Precision Agriculture Adoption Among Smallholder Women Farmers in Northern Ghana: A Review of Three-Year Impacts
Bibliographic record
Abstract
{ "background": "Precision agriculture techniques are increasingly being adopted by smallholder farmers globally to enhance productivity and sustainability. In northern Ghana, a region known for its arid climate and reliance on rain-fed agriculture, women farmers have shown interest in adopting these technologies despite limited access to resources.", "purposeandobjectives": "This systematic literature review aims to synthesize existing studies examining the adoption of precision agriculture techniques among smallholder women farmers in northern Ghana. The objectives are to identify key factors influencing their uptake and assess the three-year impacts on yields, resource use efficiency, and socio-economic outcomes.", "methodology": "A comprehensive search strategy was employed across multiple databases including Agricola, Web of Science, and Google Scholar. Studies published between and were included if they focused on smallholder women farmers in northern Ghana using precision agriculture techniques. Two reviewers independently screened titles and abstracts, followed by full-text reviews to select studies.", "findings": "The analysis revealed that the adoption of precision agriculture among these farmers was influenced primarily by socio-economic factors such as education levels (r = -0.52) and access to credit (p < 0.01). Notably, there was a positive association between the use of remote sensing data for crop monitoring and yield improvements over three years (β = 0.34, p < 0.05; CI: [0.08, 0.60]).", "conclusion": "The review underscores the importance of addressing socio-economic barriers to precision agriculture adoption among smallholder women farmers in northern Ghana.", "recommendations": "Further research should explore potential policy mechanisms to enhance access to credit and digital services for these farmers, thereby increasing their likelihood of adopting precision agriculture techniques.", "keywords": "Precision Agriculture, Smallholder Women Farmers, Northern Ghana, Three-Year Impact Study", "contributionstatement": "This review introduces a novel statistical model equation predicting the impact of remote sensing data on yield improvements among smallholder women
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".