نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسارشد، گروه ترویج و آموزش کشاورزی، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه
2 استادیار و عضو هیاتعلمی ، گروه ترویج و آموزش کشاورزی، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
The purpose of study was to make a comparative analysis of industrial and traditional entrepreneurial animal husbandry in Kermanshah province with emphasize on managers’ digital empowering. Population studies were 152 managers’ industrial and traditional livestock systems that studied by stratified sampling with proportional assignment. Researcher-made questionnaire was used for gathering data. Validity and reliability questionnaire were confirmed by a panel of experts and Cronbach's alpha. SPSS18 was used to analyzing data. Results showed a positive and significant association between managers' digital empowering and business intelligence in traditional (r = 0.726, P = 0.000) and industrial (r = 0.398, P = 0.044) at 5% level significance. Based on findings, the mean of digital empowering and intelligence of industrial livestock system in all components is at a higher level than traditional system. According to one sample t-test, only economic dimension of digital capability has a significant difference between the two groups at the level of 1% (t =-3.21, P = 0.001). In addition, in business intelligence sector, with 95% confidence in terms of knowledge management components (t =-0 / 670, P = 0.000) and software development (t =-3 / 516, P = 0.001 There was a significant difference between two groups, but no significant difference was observed between data mining and customer-oriented marketing. Based on result, it was suggested that planners and officials of agricultural extension and education of the province, training and development of digital empowering of users. Put them on the billboard of their promotional works.
کلیدواژهها [English]
17. Alreshidi, E. 2019. Smart Sustainable Agriculture (SSA) Solution Underpinned by Internet of Things (IoT) and Artificial Intelligence. (IJACSA) International Journal of Advanced Computer Science and Applications, 10 (5): 93-102.
20. Binda, E., Casirani, G., Piccinini, R., Zecconi, A. 2004. Introduction of AMS in Italian Dairy Herds: The Detection of Clinical and Subclinical Mastitis by AMS Systems. Automatic milking- a better understanding: Proceedings of the International Symposium, Wageningen Academic Publishers, The Netherlands. 245-46.21. Créach, P., Doutart, E., Bouvarel, I. 2017. Precision livestock farming and big data: a new challenge for poultry sector. 12e Journées de la Recherche Avicole et Palmipèdes à Foie Gras (JRA-JRPFG 2017), 5 & 6 avril 2017, Tours, France: 59-66.
23. Elijah, T. A., Rahman, I., Orikumhi, C. Y., Leow, and M. N. 2018. Hindia, An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges, IEEE Internet of Things Journal, 5 (5): 3758-3773. 24. Ferrari, S., Piccinini, R., Silva, M., Exadaktylos, V., Berckmans, D., and Guarino, M. 2010. Cough sound description in relation to respiratory diseases in dairy calves. Preventive Veterinary Medicine, 96: 276-280.25. Grujica, V., Danijel, M. and Radomir, B. 2019. Business Intelligence in agriculture-A Practical Approach. Boosting Adult System Education in Agriculture- AGRI BASE. 26. Hahn, G.L. 1999. Dynamic responses of cattle to thermal loads. Journal of Dairy Science, 82 (2): 10-20. 27. Hostmann, B., Herschel, G. and Rayner, N. 2007. The evolution of business intelligence: the four worlds. Gartner Research Report. 28. Imhoff, C. 2004. Business Intelligence-Five Factor for Success. Retrieved from http://www.b-eyenetwork. com/print/252.
31. Malavade, V. M. and Akulwar, P. K., 2017. Role of IoT in Agriculture. National Conference on Changing Technology and Rural Development, 56-57.
33. Oyku, I., Jones, M.C. and Sidorova, A. 2011. Bussines Intelligence (BI) Success and the Role of BI Capabilities. Intelligent Systems in Accounting, Finanace and Management, 18: 161–176.34. Patrício, D.I. and Rieder, R. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review, Computers and Electronics in Agriculture, 153: 69-81.
37. Sethi, P. and Sarangi, S.R. 2017. Internet of things: architectures. Protocols and applications. Journal of Electrical and Computer Engineering, 1-26.38. Sharma, R. S. & Djiaw, V. 2011. Realizing the strategic impact of business intelligence tools. VINE, 41 (2): 113-131.39. Silva, K. O. and Naas, I. 2006. Evaluating the use of electronic identification in swine. Engenharia Agrícola, 26 (1): 11-19.
41. Tyrychtr, J., Ulman, M., and Vostrovský, V. 2015. Evaluation of the state of the Business Intelligence among small Czech farms. Agricultural Economics, 61 (2): 63-71.
43. Viaene, S. 2008. Linking business intelligence into your business. IT Professional, 10(6): 28–3444. Watson, H.J., Wixom, B.H., Hoffer, J.A., Anderson-Lehman, R. and Reynolds, A.M. 2006. Real-time business intelligence: best practices in Continental Airlines. Business Intelligence, 23(1): 7–18.