Evaluation of growth, yield and economics of Stevia rebaudiana Bertoni under partial shade in a teak-based agroforestry system in the sub-tropical region of Madhya Pradesh
Bibliographic record
Abstract
Stevia rebaudiana, a perennial herb prized for its steviol glycosides, is increasingly being cultivated as a natural sweetener. In India, the annual demand for this herb is estimated at approximately 10 metric tons. However, the relationship between agronomic practices and agroforestry systems on leaf yield and economic viability in tropical climates remains underexplored. This study evaluated the effects of spacing and organic amendments on growth, productivity in a teak-based agroforestry system in Central India. A field experiment was conducted in a randomized block design with a factorial concept under a teak-based agroforestry model at the Non Wood Forest Produce (NWFP) nursery, Indian Council of Forestry Research and Education - Tropical Forest Research Institute (ICFRE-TFRI), Jabalpur, Madhya Pradesh. The treatments included plant spacing (S1: 45 × 45 cm, S2: 30 × 30 cm, S3: 20 × 20 cm) and organic nutrient regimes (M7: farmyard manure (FYM) + vermicompost (VC) + poultry manure (PM); M6; VC + PM; M5: FYM + PM; M4: FYM + VC; M3: PM; M2: FYM; M1: VC; M0: control). Growth parameters such as plant height, branch number and leaf count were recorded, along with biomass yield (fresh and dry weight). Economic viability was assessed through input-output ratios. The results indicated enhanced plant height, branching and leaf count perplant under M7. Spacing S3 yielded the maximum fresh and dry biomass. Economic analysis revealed that S3 and M7 are cost-effective with higher net returns. These findings underscore that integrating teak agroforestry and organic amendments enhances both yields, offering a sustainable model for tropical stevia cultivation. This study provides actionable insights into agronomic practices to balance productivity, metabolite quality and profitability in resource-constrained systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".