutions-based projects in the poultry industry. One of the main challenges of the agricultural sector, especially the poultry industry, is traditional production utilization which leads to the overuse of land capacity. Also, the globalization trends, climate changes, moving from a fossil fuel-based economy to an environment-based economy, competition for land, freshwater, and labor shortage have led to more complications in supplyingnutrition. Considering the potential of smart solutions in realizing sustainable development objectives, it is suggested to focus more on the environmental aspects of poultry industry projects.

Keywords

Internet of things

Poultry industry

SECA method

Smart solutions

Sustainable development

20.1001.1.20084722.1401.36.4.5.8

Subjects

• Agricultural Economics

---

©2022 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

References

1. Archana, M.P., &Uma, S.K.)2018(. Monitoring and controlling of poultry farm using IOT, International Journal of Innovation Research in Computer and Communication Engineering6(4).
2. Aengwanich, W., Intarakhamhaeng, M., Wandee, J., Nongbua, T., ,Chaiyasak, S., Srikot, P., Thammasar, K., Junsanitsri, N., Sritongtuam, K., &Tawinwaang, T. (2012). Poultry Production Clusters (PPCs) after AI Outbreaks in Thailand: Past, Present and Future Direction, International Journal of Poultry Science 11(8): 541-550.
3. ArunGnanaraj, A., &GnanaJayanthi, J. (2016). Smart, connected IoT applications for maximizing agricultural business performance. International Journal of Control Theory and Applications 9(27): 313–319. Retrieved from http://www.serialsjournals.com/serialjournalmanager/pdf/1477480165.
4. Astill, J., Dara, R.A., Fraser, E.D.G., &Sharif, S. (2020). Smart poultry management: smart sensors, big data, and the internet of thing, Computers and Electronics in Agriculture 170: 105291.
5. Atzori, L., Iera, A., Morabito, G., &Nitti, M.(2012). "The Social Internet of Things (SIoT) – When social networks meet the Internet of Things: Concept, architecture and network characterization", Elsevier B.V. All rights reserved.
6. Mohamadian, A., HeydariDahooie, J., &Qorbani, A.R (2020). Prioritizing the Applications of Internet of Things in the Agriculture by Using Sustainable Development Indicators.
7. Banhazi, T. (2009). Precision Livestock farming: a suite of electronic systems to ensure theapplication of best practise management on livestock farms. Aust. Journal Multi-disciplinaryEngineering https://doi.org/10.1080/14488388.2009.11464794.
8. Baniasadi,M., &ZareMehrjerdi, M.R.(2010). Studying the effects of optimal cultivation pattern on rural poverty: case study of Orzoo’iyeh District in Baft (Kerman – Iran), Journal of Agricultural Economics4(2): 183-209.
9. Bach, H., & Mauser, W. (2018). Sustainable agriculture and smart farming. In Earth Observation Open Science and Innovation (pp. 261-269): Springer, Cham.
10. Bauer, J., &Aschenbruck, N. (2018, May). Design and implementation of an agricultural monitoring system for smart farming. In 2018 IoT Vertical and Topical Summit on Agriculture-Tuscany (IOT Tuscany) (pp. 1-6). IEEE.
11. Bello, O., &Zeadally, S. (2015). Intelligent device-to-device communication in the internet ofthings. IEEE Syst. Journal 10: 1–11. https://doi.org/10.1109/JSYST.2014.2298837.
12. Benkhelifa, E., Abdel-Maguid, M., Ewenike, S. &Heatley, D. (2014). The internet of things: the eco-system for sustainable growth. Computer systems and applications (AICCSA), 2014 IEEE/ACS 11^th International Conference on, 836-842.
13. Biggs, P., Garrity, J., LaSalle, C., &Polomska, A. (2016). Harnessing the theinternet of things for global development. Switzerland: International Telecommunication Union.
