Agronomic approach to evaluate the nitrogen use efficiency of liquid, solid, and composted swine manures in corn–soybean rotation
Bibliographic record
Abstract
Evaluating the nitrogen (N) use efficiency of animal manure is essential to optimize its application for profitable crop production without impairing the environment. A four-year field study was conducted under corn ( Zea mays L.)–soybean ( Glycine max L.) rotation in a Brookston clay loam soil using the yield control approach. Treatments included inorganic fertilizer (IN), and liquid (LM), solid (SM), and composted (CM) swine manure applied at the rate equivalent to an available N of 200 kg ha −1 and a non-fertilization control (CT). Seven N use indices were employed to evaluate N use efficiency. LM obtained comparable corn yields relative to IN. Corn yield in SM was inconsistent from one year to another, and CM had minimal agronomical value in our study. Soybeans with IN posed the highest grain yields of 3,468 and 3,761 kg ha −1 in 2005 and 2007, respectively. In contrast to grain yield and total N uptake, the gain N removal of either corn or soybeans was comparable between the two alternative years. The distinctions between N supply dynamics of manures and their influences on yield, grain N removal, and total above-ground plant N uptake of corn were well discriminated by N use efficiency (NUE), N uptake efficiency (NUpE), N utilization efficiency (NUtE), N agronomic efficiency (NAE), and N recovery efficiency (NRE), but not by N physiological efficiency (NPE) and N harvest index (NHI). Legacy effects on soybean yield from the preceding corn were detected by NAE and NRE. Based on grain yield in conjunction with N use efficiency parameters, the IN performed the best, followed by LM. The NUE, NUpE, NUtE, NAE, and NRE parameters used to evaluate chemical fertilizer N were also applicable to evaluate manure N efficiency.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".