Potential of Chemically Labile Fractions to Measure Mineralizable Soil Nitrogen
Bibliographic record
Abstract
Nitrogen (N) in the soil is largely organic and is available to crops only after it is mineralized to inorganic N by microbial or enzyme action. To develop a soil test for guiding N applications, a method to predict the relative amount of organic N that will mineralize in a growing season is necessary. Several chemical analysis methods proposed in the literature to measure mineralizable N were examined for chemical interference, measurement precision, response to procedure modifications, and ability to distinguish differences among soils. The chemical analyses examined involved various acid or alkaline hydrolysis, with the resulting inorganic ammonium N measured by steam distillation and manual or automated diffusion. A gelatinous precipitate in the filtered and neutralized 6 M hydrochloric acid (HCl) hydrolysis solution interfered with magnesium oxide (MgO) diffusion traditionally used to measure inorganic ammonium N. Removing the precipitate appeared to circumvent the interference. The precipitate did not appear to interfere with the sodium hydroxide (NaOH) diffusion. The 6 M HCl hydrolysis extracted 34 to 103% of clay‐fixed ammonium in the soils. Steam distillation was shown to be an acceptable alternative to diffusion for measuring NaOH‐labile N. The vigor of NaOH measurement conditions caused differences in results, showing that precise and reproducible conditions are necessary. Several methods were closely correlated (r2 > 0.62) with N mineralized during aerobic incubations and could be considered for further evaluation for soil N testing. This study showed that modifications are required to several proposed analytical methods to improve their potential to estimate mineralizable N for fetilizer or other amendment recommendations for crop production
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".