Relationships of Total Soil Nitrogen to Several Soil Nitrogen Indices
Bibliographic record
Abstract
The usefulness of soil nitrogen (N) tests or indices for predicting crop N requirements has been controversial. This paper shows how the relationship between soil indices and total soil N varies using simple linear regressions. The soil N indices included measurement of extractable NH4 + following autoclaving or anaerobic incubation, or following “hot” (100°C) KCl extraction. Also, extractable NO3 − levels sampled four times during the early growing season (May/June) were correlated to total N. Total soil N was considered to be a relatively stable “bench mark” for this study. Soil samples (0–30 cm) were collected from field trials that focused on response of corn to N with different red clover cover crop management practices on different landscape locations over a 3-year period. Management practices included comparisons between no tillage and conventional tillage done in either the late fall or spring. It was observed that the correlations between total N and any of the soil N indices were extremely variable from year to year with each cover crop management practice. The correlations also varied with respect to slope location from year to year in an unpredictable manner. It was concluded that N indices only partially and variably reflect the N released from the large humus N pool in the soil, and are therefore unpredictably related to other more labile N pools (e.g., residues) in the soil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".