Soil-applied zinc and copper suppress cadmium uptake and improve the performance of cereals and legumes
Bibliographic record
Abstract
The present study aimed to evaluate the effect of soil-applied Zn and Cu on absorption and accumulation of Cd applied through irrigation water in legume (chickpea and mung bean) and cereal (wheat and maize) crops. The results revealed that Cd in irrigation water at higher levels (2 and 5 mg L−1) significantly (p < 0.05) reduced the plant biomass while the soil application of Zn and Cu, singly or combined, favored the biomass production. Plant tissue Cd concentration increased linearly with the increasing application of Cd via irrigation water. While Cd application caused a redistribution of metals in grains, straw, and roots with the highest concentration of Cd, Zn, and Cu occurred in roots followed by straw and grains. Zinc addition to soil alleviated Cd toxicity by decreasing Cd concentration in plant tissues due to a possible antagonistic effect. The addition of Cu to the soil had no consistent effects on Zn and Cd contents across all crops. Inhibitory effects of Cd on the uptake and accumulation of Zn and Cu have also been observed at higher Cd load. Thus, soil-applied Zn and Cu antagonized Cd helping the plant to cope with its toxicity and suppressed the toxic effects of Cd in plant tissues, thus favoring plant growth.
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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) | 0.000 | 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) | 0.001 | 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".