Effectiveness of agricultural advisory call centers in addressing real-time farmer information needs

Introduction

The problem is timing. Traditional extension models operate on weekly or monthly visit cycles, yet many farming decisions when to spray, whether to irrigate, how to respond to sudden pest pressure require answers within hours [1] . This mismatch between the rhythm of extension delivery and the tempo of farm-level decision making has driven interest in remote advisory channels, particularly phonebased services [2] . Call centers for agricultural advice emerged in the early 2000s, with India's Kisan Call Centre launching in 2004 as one of the largest experiments [3] . Similar services appeared in Kenya, Ethiopia, and Bangladesh over the following decade [4] . In Europe, where extension systems are more fragmented and often privatized, call center models have been less common but not absent. Spain's agricultural advisory system, organized through regional offices and cooperatives, began piloting centralized helplines in Andalusia around 2018 [5] . Evidence on call center effectiveness remains mixed. Cole and Fernando found positive yield effects among Indian farmers using mobile advisory services [6] , but Aker's review cautioned that information delivery alone doesn't guarantee behavior change [7] . Few evaluations have examined which types of queries call centers handle well versus poorly, or how seasonal variation affects resolution capacity. This research aimed to fill that gap by evaluating query resolution patterns and farmer satisfaction with call center services in Andalusia.

Theoretical Framework

The research was grounded in the Information Needs Assessment Model proposed by Kaniki [8] , which distinguishes between dormant information needs (recognized but not acted upon), active needs (prompting information-seeking behavior), and demands (expressed to a specific information source). Call centers primarily capture demands the subset of needs that farmers articulate to a service provider. This framework was combined with service quality theory from Parasuraman's SERVQUAL model [9] , adapted to measure gaps between farmer expectations and perceived service delivery across five dimensions: reliability, responsiveness, assurance, empathy, and tangibles.

Material and Methods

Material

Two data sources were used. First, anonymized call log records from the Andalusia Agricultural Advisory Helpline covering 14, 287 farmer inquiries made between January 2022 and December 2023 were obtained with institutional permission. Each record included query topic, call duration, resolution status, advisor ID, and seasonal coding. Second, a cross-sectional satisfaction survey was administered to 324 www.extensionjournal.com farmers who had used the service at least three times during the research period, selected through systematic sampling from the call log database. Data collection was based at the University of Cordoba, School of Agricultural Engineering.

Methods

Ethical Considerations

Call log data were fully anonymized before transfer to the research team, with farmer and advisor identifiers replaced by coded numbers. Survey participants provided written informed consent and were assured that individual responses would not be shared with call center management. Farmers were informed they could skip any question and withdraw at any point without affecting their access to the advisory service.

Results

Discussion

The results suggest call centers work well for factual, bounded queries but struggle with context-dependent problems. Market prices and seed selection both involving relatively standardized information achieved resolution rates above 82%. Irrigation scheduling, which depends heavily on local soil, microclimate, and crop stage, was the most problematic. This mirrors findings from India's Kisan Call Centre, where location-specific agronomic advice proved difficult to deliver remotely [3] . The seasonal variation in resolution rates has staffing implications. Summer's high pest identification resolution (91.3%) coincided with having entomology specialists on call. Winter's poor irrigation performance suggests the need for hydrologist availability during that period. Call centers that match advisor specialization to seasonal demand patterns should see improved performance [10] . The follow-up gap deserves attention. A satisfaction score of 3.12 for follow-up support means farmers felt largely abandoned after hanging up. Integrating call center records with field visit scheduling could address this flagging complex queries for in-person follow-up rather than treating phone advice as the final touchpoint [11] .

Practical Applications

Advisory call centers in Mediterranean agricultural contexts should implement seasonal staffing rosters matching specialist availability to predictable query peaks. Irrigation queries during winter months need dedicated hydrological support. A triage protocol should distinguish between queries resolvable by phone and those requiring field follow-up, with automatic referral pathways to local extension offices for complex cases. Call duration benchmarks should be flexible by topic rather than applying uniform time limits.

Conclusion

This research showed that agricultural advisory call centers effectively handle routine and factual queries but face clear limitations with context-dependent agronomic problems. Resolution rates varied substantially by topic and season, with market prices achieving the highest success and irrigation scheduling the lowest. Farmer satisfaction was strong for communication quality but weak for follow-up support. Extension systems incorporating call centers should treat them as a first-response layer within a broader advisory architecture rather than as standalone solutions, with explicit referral protocols connecting phone advice to field-level support.

References

1. Aker JC, Ghosh I, Burrell J. The promise (and pitfalls) of ICT for agriculture initiatives. Agricultural Economics. 2016;47(S1):35-48.
2. Anderson JR, Feder G. Agricultural extension: good intentions and hard realities. World Bank Research Observer. 2004;19(1):41-60.
3. Mittal S, Mehar M. An assessment of farmer information needs and Indian Kisan Call Centre. Journal of Agricultural Science. 2016;8(5):65-72.
4. Baumgartner P, Elias M. Agricultural information access for smallholder farmers in sub-Saharan Africa. Information Development. 2018;34(3):230-245.
5. Garzon-Diaz E, Lopez-Iglesias E. Advisory services reform in Mediterranean agriculture. Spanish Journal of Agricultural Research. 2020;18(1):e0101.
6. Cole SA, Fernando AN. Mobile'izing agricultural advice: technology adoption and extension. American Economic Journal: Applied Economics. 2021;13(1):6898.
7. Aker JC. Dial "A" for agriculture: a review of information and communication technologies for agricultural extension. Agricultural Economics. 2011;42(6):631-647.
8. Kaniki AM. Agricultural information needs in developing countries. Information Development. 2003;19(2):126-131.
9. Parasuraman A, Zeithaml VA, Berry LL. A conceptual model of service quality and its implications. Journal of Marketing. 1985;49(4):41-50.
10. Sulaiman VR, Hall A, Kalaivani NJ, et al. Necessary but not sufficient: information and communication technology and its role in rural innovation. Research Into Use Discussion Paper. 2011;No.16:1-??.
11. Faure G, Desjeux Y, Gasselin P. New challenges in agricultural advisory services from a research perspective. Journal of Agricultural Education and Extension. 2012;18(5):461-474.
12. Feder G, Birner R, Anderson JR. The private sector's role in agricultural extension systems. World Development. 2011;39(10):1781-1790.
13. Nakasone E, Torero M, Minten B. The power of information: the ICT revolution in agricultural development. Annual Review of Resource Economics. 2014;6:533-550.
14. Kiiza B, Pederson G. ICT-based market information and adoption of agricultural seed technologies. Telecommunications Policy. 2012;36(4):253-259.
15. Raj S, Maru A. Knowledge management in agricultural extension services in Asia. Journal of Agricultural Extension and Rural Development. 2018;10(8):158167.
16. Rivera WM. Agricultural extension in transition worldwide: structural, financial and managerial reform. Public Administration and Development. 2001;21(2):151-159.
17. Christoplos I, Kidd A. Guide for monitoring, evaluation and joint analyses of pluralistic extension support. Neuchatel Group. 2000:1-48.