Climate variability and agricultural productivity: A time-series regression analysis of cassava, yam, and maize yields in Wenchi, Ghana
Bibliographic record
Abstract
This study examined the impacts of climate variability and cultivated land area on the yields of cassava, yam, and maize in Wenchi Municipality, Ghana, from 2000 to 2021. Using a quantitative approach, the study employed time-series data on rainfall, minimum and maximum temperatures, crop yields, and cultivated area. Multiple linear regression models with logarithmic transformations were used to assess the influence of climate variables and cultivated area on crop yields. Diagnostic tests confirmed the validity of model assumptions. The regression results revealed that temperature variables, especially minimum temperature, had a significant positive effect on all three crop yields. Maximum temperature also showed positive effects, although with varying levels of significance. Rainfall and cultivated area had no statistically significant impact on yields. The models explained 46.87%, 51.28%, and 61.57% of the variations in cassava, yam, and maize yields, respectively. Temperature played a more critical role than rainfall or cultivated land in influencing crop yields in Wenchi over the study period. These findings underscore the need for temperature-focused adaptation strategies and climate-smart agriculture to enhance food security and resilience in the transitional zones of Ghana.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".