Assessing the effects of agroecology and conventional farming techniques on small-scale peasant farmers crop yields in the Fako and Meme divisions of Cameroon
Bibliographic record
Abstract
Small-scale farming constitutes a very important segment of the food production chain in most third world countries. In Cameroon for example, they constitute about 70% of the agrarian population. This study aimed at verifying the effects of agroecology and conventional farming techniques on crop yields in four sites in the South West Region of Cameroon from small-scale farms. Data were obtained through the administration of 200 questionnaires and two focus group discussions (FGDs). The data were analyzed using frequencies, means, coefficient of correlation, coefficient of determination, and linear regression models. The FGDs were also analysed using context analysis. All the analyses were performed in SPSS version 20 and Wordstat 7 software. The results showed that both agroecology and conventional farming techniques are used in the study sites but agroecology techniques are more responsible for yield increases than conventional techniques as seen in correlations coefficients and regression outputs. The only exceptions in which conventional farming techniques contribute more to yields was under income levels and the number of family members that live and work on the farm. This was justified by the fact that conventional techniques often require higher income levels since they are often purchased. Key words: Agroecology and conventional techniques, small-scale farmers, crop yields.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".