Determinants of Cocoa Productivity and Vulnerability to Climate Change in Central Cameroon: An In-Depth Analysis of Farmers' Perspectives
Bibliographic record
Abstract
This study aimed at analyzing the determinants of cocoa productivity and vulnerability to climate change in Central Cameroon. The methodological approach consisted of questionnaire administration to 152 cocoa farmers in the Ngoro municipality. Multiple generalized regression analyses were run to identify socio-demographic and management practices that significantly influenced cocoa productivity and vulnerability to climate change. Results showed that cocoa farming is done predominantly by men (97%) with cocoa farms covering an average of 5 ha (57%) and cocoa bean productivity varying between 0.08 and 15 tons per farm. It provides money in cash varying from 151 to 28,275 USD. Age of farm and household size were the socio-demographic determinants that significantly (positively) influenced cocoa productivity. Cocoa farm size, number of years spent, and clearing/bush fire management were management practices that significantly influenced cocoa productivity. Climate change was perceived by all farmers (100%) as the main factor that negatively influenced cocoa productivity. Given the monetary value associated with cocoa farming, which contributes to the local farmers' well-being and then food security, it is becoming more important than ever to ensure that the management practices in cocoa farming systems consider ecological aspects, to enable Cameroon meets its commitments in terms of food security, biodiversity conservation, and the fight against climate change. Received: 22 August 2024 | Revised: 28 October 2024 | Accepted: 20 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data used for this study is available and will be provided upon reasonable request by FOKABS Canada and Cameroon. Author Contribution Statement Chimi Djomo Cédric: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization. Tanougong Armand Delanot: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - review & editing. Joël Martin Atangana Owona: Methodology, Resources, Funding acquisition. Serges Okala Ndzie: Conceptualization, Investigation, Data curation, Writing - review & editing. Barnabas Neba Nfornkah: Conceptualization, Investigation, Data curation, Writing - review & editing. Kevin Enongene: Methodology, Investigation, Data curation, Writing - review & editing, Supervision, Funding acquisition. Eugene Chia Loh: Methodology. Elsie Fobissie: Investigation, Data curation. Dieudonne Alemagi: Methodology, Investigation, Writing - review & editing. Nyong Princely Awazi: Conceptualization, Writing - review & editing. Karol Lavoine Mezafack: Methodology, Investigation, Data curation, Writing - review & editing. Parfait Kamta Nkontcheu: Investigation, Data curation, Writing - review & editing. Stelle Vartant Djeukam Pougom: Investigation, Data curation, Writing - review & editing. Kevin Tchemmoe Fokou: Software, Validation, Formal analysis, Data curation, Writing - review & editing. Katty Claudia Chiteh: Project administration. Kabelong Banoho Louis Paul Roger: Writing - review & editing. Kalame Fobissie: Resources, Supervision, Project administration, Funding acquisition.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".