Social media adoption by extension agents for farmer outreach: drivers, barriers, and effectiveness outcomes

Introduction

Between 2015 and 2023, smartphone ownership among Portuguese farmers rose from roughly 41% to over 78%, fundamentally changing how rural populations access and share information [1] . Extension services, however, have been markedly slower to adapt. While farmers scroll through weather forecasts, market prices, and crop management videos on their phones, most extension agents still rely on scheduled farm visits and printed bulletins as their primary communication channels [2] . This disconnect reflects a broader pattern across European agricultural advisory systems. Digital tools are widely available but unevenly adopted by the institutional structures responsible for knowledge transfer [3] . Some individual agents have embraced social media on their own initiative, creating WhatsApp advisory groups or posting technique videos on YouTube. But these remain isolated experiments rather than coordinated strategies, leaving the extension system as a whole underequipped for a digital farming population [4] . Understanding why some agents adopt social media while others resist it and whether adoption actually improves farmer outreach requires examining both individual and institutional factors. Technology acceptance research suggests that perceived usefulness and ease of use drive adoption decisions, but organizational context, management support, and professional identity also play roles that generic technology models may underestimate [5,6] . This research investigated the drivers and barriers of social media adoption among Portuguese extension agents and measured the outreach and satisfaction effects of digital platform use on farmer engagement with advisory services [7] .

Material and Methods

Material

The research was conducted across seven agricultural districts in central and southern Portugal: Lisbon, Santarem, Leiria, Evora, Beja, Portalegre, and Faro. These districts span the country's major farming zones from intensive horticultural systems near Lisbon to extensive olive and cork oak landscapes in the Alentejo and irrigated citrus operations in the Algarve. Data were collected between February and October 2023. The University of Lisbon School of Agriculture Ethics Committee approved all protocols (Ref: ISA-EC/2023-032, January 2023). Survey instruments included a 36-item agent questionnaire covering technology use patterns, adoption drivers, barriers, and professional demographics, and a 22-item farmer questionnaire assessing information channel preferences and satisfaction [8] . www.extensionjournal.com Methods: A total of 142 extension agents completed the survey across the seven districts. Agents were classified as high adopters (regular professional social media use, n=55), moderate adopters (occasional use, n=41), or low/nonadopters (rare or no use, n=46). Social media activity was verified through platform analysis for consenting agents (n=87). Follow-up structured interviews with 86 farming households assessed information receipt patterns and satisfaction across communication channels. Adoption predictors were modeled using hierarchical multiple regression with perceived usefulness, perceived ease of use, institutional support, self-efficacy, and demographic variables as predictors. Outreach effectiveness was compared across adopter categories using independent ttests and ANOVA. Farmer satisfaction was assessed on a 5point scale across five information quality dimensions [9,10] . Social media adopters reached 2.4 times more farmers monthly and covered nearly twice the geographic area. However, farmers rated face-to-face interaction quality higher than digital contact (3.94 vs. 3.41), indicating that social media extends but does not replace the depth of personal advisory relationships. extension contexts where mobile-first strategies have been deliberately promoted [11,12] . The dominance of perceived usefulness as an adoption predictor aligns with decades of technology acceptance research, but the strong role of institutional support (β=0.34) highlights something specific to professional contexts: agents adopt tools that their organizations visibly endorse and resource [5,13] . The outreach multiplier effect 2.4 times more farmers reached by adopting agents represents a meaningful efficiency gain that extension systems operating under budget pressure cannot afford to ignore [14] . But the finding that interaction quality scored higher for face-to-face contact introduces an important nuance. Social media excels at information broadcasting weather alerts, pest warnings, market updates but falls short when farmers need personalized problem-solving or nuanced technical advice [15, 16] .

Results

The age effect warrants careful interpretation. Rather than viewing older agents as resistant, extension managers should recognize that agents trained in an era of farm-visit extension may lack both the technical skills and the professional framework to integrate digital tools into their practice [17] . Targeted training that demonstrates practical relevance rather than generic digital literacy programs is more likely to shift adoption behavior among experienced agents [18] .

