High value crops in coarse-textured soil and nitrate leaching - How risky is it?
Bibliographic record
Abstract
To identify practices that minimize the risk of NO3-N leaching to groundwater from high-value crops produced on coarse-textured soil, NO3-N movement was determined from varied water (overhead vs. drip irrigation) and nutrient (pre-plant broadcast vs. fertigation) management in cucumber (Cucumis sativus L.) over three growing seasons, and municipal compost rate and mulch type in ginseng (Panax quinquefolius L.) over 5 yr. Under cucumber, seasonal NO3-N leaching ranged from 4 to 28 kg ha-1, and was reduced by 10 kg NO3-N ha-1 yr-1 in 2 of 3 yr using drip delivery of water and nutrients as compared with pre-plant broadcast fertilizer with overhead irrigation. Under ginseng, 452, 321 and 173 kg NO3-N ha-1 leached from C260 compost (170 Mg ha-1 incorporated compost under 90 Mg ha-1 compost mulch re-applied annually), C200 bark (200 Mg ha-1 incorporated compost under pine bark mulch) and C0 straw (no compost, straw mulch), respectively, during the initial fall plus subsequent 4 yr. Following fall application of compost, subsoil solution NO3-N concentrations increased to > 100 mg NO3-N L-1 by early December. Even with no compost applied, NO3-N concentrations in water draining from the ginseng soil profile usually exceeded the drinking water standard. Key words: Compost, cucumber, fertigation, ginseng, nutrient management
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".