Tolerance Of Faba Bean, Chickpea And Lentil To Salinity: Accessions' Salinity Response Functions
Bibliographic record
Abstract
Abstract The productivity of crops irrigated with saline water or grown on salt‐affected soils depends on the salt tolerance of the crops, their accessions, and various environmental and cultural conditions such as soil properties, climate and irrigation methods. The level and ability of plants to tolerate salt stress is the most critical information for the successful management of salt‐affected agricultural lands and saline irrigation waters. In this paper, responses of three food legume crops (faba bean, chickpea and lentil) to salinity stress were analysed using the threshold‐slope linear response function and modified discount function. The response functions are calibrated using the 2009–2010 season data and validated using the 2010–2011 season data from faba bean, chickpea and lentil experiments conducted in Raqqa, Syria. The comparison was also made through SALTMED model predictions. The results of this study show that the salinity response functions and productivity of grain yield are highly variable within the accessions of the same crop. For optimum outcome, practitioners need to consider salinity response functions and also the productivity of different accessions and their response to salinity in relation to the soil and available irrigation water salinity levels. 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 | 0.000 | 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".