Dry Leaf and Steviol Glycoside Productivity of Stevia rebaudiana in the Western United States
Bibliographic record
Abstract
Stevia ( Stevia rebaudiana Bertoni) is a perennial herbaceous plant native to Paraguay, where it was used by the native Guarani peoples for centuries. Although steviol glycosides from stevia are powerful natural noncaloric sweeteners, stevia has been cultivated and commercially available only for the past 50 years. Cultural practices are still in development, and productivity potential in the United States is largely unknown. Currently commercial growers and processors worldwide are seeking to maximize the productivity of rebaudioside A, a steviol glycoside. The trials reported here examined the effects of location, harvest strategy, and cultivar on stevia dry leaf yield, steviol glycoside content, and steviol glycoside yield. Six or seven stevia cultivars were grown for ≈5 months at four western U.S. locations, with an irrigation criterion of 20 kPa. Stevia at every location was subjected to two harvest strategies: either one harvest at the end of the season or two harvests, one midseason and another at the end of the season. The main plots at each location were the stevia cultivars, and the split plots were the harvest strategies in a randomized complete block, split-plot design with four replicates. Dry leaf yield, leaf steviol glycoside content, and leaf steviol glycoside yield varied by cultivar, location, and cultivar by location, but not by harvest strategy or interactions of harvest strategy with location or cultivar. Dry leaf yield averaged 4.12 Mg·ha −1 with significant differences by cultivar and location. One of the steviol glycosides, rebaudioside A yield averaged 300 kg·ha −1 with significant differences by cultivar and by interactions of location with cultivar. Leaf productivity was greater at Ontario, OR, than at Hanford, CA, Indio, CA, or Yuma, AZ. Dry leaf yield greater than 7 Mg·ha −1 and rebaudioside A yields greater than 500 kg·ha −1 were observed at Ontario. Stevia perenniated at Hanford and Indio, providing the option of multiyear harvests from a single planting.
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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 | 0.000 | 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".