Importance of yam in the role of agrobiodiversity in Mayombe and Batéké Plateau ecozones in Democratic Republic of Congo
Bibliographic record
Abstract
The Mayombe and Batéké Plateau ecozones of the Democratic Republic of Congo (DRC) are experiencing differentiated deforestation and forest degradation, together with a trend toward homogenization of their agricultural diversity. These may undermine efforts to sustainably reverse household food, nutrition, and livelihood insecurity. In this context, this study seeks to assess the importance of yam in the role of agrobiodiversity among populations in the two contrasting ecozones. A sample of 351 households was surveyed. A dataset of about 202 testimonies from six focus groups and observations in 86 peasant agroforestry fields was also analyzed using descriptive statistics, correlation and regression, and calculations of different indices of crop importance. Overall, plant, animal, and fish species represent respectively 60.9%, 26.7% and 12.4% of genetic resources. About 50 of 72 species of these resources are found in both study areas. Regarding the overall use of species, the five top-ranked species that were utilized as food included Manihot esculenta, followed by Arachis hypogaea, Zea mays, Dioscorea alata, and Musa acuminata. Living in the Mayombe ecozone increases the household's preference for growing yams by up to 5.7 times. Population density was correlated with agricultural diversity. Villages with a high population density showed greater crop diversity than those with a low population density. In short, yam remains an important but under-represented crop, the contribution of which could be increased to secure sustainable livelihoods through biodiversity-rich peasant agroforestry systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".