A Survey on Energy Use in Agricultural Irrigation and Determination of Saving Measures in Sanliurfa, Diyarbakir and Mardin Provinces in Turkey
Bibliographic record
Abstract
The main objective of this study is to determine the necessary measures to reduce energy consumption and save energy in agricultural irrigation in the Southeastern Anatolia Region of Turkey. The primary data of the survey study consists of the primary data collected through face-to-face surveys with producers in Sanliurfa, Diyarbakir and Mardin provinces. In the survey, the number of questionnaires to be applied to the producers was determined as 300 in total and the farms to be surveyed were determined by using stratified random sampling method. Flood and furrow irrigation methods are commonly used (62%) in the region. About a quarter of the farmers apply sprinkler irrigation. Nearly four-fifths (78%) of the farmers in the region report that there is a loss-leakage in the irrigation system. A very high proportion (95%) of the farmers in the region apply non-pressure irrigation, and approximately three-quarters (76%) report that they do not know whether the pumps and irrigation systems used are working at the recommended flow and pressure. Almost all of the farmers in the region (98%) do not use solar energy systems. A very high proportion (94%) of regional farmers does not use engine drivers in pumps. The responses of the farmers to the survey questions were interpreted and discussed and suggestions were developed based on the responses of the farmers to the survey questions.
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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 | 0.001 |
| 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".