Irrigation and fertilizer management effects on processing cucumber productivity and water use efficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it