Research progress on climate change adaptation strategies to control invasive crop pest in sub-Saharan Africa: a bibliometric and systematic review
Bibliographic record
Abstract
This bibliometric and systematic review assesses research progress and climate change adaptation strategies to control invasive crop pests in sub-Saharan Africa. Scientific publications on crop pest management in sub-Saharan Africa in a context of climate change adaptation were extracted from papers published between 1991 and 2024. A literature search was conducted on Scopus, dimension, and google scholar, followed by screening and data extraction in compliance with ROSES standards. Findings indicated that pests such as armyworms, fruit flies and coffee berry borer cause huge losses. Communities are adopting integrated pest management, water harvesting, drip irrigation, resistant varieties, and improving production efficiency. Agro-ecological practices reduce pest invasions while preserving the environment. Meanwhile, chemical insecticide use remains an emergency solution as its effects on pest control would be more efficient. However, promising approaches emerge around biocontrol, agroforestry integrating pest management, and gender-tailored strategies. Nevertheless, regional disparities persist in scientific output. In conclusion, while invasive pests represent a major plant health crisis in sub-Saharan Africa, this review highlights innovative adaptation strategies. Their development will require coordinated mobilization to catalyze the sustainable agro-ecological transition that sub-Saharan Africa needs to address these multidimensional challenges. Future research should assess farmer’s perception on the effectiveness of the existing pest management practices for invasive crop pests.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.067 | 0.068 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".