IMPACT of Irrigation Management Strategy on Sizing of a Solar-powered Center Pivot Irrigation System
Bibliographic record
Abstract
Abstract. The potential for application of photovoltaic (PV) technology in irrigated agriculture has increased in recent years due to a wider availability of affordable PV modules, and a desire to reduce dependencies on conventional non-renewable energy sources. The irrigation management strategy determines the frequency and duration for which pumping is required, thus influencing the PV system sizing. The objective of this research is to investigate the variability of PV system sizing with alternate irrigation management strategies. A model interlinking the daily crop water requirement, the soil moisture status, the irrigation requirement, the power production from the PV array, the power used by solar pumps, and the state of charge of the battery bank was used for determining the reliability of a chosen PV system size under variable operating and meteorological conditions. The model was used for determining the PV sizing requirement to achieve a desired reliability for operating a 1.4 ha center pivot installed in Outlook, Saskatchewan, Canada considering a typical, moderate application depths (20-35 mm) as well as more frequent light irrigations (5-8 mm) management strategies. The strategy of using frequent light irrigations required a significantly smaller PV system than the standard soil moisture threshold based strategy to achieve the desired reliability. These results emphasize that the chosen irrigation management strategy can have a significant impact upon the economic and technological feasibility of a PV irrigation system. The modeling tools demonstrated here can be used to determine the optimum size of the PV irrigation systems while taking into consideration the interrelated factors of irrigation management, soil water characteristics, and climatic variations.
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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 | 0.001 |
| 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 | 0.000 | 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".