Availability of Phosphorus after Long‐term Whole and Separated Slurry Application to Perennial Grass prior to Corn Silage
Bibliographic record
Abstract
Removing solids from whole dairy slurry (WS) can reduce P loading on fields. This study investigated the effects of 11 yr of applications of WS, separated liquid fraction (LF), commercial fertilizer, WS plus fertilizer, and a control on total and extractable (Kelowna) soil P in perennial grass (Festuca arundinacea Schreb.) and two subsequent silage corn (Zea mays L.) crops. Plots received 200 kg N ha−1 with or without 40 kg P ha−1. Shoot biomass, P uptake, arbuscular mycorrhizal fungi root colonization, and soil microbial P were determined for corn at six leaves. Whole‐crop and grain yield, P uptake, dry matter, and grain content were determined at maturity. The LF treatment received 30% less manure P than WS, had lower extractable soil P at 0 to 15 and 15 to 30 cm, and had lower total P at 0 to 15 cm. Historical treatments had no effect on soil microbial P or arbuscular mycorrhizal fungi colonization. At six leaves, corn shoot biomass, P concentration, and P uptake were related to historical P rates in Year 2, but the relationship was not apparent in Year 1. Starter P fertilizer increased shoot yield and P uptake for all treatments in both years. At maturity, most yield parameters for WS and LF were similar in Year 1, but in Year 2, there was a significant response to starter P fertilizer and a positive relationship between historical P rate and corn performance. This study shows that long‐term LF treatments reduced the risk of P pollution with relatively little effect on the P response of subsequent corn crops. Core Ideas Historical N‐based LF treatments had lower P surplus and soil P than WS. Long‐term WS and LF treatments had no effect on AMF and soil microbial P. Juvenile corn responds to starter P in high‐P‐testing soils. The residual effects of long‐term LF on subsequent corn were small. Use of LF may help reduce soil P accumulation on dairy farm soils.
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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) | 0.001 | 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".