Effect of Every-Other Furrow Irrigation on Water Use Efficiency, Starch and Protein Contents of Potato
Bibliographic record
Abstract
The every-other furrow irrigation is one of the mothods of deficit irrigation in furrow irrigation system. In this research,a randomized complete block design with three irrigation treatment and four replication on potato was stablished inAgricultural Research Center,Shahrekord, Iran. The irrigation treatments were: normal furrow irrigation(N), fixedevery-other furrow irrigation(F) and alternative(variable) every-other furrow irrigation(V). The frequency of irrigationwas constant and depth of it was calculated by measurement of soil moisture deficit and the volume of irrigation waterwas measured by a volumetric counter. The water and soil quality was normal (EC less than 1 ds/m). The differentfertilizers were used. After harvesting, water use efficiency, starch and protein content were measaured for each plot.There was significant difference between water use efficiency under different treatments, so that, the F treatment hadthe most water use efficiency. The every-other furrow irrigation decreased the starch content significantly. The Vtreatment increased the starch content significantly related to F treatment. There was no significant difference betweenthe protein contents in the three treatments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".