Development and use of a variable-speed lateral boom irrigation system to define water requirements of 11 turfgrass genotypes under field conditions
Bibliographic record
Abstract
Improved irrigation scheduling is one strategy by which water management can be improved in turfgrass systems. The development and testing of a variable-speed lateral boom irrigation system for use in field-based irrigation trials is reported. Christiansen’s coefficient of uniformity was greater than 92% and the efficiency of irrigator discharge was greater than 90% for application depths (mm/unit land area) of 0.5–13 mm. The minimum irrigation requirements were determined for 11 turfgrass genotypes from a summer irrigation dose–response field trial that applied daily treatments of 100 (control), 80, 60, 40 and 20% of the previous day’s net evaporation measured using a US Class A pan. Responses of several shoot parameters, including clipping production, green leaf area index, leaf chlorophyll and leaf water status were evaluated to define minimum irrigation requirements for the turfgrasses. Minimum irrigation requirements (as defined by declines of 10% in several shoot responses) for C3 and C4 turfgrasses were 64–94% and 32–78% of US Class A pan, respectively. Variability in irrigation requirements within C3 or C4 types was due mainly to variations in estimates based on the different shoot parameters. The results demonstrate the opportunity for water conservation by using C4 rather than C3 turfgrasses in locations with hot dry summers (and mild winters) typical of a Mediterranean-type climate.
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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 | 0.001 | 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 | 0.000 |
| 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".