Farmers’ Perceptions of Land Degradation and Adaptation Strategies Adopted by Farmers in the Geographical Area of Bagaroua in Niger
Bibliographic record
Abstract
The study was conducted in the commune of Bagaroua, (Tahoua region). The Tahoua area has agro-climatic characteristics favorable to agricultural production. This area is now threatened by the rapid degradation of its natural resources due to climatic hazards and human activities. It is in this context that the present study proposes to analyze the perceptions of farmers on land degradation and the adaptation strategies of producers faced with the impact of this degradation. The data was collected by interview using a questionnaire submitted to 254 agricultural producers sampled using the formula n=t² *p*(1-p) /m². The results showed that agricultural producers clearly perceive the manifestations of soil degradation by the appearance of glacis with percentages by 66% of respondents; formation of “erosion” ravines (13.17%); presence of pebbles and sandy bulge (10.07%). Farmers perceive the impacts of this soil degradation through parameters such as the reduction in cultivable areas (12%); attacks by crop enemies (7%); increased food insecurity (32%); the influx of able-bodied young people to big cities (11%) inside and outside the country and delinquency (6%). This situation puts the population in a situation of extreme poverty (16%), indebtedness (8%) and conflicts between households (8%). Faced with this shock, producers adopt adaptation strategies, the most widespread of which are, among others, the use of water and soil conservation techniques, the use of organic and mineral manure, the use of improved varieties early.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".