Quantifying Nitrate Leaching under Commercial Red Raspberry Using Passive Capillary Wick Samplers
Bibliographic record
Abstract
Core Ideas NO 3 leaching was quantified by passive capillary wick samplers over 2 yr. NO 3 leaching was three times greater in Year 1 than Year 2 (240 vs. 80 kg N ha −1 ). Increased Year 1 NO 3 leaching reflected field renovation prior to monitoring period. There was strong seasonality in NO 3 leaching from the field. Despite fertilizer banding in rows, 60% of NO 3 leached from alleys between rows. Groundwater NO 3 –N contamination in the Abbotsford‐Sumas Aquifer in British Columbia, Canada, has been attributed primarily to NO 3 –N leaching from red raspberry ( Rubus idaeus L.); however, direct estimates of NO 3 –N leaching are lacking. This study quantified the magnitude and timing of NO 3 –N leaching under a commercial red raspberry field over 30 mo (October 2010–March 2013) using passive capillary wick samplers installed below the root zone at three row locations (irrigated row, nonirrigated row, and alley) after the critical period of field renovation and replanting. Substantial NO 3 –N leaching (240 kg N ha −1 ) during the first year of monitoring was attributed to the effects of field renovation (including autumn chopping and incorporation of raspberry canes and soil fumigation and spring poultry broiler manure application) in the year prior to the initiation of monitoring. Lower NO 3 –N leaching (80 kg N ha −1 ) occurred in the second year of monitoring under typical mineral fertilizer management practices. Strong seasonality of NO 3 –N leaching was observed in both years, with ∼48% in autumn, 34% in spring and summer, and 17% in winter. Approximately 60% of the NO 3 –N leaching was attributed to the alleys between raspberry rows, which did not receive mineral fertilizer or irrigation. The high proportion of leaching during spring and summer and from the alleys suggests that growing‐season irrigation practices and alley vegetation management, respectively, would be good targets for the development of improved practices. The samplers were effective in quantifying the magnitude and timing of NO 3 –N leaching from a commercial agricultural field and informing the development of improved practices.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".