The Role of Social Relations on Sustainable Agricultural Practices and Innovation Adoption among Smallholder Orange Farmers in Muheza District, Tanzania
Bibliographic record
Abstract
This study aimed to examine the role of social relations in influencing sustainable agricultural practices, innovation adoption, and market participation among smallholder orange farmers in Mkuzi Village, Muheza District, Tanzania. Specifically, the study investigated: first how trust, cooperation, and networks shape productivity and innovation uptake; second, the barriers posed by mistrust and weak social ties; and third, the institutional and social interventions that can support sustainable farming outcomes. A mixed-methods design was employed, integrating both qualitative and quantitative approaches. Data were collected through household surveys (n = 60), semi-structured interviews, focus group discussions, and participant observation. Quantitative data were analysed using descriptive statistics and regression analysis via SPSS Version 25, while qualitative data were thematically coded using NVivo 12. Findings reveal that strong social relations, characterized by trust, cooperation, and active group membership, were associated with higher adoption of improved inputs, greater market access, and increased resilience. Conversely, weak social networks, insecure land tenure, and theft undermined innovation and investment, particularly among smallholder farmers. The implications of these results underscore the need to strengthen local institutions, promote inclusive cooperatives, improve land tenure systems, and rebuild community trust. The study recommends that policymakers and development practitioners integrate social capital considerations into agricultural programs to enhance sustainability, innovation uptake, and rural livelihoods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".