Relationships Between Soil Nitrogen Availability Indices, Yield, and Nitrogen Accumulation of Wheat
Bibliographic record
Abstract
The success of variable rate N fertilizer application rests on our ability to predict the contribution of soil N to growing crops. We assessed relationships between soil N availability indices (SNAIs), yield, and total N accumulation of wheat ( Triticum aestivum L.) grown in a typical glacial till landscape in Saskatchewan, Canada. Soil samples were collected at 3‐m intervals along a 300‐m transect comprised of low (LCFS) and high catchment footslopes (HCFS), and low (LCSH) and high catchment shoulders (HCSH). Total soil N and C, organic C, mineral N, depth of A horizon, spring soil moisture, grain yield, and total plant N were measured. Soil N availability indices used in this study included: (i) cumulative N released during a 2‐wk aerobic incubation (N MIN ); (ii) potentially mineralizable N estimated using a 16‐wk aerobic incubation (N 0 ); (iii) NO 3 sorbed on anion‐exchange membranes (NO 3AEM ); (iv) N extracted with hot KCl (N KCl ); and (v) N hydrolyzed with hot KCl (N HYDR ). Although all SNAIs were significantly correlated to yield and, with the exception of N 0 , total plant N when analyzed across the transect, typically <40% of the yield variability was explained. Forward stepwise regression revealed that most SNAIs failed to explain more variability in crop N accumulation than did basic soil properties or relative elevation. Although these results do not invalidate the use of SNAIs for soil testing purposes, it is clear that SNAIs must be combined with additional information about field scale variability for predicting fertilizer N requirements. Without this information, grid sampling as a means of assessing N requirements remains ill‐advised for glacial till semi‐arid 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 | 0.000 | 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 | 0.000 | 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".