Barley Grain Yield and Protein Content Response to Deficit Irrigation and Sowing Dates in Semi-Arid Region
Bibliographic record
Abstract
The present study was conducted to investigate the relational effects of various sowing dates and deficit irrigation on grain yield, protein and yield components of barley, in a semi-arid region (southern part of Iran) during growing seasons 2011-2012 and 2012-2013. A Split plot layout within a randomized complete block design with three replications was used. Main plots were selected as Irrigation treatments with varying water irrigation levels consisting of: (1) full irrigation, FI, (2) 0.75 FI, (3) 0.5 FI and (4) Dry land (rain-fed) during both growing seasons. Sub plots were the sowing dates consisted of: (1) 23th October (T1), (2) 6th and (3) 22th November (T2 and T3) and (4) 6th December (T4). The interaction of different irrigation levels and sowing dates had a significant impact (p<0.05) on grain yield, grain and straw protein, 1000-grain weight, plant height, biomass, water use efficiency (WUE), and crop evapotranspiration. Results revealed that the largest amount of protein was obtained in the latest sowing date (T4) at dry land treatment in two consecutive years. Full irrigation treatment showed the largestrate of dry matter accumulation (14.72 and 15.25 Mg.h-1 for the first and second years, respectively), while the smallest rate was seen in the rain fed treatment (4.22 and 7.43 Mg.h-1 for the first and second years, respectively). The largest yield was obtained with the 23th October (T1) sowing date in full irrigation treatments (FI). The largest water use efficiency was achieved with 0.5FI and the earliest sowing date (T1).
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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 | 0.000 |
| 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".