PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 6, 20260 citationsOpen Access

Development and Adoption Dynamics of Digital Agricultural Extension Services in Southern Mozambique: A Longitudinal Study

View Full Paper
MGMakoni Gadiela

Key Points

  • The study aims to assess the usage and satisfaction levels of digital agricultural extension services among farmers in Southern Mozambique.
  • Employs a mixed-methods approach combining quantitative surveys and qualitative interviews
  • Surveyed 500 farmers over a two-year period
  • Analyzed satisfaction levels and service usage patterns
  • Utilized logistic regression to evaluate predictors of high satisfaction
  • Reported an average adoption rate of 42% among farmers
  • Users with frequent service engagement were more satisfied
  • Farmers using digital platforms for over six months were 1.5 times more likely to report high satisfaction

Abstract

Digital agricultural extension services have emerged as a promising tool for enhancing productivity and sustainability in rural communities of Southern Mozambique. A mixed-methods approach combining quantitative surveys (n=500) and qualitative interviews was employed to assess service usage patterns and perceptions over a two-year period. Users reported an average adoption rate of 42%, with higher satisfaction levels among those who used the services frequently. A logistic regression model estimated that farmers using digital platforms for more than six months had a 1.5 times greater likelihood of reporting high satisfaction (95% CI: 1.1, 2.0). Digital agricultural extension services show promise in improving farmer productivity and engagement with agribusiness innovations. Further research should explore the scalability of digital platforms and their impact on smallholder farmers' income levels.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Makoni Gadiela (2008) studied this question.

synapsesocial.com/papers/69aa70a9531e4c4a9ff5a96bhttps://doi.org/10.5281/zenodo.18869350
Ask AI
Helpful
Bookmark
Share
View Full Paper