Response of two legume crops to soil salinity in gypsiferous soils
Bibliographic record
Abstract
Abstract Competition for water resources, increasing land salinization and the need to feed a growing population are challenges facing irrigated agriculture in the Fergana Valley of Uzbekistan. Growing short‐season legumes with water‐saving irrigation technologies is one strategy for increasing food production and land productivity using relatively less water. However, little information is available to assess how these crops will respond when produced with deficit irrigation on the gypsiferous soils of the region. This greenhouse study evaluated various growth components of common bean and green gram irrigated with deficit irrigation in soils with and without gypsum and at three levels of soil salinity. Results showed that biomass and leaf area decreased by approximately 20% for both crops, as ECe increased from 2.8 to 7 dS m−1. Yields were higher at all salinities for green gram than in common bean. However, relative yield reductions with increasing salinity were greater for green gram (43%) compared to common bean (19–31%). The presence of gypsum enabled both crops to maintain reasonable yield at ECe values which would be lethal in soils dominated both other salts. The effect of increasing salinity was the same at all levels of deficit irrigation. Copyright © 2008 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".