This paper investigates how automation technologies are reshaping agricultural labor, focusing on both labor market dynamics and changes in work organization. Drawing on a systematic literature review of 33 peer-reviewed studies and following the PRISMA protocol, the analysis adopts an inductive approach to extract empirical patterns. Findings reveal a dual transformation: while automation reduces the demand for low-skilled, repetitive labor, it simultaneously generates new opportunities for workers with technical and cognitive skills. The study identifies key risks—displacement, polarization, and digital exclusion for vulnerable groups—alongside potential benefits such as professional upskilling and improved working conditions. By introducing a dual-level thematic framework, Occupational and Worker level, the paper provides a granular understanding of labor impacts across macro and micro dimensions. It offers a critical and interdisciplinary contribution to ongoing debates on the social consequences of technological change, with implications for policy, workforce development, and equitable innovation in the agricultural sector.
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Alarcón, C. (2021). Agrarian questions, digitalisation of the countryside, immigrant labour in agriculture and the official discourses on rural development in the Uppsala region, Sweden. Rivista di Economia Agraria, 76(1), 19–32. https://doi.org/10.36253/rea-12824
Apicella, A., & Tarabella, A. (2024). Innovation in farming: Drivers of adoption of Precision Agriculture amongst farmers. International Journal on Food System Dynamics, 15(6), 671–683. https://doi.org/10.18461/ijfsd.v15i6.n7
Apicella, A., & Tarabella, A. (2024b). The technological translation from Industry 4.0 to Precision Agriculture: Adoption and perception of Italian farmers. In Volume XL - L’aziendalismo crea valore! Il ruolo dell'accademia nelle sfide della società, dell'economia e delle istituzioni (pp. 1-20).
Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis (OECD Social, Employment and Migration Working Papers No. 189). OECD Publishing. https://doi.org/10.1787/5jlz9h56dvq7-en
Assante, D., Castro, M., Crudele, M., Alonso, G., & Vicentini, F. (2018). Internet of Things education: Labor market training needs and national policies. In 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 1273–1279). IEEE. https://doi.org/10.1109/EDUCON.2018.8363459
Bazargani, K., & Deemyad, T. (2024). Automation’s impact on agriculture: Opportunities, challenges, and economic effects. Robotics, 13(2), 33. https://doi.org/10.3390/robotics13020033
Binswanger, H. P. (1986). Agricultural mechanization: A comparative historical perspective. The World Bank Research Observer, 1(1), 27–56.
Bissadu, K. D., Sonko, S., & Hossain, G. (2025). Society 5.0 enabled agriculture: Drivers, enabling technologies, architectures, opportunities, and challenges. Information Processing in Agriculture, 12(1), 112–124. https://doi.org/10.1016/j.inpa.2024.04.003
Campos González, J., Williams, J., Stojanovic, M., & Phillips, M. (2025). How fast is Agriculture 4.0 advancing in the UK? An analysis using employment routine intensity. Working Paper, 52–60.
Carbonero, F., Ernst, E., & Weber, E. (2018). Robots worldwide: The impact of automation on employment and trade. ResearchGate. https://doi.org/10.13140/RG.2.2.10507.13603
Chadegani, A. A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A comparison between two main academic literature collections: Web of Science and Scopus databases. Asian Social Science, 9(5), 18–26. https://doi.org/10.5539/ass.v9n5p18
Charlton, D., Hill, A. E., & Taylor, J. E. (2022). Automation and social impacts: Winners and losers. Food and Agriculture Organization (FAO). https://doi.org/10.4060/cc2610en
Cheein, F. A., Carelli, R., Larrauri, E., & Perez, J. (2015). Human-robot interaction in precision agriculture: Sharing the workspace with service units. In 2015 IEEE International Conference on Industrial Technology (ICIT) (pp. 2134–2139). IEEE. https://doi.org/10.1109/ICIT.2015.7125113
Cho, Y., Moon, J., & Yoe, H. (2010a). A context-aware service model based on workflows for u-agriculture. In Computational Science and Its Applications – ICCSA 2010 (Lecture Notes in Computer Science, pp. 258–268). Springer. https://doi.org/10.1007/978-3-642-12179-1_23
Cho, Y., Moon, J., Lee, J., & Yoe, H. (2010b). A workflow service scenario based on uWDL for smart agriculture. In 2010 5th International Conference on Embedded and Multimedia Computing (EMC) (pp. 1–6). IEEE. https://doi.org/10.1109/EMC.2010.5575749
Christiaensen, L., Rutledge, Z., & Taylor, J. E. (2020). The future of work in agriculture: Some reflections (World Bank Policy Research Working Paper No. 9193). World Bank. https://doi.org/10.1596/1813-9450-9193
Christiaensen, L., Rutledge, Z., & Taylor, J. E. (2021). Viewpoint: The future of work in agri-food. Food Policy, 99, 101963. https://doi.org/10.1016/j.foodpol.2020.101963
FAO. (2022). The State of Food and Agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems. Food and Agriculture Organization of the United Nations.
