Projecting Climate Change Impacts on Benin’s Cereal Production by 2050: A SARIMA and PLS-SEM Analysis of FAO Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Globally, agriculture is facing significant challenges due to climate change, which is seriously affecting grain yields. This research aims to analyze the significant effect of climate change (temperature and rainfall) on cereal production in Benin. The choice of Benin is explained by its strong dependence on agriculture and its vulnerability to climatic variations. This study employed climate and agricultural data from FAO and ASECNA (1990–2020) to evaluate the impacts of climate change on cereal production. SARIMA time-series models were used for forecasting, while the PLS-SEM approach assessed the relationships between climate variables and cereal production. The findings reveal a rise in temperatures and a gradual decline in precipitation. Despite these challenges, the time-series analysis suggests that Beninese farmers are expanding cultivated areas, successfully increasing production levels, and improving yields. Projections to 2050 indicate an increase in areas and production for maize and rice, while sorghum shows a constant trend. However, even with these projections, it is recommended to explore, in more depth, the resilience strategies used by cereal producers to better understand their influence and refine the orientations of future agricultural policies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it