Nitrogen loss by surface runoff from different cropping systems
Bibliographic record
Abstract
Reducing nitrogen (N) loss from agricultural soils as surface runoff is essential to prevent surface water contamination. The objective of 3-year study, 2007–09, was to evaluate surface runoff and N loss from different cropping systems. There were four treatments, including one single-crop cropping system with winter wheat (Triticum aestivum L.) followed by summer fallow (wheat/fallow), and three double-cropping systems: winter wheat/corn (Zea mays L.), wheat/cotton (Gossypium hirsutum L.), and wheat/soybean (Glycine max L. Merrill). The wheat/fallow received no fertiliser in the summer fallow period. The four cropping systems were randomly assigned to 12 plots of 5 m by 2 m on a silty clay soil. Lower runoff was found in the three double-cropping systems than the wheat/fallow, with the lowest runoff from the wheat/soybean. The three double-cropping systems also substantially reduced losses of ammonium-N (NH4+-N), nitrate-N (NO3–-N), dissolved N (DN), and total N (TN) compared with the wheat/fallow. Among the three double-cropping systems, the highest losses of NO3–-N, DN, and TN were from the wheat/cotton, and the lowest losses were from the wheat/soybean. However, the wheat/soybean increased NO3–-N and DN concentrations compared with wheat/fallow. The losses in peak events accounted for >64% for NH4+-N, 58% for NO3–-N, and 41% for DN of the total losses occurring during the 3-year experimental period, suggesting that peak N-loss events should be focussed on for the control of N loss as surface runoff from agricultural fields.
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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 | 0.001 | 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".