Sustainable intensification of cocoa production under a changing climate in Southwest, Nigeria
Bibliographic record
Abstract
This study examines the sustainable intensification of cocoa production in Southwest Nigeria, focusing on Ondo State, under the pressures of a changing climate. West Africa dominates global cocoa production, with Nigeria ranking as the fourth-largest producer, yet its yields remain low compared to higher outputs in countries like Côte d’Ivoire. Climate change, coupled with low adoption of intensification technologies and extreme weather events, has contributed to declining productivity in Nigeria. This research investigated the determinants and impacts of adopting intensification technologies, such as improved seedlings, fertilizers, and pesticides, on cocoa yields in Ondo State, a major production hub. Using a multi-stage sampling technique, we collected data from smallholder farmers and analyzed with descriptive statistics, a multinomial logit model, and multinomial endogenous switching regression (MESR). Results reveal that farm size, access to credit, membership in associations, age, gender, and positive perceptions significantly influenced technology adoption. The MESR analysis shows substantial yield increases with the adoption of the intensification technologies, notably an 80.62% boost when combining all technologies. The study underscores the potential of sustainable intensification to enhance cocoa productivity and resilience to climate variability, offering policy recommendations including improved credit access, enhanced extension services, and supply chain optimization for inputs. This research bridges climate science and agronomic innovation, providing actionable insights for sustaining Nigeria’s cocoa economy amidst environmental challenges.
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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 | 0.001 | 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 | 0.000 |
| 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 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".