Root Water Uptake by Romaine Lettuce in a Muck Soil: Linking Tip Burn to Hydric Deficit
Bibliographic record
Abstract
Canadian lettuce is mostly grown in organic soils in Quebec. Lettuce is highly sensitive to tip burn, a physiological disorder that can lead to significant marketable yield losses. Tip burn losses have been linked to multiple causes, such as inadequate water and Ca fluxes during transpiration, which in turn are linked to the water fluxes in the root zone to and within the plant. The aim of this study was to investigate the dynamic relationship between water fluxes in the root zone and the occurrence of tip burn in romaine lettuce ( Lactuca sativa L.) grown in muck soils. Water fluxes in the root zone were modeled with HYDRUS‐2D using measured soil properties and weather conditions. The model results showed that mean unsaturated hydraulic conductivity in the root zone was sufficient to meet transpiration demands down to a matric potential of −31.14 kPa at a depth of 15 cm. Simulations based on field observations revealed that as soon as a root water uptake deficit of 0.14 mm h −1 was reached, tip burn developed rapidly. Hence, the simulations confirmed that below this matric potential threshold, hydraulic conductivity <1.6 × 10 −4 mm h −1 and water flow velocity in the root zone <4 × 10 −3 mm h −1 became insufficient for plant needs, leading to tip burn occurrence in lettuce, consistent with field observations. Understanding the water root‐zone fluxes to anticipate water stress and reduce tip burn damage in Romaine lettuce is a major improvement for growers, giving them a longer time window to initiate irrigation.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".