Yield and Nutrient Export of Grain Corn Fertilized with Raw and Treated Liquid Swine Manure
Bibliographic record
Abstract
Treatment of liquid swine manure (LSM) is an option to improve nutrient management. Mineral fertilizer, raw LSM, and LSM treated by anaerobic digestion, flocculation, filtration, or natural decantation were sidedressed (100 kg N ha−1) to grain corn (Zea mays L.) on a clay and a loam soil. Over 3 yr, corn grain yield (6 to 11 Mg ha−1), N export (83 to 176 kg ha−1), and P export (19 to 40 kg ha−1) were similar among LSM types and between LSMs and mineral fertilizer. This was attributed to the immediate incorporation of LSM to minimize N volatilization. Treated LSMs reduced P input to soil by 3 to 24 kg ha−1, compared with raw LSM. This reduced corn P export by 2 to 4 kg ha−1 on the clay soil, but had no effect on the loam soil. Soil NO3 content after harvest was higher with the mineral fertilizer (19–31 kg NO3–N ha−1) than with LSMs (13–20 kg NO3–N ha−1) in the clay soil, but was similar for all treatments in the loam soil. We conclude that when sidedressed to corn and immediately incorporated, raw and treated LSMs have a fertilizer value similar to the mineral fertilizer. Moreover, the risk of postharvest NO3 accumulation with the raw and treated LSMs was similar to mineral fertilizer on the loam and lower on the clay.
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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) | 0.001 | 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".