Digitalization and digital technologies: The obstacles to adaptation among Hungarian farmers
DOI:
https://doi.org/10.24136/eq.3237Keywords:
digitalization, Hungarian farmers, clustering, precision agricultureAbstract
Research background: Digital technologies aim to enhance efficiency, competitiveness, and sustainability. Despite ongoing efforts, Hungary is encountering various challenges in digitalization. This research seeks to investigate the potential of digital technologies in addressing these challenges, with a particular focus on precision agriculture technologies (PA).
Purpose of the article: This study explores the utilization of digital technologies among 81 agricultural and food companies in Hungary. The study evaluates the use of advanced and less advanced digital technologies and their impact on rural areas. Additionally, the research seeks to understand the economic and social impacts resulting from the adoption of these technologies, focusing specifically on precision farming.
Methods: The study uses advanced clustering methods to categorize agricultural companies based on their use of digital technology. The research employed the two-step clustering method, which has been recognized for its robustness in clustering mixed-level variables.
Findings & value added: Farmers and food producers generally have low adoption rates of advanced digital technologies, preferring common but less advanced tools. This is mainly due to high investment costs, economies of scale, limited experience and expertise, and a lack of willingness to cooperate among farmers. The respondents could be classified into two groups: one group is aware and even understands advanced technology, but rarely uses it. In contrast, the other group is largely unaware of these technologies. The results indicate that a lack of knowledge and expertise can significantly impede the adoption of advanced technologies in agriculture. Policymakers must develop a mix of policies that collectively reduce the economic barriers to technology adoption while engaging stakeholders who may not fully understand the benefits of advanced technologies.
Downloads
References
Bacher, J., Wenzig, K., & Vogler, M. (2004). SPSS twostep cluster-a first evaluation. Nürnberg: Universität Erlangen-Nürnberg, Wirtschafts- und Sozialwissenschaftliche Fakultät, Sozialwissenschaftliches Institut Lehrstuhl für Soziologie. Retrieved from https://nbn-resolving.org/urn:nbn:de:0168-ssoar-327153.
View in Google Scholar
Balogh, P., Bai, A., Czibere, I., Kovách, I., Fodor, L., Bujdos, Á., Sulyok, D., Gabnai, Z., & Birkner, Z. (2021). Economic and social barriers of precision farming in Hungary. Agronomy, 11(6), 1112. DOI: https://doi.org/10.3390/agronomy11061112
View in Google Scholar
Bramley, R., & Trengove, S. (2013). Precision agriculture in Australia: Present status and recent developments. Engenharia Agrícola, 33(3), 575–588. DOI: https://doi.org/10.1590/S0100-69162013000300014
View in Google Scholar
Brodny, J., & Tutak, M. (2021). Assessing the level of digitalization and robotization in the enterprises of the European Union Member States. Plos One, 16(7), e0254993. DOI: https://doi.org/10.1371/journal.pone.0254993
View in Google Scholar
Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 2053951716648174. DOI: https://doi.org/10.1177/2053951716648174
View in Google Scholar
Butollo, F. (2021). Digitalization and the geographies of production: Towards reshoring or global fragmentation? Competition & Change, 25(2), 259–278. DOI: https://doi.org/10.1177/1024529420918160
View in Google Scholar
Chiu, T., Fang, D., Chen, J., Wang, Y., & Jeris, C. (2001). A robust and scalable clustering algorithm for mixed type attributes in large database environment. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 263-268). ACM Digital Library. DOI: https://doi.org/10.1145/502512.502549
View in Google Scholar
Creutzig, F., Roy, J., Lamb, W. F., Azevedo, I. M. L., Bruine de Bruin, W., Dalkmann, H., Edelenbosch, O. Y., Geels, F. W., Grubler, A., Hepburn, C., Hertwich, E. G., Khosla, R., Mattauch, L., Minx, J. C., Ramakrishnan, A., Rao, N. D., Steinberger, J. K., Tavoni, M., Ürge-Vorsatz, D., & Weber, E. U. (2018). Towards demand-side solutions for mitigating climate change. Nature Climate Change, 8(4), 260–263. DOI: https://doi.org/10.1038/s41558-018-0121-1
View in Google Scholar
Czibere, I., Kovách, I., & Loncsák, N. (2023). Hungarian Farmers and the Adoption of Precision Farming. European Countryside, 15(3), 366–380. DOI: https://doi.org/10.2478/euco-2023-0020
View in Google Scholar
Daberkow, S. G., & McBride, W. D. (2003). Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precision Agriculture, 4, 163–177. DOI: https://doi.org/10.1023/A:1024557205871
View in Google Scholar
Dasgupta, P. (2021). The economics of biodiversity: the Dasgupta review: Hm Treasury.