Policy implications

Portuguese agricultural policy should embed digital extension as a funded mandate rather than leaving social media adoption to individual initiative. This means allocating budget for platform management, content creation training, and data connectivity in rural advisory offices. Extension performance metrics should include digital outreach indicators alongside traditional visit counts. EU Common Agricultural Policy advisory provisions should explicitly recognize digital channels as eligible extension activities to incentivize member state investment [19,20] .

Future research directions

Three research priorities emerge from these findings. First, longitudinal tracking of how social media adoption evolves within extension organizations as younger agents enter the workforce. Second, randomized controlled trials comparing farmer outcomes between digitally enhanced and traditional-only extension delivery. Third, examination of information quality and misinformation risks when extension content circulates through informal social media sharing beyond the original intended audience [21] .

Conclusions

This research showed that social media adoption among Portuguese extension agents remains modest (38.7%) but produces clear outreach advantages when it occurs. Perceived usefulness and institutional support drive adoption more than individual technical skills, suggesting that organizational commitment matters more than agent training alone. Social media extends reach and improves information timeliness but does not match the interaction depth of face-to-face advisory contact. The practical recommendation is to position social media as a complement that handles information broadcasting efficiently, freeing agent time for the personalized advisory work where human contact remains irreplaceable.

References

1. INE Portugal. Agricultural census 2019: Digital technology adoption in farming households. Instituto Nacional de Estatistica. 2020;1-124.
2. Trendov NM, Varas S, Zeng M. Digital technologies in www.extensionjournal.com agriculture and rural areas: Status report. FAO Publications. 2019;1-152.
3. Klerkx L, Jakku E, Labarthe P. A review of social science on digital agriculture, smart farming and Agriculture 4.0. NJAS-Wageningen Journal of Life Sciences. 2019;90-91:100315.
4. Munthali N, Leeuwis C, van Paassen A, et al. Innovation intermediation in a digital age. NJASWageningen Journal of Life Sciences. 2018;86-87:6876.
5. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;13(3):319-340.
6. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Quarterly. 2003;27(3):425-478.
7. Jain R, Garg A, Khera S. Adoption of social media in agricultural extension: A systematic review. Journal of Agricultural Education and Extension. 2021;27(5):545563.
8. Eurostat. Farm structure survey: Agricultural advisory services in EU member states. European Commission Statistical Reports. 2020;1-86.
9. Rogers EM. Diffusion of Innovations. Free Press, New York. 2003;5th edition:1-551.
10. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. Cengage Learning. 2019;8th edition:1-813.
11. Sulaiman VR, Davis K, Holt D. Is extension fit for purpose? Considerations for policy and practice. GFRAS Policy Brief 4. 2020;1-8.
12. Babu SC, Joshi PK. Agricultural extension reforms in South Asia. Academic Press. 2019;1-382.
13. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991;50(2):179-211.
14. Anderson JR, Feder G. Agricultural extension: Good intentions and hard realities. The World Bank Research Observer. 2004;19(1):41-60.
15. Leeuwis C. Communication for Rural Innovation: Rethinking Agricultural Extension. Blackwell Science Publishing. 2004;3rd edition:1-412.
16. Christoplos I. Mobilizing the potential of rural and agricultural extension. FAO and Global Forum for Rural Advisory Services. 2010;1-65.
17. Labarthe P, Laurent C. Privatization of agricultural extension services in the EU. Food Policy. 2013;38:240-252.
18. Birner R, Davis K, Pender J, Nkonya E. From best practice to best fit. Journal of Agricultural Education and Extension. 2009;15(4):341-355.
19. European Commission. The future of food and farming: Communication from the Commission. COM(2017) 713 Final. 2017;1-26.
20. Swanson BE, Rajalahti R. Strengthening agricultural extension and advisory systems. World Bank Agriculture and Rural Development Discussion Paper 45. 2010;1-182.
21. Spielman DJ, Ekboir J, Davis K. The art and science of innovation systems inquiry. Technology in Society. 2009; 31(4):399-405.