Soil nitrogen dynamics as affected by landscape position and nitrogen fertilizer
Bibliographic record
Abstract
The influence o f landscape position on the dynamics of N in the soil-plant system has not been adequately studied. Our aim with this study on a predominantly Black Chernozem soil was to evaluate the effect of slope position (upper vs. lower) and N fertilizer application (none vs. 60 kg N ha-1) on soil and wheat (Triticum aestivum L.) N through the growing season. Landscape position had a dominant effect on soil NO3− and soluble organic N (SON) concentrations, especially in the surface 15 cm. These pools of soil N and net N mineralization were greater at the lower than at the upper slope position. The landscape effect is attributed to higher organic matter content (as measured by organic C) and water availability in lower compared with upper slope positions. Nitrogen application had no measurable effect on soil NO3− and SON concentrations. Exchangeable and non-exchangeable NH4+ were little affected by slope position or N fertilization. Nitrogen application increased wheat N uptake; however, its influence was less than that of slope position, especially on N accumulation in wheat heads during grain-filling. Although N application increased wheat yields, landscape position exerted the greater influence: grain yield was less on upper than lower slope positions due to earlier onset of crop maturity. During grain filling, net N mineralization was suppressed at the upper slope position and by N application. The increase in crop yield and N uptake due to N application was not significantly different between slope positions. This study demonstrated that landscape position had a greater influence on N dynamics and availability than the application of typical amounts of fertilizer N and that the two effects were mostly independent of each other. Key words: Available N, landscape position, N uptake, net N mineralization, soluble organic N
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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 | 0.000 | 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 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".