Factors Contributing to Changes in Plant Available Nitrogen across a Variable Landscape
Bibliographic record
Abstract
The maximum benefits that may be obtained from site‐specific N management will not be realized until we improve our understanding of the spatial variability in plant available nitrogen (PAN) under different soil and crop management practices across the landscape. The objectives of this study were to quantify the influence of soil factors on the spatial and temporal variability of N availability to corn ( Zea mays L.) grown under different tillage and legume cover crop conditions. Three management treatments were established on a variable landscape in southern Ontario in 1999, 2000, and 2001: barley ( Hordeum vulgare L.) followed by corn under no‐tillage, barley followed by corn under spring plowing and secondary tillage, and barley underseeded with red clover ( Trifolium pratense L.) that was plowed down the next spring and followed by corn. The PAN contents were measured through the growing seasons and related to soil properties, management, and their interactions using regression analysis. The spatial patterns of PAN were temporally stable, suggesting a temporal consistency in the spatial patterns of factors influencing PAN. The variation in soil temperature among landscape positions was very small, often only 1 to 2°C, and its contribution to the spatial variation in PAN was considered negligible. Soil water made only a small contribution to the variability in PAN because the seasonal average water‐filled pore space (SAWFPS) exhibited little variation across the landscape and often fell within the nonlimiting water range (NLWR) for N mineralization. Most of the variation in PAN within a season in this landscape was accounted for by variation in organic carbon (OC) content.
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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 | 0.001 | 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".