Overview of the scientific, political and financial landscapes of Climate-Smart Agriculture in West Africa
Bibliographic record
Abstract
The agricultural sector plays a key role in the Economic Community of West African States (ECOWAS).As the backbone of the economy, it affects society at many levels since national economies and people's jobs, incomes and food security depend upon it.Climate change and variability pose a major threat to farmers in the region, which is already experiencing rising temperatures, shifting precipitation patterns, and increasing extreme events.The ECOWAS has put in place various policy instruments such as the Economic Community of West Africa States Agricultural Policy (ECOWAP) and its derived Regional Agricultural Investment Plan (RAIP) in order to promote a modern and sustainable agriculture based on effective and efficient family farms and the promotion of agricultural enterprises through the involvement of the private sector.Taking stock on member States' expressed needs, ECOWAS would like to integrate a new type of public policy instruments into the RAIP: instruments for adapting the West-African agriculture to climate change, towards a Climate-Smart Agriculture (CSA) focusing on adaptation, mitigation and food & nutrition security joint objectives.This book documents and analyses specific features of the scientific, institutional, policy and funding CSA landscape in West Africa.It provides relevant information that could guide the definition of the ECOWAS Framework for CSA Intervention, Funding, Monitoring and Evaluation.Five major agricultural sectors have been covered: crop production, livestock, fisheries, forestry/agroforestry, and water.For each sector, a particular emphasis was given to the current status, the climate projections and likely socioeconomic and environmental impacts expected, the bottlenecks to action and suggested next steps for adaptation and mitigation.Actionable messages and recommendations have been directed to ECOWAS stakeholders so as to incentivise CSA in West 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".