Response of the Illinois Soil Nitrogen Test to liquid and composted dairy manure applications in a corn agroecosystem
Bibliographic record
Abstract
Dairy manure is important for corn (Zea mays L.) production in New York. Optimizing corn yield while minimizing environmental loss with manure nutrients is often a challenge. A potential tool for improving N management is the Illinois Soil Nitrogen Test (ISNT), which estimates amino sugar N, a pool of potentially mineralizable N for corn uptake. The objectives of this study were to determine (1) the short-term effects of manure applications on ISNT-N, and (2) the longer-term impacts of annual additions of composted and liquid dairy manure on ISNT-N. A 6-wk incubation study showed that NH4-N from manure temporarily (< 2 wk) increased ISNT results. A 4-yr field study was conducted with annual spring applications of two rates of composted dairy manure (45 and 77 Mg ha-1) and two liquid dairy manure rates (63.5 and 180 kL ha-1). Results showed that ISNT-N slightly decreased over time in check plots (no manure or fertilizer additions) and that increases in ISNT-N over time in compost and liquid manure amended plots were consistent with changes in N credits currently given to manures in New York. Our results suggest that the ISNT accounts for N in previously applied compost and manure amendments, but that samples should not be taken within 2-wk following manure addition. Key words: Amino sugar, compost, Illinois Soil Nitrogen Test, nitrogen, manure, nutrient management
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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 | 0.001 | 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".