Production potential and mapping of medicinal and aromatic plants in the Azilal forest (central High Atlas of Morocco)
Bibliographic record
Abstract
Background: In Morocco, a Mediterranean biodiversity hotspot, medicinal and aromatic plants (MAPs) play a key socioeconomic and cultural role, particularly in traditional healthcare and rural livelihoods. Yet, increasing anthropogenic and climatic pressures threaten their sustainability, especially in mountainous forest ecosystems where quantitative data on species distribution and productivity remain scarce. Methods: To address this gap, a phytoecological inventory was conducted in the Azilal forest using a stratified random sampling design across nineteen plots of 400 m² each, along with aboveground phytomass assessment of dominant MAP species to assess floristic diversity and production potential. Results: A total of 40 MAP species belonging to 22 botanical families were recorded, with Asteraceae, Fabaceae, and Lamiaceae being the most represented. Three distinct floristic facies were identified, reflecting pronounced spatial heterogeneity and areas of ecological fragility. Phytomass estimates showed substantial interspecific variability, with fresh biomass ranging from 66.84 to 998.94 kg·ha⁻¹ and dry biomass from 4.73 to 430 kg·ha⁻¹. High production potential was observed in a limited number of species, notably Chamaerops humilis L., Cistus laurifolius L.., and Asphodelus ramosus L., whereas approximately 80% of the recorded species exhibited low cover rate and limited biomass, indicating high vulnerability to disturbance and overharvesting. Conclusions: This study provides the first quantitative and spatially explicit evaluation of MAPs diversity and productivity in the Azilal forest. The resulting phytomass estimates and ecological facies mapping provide practical tools for identifying high-potential economic zones and prioritizing conservation areas, thereby supporting adaptive management strategies that balance local development and ecosystem preservation. Keywords: medicinal and aromatic plants (MAPs), Azilal forest, non-timber forest products (NTFP), inventory, biomass production, sustainable management, conservation, Morocco
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".