14. Bodi, S.G., Garcia, A.V., Garcia, S.V., &Orenga, C.M. (2013). Litter aeration andspread ofSalmonella in broilers. Poultion Science 92: 2005–2011.https://doi.org/10.3382/ps.2013-03078.
15. Bora, R.R.,  Lei, M.,  Tester, J.W., Lehmann, J., & You, F. 2020. Life cycle assessment and technoeconomic analysis of thermochemical conversion technologies applied to poultry litter with energy and nutrient recovery. ACS Sustain. Chemistry Engineering 8(22):8436–8447. https://doi.org/10.1021/acssuschemeng.0c02860.
16. Bordin, F.M., Linke, B.S., Fredel, M.C., Xavier, F.A., &Weingaertner, W.L. (2022). Grinding sustainability analysis using a fatigue failure-related product life indicator, CIRP Journal of Manufacturing Science and Technology 38: 1-15.
17. Bright, A. (2008). Vocalisations and acoustic parameters of flock noise fromfeatherpecking and non-feather pecking laying flocks. Poultion Science 49: 241249. https://doi.org/10.1080/00071660802094172.
18. Bumanis, N., Arhipova, I., Paura, L., Vitols, G.,&Jankovska, L. (2022).Data conceptual model for smart poultry farm management system, 3^rd International Conference on Industry 4.0 and Smart Manufacturing, 517-526.
19. Bustamante, E., Guijarro, E., García-Diego, F.J., Balasch, S., Hospitaler, A., &Torres,A.G. (2012). Multisensor system for isotemporal measurements to assess indoorclimaticconditions in poultry farms. Sensors (Switzerland) 12: 5752–5774.https://doi.org/10.3390/s120505752.
20. Calvet, S., Campelo, J., Estellés, F., Perles, A., Mercado, R., Serrano, J., Calvet, S., Campelo, J.C., Estellés, F., Perles, A., Mercado, R., & Serrano, J.J.(2014). Suitability evaluation of multipoint simultaneous CO₂ sampling wireless sensors forlivestockbuildings. Sensors 14: 10479–10496. https://doi.org/10.3390/s140610479.
21. Carroll, B.T., Anderson, D. V., Daley, W., Harbert, S., Britton, D.F., &Jackwood, M.W. (2014). Detecting symptoms of diseases in poultry through audio signalprocessing.2014 IEEE Glob. Conf. Signal Inf. Process. 1132–1135.https://doi.org/10.1109/GlobalSIP.2014.7032298.
22. Chakchai, S., Mongkonchai, N., Aimtongkham, P., Wijitsopon, K., &Rujirakul, K. (2014). An evaluation of data-mining classification models for network intrusion detection. In 4^th international conference on digital information and communication technology and it’s applications (pp. 90–94). Bankok.
23. Chen, J., Hu, T., Wu, J.H., Si, H.P., & Lin, K.Y. (2014). Applications of internet of things in facility agriculture. Applied Mechanics and Materials 685: 517–523. https://doi.org/10.4028/www.scientific.net/AMM.685.517.
24. Chen, L., &Neethirajan, S. (2015). A homogenous fluorescence quenching basedassayforspecific and sensitive detection of influenza virus Ahemagglutininantigen. Sensors (Basel) 15: 8852–8865.https://doi.org/10.3390/s150408852.
25. Chen, M., Mao, S., & Liu, Y.(2014). Big data: A survey. Mob. Networks Appl. 19: 171–209.https://doi.org/10.1007/s11036-013-0489-0.
26. Chowdhury, E.U., &Morey, A. (2019). Intelligent packaging for poultry industry, PultryScince Association Inc. 791-800.
27. Christen, O., &Heyer, W. (2010). Sustainable development of agriculture, fisheries, and forestry. In O. Christen, V. Squires, R. Lal, & R. Hudson, Interdisciplinary and Sustainability Issues in Food and Agriculture (pp. 68-96). EOLSS Publications.
