Farmer attitude formation toward extension services and its influence on advisory seeking behavior

Introduction

A farmer in Punakha Valley may live within walking distance of an extension office and still never walk through the door. Another farmer in a remote Haa district village might travel hours to attend a training session organized by the same service. The difference between these two farmers isn't distance or opportunity it's attitude [1] . How farmers feel about extension services determines whether they seek advisory contact, and those feelings are shaped by accumulated experiences, social signals, and perceptions of service quality that extension planners rarely measure or manage [2] . Attitude, in the psychological sense, refers to a learned predisposition to respond consistently favorably or unfavorably toward an object, person, or institution [3] . In extension contexts, farmer attitude encompasses beliefs about whether advisory services offer useful information (cognitive component), feelings about the experience of interacting with extension agents (affective component), and readiness to actively seek out or avoid advisory contact (behavioral component) [4] . These three dimensions together determine the farmer's overall orientation toward the extension system. Research on extension effectiveness has overwhelmingly focused on supply-side factors agent training, technology packages, delivery methods while treating farmer demand as relatively passive [5] . The assumption that farmers will engage with extension if services are made available underlies most extension reform programs. But availability and utilization are different things, and the gap between them is largely an attitudinal gap [6] . This research investigated how attitudes toward extension form among Bhutanese farmers and how strongly those attitudes predict actual advisory seeking behavior and technology adoption outcomes [7,8] .

Theoretical framework:

The research drew on Ajzen's Theory of Planned Behavior, which positions attitude as a primary predictor of behavioral intention alongside subjective norms and perceived behavioral control [9] . In extension contexts, attitude toward extension services functions as the evaluative lens through which farmers judge whether seeking advisory contact is worth the effort. The framework was extended with Petty and Cacioppo's Elaboration Likelihood Model, which distinguishes between central and peripheral routes to attitude formation [10] . Central processing occurs when farmers evaluate extension based on direct experience with service quality, while peripheral processing involves relying on social cues like neighbor opinions or agent appearance. This dual-route model helps explain why some attitude antecedents (direct experience) carry more predictive weight than others (social influence).

Material and Methods

Field research design:

A cross-sectional survey design was implemented across four dzongkhags in western and central www.extensionjournal.com Bhutan: Punakha, Wangdue Phodrang, Trongsa, and Bumthang. These districts were selected to represent varied agroecological zones from subtropical rice-growing valleys in Punakha (1,200m elevation) to temperate buckwheat and potato zones in Bumthang (2,600m). The four districts also differ in extension service density, with Punakha having the highest agent-to-farmer ratio (1:380) and Bumthang the lowest (1:720). Respondents were selected using stratified random sampling with gewogs (sub-district blocks) as primary sampling units and farming households as secondary units [7] . Technology adoption was assessed through a checklist of 12 recommended practices promoted by the Department of Agriculture [11] .

Material

Methods

Surveys were administered face-to-face to 284 farming households (Punakha: 82, Wangdue Phodrang: 74, Trongsa: 68, Bumthang: 60). Multiple regression analysis modeled attitude formation with past extension experience, social influence, perceived agent credibility, and service accessibility as predictors. A second regression examined attitude as a predictor of advisory seeking frequency, controlling for age, education, farm size, and district. Respondents were divided into attitude quartiles for descriptive comparison of seeking behavior and adoption outcomes. Structural equation modeling tested the full conceptual pathway from attitude antecedents through attitude to behavioral outcomes using AMOS 26 [12] . A clear dose-response pattern emerged: advisory seeking frequency increased nearly fourfold from the lowest to highest attitude quartile. Adoption rates followed a parallel trajectory, with Q4 farmers four times more likely to adopt recommended practices than Q1 farmers.

Results

Discussion

The finding that past extension experience dominates attitude formation (β=0.41) carries a practical warning: first impressions matter enormously. A farmer whose initial encounters with extension were unhelpful, condescending, or irrelevant develops negative attitudes that subsequently reduce the likelihood of future engagement [1,4] . This creates a self-reinforcing cycle where dissatisfied farmers disengage, receive less service, and develop increasingly negative attitudes a pattern difficult to break once established.

Perceived agent credibility (β=0.36) as the second-strongest predictor aligns with communication research showing that source credibility shapes message reception more powerfully than message content [10] . Farmers assess whether an agent knows what they're talking about and whether they can be trusted, and these judgments form quickly and persist stubbornly. Extension systems that invest in agent competence and interpersonal skills may therefore generate larger returns in farmer engagement than systems that focus primarily on expanding geographic coverage [13] .

The relatively weak effect of service accessibility (β=0.16) challenges the assumption underlying many extension reform programs that making services more available will automatically increase farmer uptake. Accessibility matters, but it matters less than the quality of the experience farmers encounter when they do make contact. Extension planners in Bhutan and similar contexts may be directing resources toward the wrong bottleneck [6,14] .

Limitations

The cross-sectional design captures associations but cannot confirm causal direction positive attitudes may cause advisory seeking, but advisory seeking may also reinforce positive attitudes. Self-reported extension contact is subject

Conclusions

This research demonstrated that farmer attitudes toward extension services powerfully predict advisory seeking behavior and technology adoption in Bhutan. Past experience and agent credibility shape attitudes more strongly than service accessibility or social influence, suggesting that quality of engagement matters more than quantity of opportunities. Extension systems seeking to increase farmer participation should prioritize positive early interactions and sustained agent credibility rather than simply expanding the number of access points. Attitude measurement should become a routine monitoring indicator alongside conventional metrics of extension coverage and technology transfer.

References

1. Leeuwis C. Communication for Rural Innovation: Rethinking Agricultural Extension. Blackwell Science Publishing. 2004;3rd edition:1-412.
2. Anderson JR, Feder G. Agricultural extension: Good intentions and hard realities. The World Bank Research Observer. 2004;19(1):41-60.
3. Eagly AH, Chaiken S. The Psychology of Attitudes. Harcourt Brace Jovanovich. 1993;1-794.
4. Ragasa C, Berhane G, Tadesse F, Taffesse AS. Gender differences in access to extension services and agricultural productivity. Journal of Agricultural Education and Extension. 2013;19(5):437-468.
5. Birner R, Davis K, Pender J, Nkonya E. From best practice to best fit: A framework for designing pluralistic advisory services. Journal of Agricultural Education and Extension. 2009;15(4):341-355. www.extensionjournal.com
6. Swanson BE. Global Review of Good Agricultural Extension and Advisory Service Practices. FAO Research and Extension Division. 2008;1-82.
7. Tobgay S, McCullough EB. Agricultural extension in Bhutan: Challenges and opportunities in a changing environment. Journal of Bhutan Studies. 2018;38:2347.
8. Christoplos I. Mobilizing the potential of rural and agricultural extension. FAO and Global Forum for Rural Advisory Services. 2010;1-65.
9. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991;50(2):179-211.
10. Petty RE, Cacioppo JT. The Elaboration Likelihood Model of persuasion. Advances in Experimental Social Psychology. 1986;19:123-205.
11. DeVellis RF. Scale Development: Theory and Applications. SAGE Publications. 2017;4th edition:1262.
12. Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. Cengage Learning. 2019;8th edition:1-813.
13. Rogers EM. Diffusion of Innovations. Free Press, New York. 2003;5th edition:1-551.
14. Davis K. Extension in Sub-Saharan Africa: Overview and assessment of past and current models. Journal of S. 2008;15(3):15-28.