Urea Fertilizer Placement Impacts on Corn Growth and Nitrogen Utilization in a Poorly-Drained Claypan Soil
Bibliographic record
Abstract
<p>Practices to increase nitrogen (N) use efficiency (NUE) include selecting appropriate N fertilizer sources and application methods, but minimal research has focused on these practices in poorly-drained claypan soils which are prone to N loss. This research assessed the impact of different urea fertilizer placement practices on corn (<em>Zea mays</em> L.) production and N utilization in a poorly-drained claypan soil. Field trials were conducted in 2014 and 2015 in Missouri. Treatments consisted of pre-plant deep banding (20 cm) urea at 202 kg N ha<sup>-1</sup> or urea plus a nitrification inhibitor (NI) (nitrapyrin) compared to pre-plant urea broadcast surface-applied or incorporated to a depth of 8 cm. In 2014, incorporating urea, deep banding urea, and deep banding urea plus NI had higher yields (&gt; 10%) of corn compared to the control with grain yields ranging from 13.73 to 14.05 Mg ha<sup>-1</sup>. In 2015, grain yields were lower than in 2014, ranging from 4.1 to 7.9 Mg ha<sup>-1</sup>. Deep placing banded urea with a NI yielded an increase in grain yield up to 48% compared to the other treatments. Rainfall amounts were higher in 2015, which could have resulted in poorer root growth and greater N loss in deep banded treatments. In 2014, deep banding urea with a NI produced the highest NUE. Similar to NUE, silage tissue N concentrations in 2014 were greater with deep banded urea plus NI, while in 2015 silage tissue N concentrations were higher with surface applied urea. The results suggest that urea fertilizer incorporation including deep banding may improve corn grain production, N uptake, and NUE, but response was affected by climatic conditions. The addition of an NI may be an important safeguard when deep banding urea in years with excessive precipitation.</p>
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".