Optimal Rate of Organic Fertilizer during the Flowering Stage for Cannabis Grown in Two Coir-based Substrates
Bibliographic record
Abstract
In the expanding North American medical cannabis industry, growers lack reliable and systematically investigated information on the horticultural management of their crops, especially with regard to nutrient management and growing substrates. To evaluate organic substrates and their optimal nutrient management, five rates that supplied 57, 113, 170, 226, and 283 mg N/L of a liquid organic fertilizer (2.00N–0.87P–3.32K) were applied to container-grown plants [ Cannabis sativa L. ‘WP:Med (Wappa)’] in two coir-based organic substrates. The trial was conducted in a walk-in growth chamber and the two substrates used were ABcann UNIMIX 2-HP (U2-HP) with lower container capacity (CC) and ABcann UNIMIX 2 (U2) with higher CC. U2-HP produced 11% higher floral dry weight (yield), 13% higher growth index (GI), 20% higher ∆ 9 -tetrahydrocannabinol (THC) concentration, 57% higher THC yield (per plant), 22% higher Δ 9 -tetrahydrocannabidiolic acid (THCA) yield, and 20% higher cannabigerolic acid (CBGA) yield than U2. Increasing fertilizer rate led to increased growth and yield but also to a dilution of THC, THCA, and CBGA. In U2-HP, to maximize both yield and cannabinoid yield, the optimal organic fertilizer rates were those which supplied 212–261 mg N/L. For U2, the highest applied rate, that supplied 283 mg N/L, maximized yield; although lower rates delivered higher cannabinoid concentrations in dry floral material. The results on these substrates and recommended fertilizer rates can serve as a guide when using other organic fertilizers and substrates; although results may differ with cannabis variety.
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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) | 0.001 | 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".