Comprehensive Analysis of Agricultural Practices in Adapting Soil, Water and Pest Management to Climate Change in Sub-Saharan Africa
Bibliographic record
Abstract
Agriculture constitutes the primary economic sector in Sub-Saharan African (SSA) countries, engaging over 70% of the population, predominantly vulnerable rural communities. For decades, agriculture in SSA has grappled with climatic constraints, intensifying the vulnerability of impoverished communities. In response, communities have developed indigenous practices to adapt to these climatic hazards, warranting greater recognition for potential optimization. This review identifies agricultural practices related to climate change adaptation, specifically focusing on soil management, water, and pests. The operational mechanisms of the most widely utilized practices were scrutinized through a thorough analysis. The study concludes by identifying nine practice categories. Results indicate that water collection practices and the use of organic fertilizers are the most prevalent in soil and water management. Additionally, agricultural and biological control practices dominate pest management. The comprehensive analysis underscores that the most frequently cited practices may not always be the easiest to implement. Nevertheless, these practices are agro-ecologically sustainable, contributing to soil health restoration, efficient water management, and pest control in Sub-Saharan African countries. The findings suggest a need for a research program that concentrates on the simultaneous application of these practices, enabling their optimization for more sustainable agriculture, particularly in the context of climate change adaptation and soil fertility restoration in Sub-Saharan Africa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".