Influence of Nitrogen Fertilization on the Growth and Yield of North American Ginseng
Bibliographic record
Abstract
Information on nitrogen (N) fertilization of North American ginseng (Panax quinquefolius L.) is needed to optimize dry matter accumulation, ginsenoside concentration, and root yields. Field experiments were initiated in 1995 and 1996 to study the effects of N fertilization on dry matter accumulation, N concentration and accumulation, final root yield, and ginsenoside concentration of American ginseng production in a Fox loamy sand (Psammentic Hapludalf). Ammonium sulfate was broadcast-applied at rates of 0, 10, 20, 30, 40, 50, 60, and 70 kg N/ha prior to seedling emergence in the spring of each of the four growing seasons. The eight N treatments were arranged in a randomized complete block design with four replications. Ginseng plant dry weight showed a quadratic response to increasing N rate. As the rate of N fertilizer applied was increased, significant increases in tissue N concentration resulted. Approximately, 50 percent of the total N accumulated was allocated to the roots in the first year of growth while approximately 66 percent of the total N accumulated was allocated to the roots in the subsequent years of growth. Ginsenoside contents of the harvested roots were not significantly affected by N fertilizer application rates. Based on the quadratic model fitted to the data, maximum yields of four-year-old commercially harvested ginseng roots can be obtained in Ontario with annual additions of about 40 kg N/ha in the spring, prior to plant emergence.
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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".