Field Crop Responses and Management Strategies to Mitigate Soil Salinity in Modern Agriculture: A Review
Bibliographic record
Abstract
The productivity of cereal crops under salt stress limits sustainable food production and food security. Barley followed by sorghum better adapts to salinity stress, while wheat and maize are moderately adapted. However, rice is a salt-sensitive crop, and its growth and grain yield are significantly impacted by salinity stress. High soil salinity can reduce water uptake, create osmotic stress in plants and, consequently, oxidative stress. Crops have evolved different tolerance mechanisms, particularly cereals, to mitigate the stressful conditions, i.e., effluxing excessive sodium (Na+) or compartmentalizing Na+ to vacuoles. Likewise, plants activate an antioxidant defense system to detoxify apoplastic cell wall acidification and reactive oxygen species (ROS). Understanding the response of field crops to salinity stress, including their resistance mechanisms, can help breed adapted varieties with high productivity under unfavourable environmental factors. In contrast, the primary stages of seed germination are more critical to osmotic stress than the vegetative stages. However, salinity stress at the reproductive stage can also decrease crop productivity. Biotechnology approaches are being used to accelerate the development of salt-adapted crops. In addition, hormones and osmolytes application can mitigate the toxicity impact of salts in cereal crops. Therefore, we review the salinity on cereal crops physiology and production, the management strategies to cope with the harmful negative effect on cereal crops physiology and production of salt stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".