Nitrogen, irrigation, and alley management affects raspberry crop response and soil nitrogen and root‐lesion nematode dynamics
Bibliographic record
Abstract
Abstract There is a need to improve raspberry crop nitrogen (N) management practices, particularly when grown over aquifers vulnerable to nitrate (NO 3 ) leaching. This study quantified the effects of N, irrigation and alley management strategies on berry yield, indices of crop vigor and N status, growing season soil N dynamics, and root‐lesion nematode (RLN) population dynamics under red raspberry production in British Columbia, Canada. Conventional management (100 kg N ha −1 surface broadcast on the row, clean cultivation of alleys, and drip irrigation for a fixed duration regardless of evapotranspiration [ET]) was compared with different mineral fertilizer N rates, application of N as manure, seeding the alley to either a perennial forage grass (perennial ryegrass [ Lolium perenne L.] and ‘Bridgeport II’ chewings fescue [ Festuca rubra subsp. commutate]) or an autumn‐seeded spring barley crop, or ET‐scheduled irrigation. In addition, the combination of ET‐scheduled irrigation plus fertigation of a reduced rate of N was compared with conventional practices at a reduced N rate. There was little or no crop response to N source and rate, a finding attributed primarily to high nonmanaged N inputs, and possibly also to RLNs present at population densities (grand mean = 4 per cm 3 soil) expected to suppress raspberry growth. ET‐scheduled irrigation reduced water use ∼50% compared with fixed‐duration irrigation without compromising crop performance. The perennial forage grass in the alley reduced soil mineral N but not yield. Taken together, these findings demonstrate that more environmentally sustainable raspberry production can be achieved through integrated management systems even in soils vulnerable to NO 3 leaching.
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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".