View in Google Scholar
Ehlers, M.-H., Finger, R., El Benni, N., Gocht, A., Sørensen, C. A. G., Gusset, M., Pfeifer, C., Poppe, K., Regan, Á., Rose, D. C., Wolfert, S., & Huber, R. (2022). Scenarios for European agricultural policymaking in the era of digitalisation. Agricultural Systems, 196, 103318. DOI: https://doi.org/10.1016/j.agsy.2021.103318
View in Google Scholar
Ehlers, M.-H., Huber, R., & Finger, R. (2021). Agricultural policy in the era of digitalisation. Food Policy, 100, 102019. DOI: https://doi.org/10.1016/j.foodpol.2020.102019
View in Google Scholar
European Commission (2023a). At a glance: Hungary's CAP Strategic Plan. Retrieved from https://agriculture.ec.europa.eu/system/files/2023-04/csp-at-a-glance-hungary_en.pdf.
View in Google Scholar
European Commission (2023b). Digitalisation of the European agricultural sector: Activities in Horizon 2020. Retrieved from https://digital-strategy.ec.europa.eu/en/policies/digitalisation-agriculture-horizon-2020.
View in Google Scholar
European Commission (2023c). Shaping Europe’s digital future. Retrieved from https://digital-strategy.ec.europa.eu/en/library/digital-economy-and-society-ind ex-desi-2022.
View in Google Scholar
European Commission (2023d). Shaping Europe’s digital future. Retrieved from https://digital-strategy.ec.europa.eu/en/policies/digitalisation-agriculture.
View in Google Scholar
FAO (2020). Biodiversity and the livestock sector – Guidelines for quantitative assessment – Version 1. Rome, Italy, Livestock Environmental Assessment and Performance Partnership (FAO LEAP), 1–142. doi:
View in Google Scholar
Gaál, M., Molnár, A., Illés, I., Kiss, A., Lámfalusi, I., & Kemény, G. (2021). Where do we stand with digitalization? An assessment of digital transformation in Hungarian agriculture. In D. Bochtis, C. Achillas, G. Banias & M. Lampridi (Eds.). Bio-economy and agri-production: Concepts and evidence (pp. 195–206). Academic Press. DOI: https://doi.org/10.1016/B978-0-12-819774-5.00011-4
View in Google Scholar
Gabriel, A., & Gandorfer, M. (2023). Adoption of digital technologies in agriculture—an inventory in a european small-scale farming region. Precision Agriculture, 24(1), 68–91. DOI: https://doi.org/10.1007/s11119-022-09931-1
View in Google Scholar
Galanakis, C. M., Rizou, M., Aldawoud, T. M., Ucak, I., & Rowan, N. J. (2021). Innovations and technology disruptions in the food sector within the COVID-19 pandemic and post-lockdown era. Trends in Food Science & Technology, 110, 193–200. DOI: https://doi.org/10.1016/j.tifs.2021.02.002
View in Google Scholar
Garske, B., Bau, A., & Ekardt, F. (2021). Digitalization and AI in European agriculture: a strategy for achieving climate and biodiversity targets? Sustainability, 13(9), 4652. DOI: https://doi.org/10.3390/su13094652
View in Google Scholar
Giannakis, E., & Bruggeman, A. (2015). The highly variable economic performance of European agriculture. Land Use Policy, 45, 26–35. DOI: https://doi.org/10.1016/j.landusepol.2014.12.009
View in Google Scholar
Griffin, T. W., Miller, N. J., Bergtold, J., Shanoyan, A., Sharda, A., & Ciampitti, I. A. (2017). Farm’s sequence of adoption of information-intensive precision agricultural technology. Applied Engineering in Agriculture, 33(4), 521. DOI: https://doi.org/10.13031/aea.12228
View in Google Scholar
Hoyk, E., Szalai, Á., Palkovics, A., & Farkas, J. Z. (2022). Policy gaps related to sustainability in Hungarian agribusiness development. Agronomy, 12(9), 2084. DOI: https://doi.org/10.3390/agronomy12092084
View in Google Scholar
Huang, J.-k. (2020). Impacts of COVID-19 on agriculture and rural poverty in China. Journal of Integrative Agriculture, 19(12), 2849–2853. DOI: https://doi.org/10.1016/S2095-3119(20)63469-4
View in Google Scholar
Jorge-Vázquez, J., Chivite-Cebolla, M. P., & Salinas-Ramos, F. (2021). The Digitalization of the European agri-food cooperative sector. Determining factors to embrace information and communication technologies. Agriculture, 11(6), 514. DOI: https://doi.org/10.3390/agriculture11060514
View in Google Scholar
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23–37. DOI: https://doi.org/10.1016/j.compag.2017.09.037
View in Google Scholar
Khanna, M. (2021). Digital transformation of the agricultural sector: Pathways, drivers and policy implications. Applied Economic Perspectives and Policy, 43(4), 1221–1242. DOI: https://doi.org/10.1002/aepp.13103
View in Google Scholar
Kosior, K. (2019). Towards a new data economy for EU agriculture. Studia Europejskie-Studies in European Affairs, 23(4), 91–107. DOI: https://doi.org/10.33067/SE.4.2019.6
View in Google Scholar
Kovac, N., Żmija, K., Roy, J. K., Kusa, R., & Duda, J. (2024). Digital divide and digitalization in Europe: A bibliometric analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(2), 463–520. DOI: https://doi.org/10.24136/eq.2899
View in Google Scholar
Kovács, I., & Husti, I. (2018). The role of digitalization in the agricultural 4.0–how to connect the industry 4.0 to agriculture? Hungarian Agricultural Engineering, 33, 38–42. DOI: https://doi.org/10.17676/HAE.2018.32.38
View in Google Scholar
KSH (2023). Agrárcenzus eredmények – Agrárdigitalizáció. Retrieved from https://www.ksh.hu/docs/hun/xftp/ac2020/agrardigitalizacio/index.html.
