Evaluating Fertilization and Water Practices to Minimize NO3-N Leachate from Container-grown Forsythia
Bibliographic record
Abstract
To minimize fertilizer and water use, and NO 3 -N runoff from container culture, growth, and nutrient status of forsythia ( Forsythia × intermedia Zab. ‘Spring Glory’) in No. 2 containers were compared in response to a controlled-release fertilizer (CRF; Nutricote 18-6-8 100-day at rates of 2, 4, and 6 kg·m −3 ) and placements (incorporation and topdress) under three irrigation strategies [drip-irrigated low (25% or less) leaching fraction (DrLoLF), hand-sprinkled low leaching fraction (HsLoLF), and hand-sprinkled high (50% or less) leaching fraction (HsHiLF)]. In a coexperiment under drip irrigation only, forsythia response was also examined under incorporation, topdress, and dibble fertilizer placements with the same CRF rates applied as single or split dose. Dibble fertilizer placement was superior to both incorporation and topdress in this order. Maximum growth of forsythia occurred at rates of 4.7 kg·m −3 with dibble. With incorporation and topdress, maximum growth was not achieved even at the 6 kg·m −3 maximum rate tested. Forsythia grew better with incorporated than with topdressed CRF with the DrLoLF treatment. The response was reverse with HsHiLF or showed no differences with HsLoLF. Under drip irrigation, greater concentrations of NO 3 -N generally leached from incorporation and dibbled containers in this order than from topdress. Less nitrate was leached from the topdressed containers because less was released from the CRF prills. At the 6 kg·m −3 CRF rate, total cumulative NO 3 -N leachings were 76, 85, and 22 kg·ha −1 (45 × 45-cm container spacing) for dibbling, incorporation, and topdress, respectively, under drip irrigation. Split application of CRF greatly reduced NO 3 -N in leachate, although plant growth also was reduced as a result of less availability of and uptake of nutrients under this strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".