Assessment of the level of digitalization of sheep farming in Bulgaria
Yovka Popova

, Nikolay Ivanov

Abstract: The aim of this study is to assess the level of digitalization of sheep farms with different productive areas - dairy and meat in different regions of the country - plain, semi-mountainous and mountainous.
A study was conducted of 324 farms from different regions of the country of different sizes, where sheep are raised. For this purpose, a survey was developed, including 20 questions. The surveys were conducted by telephone or through a site visit. The method of descriptive statistics is used to analyze the results of the study.
It was found that:
Of all employed farmers, men predominate, with the largest share of employed farmers, aged 41 to 50 and 51 to 60, and having completed secondary education. The share of farmers, who are not familiar with digital technologies is high, and a negative attitude towards their application was found. The most used digital technologies are camera surveillance, milk temperature sensors, sensors for concentrated feed consumption, automatic lamb feeders and milk conductivity sensors, and the most widespread is the pasture management system. The comprehensive assessment of the level of digitalization of sheep farms is low.
Keywords: data transfer into management systems; digital technologies; level of digitalization; pasture management; sheep farms
Citation: Popova, Yo. & Ivanov, N. (2025). Assessment of the level of digitalization of sheep farming in Bulgaria. Bulgarian Journal of Animal Husbandry, 62(3), 12-23 (Bg).
References: (click to open/close) | Annual report on the state and development of agriculture. Agricultural report, 2024. https://www.mzh.government.bg/en/policies-and-programs/reports/agricultural-report/. Bachev, H. (2020). Digitalisation of Bulgarian Agriculture and Rural Areas. Ikonomika i upravlenie na selskoto stopanstvo, 65(2), 3 - 24 (Bg). Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T. & Gomez-Barbero, M. (2019). Exploring the adoption of precisionagricultural technologies: a cross regional study of EU farmers. Land Use Policy, 80, 163 - 174. Borchers, M. R. & Bewley, J. M. (2015). An assessment of producer precision dairyfarming technology use, prepurchase considerations, and usefulness. Journal of Dairy Science, 98, 4198 - 4205. Edwards, J. P., Rue, B. T. D. & Jago, J. G. (2015). Evaluating rates of technology adoption and milking practices on New Zealand dairy farms. Animal Production Science, 55, 702 - 709. Gargiulo, J. I., Eastwood, C. R., Garcia, S. C. & Lyons, N. A. (2018). Dairy farmers with larger herd sizes adopt more precision dairy technologies. Journal of Dairy Science, 101, 5466 - 5473. Groher, T., Heitkämper, K. & Umstätter, С. (2020). Digital technology adoption in livestock production with a special focus on ruminant farming. Animal, 14(11), 2404 - 2413. Kovljenić, М, Škorić, J., Galetin, M. & Škorić, S. (2023). Digital technology in agriculture: evidence from farms on the territory of AP. Economics of Agriculture, 70(2), 583 – 596. Belgrade, Serbia. Konrad, M. T., Nielsen, H. Ø., Pedersen, A. B. & Elofsson, K. (2019). Drivers of farmers’ investments in nutrient abatement technologies in five Baltic Sea countries. Ecological Economics, 159, 91 - 100. Lima, E., Hopkins, T., Gurney, E., Shortall, O., Lovatt, F., Davies, P., Williamson, G. & Kaler, J. (2018). Drivers for precision livestock technology adoption: a study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales. PLoS ONE, 13, e0190489. Nikolov, D., Boevski, Iv., Borisov, P., Atanasova-Chopeva, M., Kostenarov, Kr., Petkov, Ev. , Fidanska,B. (2022). Digitalizatsiya v zemedelieto – konkurentnosposobnost i biznes modeli, Sofia, p. 307(Bg). Ordolff, D. (2001). Introduction of electronics into milking technology. Computersa and Electronics in Agriculture, 30, 125 - 149. Paustian, M. & Theuvsen, L. (2017). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18, 701 - 716. Pierpaoli, E., Carli, G., Pignatti, E. & Canavari, M. (2013). Drivers of precision agriculture technologies adoption: a literature review. Procedia Technology, 8, 61 - 69. Strategiya za tsifrovozatsiya na zemedelieto v selskite rayoni na Republika Balgariya. (2019). https://www.mzh.government.bg/bg/politiki-i-programi/politiki-i-strategii/strategiya-za-cifrovizaciya-na-zemedelieto-i-selskite-rajoni-na-/. (Bg). The Digital Economy and Society Index (DESI). (2022). https://digital-strategy.ec.europa.eu/bg/policies/desi МЗ. Tamirat, T. W., Pedersen, S. M. & Lind, K. M. (2018). Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany. Acta Agriculturae Scandinavica, Section B - Soil & Plant Science, 68, 349 - 357. Tey, Y. S. & Brindal, M. (2012). Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision Agriculture, 13, 713 - 730. |
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| Date published: 2025-06-25
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