Optimal Irrigation for Onion and Celery Production and Spinach Seed Germination in Histosols
Bibliographic record
Abstract
Increasing water scarcity in humid regions requires that food production increase its water use efficiency. Because the hydraulic characteristics of Histosols are different from those of mineral soils, water management for vegetable production must be adapted accordingly. The objective of this research was to determine the optimal soil water potential for irrigating onion (Allium cepa L.), celery (Apium graveolens L.), and spinach (Spinacia oleracea L.) crops in muck soils. Onion and celery were subjected to three irrigation treatments scheduled when tensiometer readings reached –10 or –20 kPa for onion and –30 or –50 kPa (2008) and –15 or –30 kPa (2009) for celery compared with drier control treatments for both crops. For spinach, two irrigation treatments (–10 and –20 kPa) and a control (drier) were tested. Optimal onion marketable yields and jumbo size were obtained from irrigation at potentials above –20 kPa at the bulbing stage. Celery had the best yields with the treatments of 2009 relative to the drier control. The highest spinach germination rate and yield were obtained at –10 kPa. Reliable estimates of the optimal thresholds were consistent with calculations performed using a simple analytical solution to Richards’ equation and soil characteristics. Irrigation thresholds for matric potential in a muck soil were shown to be crop specific and could be derived from a model and basic soil hydraulic characteristics.
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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) | 0.000 | 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 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".