Does the Nitrogen Rates, Methods and Times of Application Influences the Corn Nutrition and Yield?
Bibliographic record
Abstract
Alternatives to reduce N losses in soil, increasing the nutrient utilization efficiency by the plant with increased grain yield are necessary for the adequate and sustainable management of this nutrient. In this context, the objective of this study was to evaluate the use of N rates at different times and in different methods of application, evaluating the nutritional status and corn grain yield in the Cerrado (Savannah) region. The study was conducted in Selvíria - MS, Brazil, in an Oxisol. The experimental design was a randomized complete block design with four replicates, arranged in a factorial scheme 6 × 2 × 2, being: 6 rates of N (0, 50, 100, 150, 200, 250 kg ha-1), 2 times of application (at seeding or top-dressing) and 2 methods of application (surface or incorporated) with the urea source. The evaluations that were performed: leaf chlorophyll index (LCI), macro and micronutrients concentrations in leaf tissue (N, P, K, Ca, Mg, S, Cu, Fe, Mn and Zn), and corn grain yield. The N supply at sowing time provided higher levels of K, Ca, Mg, Fe and Zn in leaf tissue, while nitrogen fertilization at the V4 stage provided greater LCI. The incorporation of urea into the soil provided higher LCI and N, P and K foliar contents. The increase of N doses influenced positively the LCI, N, P, S, Cu, Fe, Mn and Zn foliar contents, and increased corn grain yield up to the dose of 164 kg ha-1 of N. The urea application to the surface is more interesting to the incorporated application due to the ease of application, cost, besides propitiating similar corn grain yield.
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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 | 0.001 | 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 | 0.001 |
| 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".