Fassakhova, G. R., Zarubina, A. Y., Voronina, I. S., & Rudenko, M. Y. (2020). Staffing of agrarian economy: Challenges and solutions. BIO Web of Conferences, 27, 00146. https://doi.org/10.1051/bioconf/20202700146
Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107
Filippi, E., Bannò, M., & Trento, S. (2023). Automation technologies and their impact on employment: A review, synthesis and future research agenda. Technological Forecasting and Social Change, 191, 122448. https://doi.org/10.1016/j.techfore.2023.122448
Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerisation? Oxford Martin Programme on Technology and Employment. https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
Fuentes-Peñailillo, F., Soto, M., Díaz, P., Torres, A., & Rodríguez, C. (2024). New generation sustainable technologies for soilless vegetable production. Horticulturae, 10(1), 49. https://doi.org/10.3390/horticulturae10010049
Grift, T. E. (2008). A review of automation and robotics for the bioindustry. Journal of Biomechanical Engineering, 1, 37–54.
Gwagwa, A., Kazim, E., Shawe-Taylor, J., Hudzia, B., Taddeo, M., & Floridi, L. (2021). Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. Patterns, 2(12), 100381. https://doi.org/10.1016/j.patter.2021.100381
Hansen, B. G., & Stræte, E. P. (2020). Dairy farmers’ job satisfaction and the influence of automatic milking systems. NJAS: Wageningen Journal of Life Sciences, 92, 100328. https://doi.org/10.1016/j.njas.2020.100328
Homrich, A. S., Theodoro, D. S., & Carvalho, M. M. de. (2017). PSS creating business for sustainability: The Brazilian olive oil case in Mantiqueira community. Procedia CIRP, 64, 405–410. https://doi.org/10.1016/j.procir.2017.03.110
Kron, R. V., Shapovalov, A. G., Bratukhin, A. A., & Galkin, A. N. (2019). Analysis and forecasting of the availability of sufficient personnel complexities of the digital economy mechanisms in the agrarian sector of the Stavropol territory. IOP Conference Series: Earth and Environmental Science, 315(3), 032010. https://doi.org/10.1088/1755-1315/315/3/032010
Lowenberg-DeBoer, J., Huang, I. Y., Grift, T. E., Miller, D., Schimmelpfennig, D., & Reichardt, M. (2020). Economics of robots and automation in field crop production. Precision Agriculture, 21(2), 278–299. https://doi.org/10.1007/s11119-019-09667-5
Ludwig-Ohm, S., Henn, M., Müller, M., Frank, S., Böttcher, S., Schock, R., Böttrich, B., & Mempel, H. (2023). The contribution of Horticulture 4.0 innovations to more sustainable horticulture. Procedia Computer Science, 217, 465–477. https://doi.org/10.1016/j.procs.2022.12.242
Marinoudi, V., Pearson, S., Godwin, R. J., & Fountas, S. (2024). Adapting to the agricultural labor market shaped by robotization. Sustainability, 16(16), 7061. https://doi.org/10.3390/su16167061
Marinoudi, V., Pearson, S., Godwin, R. J., & Fountas, S. (2021). The future of agricultural jobs in view of robotization. Sustainability, 13(21), 12109. https://doi.org/10.3390/su132112109
Marinoudi, V., Pearson, S., Godwin, R. J., & Fountas, S. (2019). Robotics and labour in agriculture: A context consideration. Biosystems Engineering, 184, 111–121. https://doi.org/10.1016/j.biosystemseng.2019.06.013
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535
Perez-Silva, R., & Campos, J. (2021). Agriculture 4.0? Studying the evidence for automation in Chilean agriculture. International Journal of Agriculture and Natural Resources, 48(3), 233–247. https://doi.org/10.7764/ijanr.v48i3.2339
