Effect of crop rotations on NO<sub>3</sub> leached over 17 years in a medium-textured Brown Chernozem
Bibliographic record
Abstract
High NO3 concentration in drinking water can be a health hazard, but properly fertilized rotations containing cereals and pulses or perennial grasses reduce the risk of NO3 leaching. Over fertilization, and sometimes under fertilization, frequent summer fallowing, and use of legume green manure may increase the risk of NO3 leaching in subhumid areas. We used a crop rotation study, initiated in 1987 on a medium-textured Brown Chernozem at Swift Current, Saskatchewan, to determine the influence of cropping frequency, legume green manure, wheat class and grass hay crop on NO3-N leached beyond the rooting depth of cereals (1.2 m) after 17 yr. Nitrate distribution in the soil to 2.4 m was measured in 1987 and again in 2003. All rotations received N and P fertilizer based on soil tests, and were generally managed using no-tillage. The period had 4% more precipitation than the long-term average (367 mm) with 5 yr exceeding the average by >13%. A comparison of NO3-N content below 1.2 m depth in 1987 and 2003 showed no significant (P < 0.05) leaching has occurred, although the legume (Lens culinaris L.) green manure-wheat-wheat (Triticum aestivum L.) system showed evidence it may eventually leach NO3. Contrary to expectations, continuous-wheat, because of higher N applied and possibly because net N mineralization is small under no-tillage, tended to leach more NO3 than fallow-containing rotations (P = 0.09). Crested Wheatgrass (Agropyron cristatum L. Gaertn) reduced NO3 content to 2.4 m because it is a perennial with deep and extensive roots. There was no effect of wheat class on the amount of NO3 leached. Key words: Cropping frequency, wheat class, lentil green manure, crested wheatgrass
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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 | 0.001 |
| 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) | 0.001 | 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".