Nitrogen and phosphorus uptake from solid and liquid pig manure in perennial and annual cropping systems
Bibliographic record
Abstract
Applying pig manure in excess of crop demand can result in nutrient loss to water bodies. We studied the effect of liquid and solid pig manures and their N- and P-based application rates on yield and nutrient uptake in annual and perennial cropping systems for 3 yr. The experiment had a split-plot design with five nutrient management treatments including liquid-N (annual N-based liquid pig manure), liquid-P (P-based liquid pig manure once every 5 yr), solid-N (annual N-based solid pig manure), solid-P (P-based solid pig manure once every 5 yr), and control (no manure). The liquid-P treatment showed high apparent nitrogen and phosphorus recovery (ANR and APR, respectively) in each cropping system and yields similar to that of the liquid-N treatment. The solid-N treatment had the smallest ANR in the perennial cropping system (9%–27%) and also the smallest APR in both cropping systems (typically <4%) possibly due to N deficiency and high P application rate. The current formula for estimating organic N mineralization overestimated the available N, particularly for solid pig manure, reducing crop yield. Based on the ANR obtained in this study, a more appropriate coefficient is 10%–15% of the organic N from solid pig manure for no-till perennial cropping systems.
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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 | 0.001 |
| 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".