Organic farming for local markets in Kenya: Contribution of conversion and certification to environmental benefits
Bibliographic record
Abstract
Abstract Organic farming is a way to address environmental issues. In Kenya, organic production for domestic markets based on local certification represents a solution to both economic and environmental issues. We propose to address this latter issue. Indeed, no quantitative studies have been dedicated to these systems’ impacts on the environment. However, their theoretical benefits can be weakened, first by their functioning based on internal control and indirect external control, and second by the risk of self‐selection since farmers using low levels of synthetic inputs have less effort to make in order to enter in conversion process. Thanks to unique farm‐level survey data along with the propensity score matching method, we assess the producer‐level effects of organic certification for fruits and vegetables on agro‐ecological practices. We show that conversion and certification are associated with organic farming techniques and positive perceptions of different statements about environmental values. However, we do not notice any additional effects of certification compared to conversion alone. Although economic issues are important, we focus on environmental issues that appear as important for smallholders. In a context with no public regulation, conversion‐only farmers and locally certified farmers could be a lever for a more sustainable 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".