Effect on forage yield and nutrient uptake of long-term surface application of manure at various rates and times in the growing season to timothy grown on two contrasting soils
Bibliographic record
Abstract
Although much of the manure in Canada is surface-applied to forages, little research exists evaluating time of year (Time) and rate (Rate) of application on forage yield and nutrient uptake. Field trials (10 yr) on two soils (sandy loam upland and silty clay loam dykeland) investigated this. Experimental arrangement was a factorial [Time (spring, summer, early, and late fall manure applications)] plus a control [spring-applied ammonium nitrate fertilizer (ANF)] in a Latinized split plot. ANF at 0, 25, 50, 100 and 200 kg N·ha−1; 0, 75, 150, and 300 as semi-solid beef (SSM) and 150 kg N·ha−1 as liquid dairy manure (LDM), constituted respective splits. The Time × Rate interaction, later in the trial on the upland soil, showed higher yields and nutrient uptakes with fall manure application. There was little interaction on the dykeland soil; summer application resulted in higher yields at times. For both soils, the optimal long-term application rate of SSM was approximately 150 kg N·ha−1 while that of ANF was approximately 100 kg N·ha−1. Inherent fertility of dykelands resulted in lesser responses to manure addition. Negligible and significant residual N occurred with fertilizer and manure, respectively. Nitrogen, phosphorus, potassium, calcium, magnesium, manganese, copper, zinc, and boron uptakes were due to amendment impact on yield. The recommended rate is 150 kg N·ha−1 of SSM or LDM applied in fall and summer to Maritime grasslands grown on upland and dykeland soils, respectively. Yield differences may not warrant producers adjusting timing of in-season manure application.
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 | 0.001 | 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".