Grain protein as a post-harvest index of nitrogen status for winter wheat in the northern Great Plains
Bibliographic record
Abstract
The use of grain protein as a post-harvest index of N fertility status has been promoted for spring wheat (Triticum aestivium L.) through the establishment of critical levels for segregating wheat into N deficient vs. N sufficient classes. The objectives of this study were to evaluate this concept for winter wheat in the northern Great Plains; and to estimate the added N requirements necessary to achieve maximum yield when protein concentrations fall below the critical level. A field study consisting of three water regimes, four cultivars, and five fertilizer N levels was conducted near Havre, MT. A consistent relationship between relative yield and grain protein was found and a critical protein concentration of 121 mg g-1 was defined using Cate-Nelson R2 statistics. Protein concentrations below the critical level were associated with yield losses from N deficiency (79% frequency), while protein concentrations ≥ the critical level were associated with N sufficiency (93% frequency). Under conditions of mode rate N deficiency (68–99% of maximum), protein concentration could be used to estimate the amount of additional N that would have been needed to achieve maximum yields. This is accomplished by first calculating the difference between the critical and actual protein concentration (expressed in mg g-1 protein). This protein deficit is then multplied by a fertilizer N equivalent that varied from 20 to 38 kg N ha-1 (according to the precipitation environment) for each 10 mg g-1 rise in protein desired. Key words: N sufficiency, N deficiency, critical protein concentration, plant available N
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".