Field‐Scale Application of Oily Food Waste and Nitrogen Fertilizer Requirements of Corn at Different Landscape Positions
Bibliographic record
Abstract
Oily food waste (FOG; fat + oil + greases) containing high concentrations of fat, oil and grease is produced by the food service, production, and processing industries. It has a high C to N ratio (90:1) and can recycle soil available N through immobilization and remineralization during its decomposition. Experiments were conducted at a farm (Hillsburg fine sandy loam; Typic Hapludalf) having rolling topography (5 and 9% slope) during 1995 and 1996. Objectives of this study were to (i) examine the variability of available N and corn (Zea mays L.) grain yield at different landscape positions of FOG-amended fields and (ii) determine whether N fertilizer management could be improved by considering the spatial variability of soil NO(3)-N at different landscape positions in FOG-amended fields. A spatial and temporal variability in soil NO(3)-N was observed during both years. Corn grain yields at all N fertilizer application rates were affected by slope position and followed the pattern: lower > upper > or = middle. Nitrogen fertilizer requirements for corn production in conjunction with FOG management were also affected by slope position. Essentially no additional fertilizer N was required for corn production at the lower landscape position. It was estimated that site-specific fertilizer N management on FOG-amended fields could result in an average savings of 51 and 63 kg N ha(-1) (with a potential economical savings of US 42 dollars and US 52 dollars ha(-1)) during 1995 and 1996, respectively.
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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 | 0.000 | 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".