Biostimulant Effects of Glutacetine® and Its Derived Formulations Mixed With N Fertilizer on Post-heading N Uptake and Remobilization, Seed Yield, and Grain Quality in Winter Wheat
Bibliographic record
Abstract
Biostimulants could play an important role in agriculture particularly for increasing N fertilizer use efficiency that is essential for maintaining both yield and grain quality in bread wheat, which is a major global crop. In the present study, we examined the effects of mixing urea-ammonium-nitrate fertilizer (UAN) or urea with five new biostimulants containing Glutacetine® or its derivative formulations (VNT1, 2, 3, and 4) on the physiological responses, agronomic traits, and grain quality of winter wheat. A first experiment under greenhouse conditions showed that VNT1, VNT3, and VNT4 significantly increased the seed yield and grain numbers per ear. VNT4 also enhanced total plant nitrogen (N) and total grain N, which induced a higher N Harvest Index (NHI). The higher post-heading N uptake (for VNT1 and VNT4) and the acceleration of senescence speed with all formulations enabled better nutrient remobilization efficiency, especially in terms of N mobilization from roots and straw toward the grain with VNT4. The grain ionome was changed by the formulations with the bioavailability of iron improved with the addition of VNT4, and the phytate concentrations in flour were reduced by VNT1 and VNT4. A second experiment in three contrasting field trials confirmed that VNT4 increased seed yield and N use efficiency. Our investigation reveals the important role of these new formulations in achieving significant increases in seed yield and grain quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".