Bibliographic record
Abstract
Land degradation poses a critical threat to food security and rural livelihoods in Sub-Saharan Africa.This study investigates the drivers and impacts of land-use change and unsustainable agricultural practices on soil health and crop productivity in Santa Sub-Division, Cameroon.Employing a mixed-methods approachincluding remote sensing analysis, laboratory soil assessments, and household surveys-the research quantifies land cover transitions, soil quality dynamics, and yield trends from 2000 to 2023.Results reveal a 30% decline in forest cover and a 22% expansion of cropland, accompanied by widespread soil acidification (65% of samples with pH<5.5) and nutrient depletion (nitrogen and phosphorus reduced by over 40%).These changes have contributed to a 12% per-decade decrease in maize yields and increased farmer-grazier conflicts over shrinking resources.While climate-smart agriculture practices such as agroforestry and crop rotation have shown promise in restoring soil fertility and stabilizing yields, their adoption is constrained by tenure insecurity and limited financial support.The findings underscore the urgent need for integrated, policy-driven interventions-including soil restoration, secure land tenure, and scaling up climate-smart agriculture-to enhance resilience and ensure sustainable agricultural development in Santa Sub-Division and similar highland regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".