Potassium Substitution by Sodium in Plants
Bibliographic record
Abstract
Soil salinity is an ever-increasing constraint to crop productivity worldwide especially in countries with irrigated agriculture. In contrast to all the soil reclamation strategies to decrease salt concentrations in root zone, the use of sodium (Na+) in plant nutrition may be an interesting tactic. The roles of potassium (K+) and Na+ in plant nutrition suggest that K+ is the only monovalent cation which is essential for most higher plants and is involved in three important functions, i.e., enzyme activation, charge balance and osmoregulation. Plants need a small amount but high concentration of K+ for specific functions in the cytoplasm and a major portion (∼90%) of it is localized in vacuoles, where it acts as an osmoticum. Maintenance of osmotic potential in vacuoles, a nonspecific function of K+, can be achieved by other cations such as Na+. For decades an ample amount of work has been done on the substitution of K+ by Na+ in plant nutrition. In this regard, Na+ has the potential to replace K+ for some of its functions. In some plants, supplementation of Na+ in reduced amounts can eliminate K+ deficiency symptoms under limited K+ supply. Thus, the question of K+ substitution by Na+ in plant physiology is not only of academic interest but has considerable practical implications in relation to fertilizer management and plant growth in salt-affected environments. In this review, we discuss the possibilities of K+ substitution by Na+ under specific soil and environmental conditions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".