Diversity of use and local knowledge of wild edible plant resources in Nepal
Bibliographic record
Abstract
BACKGROUND: Wild edible plants (WEP) provide staple and supplement foods, as well as cash income to local communities, thus favouring food security. However, WEP are largely ignored in land use planning and implementation, economic development, and biodiversity conservation. Moreover, WEP-related traditional knowledge is rapidly eroding. Therefore, we designed this study to fulfill a part of the knowledge gap by providing data on diversity, traditional knowledge, economic potential, and conservation value of WEP from Nepal. METHODS: The information was collected through focus group discussions and key informant interviews. Percentage of general utility of the plants among the study communities was evaluated using the Chi-square (χ(2)) test of homogeneity. High priority species were identified after consultation with the local stakeholders followed by scoring based on defined criteria. Pairwise ranking was used to assess ethnoecological knowledge to identify the threats to WEP. RESULTS: We documented 81 species belonging to Angiosperms (74), Pteridophytes (5), and Fungi (2). Most of the species were used as fruits (44 species) followed by vegetables (36). Almost half of the species (47%) were also used for purposes other than food. From the species with market value (37% of the total), 10 were identified as high priority species. Pairwise ranking revealed that WEP are threatened mostly by habitat destruction, land-use change and over-harvesting. Some of these plants are crop wild relatives and could thus be used for crop improvement. Interestingly, our study also revealed that young people who spend most of the time in the forest as herdsmen are particularly knowledgeable of wild fruit plants. CONCLUSION: We provide empirical evidence from a relatively large area of Nepal about diversity and status of WEP, as well as methodological insights about the proper knowledge holders to consult. Regarding the unique and important knowledge they have on WEP, young people should be included when recruiting participants to ethnobotanical studies or to any type of consultation about WEP. The habit of using wild edible plants is still alive and is a traditional culinary practice that demonstrates rich traditional knowledge of local people. WEP were found to be important for livelihood as well as showing great potential for crop improvement. Priority species should be promoted for income generation activities through sustainable collection and trade. Communities should engage in minimizing the threats to these valuable resources.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".