Exploring the nexus of climate change, energy use, and maize production in Benin: In-depth analysis of the adequacy and effectiveness of adaptation
Bibliographic record
Abstract
To mitigate the impact of climate change, farmers are increasingly opting for more efficient energy allocation in agricultural production. This study aims to evaluate the effectiveness of these methods employed by maize growers in Benin, while identifying the constraints associated with their implementation. A survey was conducted among 230 maize growers in Benin to achieve the objectives of the study. The Data Envelopment Analysis method was utilized to measure farmers' technical efficiency, followed by the application of the Tobit model to identify the factors determining this efficiency. The comparative analysis of efficiency indices reveals that farmers who prioritize increased utilization of agricultural inputs exhibit higher levels of technical efficiency while maintaining constant yields. In terms of technical efficiency at varying yields, farmers who increase their labor input demonstrate the highest level of efficiency. Subsequently, farmers who choose to augment the quantities of agricultural inputs exhibit greater scale efficiency. The Tobit model reveals that age, experience, maize production area, utilization of insecticides and NPK fertilizers are significant determinants influencing the efficiency levels of maize growers. Maize growers encounter challenges in accessing improved maize seeds and agricultural machinery, as well as facing financial constraints.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".