Nitrogen, irrigation, and alley management effects on nitrate leaching from raspberry
Bibliographic record
Abstract
Abstract High NO 3 concentrations in the Abbotsford‐Sumas aquifer are linked to raspberry ( Rubus idaeus L.) production. Passive capillary wick samplers were used to quantify the impacts of N, irrigation, and alley managements on drainage and NO 3 leaching from raspberry rows and alleys over 4 yr. Conventional management (100 kg N ha −1 surface broadcast on the row as a split application, clean cultivation of alleys, and fixed‐duration drip irrigation) was compared with different mineral fertilizer N rates, N applied as manure, alleys seeded to a perennial forage grass or an autumn‐seeded spring barley ( Hordeum vulgare L.) crop, or evapotranspiration (ET)‐scheduled irrigation. The temporal pattern of drainage and NO 3 leaching was driven by seasonal precipitation and growing season irrigation. Growing season drainage and NO 3 leaching were much lower under ET‐scheduled irrigation compared with fixed irrigation. Nitrate leaching was high (up to 90 kg N ha −1 ), even with no managed N inputs due to high inherent soil fertility and large quantities of N applied in irrigation water. Nitrate leaching was insensitive to N fertilizer rate. Application of N as poultry manure more than doubled NO 3 leaching compared with fertilizer, emphasizing the need to use organic N inputs judiciously. The perennial grass alley cover crop resulted in the greatest overall reduction in NO 3 leaching. Our data indicate that no single management strategy is sufficient to protect groundwater quality. Rather, an integrated package of improved practices (i.e., application of a reduced rate of mineral N through fertigation, combined with ET‐scheduled irrigation and perennial alley crop) is necessary to protect groundwater quality.
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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) | 0.000 | 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 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".