Optimal fertilizer application for Panax notoginseng and effect of soil water on root rot disease and saponin contents
Bibliographic record
Abstract
BACKGROUND: Blind and excessive application of fertilizers was found during the cultivation of Panax notoginseng in fields, as well as increase in root rot disease incidence. METHODS: Both "3414" application and orthogonal test designs were performed at Shilin county, Yunnan province, China, for NPK (nitrogen, phosphorus, and potassium) and mineral fertilizers, respectively. The data were used to construct the one-, two-, and three-factor quadratic regression models. The effect of fertilizer deficiency on root yield loss was also analyzed to confirm the result predicted by these models. A pot culture experiment was performed to observe the incidence rate of root rot disease and to obtain the best range in which the highest yield of root and saponins could be realized. RESULTS: The best application strategy for NPK fertilizer was 0 kg/667 m(2), 17.01 kg/667 m(2), and 56.87 kg/667 m(2), respectively, which can produce the highest root yield of 1,861.90 g (dried root of 100 plants). For mineral fertilizers, calcium and magnesium fertilizers had a significant and positive effect on root yield and the content of four active saponins, respectively. The severity of root rot disease increased with the increase in soil moisture. The best range of soil moisture varied from 0.56 FC (field capacity of water) to 0.59 FC, when the highest yield of root and saponins could be realized as well as the lower incidence rate of root disease. CONCLUSION: These results indicate that the amount of nitrogen fertilizer used in these fields is excessive and that of potassium fertilizer is deficient. Higher soil moisture is an important factor that increases the severity of the root rot disease.
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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 | 0.000 | 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".