Predicting Nitrogen Requirements for Corn Grown on Soils Amended with Oily Food Waste
Bibliographic record
Abstract
Soil and plant indices of soil fertility status have traditionally been developed using conventional soil and crop management practices. Data on managing N fertilizer for corn ( Zea mays L.) produced on soils amended with C‐rich organic materials, such as oily food waste is scarce. There is a need to identify reliable methods for making N fertilizer recommendations under these conditions. The objective of this research was to evaluate different soil and plant indices for predicting N requirements for successful corn production on fields receiving oily food waste. Experiments were conducted at Elora Research Center (43° 38′ N lat., 80° W long., 346 m above sea level), University of Guelph, and on a private farm in Bellwood, ON, over 3 yr (1995–1997) where oily food waste was applied as a C‐rich organic material. Oily food waste application rate, time, and field slope position affected the maximum economic rate of N application (MERN). The greatest MERN (182 kg ha −1 ) was for the highest food waste application rate (20 Mg ha −1 ) applied in spring. The lower slope position had the least MERN (0 kg ha −1 ), showing that no extra N as fertilizer was needed at these positions of a field amended with oily food waste. Different soil and plant N indices (NO 3 –N, NO 3 –N + NH 4 –N, hot KCl NH 4 –N, hot KCl potentially available organic N, hot K 2 SO 4 total soluble N, and chlorophyll meter readings (CMRs), were evaluated for making N fertilizer recommendations for corn grown on oily food waste amended soils. Presidedress soil NO 3 –N in the 0‐ to 30‐cm soil depth had the highest correlation with MERN and can be used as a soil index to make N fertilizer recommendations for corn grown on oily food waste amended soils.
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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".