28. Colles, F.M., Cain, R.J., Nickson, T., Smith, A.L., Roberts, S.J., Maiden, M.C.J., Lunn, D.,&Dawkins, M.S., (2016). Monitoring chicken flock behavior provides earlywarningofinfection by human pathogen Campylobacter. R. Soc. B Biology Science 283: 1–6.https://doi.org/10.1098/rspb.2015.2323.
29. Comim, F., & Hirai, T. (2022). Sustainability and human development indicators: a poset analysis, Ecological Economics 198: 107470.
30. Corkery, G, Ward, S., &Hemmingway Kenny, C. (2013).Incorporating smart sensing technologies into the poultry industry. JournalWorld"sPoult. Research 3(4): 106-128.
31. David, B., Mejdell, C., Michel, V., Lund, V., &Oppermann Moe, R. (2015). Air quality inalternative housing systems may have an impact on laying hen welfare. PartII—Ammonia. Anim. Open Access Journal from MDPI 5: 886–896. https://doi.org/10.3390/ani5030389.
32. Davies, J. (2016). How poultry farm data can improve performan, .http://www.fwi.co.uk/poultry/how-poultry-farm-data-canhtm.
33. Dlodlo, N., &Kalezhi, J. (2015). The internet of things in agriculture for sustainable rural development. In 2015 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC) (pp. 13–18). IEEE. https://doi.org/10.1109/ETNCC.2015.7184801
34. DeLonge, M.S., Miles, A., & Carlisle, L. (2016). Investing in the transition to sustainable agriculture. Environmental Science & Policy 55: 266-273.
35. Elijah, O., Abdul Rahman, T., Orikumhi, I., Yen Leow, C., &NourHindia, M. (2018). An overview of internet of things (IoT) and data analytics in agriculture: Benefits and Challenges. IEEE Internet of Things Journal 3758-3773. https://doi.org/10.1109/JIOT.2018.2844296.
36. Epp, M. (2019). Poultry Technology: Rise of the robots [WWW Document]. Can. Poult.URL https://www.canadianpoultrymag.com/production/new-technology/rise-ofthe-robots-30876?jjj=1570740094606 (accessed 10.10.19).
37. (2017). Ending poverty and hunger by investing in agriculture and rural areas. Food and Agriculture Organization of the United Nations (FAO).
38. Ferrández-Pastor, F.-J., García-Chamizo, J.-M., Nieto Hidalgo, M., & Mora-Martínez, J. (2017). User-centered design of agriculture automation systems using internet of things paradigm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10586 LNCS). https://doi.org/10.1007/978-3-319-67585-5_7.
39. Fontana, I., Tullo, E., Butterworth, A., &Guarino, M. (2015). An innovative approach to predict the growth in intensive poultry farming. Computer Electronic Agriculture 119: 178–183. https://doi.org/10.1016/j.compag.2015.10.001.
40. Galli, R., Preusse, G., Uckermann, O., Bartels, T., Krautwald-Junghanns, M.-E., Koch, E., & Steiner, G. (2016). In ovo sexing of domestic chicken eggs by Raman Spectroscopy. Chemistry 88: 8657–8663. https://doi.org/10.1021/acs.analchem.6b01868.
41. (2011). Internet of Things added to the 2011 hype cycle. Retrieved from Postscapes: https://www.postscapes.com/internet-of-things-added-to-the-2011-hype-cycle.
42. Geng, H. (2017). Internet of Things and Data Analatics Handbook. Edited by HwaiyuGeng, John Wiley & Sons.
43. (2016). How Digital Solutions Will Drive Progress Towards The Sustainable Development Goals. Brussels: Global e-Sustainability Initiative (GeSI).
44. Ghasemi, R., Mohaghar, A., Safari, H. (2016). "Prioritization of IoT technology applications in the Iranian health sector: a stimulus for sustainable development", Faculty of Management, University of Tehran, 8(1). (Persian)
45. Gharoon, Z., Shabanalifomi, H., Kalantari, Kh, &Masoomianasl, A. (2013). Factors influencing use of information technology (IT) in management of dairy farms (A case of: Western Part of Tehran Province), Iranian Journal of Agricultural Economics and Development Research 43(4): 561-572.
46. Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty,J., Robinson, S., Thomas, S.M., &Toulmin, C. (2010). Food security: the challenge offeeding 9 billion people. Science 327: 812–818. https://doi.org/10.1126/science.1185383.
47. Gondchawar, N., &Kawitkar, R.S. (2016). IJARCCE IoT based Smart Agriculture. International Journal of Advanced Research in Computer and Communication Engineering 5(6): 838-842. https://doi.org/10.17148/IJARCCE.2016.56188.
48. Gunnarsdottir, J., Davidsdottir, B., Worrell, E., &Sigurgeirsdottir, S. (2022). Indicators for sustainable energy development: an Icelandic case sturdy, Energy Policy 164: 112926.
49. Halachmi, I., Guarino, M., &Bewley, J. (2019), Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Rev. Animal Bioscience 7: 403–25
50. Huang, L., & Liu, Ch. (2016) "The Application Mode in Urban Transportation Management Based on Internet of Things", Proceedings of the 2^nd International Conference on Computer Science and Electronics Engineering (ICCSE 2013) B. Nonnecke, M.Bruch," IoT& Sustainability: Practice, Policy and Promise", Center for Information Technology Research in the Interest of Society& the Banatao Institute University of California.
51. Hadinia, S.H., Carneiro, P.R.O., Ouellette, C.A., &Zuidhof, M.J. (2018). Energy partitioning by broiler breeder pullets in skip-a-day and precision feeding systems. Poultry Science. http://doi.org/10.3382/ps/pey283.
52. Information Builders. (2011). Seven Steps to Effective Data Governance: A White Paper.
53. Jackman, P., Ward, S., Brennan, L., &Corkery, G. (2015). Application of wireless technologiesto forward predict crop yields in the poultry production chain. CIGR Journal 17: 287–295.
54. Kamilaris, A., Kartakoullis, A., &Prenafeta-Boldú, F.X. (2017). A review on the practice of big data analysis in agriculture. Computer Electronic Agriculture 143: 23–37. https://doi.org/10.1016/J.COMPAG.2017.09.037.
55. Kesharavz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turshis, Z., &Antucheviciene, J. (2018). Simultaneous Evaluation of Criteria and Alternatives (SECA) for Multi-Criteria Decision-Making, INFORMATICA 29(2): 265-280.
56. Kocovic, P., Behringer, R., Ramachandran, M., &Mihajlovic, R. (2017). Emerging Trends and Applications of the Internet of Things. Hershey, PA: IGI Global. http://doi.org/10.4018/978-1-5225-2437-3.
57. Kodali, R.K., &Sahu, A. (2016). An IoT based soil moisture monitoring on Losant platform. In Proceedings of the 2016 2^nd International Conference on Contemporary Computing and Informatics, IC3I 2016 (pp. 764–768). https://doi.org/10.1109/IC3I.2016.7918063.
58. Kruize, J. W., Wolfert, J., Scholten, H., Verdouw, C. N., Kassahun, A., &Beulens, A.J. (2016). A reference architecture for Farm Software Ecosystems. Computers and Electronics in Agriculture125: 12-28.
59. Manika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., & Byers, A.H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey GlobalInstitute.
60. Marchewka, J., Watanabe, T.T.N., Ferrante, V., & Estevez, I. (2013). Welfareassessmentinbroiler farms: Transect walks versus individual scoring. Science 92: 2588–2599. https://doi.org/10.3382/ps.2013-03229.
61. Manteuffel, G., Puppe, B., &Schön, P.C. (2004). Vocalization of farm animals as ameasureofwelfare. Appl. Animal Behavier Science 88: 163–182. https://doi.org/10.1016/j.applanim.2004.02.012.
