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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 teacher head, 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".