Irrigation and fertilizer management effects on processing cucumber productivity and water use efficiency
Bibliographic record
Abstract
Experiments were conducted to evaluate the response of processing cucumber (Cucumis sativus L.) to irrigation and fertilization strategies on a loamy sand in southwestern Ontario from 2001 to 2003. Dry matter accumulation, fruit yield, economic returns and water use efficiency were compared for (a) non-irrigated with conventional broadcast fertilizer applications (NI/B), (b) overhead sprinkler irrigated with conventional broadcast fertilizer applications (OHI/B), (c) surface drip irrigated with fertigation (DI/F) and (d) subsurface drip irrigated with fertigation (SDI/F). All irrigation methods enhanced yields, with drip irrigation coupled with fertigation showing significant advantages in terms of yield and economic returns compared with overhead irrigation and conventional fertilization practices. Irrigation increased dry matter accumulation, fruit yield and economic returns over non-irrigated treatments in a dry year, but only DI/F and SDI/F irrigation with fertigation increased these parameters in a wet year. Irrigation water use efficiency was greatest with SDI/F in 2 of 3 yr. This study indicates that processing cucumbers in Ontario benefit from irrigation, with drip irrigation/fertigation being more beneficial than overhead sprinkler irrigation. Subsurface drip irrigation systems increase irrigation water use efficiency over sprinkler and surface drip systems when higher than average temperatures coupled with lower than average rainfall are experienced on coarse-textured soils. Key words: Irrigation, fertigation, Cucumis sativus, yield
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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".