Medicinal and aromatic plants for soil and water conservation: An economic analysis, Nilgiris, Tamil Nadu, India
Bibliographic record
Abstract
This study was carried out to examine the economics and soil and water conservation value of prominent medicinal and aromatic plants in the hilly areas of Nilgiris, Tamil Nadu during 1998-99 to 2004-05. The six species of medicinal and aromatic plants were planted in 15m × 5m plots on 15% land slope to assess the relative soil and water conservation efficiency and economics. The Geranium recorded highest average green biomass yield followed by Digitalis, Cineraria, Mentha and Rosemary. Runoff and soil loss was maximum under Thyme followed by Cineraria because of their poor canopy cover. The lowest average annual soil loss and runoff over the year was observed from Digitalis followed by Mentha and Geranium due to their quick establishment and higher canopy cover. The soil and water conservation efficiency varied from 39.1 to 97.2%. Whereas, relative soil and water conservation efficiency (RSWCE) of 97.2,90.5,90.0,91 and 39.1% in Digitalis, Mentha, Geranium, Rosemary and Cineraria, respectively and observed almost zero in the case of Thyme. Based on economic criteria order of medicinal plants profitability it was observed that Digitalis, Cineraria, Rosemary had 1:9.4, 1:4.2 and 1:2.5, respectively higher benefit cost ratio (BCR) where as Geranium has given higher internal rate of returns (IRR). However, IRR for Thyme and Mentha was almost negligible. Thus, RSWCE and Economic criteria does not follow the same pattern of preference of medicinal and aromatic plants. Eight years of the study suggest that Digitalis, Mentha, Geranium and Rosemary cultivation could be the viable proposition for a diversified land use system as well as for cover crop, intercrop and vegetative barrier in the Nilgiris for effective natural resources conservation. The economic analysis also found to be viable for cultivation of these plants in Nilgiris.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".