The Impact of Climate Change on Agro-Ecological Based Livelihoods in Africa: A Review
Bibliographic record
Abstract
Several local studies have been carried out on the impact of climate change on livelihoods and development especially in developing countries. However, there is a general scarcity of literature that makes a comparative appraisal of the impacts of climate change on agro-ecological based livelihoods across the African continent. This paper seeks to address that gap by making a comparative analysis of the effects of climate change on agro-based livelihoods across the African continent, focusing on Eastern, Western, Southern Africa and the Sahel region. A cross continental perspective on this issue is important in informing current global climate change negotiations and response strategies both at global level and national levels. While some studies have been conducted at individual country levels about the projected and recorded impacts of climate change, there remains a dearth of literature that reviews and consolidates these findings to give an overall holistic picture about continental and sub-continental impacts in Africa especially as relating to local agro and ecological based livelihoods. This study finds out that the impact of climate change is invariably negative across the whole of Africa as it leads to failing agricultural yields and a reduction of bio-diversity. The paper recommends an intensification for the support of livelihood diversification strategies in rural development planning. It further recommends policy strategies that particularly targets the poor and vulnerable communities whose livelihoods hinge on agriculture and natural ecosystems as these will suffer the most from the impact of climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".