Co-Granulated and Blended Zinc Fertilizer Comparison for Corn and Soybean
Bibliographic record
Abstract
A new co-granulated formulation of monoammonium phosphate (MAP) including S and Zn could allow for more uniform nutrient distribution. A six site-year study evaluated the effects of blended phosphorus (P) sources [MAP and diammonium phosphate (DAP)] and zinc amounts (0, 2.2, and 5.6 kg Zn ha-1) compared to co-granulated fertilizer, MicroEssentials® Sulfur-10 (MES10™) (12-40-0-10S) and MicroEssentials Sulfur and Zinc (MESZ™) (12-40-0-10S-1Zn), on corn and soybean response. Fertilizers were broadcast applied for corn and the carry-over effect on soybean was determined. Ear leaf P, S, and Zn concentrations at Novelty in 2013 and 2014 were within the sufficiency range regardless of treatment, even though initial soil test values were low-medium. Yields were similar to the N only control for all site-years except at Novelty in 2013, where MAP+ZnSO4 at 2.2 kg Zn ha-1, MAP+Super Zn at 5.5 kg Zn ha-1, and DAP+AMS were 540 to 570 kg/ha greater. The amount of Zn fertilizer (2.2 vs. 5.6 kg Zn ha-1) also showed no significant effect on yield. Applications of P or Zn generally increased their concentrations in post-harvest soil samples. Fertilizer applied for corn indicated some differences in soybean plant nutrient concentrations, but it had no effect on total plant nutrient uptake, grain yield or quality. At Novelty, soybean plant Zn concentration was greater at 5.6 kg Zn ha-1 compared to 2.2 kg Zn ha-1, while Albany showed an increase in whole soybean plant Zn concentration with SuperZn compared to ZnSO4. Carry-over fertilizer from corn showed limited effects on soybean response the following year.
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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".