Precision Injection of Dairy Sludge on Crop Yield and N and P Uptake in Juvenile and Mature No-Till Silage Corn
Bibliographic record
Abstract
Starter mineral fertilizer is used by famers to provide phosphorus (P) and nitrogen (N) to emerging corn (Zea mays) plants. Recent studies have shown that dairy slurry can replace mineral fertilizer provided it is precisely positioned close to the corn rows. This 5-year study examined the multi-year effect of precision injected sludge, the thick fraction separated from dairy slurry, on growth and nutrient uptake at the 6-leaf stage and final harvest of no-till corn. The sludge was first injected 15 cm deep and the corn planted < 10 cm from the sludge furrow at least 3 days later. Sludge provided sufficient P for both early growth and full season growth. At final harvest with equivalent total N and P rates (32P 250N treatments), fertilizer and sludge had similar maximum yields (17.9 and 17.4 t ha−1, respectively) and P uptake (26 and 25 kg ha−1, respectively) but fertilizer had higher N uptake than sludge (200 and 162 kg ha−1). N uptake and recovery N use efficiency was greater for sludge than fertilizer based on equivalent min.-N which suggests crop benefits in the sludge other than min-N and P. The study shows that precisely injected dairy sludge can obviate the need for starter mineral fertilizer, and this may help to alleviate P surpluses on dairy farms. This practice also provides a use on dairy farms for the separated solids fraction remaining after the thin fraction is decanted and applied as the primary N source to grass.
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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 | 0.000 |
| 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 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".