Evaluating the Productivity Potential of Chickpea, Lentil and Faba Bean Under Saline Water Irrigation Systems
Bibliographic record
Abstract
Abstract The information on salinity threshold levels for food legumes when irrigating with saline water is limited and old. In a multi‐year study at two sites in the Euphrates Basin, we aimed at (i) evaluating the potential of saline water irrigation for chickpea, faba bean and lentil production; and (ii) using the SALTMED model to determine threshold crop yields based on irrigation water salinity in equilibrium with ambient soil solution salinity. To evaluate 15 accessions each of lentil and chickpea, and 11 accessions of faba bean, three irrigation treatments were used with salinity levels of 0.87, 2.50 and 3.78 dS m‐1 at Hassake and 0.70, 3.0 and 5.0 dS m‐1 at Raqqa. Aggregated grain yields showed significant differences (p < 0.05) among crop accessions. Calibration and validation of the SALTMED model revealed a close relationship between actual grain yields from the field sites and those predicted by the model. The 50% yield reduction (π50 value) in chickpea, lentil, and faba bean occurred at salinity levels of 4.2, 4.4 and 5.2 dS m‐1, respectively. These results suggest that of the three food legume crops, faba bean can withstand relatively high levels of irrigation water salinity, followed by lentil and chickpea. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| 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".