Agricultural Technology in the Developing World: A Meta-Analysis of the Adoption Literature
Bibliographic record
Abstract
Agricultural technology transfer to people in the developing world is a potentially powerful tool to raise productivity and improve livelihoods. Despite this, many technologies are not adopted by their intended beneficiaries. Qualitative studies have identified guidelines to follow in the design and dissemination of agricultural technology, but there has been comparatively little synthesis of quantitative studies of adoption. This study presents a meta-analysis of adoption studies of agricultural technologies in the developing world. The results confirm most earlier findings, but cast doubt on the importance of some classic predictors of adoption, such as education and landholding size. Contact with extension services and membership in farming associations are found to be the most important variables in predicting adoption. Attributes of the technologies are found to modify the relationships of predictor variables to adoption. Membership in farming associations and farmer experience are found to be positively linked to adoption in general, but for technologies that reduce labour the effect is amplified. The findings have potential implications for researchers, extension workers, and policy makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.025 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".