Recycling Soil Nitrate Nitrogen by Amending Agricultural Lands with Oily Food Waste
Bibliographic record
Abstract
With current agricultural practices the amounts of fertilizer N applied are frequently more than the amounts removed by the crop. Excessive N application may result in short-term accumulation of nitrate nitrogen (NO3-N) in soil, which can easily be leached from the root zone and into the ground water. A management practice suggested for conserving accumulated NO3-N is the application of oily food waste (FOG; fat + oil + greases) to agricultural soils. A two-year field study (1995-1996 and 1996-1997) was conducted at Elora Research Center (43 degrees 38' N, 80 degrees W; 346 m above mean sea level), University of Guelph, Ontario, Canada to determine the effect of FOG application in fall and spring on soil NO3-N contents and apparent N immobilization-mineralization of soil N in the 0- to 60-cm soil layer. The experiment was planned under a randomized complete block design with four replications. An unamended control and a reference treatment [winter wheat (Triticum aestivum L.) cover crop] were included in the experiment to compare the effects of fall and spring treatment of oily food waste on soil NO3-N contents and apparent N immobilization-mineralization. Oily food waste application at 10 Mg ha(-1) in the fall decreased soil NO3-N by immobilization and conserved 47 to 56 kg NO3-N ha(-1), which would otherwise be subject to leaching. Nitrogen immobilized due to FOG application in the fall was subsequently remineralized by the time of fertilizer N sidedress, whereas no net mineralization was observed in spring-amended plots at the same time.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".