62. Mellor, D.J.,  Beausoleil, N.J., Littlewood, K.E., McLean, A.N., McGreevy, P.D., Jones, B., &  Wilkins, C. (2020). The 2020 five domains model: including human–animal interactions in assessments of animal welfare. Animals 10(10):1870. http://doi.org/10.3390/ani10101870.
63. Merad, M., Dechy, N., Serir, L., Grabisch, M., & Marcel, F. (2013). Using a multi-criteria decision aid methodology to implement sustainable development principles within an organization. European Journal of Operational Research 224(3): 603-613. http://doi.org/10.1016/j.ejor.2012.08.019.
64. McDougal, T. (2018). Poultry World - French poultry robot up and running, https://www.poultryworld.net/Meat/Articles/2018/1/Frenchpoultry-robotup-and-running-231202E/(accessed 1.22.19).
65. Mohammadian, A., HeidariDehvaei, J., &Ghorbani, A. (2020). Prioritize IoT applications in agriculture using sustainable development indicators. Iranian Agricultural Economics and Development Research 51(4): 759-745. (Persian)
66. Mohammadzadeh, A., Ghafoori, S., Mohammadian, A., Mohammadkazemi, R., Mahbanooei, B., &Ghasemi, R. (2018). A Fuzzy Analytic Network Process (FANP) approach for prioritizing internet of things challenges in Iran. Technology in Society 53: 124-134. http://doi.org/10.1016/j.techsoc.2018.01.007.
67. Momeni, F., Dashtbani, S., &Banouei, A. (2017), The importance of agriculture in maintaining the economic and social balance of urban and rural structure of Iran, Quarterly Journal of Space Economics and Rural Development 6(4): 17-46. (Persian)
68. Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., &Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and Electronics in Agriculture 156: 467-474.
69. Nääs, I.A., Garcia, R.G., & Caldara, F.R. (2014). Infrared thermal image for assessinganimalhealth and welfare. Journal Animal Behavier Biometeorology 2: 66–72. https://doi.org/10.14269/2318-1265/jabb.v2n3p66-72.
70. Neethirajan, S., Tuteja, S., Huang, S.-T., & Kelton, D. (2017). Recent advances inwearablesensors for animal and livestock health management. Biosens. Bioelectron 98: 398–407. https://doi.org/10.1016/j.sbsr.2016.11.004.
71. Nuñez, I.A., &  Ross, T.M. (2019). A review of H5Nx avian influenza viruses .  Adv. Vaccin. Immunother. 7: 2515135518821625. http://doi.org/10.1177/2515135518821625.
72. Lotfi, A.,  Navaei, M., &  Hesketh, P.J. (2019). A platinum cantilever-based thermal conductivity detector for ammonia sensing using the 3-omega technique. ECS Journal Solid State SC. 8(6): Q126. http://doi.org/1149/2.0231906jss.
73. Luka, G., Ahmadi, A., Najjaran, H., Alocilja, E., Derosa, M., Wolthers, K., Malki, A., Aziz,H., Althani, A., &Hoorfar, M. (2015). Microfluidics integrated biosensors: A leadingtechnology towards lab-on-A-chip and sensing applications. Sensors(Switzerland) 15: 30011–30031. https://doi.org/10.3390/s151229783.
74. Obedient Langroudi, S.H., &Shamsai, E. (2007). Rural development based on agricultural continuity and sustainability, Geographical Research Quarterly 86: 85-104. (Persian)
75. Okada, H., Itoh, T., Suzuki, K., & Tsukamoto, K. (2009). Wireless sensor system fordetectionof avian influenza outbreak farms at an early stage. IEEE Sensors 1374–1377. https://doi.org/10.1109/ICSENS.2009.5398422.
76. Okada, H., Suzuki, K., Kenji, T., & Itoh, T. (2014). Applicability of wireless activitysensornetwork to avian influenza monitoring system in poultry farms. Journal Sens.Technology 4: 18–23.
