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 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 | 0.000 | 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 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".