Petcu, M. A., Popescu, G. H., V?duva, S., & Ionescu, L. (2024). Integrating artificial intelligence in the sustainable development of agriculture: Applications and challenges in the resource-based theory approach. Electronics, 13(23), 4580. https://doi.org/10.3390/electronics13234580
Posadas, B. C. (2012). Economic impacts of mechanization or automation on horticulture production firms’ sales, employment, and workers’ earnings, safety, and retention. HortTechnology, 22(3), 388–401. https://doi.org/10.21273/horttech.22.3.388
Quendler, E., Trieb, K., & Nimmerichter, A. (2017). Validation of automated detection of physical and mental stress during work in a Hühnermobil 225. Annals of Agricultural and Environmental Medicine, 24(2), 329–331. https://doi.org/10.5604/12321966.1235183
Rijnks, R. H., Crowley, F., & Doran, J. (2022). Regional variations in automation job risk and labour market thickness to agricultural employment. Journal of Rural Studies, 91, 10–23. https://doi.org/10.1016/j.jrurstud.2021.12.012
Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., & Fraser, E. D. G. (2019). Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68, 112–122. https://doi.org/10.1016/j.jrurstud.2019.01.023
Schröder, S., Lehberger, M., & Sparke, K. (2022). The impact of digitalization and automation on horticultural employees – A systematic literature review and field study. Journal of Rural Studies, 95, 560–569. https://doi.org/10.1016/j.jrurstud.2022.09.016
Singh, R. K., Berkvens, R., & Weyn, M. (2021a). AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey. IEEE Access, 9, 136253–136283. https://doi.org/10.1109/access.2021.3116814
Singh, V. K., Ghosh, R., Chatterjee, R., & Tiwari, K. (2021b). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126(6), 5113–5142. https://doi.org/10.1007/s11192-021-03948-5
Sparrow, R., & Howard, M. (2021). Robots in agriculture: Prospects, impacts, ethics, and policy. Precision Agriculture, 22(3), 818–833. https://doi.org/10.1007/s11119-020-09757-9
Sunding, D., & Zilberman, D. (2001). The agricultural innovation process: Research and technology adoption in a changing agricultural sector. In B. L. Gardner & G. C. Rausser (Eds.), Handbook of Agricultural Economics, Volume 1A: Agricultural Production (pp. 207–261). Elsevier.
Tauger, M. B. (2010). Agriculture in world history. Taylor and Francis Group. https://doi.org/10.4324/9780203847480
Thomas, D. R. (2006). A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation, 27(2), 237–246. https://doi.org/10.1177/1098214005283748
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence?informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375
Trivelli, L., Apolito, L., Chiarello, F., Rana, R., & Tarabella, A. (2019). From precision agriculture to Industry 4.0. British Food Journal, 121(8), 1730–1743. https://doi.org/10.1108/bfj-11-2018-0747
United States Department of Agriculture (USDA). (2024). Selected charts from Ag and Food Statistics: Charting the Essentials 2024 (Administrative Publication Number 121). https://www.ers.usda.gov/publications/pub-details/?pubid=106977
Zhu, J., & Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321–335. https://doi.org/10.1007/s11192-020-03387-8
Apicella, A. (2025). Automation in Agriculture: Occupational Trends, Worker Outcomes, and Labor Market Implications. International Journal of Academic Research in Business and Social Sciences, 15(8), 398–418.
Copyright: © 2025 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
