Uptake and Exportation of Micronutrients by Transgenic Cultivars of Maize Under No-tillage in the Brazilian Cerrado
Bibliographic record
Abstract
Introducing cultivars of high productive potential with adequate agronomic management has contributed to the increase of maize yield in Brazil. This study aimed to characterize the extraction and exportation of micronutrients by modern maize hybrids grown in no-tillage system in the Cerrado region (Brazilian Savannah) with two fertilization levels. We established two crop environments with differentiated levels of soil fertilization, use of products for seed treatment and leaf fertilization, in which four transgenic hybrids were grown. For each environment, we used an experimental design of randomized blocks with four replicates. There were eleven plant samplings during the crop cycle to quantify dry mass production and Cu, Fe, Mn and Zn extraction. Micronutrient uptake is increased when a hybrid with higher potential for biomass production grows in an environment with greater supply of nutrients. Uptake persists throughout the maize cycle, including during the final stages of the reproductive phase, showing late demand for the crop. On average, after tasseling, about 39, 50, 42, and 49% of the total Cu, Fe, Mn and Zn absorption still occurs, respectively. Total uptake of Cu, Fe, Mn and Zn are, respectively, around 8, 199, 58 and 40 g to produce a tonne of grain, from which 23, 5, 8, and 42% are exported by the harvest. Micronutrient uptake and exportation rates for the studied transgenic hybrids are lower than the ones previously reported in Brazil and in works abroad.
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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) | 0.000 | 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 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".