Integrating Soil and Weather Data to Describe Variability in Plant Available Nitrogen
Bibliographic record
Abstract
Although there are economic and environmental reasons to manage fertilizer‐nitrogen (N) more effectively in variable landscapes, the impact of weather and its interaction with soil properties/landscape attributes or management practices has received little attention. The objectives of this study were to assess the magnitude of temporal and spatial variability in soil and plant N in a variable landscape under different management practices and to assess the dependence of their temporal variability on readily available weather variables such as air temperature and rainfall. The experiment was conducted from 1997 to 2003 on a simple slope under three maize ( Zea mays L.) based cropping systems. Soil and shoot N were measured through the growing season and the sum used as a measure of plant available N (PAN). Values of PAN varied with year, treatment, landscape position, and year × treatment and year × treatment × position interaction terms. The effects were quantified for each management treatment using multiple regression analyses to relate PAN to soil organic carbon (OC), cumulative degree days (CDD), and cumulative rainfall (CRF) in different periods within the growing season. Plant Available Nitrogen was most strongly influenced by rainfall early in the growing season and exhibited a nonlinear response to OC and CRF. The regression model predicted spatial patterns that were generally stable when applied to historical weather data; PAN increased with OC in 12 of the 15 yr. The analyses illustrate the feasibility of combining soils and weather data to predict N dynamics in variable landscapes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".