Prediction of Soil Nitrogen Supply in Corn Production using Soil Chemical and Biological Indices
Bibliographic record
Abstract
Assessment of the soil N supply capacity is essential to optimize N fertilizer use. The soil N supply capacity of 102 soil samples (0–15 cm) from 25 sites collected from 2004 to 2007 across four Canadian provinces was evaluated by comparing a group of chemical N availability indices with soil mineralizable N pools and a field‐based measure of soil N supply. Soil N supply was estimated by corn ( Zea mays L.) N uptake corrected for starter fertilizer N. Two subgroups were created based on the soil texture and were compared to the whole data set. Grouping soils provided limited benefits in predicting soil potentially mineralizable nitrogen ( N 0 ), but improved the prediction of soil N supply. The N 0 was weakly related to soil N supply for the whole data set ( r = 0.09) and in fine‐textured soils ( r = 0.37) but the relationship was improved ( r = 0.68) in medium‐ to coarse‐textured soils. The N 0 was not necessarily a good predictor of soil N supply under field conditions which emphasizes the need to also consider environmental conditions. The UV absorbance of a 0.01 M NaHCO 3 extract at 205 nm (NaHCO 3 –205), the hot KCl extractable NH 4 –N (HotKCl–N) and Pool I (a labile mineralizable N pool) plus NO 3 –N were the most promising N availability indices because they are easy to perform and they were positively and significantly related to soil N supply in the whole data set as well as the soil texture subgroups (0.28 ≤ r ≤ 0.62). This study demonstrated that grouping soils based on texture can increase the proportion of variation in soil N supply explained by N availability indices when data from contrasting environmental conditions, soil types, and years are used.
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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 | 0.001 |
| 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) | 0.000 | 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".