Optimization of Liquid Swine Manure Sidedress Rate and Method for Grain Corn
Bibliographic record
Abstract
Sidedressing may provide a better window of opportunity for land application of liquid swine (Sus scrofa) manure than early spring or fall application. Rates could be fine‐tuned to match crop N demand using the presidedress nitrate test (PSNT) if: (i) the yield response function to sidedress rate is consistent and (ii) yield and PSNT are positively correlated. To optimize application rate and method, we measured corn (Zea mays L.) grain yield response to in‐row injection (INJ) and topdress (TD) of liquid swine manure (LSM) sidedressed at different rates on clay loam (51‐cm rows in 1999) and silt loam (75‐cm rows from 2000–2002). Yields exceeded local long‐term averages with INJ in all but the wettest year, were variable with TD, and were 2 Mg ha−1 greater with INJ than TD at 37.4 m3 LSM ha−1. From the quadratic yield response to sidedress injection rate, optimal rate (to achieve 95% maximum yield) ranged from 38 to 63 m3 ha−1 (plot‐scale data; four 6‐m sections per plot) and 37 to 49 m3 ha−1 (field‐scale data; 0.2‐ha plots). Yields were correlated with the PSNT (r = 0.75 for no LSM sidedress; r = 0.24 for all treatments). Given the consistent yield response to sidedress INJ rate and accurate (correct 88% of the time) PSNT‐based predictions of additional N requirements (from comparisons of N fertilizer recommendation and relative yield), sidedress injection of LSM using the PSNT to fine‐tune rates according to crop N requirements can be considered as a best management practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".