77. O'Grady, M.J., & O'Hare, G.M.J.I. (2017). Modelling the smart farm. 4(3): 179-187.
78. Palavalli, A., Karri, D., &Pasupuleti, S. (2010). "Semantic Internet of Things", IEEE Tenth International Conference on Semantic Computing (ICSC). Sundmaeker. Harald, Guillemin. Patrick, Friess. Peter, Woelfflé. Sylvie, "Vision and Challenges for RealisingtheInternet of Things".
79. Park, M., Britton, D., Daley, W., MsMurray, G., Navaei, M., Samoylov, A., Usher, C., & Xu, J. (2022). Artificial intelligence, sensors, robots, and transportation systems drive an innovative future for poultry broiler and breeder management, Animal Frontiers 12(2): 40-48.
80. Pitakphongmetha, J., Boonnam, N., Wongkoon, S., Horanont, T., Somkiadcharoen, D., &Prapakornpilai, J. (2017). Internet of things for planting in smart farm hydroponics style. In 20^th International Computer Science and Engineering Conference: Smart Ubiquitos Computing and Knowledge, ICSEC 2016 (pp. 1–5). IEEE. https://doi.org/10.1109/ICSEC.2016.7859872.
81. Popović, T., Latinović, N., Pešić, A., Zečević, Ž.,Krstajić, B., &Djukanović, S. (2017). Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study. Computers and Electronics in Agriculture 140: 255–265. https://doi.org/10.1016/j.compag.2017.06.008.
82. Poursaeed, A., Mirdamadi, M., Malekmohammadi, I., & Hosseini, J. (2010). The partnership models of agricultural sustainable development based on Multiple Criteria Decision Making (MCDM) in Iran. African Journal of Agricultural Research 5(23): 3185-3190.
83. Pratim Ray, P. (2017). Internet of things for smart agriculture: Technologies, practices and future direction. Journal of Ambient Intelligence and Smart Environments 9: 395-420.
84. Praveen, K., & Satish, C. (2012). An intelligent system based on sensor integration & sensor fusion. InternationalJournal of Advanced Research In Computer Science and Software Engineering 2(3): 2277–2283.
85. Quaddus, M., & Siddique, M. (2001). Modelling sustainable development planning: A multicriteria decision conferencing approach. Environment International 27(2-3): 89-95. http://doi.org/10.1016/S0160-4120(01)00066.
86. Quintero-Angel, M., & González-Acevedo, A. (2018): Tendencies and challenges for the assessment of agricultural sustainability. Agriculture, Ecosystems & Environment 254: 273-281.
87. Rayes, A., & Salam, S. (2017). Internet of Things—From Hype to Reality. Springer International Publishing. http://doi.org/10.1007/978-3-319-44860-2.
88. Regan, Á. (2019). ‘Smart farming’in Ireland: A risk perception study with key governance actors. NJAS-Wageningen Journal of Life Sciences90: 100292.
89. Rezaei-Moghaddam, K., &Karami, E. (2008). A multiple criteria evaluation of sustainable agricultural development models using AHP. Sustainable Agricultural Development Models 10(4): 407-426.
90. Rizwan, M., Carroll, B.T., Anderson, D.V., Daley, W., Harbert, S., Britton, D.F., &Jackwood, M.W. (2017). Identifying rale sounds in chickens using audio signalsfor early diseasedetection in poultry. 2016 IEEE Glob. Conf. Signal Inf. Process.Glob. 2016 - Proc. 55–59. https://doi.org/10.1109/GlobalSIP.2016.7905802.
91. Rodrigues Lima Junior, F., Osiro, L., & Cesar Ribeiro Carpinetti, L. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods tosupplier selection. Applied Soft Computing 21: 194-209.
92. Ronald, B., &Siarhei, S. (2012). Experiments for evaluating sensors' precision in wireless sensor networks. International Journal of Ad Hoc, Sensor & Ubiquitous Computing 3(3): 105–121.
