Yield and Seed Composition Responses of Rotation Corn and Soybean To Phosphorus Supplied in Liquid, Solid and Composted Swine Manures
Bibliographic record
Abstract
Abstract Purpose Optimization of phosphorus (P) fertilization from livestock manure can maximize crop production while protecting water quality. We assessed the effects of different forms (liquid, solid, and liquid composted with wheat straw) of swine manure application on yield and seed composition (N: P ratio) under corn-soybean rotation in a clay loam soil, Ontario, Canada. Methods Under an unified available nitrogen (N) rate (200 kg N ha − 1 ), P in each of liquid, solid and composted swine manures was applied to corn ( Zea mays L.) in a 4-year corn-soybean ( Glycine max L.) rotation at 0, 50 and 100 kg P ha − 1 (P-based), respectively, plus another P rate treatment from N-based application at 200 kg available N ha − 1 , forming a series of four P rates. Chemical P fertilizer was also applied at 0, 50 and 100 kg P ha − 1 , respectively, with the same unified N rate. Results The parabolic corn yield response to P rate was observed in liquid swine manure, but not in solid and composted swine manures. Corn seed N concentration and seed N: P ratio in alternative years were highly related to the yield, with optimum N: P ratio at 4.75. Soybean yields in both subsequent years responded to P rate of manure addition in a parabolic pattern with optimized P rate applied to preceding corn at 70 kg P ha − 1 . Soybean yield with swine manure application was related to seed N: P ratio but varied from the year of crop rotation, suggesting the temporal changes in soil P availability with manure addition. Conclusions The relationship between corn yield and seed N: P ratio with manure application suggests that adjusting N: P ratio in manure might lead a way to optimizing corn yield and provide a new approach for manure application recommendation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".