Industrial Hemp Response to Nitrogen, Phosphorus, and Potassium Fertilization
Bibliographic record
Abstract
Industrial hemp ( Cannabis sativa L.) production has increased in Canada in recent years, and interest for this multipurpose crop remains high. The lack of agronomic guidelines for Eastern Canada represents, however, a limiting factor for local hemp production. This study assessed biomass and seed yields and composition of two hemp cultivars (CRS‐1 and Anka), following various N, P, and K fertilization treatments (0, 50, 100, 150, and 200 kg N or K ha −1 ; 0, 25, 50, 75, and 100 kg P ha −1 ). The experiment was conducted in multiple environments in the province of Québec. Positive linear and quadratic responses of biomass yield, seed yield, and seed crude protein concentration to N fertilization were observed in all environments; the magnitude of the response depended, however, on the environment and cultivar. Across environments and cultivars, biomass and seed yields increased from 1674 to 4209 kg ha −1 and from 519 to 1340 kg ha −1 , respectively, with the application of 200 kg N ha −1 when compared with the unfertilized control. Nitrogen fertilization affected biomass cellulose and hemicellulose concentrations, but the overall response remained minimal. Phosphorus and potassium fertilizations had very limited effect on biomass and seed yields and composition in all environments. In conclusion, while industrial hemp responded to N fertilization up to 200 kg N ha −1 , response to P and K fertilization remained limited.
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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".