Effect of climate variability adaptation strategies on maize yield in the Cape Coast Municipality, Ghana
Bibliographic record
Abstract
Maize is a major staple produced by most peasant farmers in Ghana, amidst climate variabilities that potentially thwart the attainment of global sustainable development goals (SDGs), specifically SDG −2 of zero hunger. Ordinarily, one expects the extant literature to be replete on a nexus between climate variability adaptation strategies and maize yields. Ironically, there appears to be scant information on the expected nexus in Ghana’s coastal areas. The dual questions about what adaptation strategies significantly affect maize yield, and the extent (magnitude) to which climate variability strategies affect maize yield beg answering. Inspired by these research questions, the objective of this article is to examine the effect of climate variability adaptation strategies on maize yield. This study relies on a cross-sectional data covering 197 smallholder maize farmers in the Cape Coast Metropolitan Assembly of Ghana’s Central Region. The study is deeply rooted in a quantitative approach employing multiple linear regression and a treatment effect model (inverse probability weighted regression adjustment—IPWRA). Our findings reveal that adaptation strategies correlate with maize yields. Specifically, estimates from the IPWRA show that irrigation and changes in planting dates positively correlate with maize yields. The implication is that these adaptation strategies improve maize yields. Smallholder farmers are encouraged to adopt effective climate variability adaptation strategies to minimize the adverse risks associated with climate variability. The government of Ghana’s initiative for arid regions, dubbed as the “one village one dam” initiative can be upscaled to southern Ghana to ensure sustainable agricultural development.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".