Application of a 1 km2 resolution model for climate change effects upon Benin and Nigeria vegetable agriculture
Bibliographic record
Abstract
Climate change and food security are complex global issues that require multidisciplinary approaches to resolve. A nexus exists between both issues, especially in developing countries, but little prior research has successfully bridged the divide. Existing resolutions to climate change and food security are expensive and resource demanding. Climate modelling is at the forefront of climate change literature and development planning, whereas agronomy research is leading food security plans. The Benin Republic and Nigeria have grown and developed in recent years but may not have all the tools required to implement and sustain long-term food security in the face of climate change. The objective of this paper is to describe the development and outputs of a new model that bridges climate change and food security. Data from the Intergovernmental Panel on Climate Change’s 5th Regional Assessment (IPCC AR5) were combined with a biodiversity database to develop the model to derive these outputs. The model was used to demonstrate what potential impacts climate change will have on the regional food security by incorporating agronomic data from four local underutilized indigenous vegetables (Amaranthus cruentus L., Solanum macrocarpon L., Telfairia occidentalis Hook f., and Ocimum gratissimum L.). The model shows that, by 2099, there is significant uncertainty within the optimal recommendations that originated from the MicroVeg project. This suggests that MicroVeg will not have long-term success for food security unless additional options (e.g., new field trials, shifts in vegetable grown) are considered, creating the need for need for more dissemination tools.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".