93. Rani, K.S.S., &Devarajan, N. (2012). Multiobjective sensor node deployment in wireless sensor networks. InternationalJournal of Engineering Science and Technology 4(4): 1262–1266.
94. Sandelowski, M., & Barroso, J. (2006). Handbook for Synthesizing Qualitative Research. New York: Springer Publishing Company, Inc.
95. Sassi, N. Ben, Averós, X., Estevez, I. (2016). Technology and poultry welfare. Animals 6: 1–21. https://doi.org/10.3390/ani6100062.
96. Schuetz, C.G., Schausberger, S., &Schrefl, M. (2018). Building an active semantic data ware house for precision dairy farming. Journal Organ. Computer Electronic Commer. 28: 122–141. https://doi.org/10.1080/10919392.2018.1444344.
97. Senoret, A., Ramirez, M., &Rehner, J. (2022). Employment and sustainability: the relation between precarious work and spatial inequality in the neoliberal city, World Development 153: 105840.
98. Shang, X. (2012). "Internet of Things (IOT) Service: Architecture and its application in E_ Commerce", Journal of Electronic Commerce in Organizations 10(3): 44-55.
99. Shen, K.-Y., &Tzeng, G.-H. (2018). Advances in multiple criteria decision making for sustainability: Modeling and applications. Sustainability (Switzerland) 10(5): 1-7. http://doi.org/10.3390/su10051600.
100. Shinder, D., Rusal, M., Giloh, M., &Yahav, S. (2009). Effect of repetitive acute coldexposuresduring the last phase of broiler embryogenesis on cold resistancethrough the lifespan. Science 88: 636–646. https://doi.org/10.3382/ps.2008-00213.
101. Sicular, S. (2013). Gartner’s Big Data Definition Consists of Three Parts, Not to BeConfusedwith Three “V”s [WWW Document]. URLhttps://www.forbes.com/sites/gartnergroup/2013/03/27/gartners-big-datadefinition-consists-of-three-parts-notto-be-confused-with-threevs/#4912508b42f6 (accessed 5.22.18).
102. Silvera, A.M., Knowles, T.G., Butterworth, A., Berckmans, D., Vranken, E., &Blokhuis, H.J. (2017). Lameness assessment with automatic monitoring ofactivity in commercialbroiler flocks. Science 96: 2013–2017. https://doi.org/10.3382/ps/pex023.
103. Skobelev, P.O., Simonova, E.V., Smirnov, S.V., Budaev, D.S., Voshchuk, G.Y., &Morokov, A.L. (2019). Development of a knowledge base in the “smart farming” system for agricultural enterprise management. Procedia Computer Science150: 154-161.
104. Smith, D., Lyle, S., Berry, A., Manning, N., Zaki, M., & Neely, A. (2015). Internet of animal health things opportunities and challenges data and analytics: Internet of Animal Health Things. https://doi.org/10.13140/RG.2.1.1113.8409.
105. Sreekantha, D.K., &Kavya A.M. (2017). Agricultural crop monitoring using IOT - a study. In 2017 11^th International Conference on Intelligent Systems and Control (ISCO) (pp. 134–139). IEEE. https://doi.org/10.1109/ISCO.2017.7855968.
106. Chunling. (2012). "Application of RFID Technology for Logistics on Internet of Things ", AASRI Conference on Computational Intelligence and Bioinformatics, Published by Elsevier.
107. Talavera, J., Tobón, L., Gómez, J., Culman, M., Aranda, J., Parra, D., &Garreta, L. (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture 104: 283-297.
108. Thierer, A., & O’Sullivan, A. (2015). Projecting the growth and economic impact of the internet of things. George Mason University, Mercatus Center, VA, USA,   1-10, Retrieved December 6 2020 from http://mercatus.org/publication/projecting-growth-and-economic-impact-internet-things
109. Vanderhasselt, R.F., Sprenger, M., Duchateau, L., &Tuyttens, F.M. (2013). Automated assessment of footpad dermatitis in broiler chickens at the slaughter-line: Evaluation and correspondence with human expert scores. Science 92: 12–8. https://doi.org/ 10.3382/ps.2012-02153.
