Fertilization of a mixed forage crop with fresh and composted chicken manure and NPK fertilizer: Effects on dry matter yield and soil and tissue N, P and K
Bibliographic record
Abstract
An experiment was conducted for 3 yr to compare the effects of fertilization of mixed forage with fresh and composted chicken manure and inorganic NPK. Fertility amendments were applied each year at three N levels and an unfertilized treatment was included. The experiment was conducted in two hay fields near Truro, Nova Scotia. Dry matter yield and forage N, P and K contents are reported for the 1992 and 1993 seasons. The nutrient content of the amendments as well as the efficiency of P and K use are discussed. Soil Mehlich 3-extractable P and K and KCl-extractable N were measured at two depths in September 1993. By the final year of the experiment, compost amendments produced yields as high as the recommended rate of NPK fertilization at both sites on both harvest dates. Medium rates of compost application also resulted in tissue N levels as high as or higher than the equivalent NPK treatments. The medium rate of manure application was equivalent in yield and tissue N to the compost treatments at the Acadia site, but had slightly lower yields and tissue N content at the Pugwash site. Compost P and K were relatively inefficiently used by the forage; this resulted in increased levels of Mehlich 3-extractable P and K in the 0- to 15-cm layer of the compost-amended plots. It was concluded that fertilization with compost or inorganic NPK, supplying equal amounts of N, can result in comparable yields and quality of forage. Key words: Chicken manure, compost, extractable NPK, forage, forage NPK, plant protein
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 | 0.000 |
| 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".