Yield, quality and revenue of pickling cucumbers with irrigation and supplemental N fertilizer under a humid climate
Bibliographic record
Abstract
A 5-yr field experiment was conducted in southern Quebec to investigate yield, gross revenue and quality of pickling cucumbers (Cucumis sativus L. ‘Fancipak’) in response to sprinkler irrigation. In addition, supplemental nitrogen treatments (40 kg ha-1 foliar and granular) following application of 80 kg ha-1 N preplant, as well as non-fertilized and over-fertilized controls (80 kg ha-1 N preplant, 40 kg ha-1 N granular and 60 kg ha-1 N slow-release) were compared in a 2-yr trial under irrigated and non-irrigated conditions. During 1997 to 2000, rainfall was close to or above normal and irrigation did not increase marketable yields. In 1999 and 2000, irrigation reduced early marketable yields relative to non-irrigated treatments. In 2000, a relatively cool year with average rainfall, there was a positive yield response to supplemental N in the non-irrigated plots, but not in the irrigated plots. In 2001, a very hot and dry season, irrigation increased early yield by 66%, marketable yield by 160% and gross revenue by 164% compared with non-irrigated treatments. Non-irrigated treatments did not respond to supplemental N, but supplementing the irrigated treatments with 40 kg ha-1 of N increased marketable yields by 22%, generating 18% additional revenues. Applying N in either a foliar or granular form gave similar results. Overall, under a humid climate, irrigation only had a positive impact on gross crop revenue for pickling cucumbers in 1 out of 5 yr. Key words: Cucumis sativus, pickle, overhead irrigation, sprinkler, foliar nitrogen, granular nitrogen, slow release.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".