Relationship of the Illinois soil nitrogen test to spring wheat yield and response to fertilizer nitrogen
Bibliographic record
Abstract
The Illinois soil N test (ISNT) has been used to distinguish between soils that are responsive and non-responsive to fertilizer N in Illinois. We examined the suitability of this test, together with more traditional measures of soil fertility, including spring nitrate-N and soil organic carbon (SOC), for predicting yield and N fertilizer response of wheat (Triticum aestivum) on hummocky landscapes in Saskatchewan. The relationship between ISNT-N and wheat yield and fertilizer N response was assessed using data and soils previously collected for a variable-rate fertilizer study. Soils were re-analyzed for ISNT-N. Our goal was to determine if ISNT-N could be used to improve the prediction of crop yields. Although ISNT-N was correlated with both unfertilized wheat yield (r = 0.467, P = 0.01) and fertilizer N response (r = -0.671, P = 0.01) when data from all study sites were combined, correlations varied according to landscape position and site. Stronger correlations between nitrate-N and both unfertilized wheat yield (r = 0.721, P = 0.01) and fertilizer N response (r = -0.690, P = 0.01) indicated that ISNT-N offered no advantage over nitrate-N. Although both tests broadly discriminated between sites with high or low N fertility, few relationships were detected on a point-by-point basis within a field. Stepwise regression equations predicting yield and yield response did not include ISNT-N, due in part to the high degree of collinearity between ISNT-N and other variables such as SOC, suggesting that ISNT-N alone was not a key indicator of soil N supply. Key words: Illinois soil nitrogen test, potentially available N, soil N, fertilizer N recommendations
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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 | 0.000 | 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) | 0.001 | 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".