Small Scale Farmers’ Indigenous Agricultural Adaptation Options in the Face of Declining or Stagnant Crop Yields in the Fako and Meme Divisions of Cameroon
Bibliographic record
Abstract
Research has proven that, at a national scale in Cameroon, arable crop production is either declining or stagnant. In the face of these trends, governments, local and international organizations, communities and peasant farmers have developed adaptation options to sustain arable production and reduce poverty. Given this general context, and based on population perceptions and four study sites in the Southwest region of Cameroon, this study aims at verifying current trends in arable production and farmers’ adaptation options based on their indigenous knowledge. These analyses are based on the administration of 200 questionnaires and two focus group discussions (FGDs). The data were analysed using SPSS version 20 in which frequencies, percentages and means were calculated. In addition, the chi-squared statistical test of goodness of fit was calculated and the stated hypothesis was validated accordingly. The FGDs were analysed through verbatim transcriptions and with the aid of the context analysis software, Wordstat 7. The results show that current yields (2010–2014) in all the study sites are declining due to deforestation, poor governance, inadequate access to farm inputs such as fertilizers, increased economic opportunities elsewhere and a breakdown of cultural practices, while 10 years (2000–2010) previously, they had been increasing. It has also been found that the main adaptation options/coping mechanisms reported by the respondents in order of highest frquencies are: expansion of farm size, help from relatives and dependents that live on the farm, supplemental occupations or livelihood diversification and usage of organic fertilizers. From the chi-squared test, the alternate hypothesis that, “there is some difference between population proportions for different adaptation options or coping mechanisms” is validated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".