Is agricultural digitization a reality among smallholder farmers in Africa? Unpacking farmers' lived realities of engagement with digital tools and services in rural Northern Ghana
Bibliographic record
Abstract
Abstract Background Digital technologies are promoted as transformational for smallholders in Africa through the potential to enhance access to knowledge, increase productivity and food security. Despite the anticipations for agricultural digitalization in Africa, smallholders' engagement with digitalization is empirically underexplored. Hence, we surveyed 1565 rural farmers in Northern Ghana to explore how farmers interact with digital tools and services, and the variations in their engagements. Results We found that despite the growing array of digital opportunities (with diverse tools and services available to farmers), farmers are mainly confined to simple devices (mobile phones, radio, and TV) as access to digital resources, including the internet remains limited. Meanwhile, the main sources of digitalization services for smallholders remain largely the highly subisidized, development-orieted. NGOs and private-sector projects, which generally leverage SMS, Interactive Voice Response (IVR), radio, or field agents to reach farmers. Nonetheless, participation in digitalization services remains limited, unimpressive at best, and often fades over time because of weak building blocks evident in low literacies, lack of digital competencies and the limited access to digital resources. Conclusions Thus, full-scale digitalization remains a distant goal, and transformation claims are disconnected from smallholders' lived realities. However, opportunities exist to create a ‘digitalization for smallholders’ that is sensitive to the current and future structural limitations of smallholder agriculture, including low literacy and limited access to digital tools, to make agriculture digitalization reach its full potential in Africa.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".