Performance Evaluation of Constant Versus Variable Rate Irrigation
Bibliographic record
Abstract
Abstract Variable rate irrigation (VRI) can increase water use efficiency and productivity by applying water based on site‐specific needs. In this study, the performance of a five‐span centre‐pivot irrigation system (CPIS) retrofitted with a commercial variable‐rate irrigation package was evaluated at constant and variable application depths at the Alberta Irrigation Technology Centre (AITC) in southern Alberta, Canada. Two sets of experiments were designed to investigate the uniformity of application of the system during the 2013 and 2014 growing seasons. The first set of catch‐can trials were carried out with three irrigation rates in the direction of pivot travel. Three different wind regimes were observed during the catch‐can trials. Catch‐cans were arranged in grid configurations within the experimental plots located under one irrigation zone in span 4. The Christiansen coefficient of uniformity (CU) ranged from 90.4 to 94.4%. Wind speeds of 3.3 and 6.5 m s‐1 negatively and significantly impacted the CU values. The second set of catch‐can trials were performed with used and new sprinklers in a transect along the pivot lateral during the 2014 growing season. The Heermann and Hein coefficient of uniformity (CUHH) ranged from 89.0 to 93.5% and from 81.7 to 94.4% with constant and variable application depths, respectively. The greatest (94.4%) and least (81.7%) CUHH values were observed where water applications were 100 and 40% of the set point, respectively. Overall, the uniformity of application of CPIS retrofitted with the commercial VRI package both along the system's lateral and in the travel direction were above 90% for the majority of the trials under the different wind speeds and water application depths. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".