Yield and Quality Traits of Some Flax Cultivars as Influenced by Different Irrigation Intervals
Bibliographic record
Abstract
Flax is a potential winter crop for Egypt that can be grown for both seed and fiber. The study was conducted during two successive winter seasons of 2013/14 and 2014/15 in the experimental farm of El-Gemmeiza Agricultural Research Station, Agriculture Research Centre, Egypt. The objective of this work was to evaluate the effect of irrigation intervals (25, 35 and 45) on the straw, seed, oil, fiber yields and quality of flax cultivars (Sakha1, Giza9 and Giza10). Irrigation intervals significantly influenced all studied traits except oil percentage. Irrigated flax plants every 35 days gave the maximum values for all traits, while irrigation every 45 days gave the minimum values. In respect to cultivars, significant differences were found in most yield and quality characters. Furthermore, the performance of Sakha 1 cultivar was superior in main stem diameter, biological, straw yields per faddan, seed index, seed, oil yields per faddan and oil percentage. Meanwhile, Giza 10 cultivar highly significantly out yielded Giza9 and Sakha1 in plant height, fiber fineness, fiber length, total fiber percentage and fiber yield per faddan. The interactions between irrigation intervals and flax cultivars were highly significant for all traits. Based on the results, Sakha1 cultivar recorded the maximum values for main stem diameter, biological, straw yields per faddan, seed, oil yields per faddan and oil percentage and Giza 10 recorded the maximum values for plant height, fiber fineness, fiber length, total fiber percentage and fiber yield per faddan under irrigation of plants every 35 days.
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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".