Residual Effect of Copper and Zinc from Fertilizers on Plant Concentration, Phytotoxicity, and Crop Yield Response
Bibliographic record
Abstract
Abstract To avoid toxicity resulting in reduced crop yields and/or phytotoxic symptoms on the foliage, information on the residual effect of micronutrient fertilization after periods of application is desirable. This article includes discussion on the micronutrients copper (Cu) and zinc (Zn), because they are essential and are of concern from plant and animal standpoints. In general, no detrimental effects in yield reduction or phytotoxicity were noted from Cu applications of up to 50 kg Cu ha−1 to barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). Barley was an exception where 50 kg Cu ha−1 decreased yield by about 12% in the first‐year crop. The Cu levels in plants did not exceed 9 mg kg−1. Studies showed that Zn applications of 50 kg ha−1 for 2 years in succession and the resulting crop tissue Zn levels as high as 105 mg kg−1 did not cause any phytotoxicity in cereals. The results of this study suggest that cereals can tolerate high levels of Cu and Zn.
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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".