Potential Role and International Trade of Medicinal and Aromatic Plants in the World
Bibliographic record
Abstract
Before the dawn of civilization, humans have hunted and gathered, and the only trustworthy sources of medicine were plants and herbs. The needs for traditional medicine, which are present in both local and international markets, are met in large part by medicinal and aromatic plants (MAPs). Due to the abundance of medicinal plants, people in many tribal and rural parts of Bangladesh, like many other nations, have traditionally relied on nature and natural remedies to heal themselves and avoid ailments. The market is flooded with items made from medicinal and aromatic herbs. Plants can be used to produce specialty materials like biocides, cosmetics, medicines, essential oils, dyes, and colorants. Most of the species of MAPs are produced for such industrial uses, but most of them are still wild collected. The increasing demand in botanicals results in a great trade from local to international level. Identifying the significant role played by MAPs in serving health and well-being security, it is very important for the countries to utilize the need to conserve, sustainably use, and commercialize the MAP biodiversity resources responsibly throughout the world. The total trades in MAPs have increased from 2.4 billion USD in 1996 to 6.2 billion USD in 2013 with annual growth rate of 5.4% in past 18 years, and growth rate of 10.7% is noticed in recent years. By 2023, it will be expected that the market for herbal medicines would develop at a compound annual growth rate (CAGR) of 5.88% and reach USD 1,29,689.3 million. Nowadays, roughly 80% of the world’s population gets their healthcare mostly from plants and plant extracts. According to a World Health Organization (WHO) forecast, the worldwide herbal industry would reach $5 trillion by the year 2050. China, India, Hong Kong, USA, Germany, Republic of Korea, Canada and Poland are the top export countries while top destinations include USA, Hong Kong, Japan, Germany, France, Republic of Korea, China and Singapore. The study suggests five major trade centers of MAPs worldwide viz. USA, Hong Kong, Germany, Republic of Korea and China. A number of Non-Governmental Organizations (NGOs), Governmental Organizations (GOs) and International Non-Governmental Organization (INGOs) have been working on improvement and expansion of commodity-wise value chains for selected MAPs throughout the world. For commercial developments of MAPs sub-sector, it is required to gather experiences of technical, socio-economical, institutional and policy inputs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".