A missing element in the practice of digital farming: farmer access to telecommunication and internet infrastructure in Ghana and Sub-Saharan Africa
Bibliographic record
Abstract
Governments in Sub-Sahara Africa (SSA) are introducing digital tools and advisory services into farming landscapes. In this paper, we explore the region’s readiness for digital agriculture through a theoretical lens of material elements of social practices. We reviewed secondary data on telecommunication infrastructure from two databases (GSMA database and Our World in Data) and complemented it with a case study of smallholder farmers’ (N=1565) access to mobile phones and internet services in Northern Ghana. Our analysis show that multiple infrastructural (material) challenges persist for agriculture digitalization even in some of the perceived digitally advanced countries in Africa. Farmer access to telecommunication systems and services relevant for digital farming remain relatively limited and unequal. In the case of Ghana, farmer access to digital tools is moderated by socio-economic characteristics. In light of these findings, we argue that for digital agriculture practice(s) to manifest in its true sense, governments must deliberately work to lay a foundation for widespread access to digital material infrastructure and tools upon which smallholder services are built. Governments must prioritize expanding telecommunication and internet infrastructure, and leverage private actors to build required digital services.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".