Statistical grouping in studying farmers attitudes for adopting practices of precise agriculture
DOI:
https://doi.org/10.61308/HUBS9978Keywords:
attitudes, precise agriculture, representativeness, statistical grouping, field surveyAbstract
The application of precision farming techniques leads to significant improvements both in economical and environmental point of view, facilitates the optimization of resource use and reduces farmers’ risks in terms of natural and disease hazards but enhances the risk from technological faults. The whole article is to explore the attitudes of farmers regarding the possibilities and prospects for the implementation of precision agriculture technologies, identifying and establishing the most possible and unifying views and perceptions obtained from a field study. In this article, a statistical grouping is used to process surveys conducted in farms in Bulgaria. Given the growing role of technology and its importance in expanding, improving and stabilizing farms, the role of precision agriculture is becoming stronger in the agriculture industry.
References
Beluhova-Uzunova, R., & Hristov, K. (2020). Models for balanced development of Bulgarian rural regions in the context of CAP post-2020. Trakia Journal of Sciences, 18(1), 491-497. https://doi.org/10.15547/tjs.2020.s.01.080
Bojinov, B., Ivanov, B., & Vasileva, S. (2022). Current state and usage limitations of vegetation indices in precision agriculture. Bulgarian Journal of Agricultural Science, 28(3), 387-394.
Cochran, W. G. (1963). Sampling Techniques. 2nd Ed., New York: John Wiley and Sons, Inc.
Da Silveira, F., Da Silva, S. L. C., Machado, F. M., Barbedo, J. G. A., & Amaral, F. G. (2023). Farmers’ perception of the barriers that hinder the implementation of agriculture 4.0. Agricultural Systems, 208, 103656. https://doi.org/10.1016/j.agsy.2023.103656
Deming, W. E. (1986). Out of the crisis. Cambridge, MA: Massachusetts Institute of Technology.
Foster, L. A., Szilagyi, K., Wairegi, A., Oguamanam, C., & De Beer, J. (2023b). Smart farming and artificial intelligence in East Africa: Addressing indigeneity, plants, and gender. Smart Agricultural Technology, 3, 100132. https://doi.org/10.1016/j.atech.2022.100132
Ivanov, B. (2020). Raboten document na metodika za izchislitelnite analizi I opredelyane na predstavitelnostta na izvadkata pri anketno prouchvane i polouchavane na srednite I razlichnite grupirovki. Institut po agrarna Ikonomika po proekt “Optimizirane parametrite na preciznoto zemedelie za podobriavane efektivnostta na proizvodstvoto i proslediaemostta na produktite ot selskoto stopanstvo” (Bg).
Ivanov, B.; Vasileva, S.; Bachev, Hr.; Toteva, D.; Sarov, A.; Mihaylova, M. (2022). Classification of farm scale and approach for sample’s processing. Ikonomika i upravlenie na selskoto stopanstvo, 67(1), 60-70 (Bg).
Otieno, M. (2023). An extensive survey of smart agriculture technologies: Current security posture. World Journal of Advanced Research and Reviews, 18(3), 1207-1231. https://doi.org/10.30574/wjarr.2023.18.3.1241
Ramsey, C. A., & Hewitt, A. D. (2005). A methodology for assessing sample representativeness. Environmental Forensics, 6(1), 71-75. https://www.researchgate.net/publication/239820911_A_Methodology_for_Assessing_Sample_Representativeness
Salam, A., Usman, R. (2020). Decision Agriculture - Purdue e-Pubs. Paper 47. https://docs.lib.purdue.edu/cit_articles/47 (n.d.) Retrieved January 11, 2024, from docs.lib.purdue.edu https://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1049&context=cit_articles
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Bulgarian Journal of Agricultural Economics and Management
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