110. Wiseman, L., Sanderson, J., Robb, L. (2018). Rethinking Ag Data Ownership. Farm Policy Journal 15: 71–77.
111. Veisi, H., Liaghati, H., &Alipour, A. (2016). Developing an ethics-based approach to indicators of sustainableagriculture using analytic hierarchy process (AHP). Ecological Indicators60: 644-654.
112. Verma, N., & Usman, A. (2016). Internet of Things (IoT): A relief for Indian farmers. 2016 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 831-835). Seattle: IEEE.
113. Vermesan, O., &Friess, P. (2014). Internet of things from research and innovation to market deployment. River Publishers.
114. Vermesan, O., &Friess, P. (2016 (b)). Degitising the industry internet of things connecting the physical, Digital and Virtual Worlds. River Publisher.
115. Walters, S.J. (2009). Quality of Life Outcomes in Clinical Trials and Health-Care Evaluation: A Practical Guide to Analysis and Interpretation. Wiley, New York.
116. Walter, A., Finger, R., Huber, R., &Buchmann, N. (2017). Opinion: Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences 114(24): 6148–6150. http://doi.org/10.1073/pnas.1707462114.
117. Wang, T.-C., & Lee, H.-D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications 36(5): 8980–8985.
118. Wang, Y., & Qi, H. (2012). "Research of intelligent transportation system based on the internet of things frame", Wireless Engineering and Technology 3(3): 21471. http://doi.org/10.4236/wet.2012.33023.
119. Weersink, A., Fraser, E., Pannell, D., Duncan, E., &Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics 10: 19-37.
120. Williams, J., Alter, T., &Shrivastava, P. (2018). Systemic governance of sustainable agriculture: Implementing sustainable development goals and climate-friendly farming. Outlook on Agriculture47(3): 192-195. http://doi.org/10.1177/0030727018795907.
121. Wolfert, S., Ge, L., Verdouw, C., &Bogaardt, M.J. (2017). Big data in smart farming–a review. Agricultural Systems153: 69-80.
122. Xin, J., &Zazueta, F.J.A.E.I.C.J. (2016). Technology trends in ICT–towards data-driven, farmercentered and knowledge-based hybrid cloud architectures for smart farming 18(4): 275-279.
123. Xin, J., &Zazueta, F. (2016). Technology trends in ICT–towards data-driven, farmer-centered and knowledge-based hybrid cloud architectures for smart farming. Agricultural Engineering International: CIGR Journal 18(4): 275-279.
124. Xin, H., & Liu, K. (2017). Precision livestock farming in egg production. Animal Front. 7: 24. https://doi.org/10.2527/af.2017.0105.
125. Wolfert, S., Ge, L., Verdouw, C., &Bogaardt, M.J. (2017). Big data in smart farming–a review. Agricultural Systems 153: 69-80.
126. Zarei, M., Mohammadian, A., &Ghasemi, R. (2016). Internet of things in industries: a survey for sustainable development. Innovation and Sustainable Development 10(4): 419-442.
127. Zhang, Q,, Yang, X.L., Zhou, Y.M., Wang, L.R., Guo, X.S. (2007). A wireless solution for greenhouse monitoring and control system based on ZigBee technology. Journal of Zhejiang University Science 8(10): 1584–1587.
128. Zuidhof, M.J. (2018). Lifetime productivity of conventionally and precision-fedbroilerbreeders. Poult. Science In Press. https://doi.org/10.3382/ps.
129. Zuidhof, M.J., Fedorak, M.V., Ouellette, C.A., & Wenger, I.I. (2017). Precision feeding:Innovative management of broiler breeder feed intake and flock uniformity. Science 96: 2254–2263. https://doi.org/10.3382/ps/pex013.