Short message service-based agricultural advisory systems: usage patterns and farmer response behavior

Introduction

As Aker [1] noted in her comprehensive review of ICT for agricultural extension, the mobile phone has become the most ubiquitous information technology in rural areas worldwide, yet our understanding of how farmers actually engage with mobile advisory content remains surprisingly shallow. Most evaluations of SMS agricultural services focus on whether messages were delivered and whether adoption rates changed, treating the space between delivery and action as a black box [2] . What happens when a farmer receives an advisory text message? Do they read it immediately or later? Do they act on it, share it, save it, or ignore it? And what determines which of these responses occurs? The Baltic states offer an unusual research context for these questions. Latvia has near-universal mobile phone penetration even in rural areas, relatively high farmer literacy levels, and an agricultural sector dominated by small to medium-sized family farms transitioning from Soviet-era collective structures to market-oriented production [3] . The Latvian Rural Advisory Centre launched an SMS advisory service in 2018, providing registered farmers with weekly text messages covering crop management, pest alerts, market information, and regulatory updates. By 2021, the service had over 3,400 registered subscribers across the country [4] . This research analyzed usage patterns and response behavior among SMS advisory subscribers in three Latvian regions over a 12-month period, combining objective server log data with farmer self-reports to develop a comprehensive model of advisory response behavior.

Conceptual Framework

The research adapted the Elaboration Likelihood Model from persuasion research to the agricultural SMS context [5] . Central route processing occurs when farmers carefully evaluate message content against their current farming situation, leading to considered behavioral responses. Peripheral route processing occurs when farmers use heuristic cues such as message source credibility and timing convenience to make quick judgments about message relevance. Message characteristics (timing, relevance, clarity) and farmer characteristics (literacy, experience, farm type) were hypothesized to determine which processing route predominated and consequently which response type resulted.

Materials and Methods

Materials

The research covered three Latvian agricultural regions: Zemgale, Kurzeme, and Latgale, between January and December 2021. The institutional base was the Department of Agricultural Economics, University of Latvia, Jelgava. Materials included server log data from the Latvian Rural Advisory Centre SMS platform recording delivery timestamps, read receipts, and subscriber interactions for all www.extensionjournal.com messages sent during the research period. A structured quarterly survey (38 items, Cronbach alpha = 0.82) measured response behavior, perceived message value, and advisory impact on farming decisions. The sample comprised 278 registered subscribers who consented to participate. Ethical approval was obtained from the University of Latvia Research Ethics Committee (Protocol UL/REC/2020-089, approved November 2020).

Methods

A longitudinal observational design tracked message interactions over 12 months. Server logs provided objective data on message delivery, read rates, and read timing. Quarterly surveys assessed self-reported response behavior categorized into four types: immediate action (behavioral change within 48 hours), delayed action (change within one month), information storage (saved for future reference), and no response. Analysis included descriptive statistics of monthly usage patterns, repeated measures ANOVA testing seasonal variation in read and action rates, and multiple regression identifying predictors of farmer response type. Qualitative data from 24 semi-structured interviews explored the reasoning behind different response patterns [6] . The model explained 47.2% of variance in action rates (adjusted R-squared = 0.472). Content relevance to the current farming season was the strongest predictor (beta = 0.41), followed by message timing (beta = 0.38). Messages sent on Monday and Tuesday mornings received higher action rates than those sent later in the week, likely because farmers plan weekly activities at the start of the work week. Read rates and action rates followed parallel seasonal curves but with a persistent gap averaging 47.6 percentage points. The gap narrowed during peak growing season (March to June) when farming decisions were most time-sensitive, and widened during off-season months when advisory content was less immediately actionable.

Results

Comprehensive Interpretation

The data reveal that SMS advisory effectiveness is fundamentally seasonal. The same message content can produce dramatically different response rates depending on whether it arrives during a period of active farming decision-making. Content relevance and timing together explained more variance in farmer response than all farmer demographic characteristics combined, suggesting that system design factors are more amenable to optimization than farmer characteristics.

Discussion

The seasonal pattern in SMS advisory response confirms what Cole and Fernando [7] observed in Indian agricultural advisory services: farmers engage most actively with information that aligns with their current decision-making needs. The Latvian data extend this finding by showing that even read behavior, not just action, follows seasonal patterns. During off-season months, farmers shifted toward information storage behavior, saving messages as future reference rather than acting immediately. This suggests that SMS systems should differentiate between actionable season-specific advisories and reference content designed for storage and later retrieval [8] . The importance of message timing within the week has not been previously documented in agricultural SMS research. The Monday-Tuesday effect likely reflects farm planning routines where weekly work schedules are set at the beginning of the week. Extension services typically design message schedules around content production workflows rather than farmer decision-making rhythms, representing a missed optimization opportunity [9] . Aligning delivery timing with farmer planning cycles could improve action rates without any change to message content itself.

Policy Recommendations

References

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