Zn application through seed priming improves productivity and grain nutritional quality of silage corn
Bibliographic record
Abstract
The micronutrient application in agriculture takes place through soil application, foliar spraying or added as seed treatments. The latter method, the nutri-priming, is an appealing option due to the easiness in handling it, environment-friendly, cost effectiveness and efficient against multiple environmental stressors. To assess the feasibility of Zn-priming technique on seeds germination, two experiments were conducted and assessed the efficiency on the growth rate, yield and biofortification on the forage maize (Zea mays L.). The first laboratory experiment assessed the effect of Zn-priming for three-time exposures (i.e., 8, 16 and 24 h) on germination parameters. The second experiment was done in a greenhouse, by using the 10 seeds obtained from 24 h priming. Five seed pretreatments were studied (0, 0.1, 0.5, 1 and 11 2 % of zinc sulfate heptahydrate (ZnSO4·7H2O)) compared to the recommended dose (5 ppm of Zn at 5–9 leaf stage) provided by soil application. The obtained results revealed that all seed priming, including hydro-priming, improve seed germination performance. Zn-priming increased the grain yield and helped to enrich the seeds in this element, especially seedlings treated with 0.5 % Zn sulphate for 24 h leading to an increase in yield by 47 % and in Zn content by 15 %. The comparison of the results from both techniques showed that Zn-priming could be was very effective than the traditional direct application in soil.
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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".