Shifting perceptions, preferences and practices in the African fruit trade: the case of African plum (Dacryodes edulis) in different cultural and urbanization contexts in Cameroon
Bibliographic record
Abstract
BACKGROUND: Understanding the perceptions, preferences and management practices associated with intraspecific variability of emblematic African tree crops is critical for their sustainable management. In this paper, we examine how the agrobiodiversity of a fruit tree species native to Central Africa, the African plum tree (Dacryodes edulis), is perceived and managed by Cameroonian cultivators. METHODS: Semi-structured interviews and tree surveys were conducted over four months with 441 African plum tree owners from three different ethnic groups (Bamileke, Bassa, Beti) in urban, peri-urban and rural areas. Questions focused on trees owners' perceptions-including the local nomenclature-preferences and management practices related to African plum trees and their intraspecific agrobiodiversity. RESULTS: Across the three ethnic groups in the study area, more than 300 different local varietal names were recorded. These were mainly based on morphological and organoleptic traits, with two-thirds of the names referring to fruit size, skin color and fruit taste. The same traits were used by tree owners to describe their fruit preferences, but their relative importance in shaping fruit preferences varied among groups. The preferences of urban dwellers from different ethnic groups when purchasing African plum fruit focused on the fruit's taste characteristics, while those of rural dwellers differed among ethnic groups. In rural areas, where African plums are sold and consumed by their growers, the preferences of Bassa consumers reflect quantity (fruit size) over quality (fruit taste or skin color) considerations. These preferences are reflected in the choice of seeds used for planting. Bassa owners sought seeds from trees with large fruits (with 34.8% of Bassa owners giving top priority to this trait as a selection criterion) to a significantly greater extent than Bamileke and Beti owners who prioritized taste and skin color instead. Among tree growers who selectively retained African plum trees in their fields, 44% considered tree productivity as a primary selection criterion. CONCLUSIONS: Findings linking perceptions of and preferences for fruit traits to intraspecific tree diversity, with attention to inter-ethnic and rural-urban differences, will help design locally specific measures to conserve the agrobiodiversity of African plum in the context of its ongoing domestication.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".