Comparison of nitrogen application via a trickle irrigation system with surface banding of granular fertilizer on red raspberry
Bibliographic record
Abstract
A field trial compared applications of ammonium nitrate N as soil-surface bands of granular fertilizer with applications as a solution through a trickle irrigation system (i.e., fertigation) on yield, plant growth and end-of-season extractable soil inorganic nitrogen of red raspberries (Rubus idaeous L.). The granular application was made once in early spring, and the fertigation was scheduled as eight weekly applications from early to late spring in each of 4 successive years. Three rates of N (33, 67 and 134 kg N ha−1) were applied by both methods and a zero N control was also included. The N treatments had relatively small effects on berry yields, being significant in the last 2 years (1988 and 1989) only. Applications of granular N resulted in equal or greater yields than the control, whereas the fertigation method resulted in equal or lower yields than the control. Granular applications tended to enhance cane growth and N concentrations in the fruiting cluster, laterals and leaves of fruiting canes. Leaching of N during the growing season was greater with the fertigation than with the granular method of application. The poorer performance of fertigation relative to granular application of N was due to the timing of application in relation to crop requirement. Raspberries require a majority of their N early in the growing season. The utility of applying fertilizer N via irrigation systems on crops such as raspberry in climatic regions where water deficits do not usually occur early in the growing season, as in coastal British Columbia, is limited. Key words: Fertigation, soil N, yield, soil inorganic N, leaching, Rubus idaeous L.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".