Response of No‐Till Grain Crops to Pig Slurry Application Methods and a Nitrification Inhibitor
Bibliographic record
Abstract
Core Ideas Mineral N fertilization of no‐till crops may be replaced by pig slurry. Pig slurry with and without dicyandiamide was injected or broadcast in a no‐till soil. Pig slurry injection in a no‐till soil increased grain yield and crop N use efficiency. Dicyandiamide added to slurry improved crop yield and N use efficiency only in winter crops. The effects of application methods and nitrification inhibitors on the fertilizer value of pig slurry (PS) in no‐till crops are still poorly documented. We evaluated grain and straw yield, and N accumulation of no‐till corn ( Zea mays L.), oat ( Avena strigosa Schreb.), and wheat ( Triticum aestivum L.) from 2011 to 2015 on a loam soil under a subtropical climate. The crops received either: (i) no fertilizer, no dicyandiamide (DCD) (control), (ii) surface‐broadcast of urea‐N (reference treatment), (iii) surface‐broadcast pig slurry (PSs), (iv) PSs + DCD, (v) shallow‐injected pig slurry (PSi), or (vi) PSi + DCD. Broadcast applications were performed manually whereas injection in furrows (≈10 cm) was made with a commercial applicator. Corn and wheat grain yields were similar with pig slurry and mineral fertilizer, confirming the good fertilizer value of pig slurry for no‐till grain crops. Compared with surface broadcast, shallow injection of pig slurry increased grain yields of corn (+1.5 Mg ha −1 ) and wheat (+0.3 Mg ha −1 ), nitrogen agronomic efficiency (NAE) of corn (+9 kg grain kg −1 total N applied) and wheat (+2 kg grain kg −1 N), and apparent nitrogen recovery (ANR) in corn (46–68%) and wheat (29–38%). Crop performance was generally not affected by DCD, except for wheat in 2013 with increased yield (+15%), NAE (+2.7 kg grain kg −1 N), and ANR (31–39%). Pig slurry injection improved yield and N use efficiency of no‐till grain crops, whereas DCD addition to pig slurry appeared more favorable for winter than summer crops.
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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 | 0.002 | 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".