Above Ground Drip Application Practices Alter Water Productivity of Malbec Grapevines under Sustained Deficit
Bibliographic record
Abstract
An objective of this study was to identify above ground drip application practices for winegrape that increase water productivity and mitigate water deficit-associated loss of yield. The influence of irrigation frequency on water productivity under two severities of sustained deficit irrigation was evaluated in field grown Malbec grapevines (Vitis vinifera L.) over three growing seasons. A weekly amount of water was delivered in a single irrigation event (1X) or apportioned into thirds and delivered in three irrigation events per week (3X). The least severe deficit (STD) had a 3-yr average maximum duty cycle (ratio of irrigation duration to irrigation interval) of 0.3, and vines irrigated 3X relative to 1X had higher water productivity each year due to a decrease in pruning weight. The most severe deficit (STD50) had a 3-yr average maximum duty cycle of 0.02 and vines irrigated 1X relative to 3X had higher water productivity due to a reduction in pruning weight in 2012 and an increase in yield in 2013. The fruit produced from vines irrigated at the frequency with highest water productivity under each deficit severity contained a lower concentration of anthocyanins. While treatment combinations did not alleviate a water-stress associated reduction in yield, results provided new information about grapevine water use efficiency that can be used to select combinations of irrigation frequencies and duty cycles with potential for increasing water productivity.
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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".