Lab to farm: mapping knowledge transfer channels and determinants from researchers’ perspective – A systematic literature review
Bibliographic record
Abstract
The literature on the research-practice gap in agriculture has evolved significantly in recent decades. Although there is a well-established body of work on how farmers adopt agricultural research outcomes and the factors that influence their adoption, research on how researchers perceive the process of transferring their results to practical applications, along with the factors that facilitate or hinder this process, remains inadequate. This study addresses this gap by conducting a systematic literature review of empirical studies on knowledge transfer and its determinants from the perspective of agricultural researchers, covering publications from 1960 to 2024. It offers two key contributions: first, an original taxonomy of the channels through which agricultural research is transferred to farmers, and second, an integrative conceptual framework that links knowledge transfer to three categories of influential factors, related to researchers' individual characteristics, the organizational context within research institutions, and the external environment. Based on the findings, a research agenda has been developed to serve as a foundation for future investigations into persistent gaps in the field. The findings hold value for both academic and practitioner communities as they provide deeper insights to improve the understanding and practice of knowledge transfer in agriculture.
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 | 0.044 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.037 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".