Nitrogen Recovery and Partitioning with Different Rates and Methods of Sidedressed Manure
Bibliographic record
Abstract
Animal manure is an important source of N for crops in areas with intensive livestock production. Variable manure N availability can incite over‐application of manure or supplemental fertilizer leading to low N recovery and possible negative environmental and economic impacts. To improve manure N use efficiency, the effects of rate and method of sidedress application of liquid swine ( Sus scrofa ) manure (LSM) on N recovery by corn ( Zea mays L.) were determined. We used in‐row injection (INJ) or topdressing (TD) to sidedress LSM from 1999 to 2002 at rates ranging from 0 to 93.5 m 3 ha −1 , and measured grain N uptake and NO 3 –N in drainage tile water, stalks, and topsoil postharvest. Apparent recovery of manure total N (LSM‐N) ranged from 0 to 57% and was greatest with injection of 37.4 m 3 ha −1 (194 kg LSM‐N ha −1 ). Injection rate to achieve 95% of maximum grain yield averaged 216 kg LSM‐N ha −1 over 4 yr. Transport of LSM‐N to ground‐ and surface waters was minimized when sidedressed at or below rates for optimal yield. When injected N exceeded crop demand, NO 3 –N increased to over 10 mg kg −1 in topsoil, 20 mg L −1 in drainage water, and to excessive (3.6 g kg −1 ) levels in stalks. Due to greater LSM‐N recovery, injection (59%) is recommended rather than topdress (41%) for sidedress application of manure.
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 | 0.000 | 0.001 |
| 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 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".