Geographical distribution, utilization and farmer’s knowledge of Kersting’s groundnut [ <i>Macrotyloma geocarpum</i> (Harms) Maréchal & Baudet] in Togo
Bibliographic record
Abstract
Abstract Legumes are key to improving food security due to their nutritional value. In Togo, however, the diversity of local legumes, particularly Kersting’s groundnut ( Macrotyloma geocarpum ), is rapidly declining. The ethnobotanical survey aimed to assess the geographical distribution, varietal diversity, uses, and sociodemographic characteristics of Kersting's groundnut producers in the five administratives regions and four big agroecological zones of Togo. Semi-structured interviews, group discussions, and field visits were conducted. A total of 238 producers were identified across 132 villages. Descriptive statistics, correlation and correspondence analysis were used to explore relationships between varietal diversity, socio-demographic factors, and regional uses. The crop was found to be most prevalent in northern Togo, particularly the Kara region (60.5%), which belongs to the dry savannah zone. In contrast, production is almost nonexistent in the coastal and subequatorial southern regions. Most producers were women (56.7%), and 47% reported having no formal education. The number of varieties grown per household was positively correlated with farming experience. Varietal preferences varied by gender and ethnic group. While consumption and sale remain the primary motivations for cultivation, ritual and medicinal uses were significantly associated with ethnic groups and regions. These findings underscore the combined influence of ecological conditions and ethnocultural heritage on varietal distribution. They offer a valuable basis for developing strategies to conserve and promote Kersting’s groundnut in Togo.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".