Foliar Symptomology, Nutrient Content, Yield, and Secondary Metabolite Variability of Cannabis Grown Hydroponically with Different Single-Element Nutrient Deficiencies
Bibliographic record
Abstract
(hereafter cannabis) is a commodity with high nutrient demands due to prolific growth under optimized environmental conditions. Since nutrient deficiencies can reduce yield and quality, cultivators need tools to rapidly detect and evaluate deficiency symptoms so corrective actions can be taken quickly to minimize losses. We grew cannabis plants in solution culture with different individual nutrient elements withheld from the solutions to identify deficiency symptoms. Control plants received a complete nutrient recipe, whereas the following single elements were withheld from the respective nutrient deficiency treatments: N, P, K, Ca, Mg, S, Fe, and Mn. The nutrient treatments began when the photoperiod was switched to a 12/12 h (light/dark), and plants were grown to commercial maturity. Plants were monitored daily, and the development of visual deficiency symptoms were recorded. Photographs of each plant were taken weekly. Upon the onset of visual deficiency symptoms, both upper- and lower-canopy foliage were analyzed for nutrient element concentrations. At harvest, plants were evaluated for biomass partitioning, and the cannabinoid composition of inflorescence tissues. This manuscript describes the onset and progression of nutrient deficiency symptoms (with pictures), relates symptomology to foliar nutrient analyses, and contextualizes the relationships between nutrient deficiencies and cannabis growth, yield, and quality. Aboveground vegetative fresh weights were reduced by 73% in the -N treatment and 59% in the -P treatment, compared with the control. All deficiency treatments except for -Fe and -Mn had floral yields reduced by between 33% to 72%, compared with the control. Overall, deficiencies of individual nutrients can substantially reduce vegetative growth and inflorescence yield, although only minor effects were observed in secondary metabolite composition. The onset of individual deficiency symptoms did not always correspond with elemental analysis of foliar tissues. Cultivators should take an integrated approach in diagnosing nutrient deficiencies and take timely corrective actions to optimize productivity and minimize losses to yield and quality.
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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 | 0.001 | 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 | 0.001 |
| 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".