Poultry manure effects on soil nitrogen processes and nitrogen accumulation in red raspberry
Bibliographic record
Abstract
This study examined the effects of solid poultry layer manure addition on soil N processes and on dry matter and N accumulation in red raspberry (Rubus idaeus L.). In trials conducted in two years, approximately 50% of the 400 kg total N ha−1 applied as manure was recovered as soil inorganic N 1 mo after manure application when manure was incorporated within 4 h of application. Three trials were conducted in two commercial raspberry fields: one with no history of manure use and one other with a history of heavy annual applications of poultry manure. Treatments included 55 kg N ha−1 as NH4NO3, 100 or 200 kg total N ha−1 as manure, and a control that received no manure or fertilizer N. Soil inorganic N to 60 cm depth was measured throughout the growing season. Berry yield was estimated, and dry matter and N accumulation was determined in floricanes at first berry ripening and in primocanes at the end of the growing season. Few significant effects of N fertilization were measured for any crop yield, growth or N accumulation parameter. This was attributed to the large (>150 kg N ha−1) supply of N to the crop in the unamended soil, primarily from soil N mineralization. Dry matter accumulation in the fruiting clusters was strongly correlated to estimated berry yield, and may provide a simple means for assessing relative yield within experiments. Soil nitrate measured in August after berry harvest may serve as a "report card" to assess N management in the current growing season, to refine fertilizer N management for subsequent growing seasons, and as an index of the risk of nitrate leaching over the following fall and winter in south coastal British Columbia. Key words: Nitrogen mineralization, nitrate leaching, manure N availability, Rubus idaeus
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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".