Optimal Rate of Organic Fertilizer during the Vegetative-stage for Cannabis Grown in Two Coir-based Substrates
Bibliographic record
Abstract
Cannabis producers, especially those with organic operations, lack reliable information on the fertilization requirements for their crops. To determine the optimal organic fertilizer rate for vegetative-stage cannabis ( Cannabis sativa L.), five rates that supplied 117, 234, 351, 468, and 585 mg N/L of a liquid organic fertilizer (4.0N–1.3P–1.7K) were applied to container-grown plants with one of two coir-based organic substrates. The trial was conducted in a walk-in growth chamber and the two substrates used were ABcann UNIMIX 1-HP with lower water-holding capacity (WHC) and ABcann UNIMIX 1 with higher WHC. No differences in growth or floral dry weight (yield) were found between the two substrates. Pooled data from both substrates showed that the highest yield was achieved at a rate that supplied 389 mg N/L (interpolated from yield-fertilizer responses) which was 1.8 times higher than that of the lowest fertilizer rate. The concentration of ∆ 9 -tetrahydrocannabinol (THC) in dry floral material was maximized at a rate that supplied 418 mg N/L, and no fertilizer rate effects were observed on Δ 9 -tetrahydrocannabidiolic acid (THCA) or cannabinol (CBN). The highest yield, cannabinoid content, and plant growth were achieved around an organic fertilizer rate that supplied 389 mg N/L during the vegetative growth stage when using the two coir-based organic substrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".