Farmer-centric On-Farm Experimentation: digital tools for a scalable transformative pathway
Bibliographic record
Abstract
Abstract This virtual issue reports on the use of digital technologies in On-Farm Experimentation (OFE) in varied farming systems across the world. The authors investigated diverse questions across contrasted environments and scientific domains, with methodologies that included review, empirical studies, interviews, and reflexive accounts. The contributions thus showcase the multiplicity of research directions that are relevant to OFE. This includes addressing the two intertwined types of research objects in OFE: the farmers’ questions (how to improve management) and the methodologies required to address these (how to improve research through OFE)—with the notable support of digital tools. The issue includes a systematic review exploring OFE practices and farmer-researcher relationships as reported in the scientific literature; a meta-analysis comparing experimental scales in the USA; reflexive analyzes on a feed assessment tool and a tree crop decision support system rooted in OFE that are connecting farmers and researchers in Africa; a retrospective on a large CGIAR program combining citizen sciences and OFE; the use of video recordings and work analysis to characterize farmers’ knowledge in French vineyards; and in the same sector in Australia, two accounts of the use of digital tools in spatially explicit OFE: one an investigation into farmers’ and consultants’ perceptions, the other a retrospective on the roles of precision agriculture. Findings from these examples validate the use of varied digital tools to scale the design, implementation, and learning stages of OFE processes. These include how to better harness and bridge the knowledge of farmers, researchers and other parties, examples of data management and analytics, the improved interpretation of results, and capitalizing on experiences. The international conference this issue was part of also led to acknowledgement of a lack of policy linkages, required to scale OFE endeavors by incentivizing institutional change toward more farmer-centric research practices and responsible digital deployment.
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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".