View in Google Scholar
Lioutas, E. D., Charatsari, C., & De Rosa, M. (2021). Digitalization of agriculture: A way to solve the food problem or a trolley dilemma? Technology in Society, 67, 101744. DOI: https://doi.org/10.1016/j.techsoc.2021.101744
View in Google Scholar
Lu, L., Reardon, T., & Zilberman, D. (2016). Supply chain design and adoption of indivisible technology. American Journal of Agricultural Economics, 98(5), 1419–1431. DOI: https://doi.org/10.1093/ajae/aaw076
View in Google Scholar
MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals. A review. Agronomy for Sustainable Development, 42(4), 70. DOI: https://doi.org/10.1007/s13593-022-00792-6
View in Google Scholar
McFadden, J., Casalini, F., Griffin, T., & Antón, J. (2022). The digitalisation of agriculture: A literature review and emerging policy issues. OECD Food, Agriculture and Fisheries Papers, 176.
View in Google Scholar
Osinga, S. A., Paudel, D., Mouzakitis, S. A., & Athanasiadis, I. N. (2022). Big data in agriculture: Between opportunity and solution. Agricultural systems, 195, 103298. DOI: https://doi.org/10.1016/j.agsy.2021.103298
View in Google Scholar
Rijswijk, K., Klerkx, L., Bacco, M., Bartolini, F., Bulten, E., Debruyne, L., Dessein, J., Scotti, I., & Brunori, G. (2021). Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation. Journal of Rural Studies, 85, 79–90. DOI: https://doi.org/10.1016/j.jrurstud.2021.05.003
View in Google Scholar
Rolandi, S., Brunori, G., Bacco, M., & Scotti, I. (2021). The digitalization of agriculture and rural areas: Towards a taxonomy of the impacts. Sustainability, 13(9), 5172. DOI: https://doi.org/10.3390/su13095172
View in Google Scholar
Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture. ERR-217, U.S. Department of Agriculture, Economic Research Service, 46. http://dx.doi.org/10.22004/ag.econ.249773.
View in Google Scholar
Stechemesser, A., Koch, N., Mark, E., Dilger, E., Klösel, P., Menicacci, L., Nachtigall, D., Pretis, F., Ritter, N., Schwarz, M., Vossen, H., & Wenzel, A. (2024). Climate policies that achieved major emission reductions: Global evidence from two decades. Science, 385(6711), 884–892. DOI: https://doi.org/10.1126/science.adl6547
View in Google Scholar
Szenderák, J., Fróna, D., & Harangi-Rákos, M. (2021). National policy report Hungary. Retrieved from https://desira2020.eu/.
View in Google Scholar
Takácsné György, K., Lámfalusi, I., Molnár, A., Sulyok, D., Gaál, M., Domán, C., Illés, I., Kiss, A., Péter, K., & Kemény, G. (2018). Precision agriculture in Hungary: Assessment of perceptions and accounting records of FADN arable farms. Studies in Agricultural Economics, 120(1316-2018-2929), 47–54. DOI: https://doi.org/10.7896/j.1717
View in Google Scholar
Vasilescu, M. D., Serban, A. C., Dimian, G. C., Aceleanu, M. I., & Picatoste, X. (2020). Digital divide, skills and perceptions on digitalisation in the European Union—Towards a smart labour market. PloS One, 15(4), e0232032. DOI: https://doi.org/10.1371/journal.pone.0232032
View in Google Scholar
Yang, M., Fu, M., & Zhang, Z. (2021). The adoption of digital technologies in supply chains: Drivers, process and impact. Technological Forecasting and Social Change, 169, 120795. DOI: https://doi.org/10.1016/j.techfore.2021.120795
View in Google Scholar
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Equilibrium. Quarterly Journal of Economics and Economic Policy
This work is licensed under a Creative Commons Attribution 4.0 International License.
