Preliminary trial to determine the performance of micropropagated turmeric ( <i>Curcuma longa</i> L.) plants in Virginia, USA
Bibliographic record
Abstract
Turmeric ( Curcuma longa L.) is a high-value spice primarily cultivated in India and increasingly in demand in the United States. It is propagated vegetatively through rhizomes; however, this method has a low multiplication rate and is prone to high disease incidence. Disease control via fungicide application is often ineffective and environmentally unsustainable. Micropropagation is an ideal method for the mass production of disease-free plant material. However, there is evidence that the micropropagated plants have lower yields during the first growing season. This experiment tested the hypothesis that turmeric plants propagated conventionally (via rhizome cuttings) and through tissue culture produce comparable yield and quality under the subtropical climate of Virginia. Twenty-four of each propagation method (cultivar ‘Hawaiian Red’) were planted in a split-plot design at the research farm and irrigated using a sprinkler system. Chlorophyll concentration index (µmol m −2 ) was measured twice during the growing season using three fully expanded leaves from the south-facing canopy. At harvest, data were collected on plant height, shoot number, fresh and dry weight of shoots, rhizomes and roots, canopy diameter, rhizome total soluble solids (TSS), and titratable acidity (TA). The micropropagated turmeric plants had significantly lower rhizome yield but higher TSS and TA compared to conventionally propagated plants. Despite being smaller at planting, micropropagated turmeric plants reached comparable height, shoot number, and root weight, and showed no sign of disease or rhizome contamination at harvest.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".