Comparison of Tape Drip Irrigation and Furrow Irrigation Systems on Base of Water Use Efficiency and Yield of Potato in West of Iran
Bibliographic record
Abstract
Potato is a major crop of Kermanshah province but shortage of water is important its production. To investigate yield and water use efficiency (WUE) of potato with trickle (T-Tape) and furrow irrigations, an experiment was performed in Kermanshah agriculture university research farm in 2007. The experiment was in complete randomized blocks design with 3 levels of irrigation-water amount (50%, 75% and 100% of cumulative evaporation from Class A pan) and irrigation methods (tape and furrow irrigations) with 3 replications. The results indicated that regardless to irrigation method, the maximum yield of potato (34.455 ton/ha) was achieved in irrigation water treatment of 100% and the minimum yield of potato (19.168 ton/ha) was achieved in irrigation water treatment of 50%. The effect of irrigation method on WUE was significant (P<0.01). The highest WUE (3.93 kg/m3) was belonged to 50% tape irrigation and the lowest WUE (1.2 kg/m3) was belonged to furrow irrigation. Tape irrigation caused an increase of 66% in WUE as compared to furrow irrigation. The difference in WUE between 50%, 75% and 100% irrigation treatment was not significant. The least percent of tubers smaller than 35 mm (26.42%) was obtained in tape irrigation method with 50% irrigation-water amount and the highest percent of tubers smaller than 35 mm (37.99%) was obtained in furrow irrigation method. The highest amount of tuber production (37.17%) in marketable size (tubers larger than 55 mm) was obtained in tape irrigation method with 100% irrigation-water amount. As the irrigation water increased, percentage of tubers larger than 55 mm increased. The effect of irrigation method on dry matter was not significant, but with reduction of irrigation water, the specific gravity of tubers of potato was decreased. It is suggested to use tape irrigation method, with irrigation water amount of 100% for producing potato.
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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 | 0.001 |
| 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) | 0.000 | 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".