Residual Soil Nitrate after Potato Harvest
Bibliographic record
Abstract
Nitrogen loss by leaching is a major problem, particularly with crops requiring large amounts of N fertilizer. We evaluated the effect of N fertilization and irrigation on residual soil nitrate following potato (Solanum tuberosum L.) harvests in the upper St-John River valley of New Brunswick, Canada. Soil nitrate contents were measured to a 0.90-m depth in three treatments of N fertilization (0, 100, and 250 kg N ha(-1)) at two on-farm sites in 1995, and in four treatments of N fertilization (0, 50, 100, and 250 kg N ha(-1)) at four sites for each of two years (1996 and 1997) with and without supplemental irrigation. Residual soil NO3-N content increased from 33 kg NO3-N ha(-1) in the unfertilized check plots to 160 kg NO3-N ha(-1) when 250 kg N ha(-1) was applied. Across N treatments, residual soil NO3-N contents ranged from 30 to 105 kg NO3-N ha(-1) with irrigation and from 30 to 202 kg NO3-N ha(-1) without irrigation. Residual soil NO3-N content within the surface 0.30 m was related (R2 = 0.94) to the NO3-N content to a 0.90-m depth. Estimates of residual soil NO3-N content at the economically optimum nitrogen fertilizer application (Nop) ranged from 46 to 99 kg NO3-N ha(-1) under irrigated conditions and from 62 to 260 kg NO3-N ha(-1) under nonirrigated conditions, and were lower than the soil NO3-N content measured with 250 kg N ha(-1). We conclude that residual soil NO3-N after harvest can be maintained at a reasonable level (<70 kg NO3-N ha(-1)) when N fertilization is based on the economically optimum N application.
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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 | 0.000 |
| 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) | 0.002 | 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".