Assessing Management of Nitrapyrin with Urea Ammonium Nitrate Fertilizer on Corn Yield and Soil Nitrogen in a Poorly-Drained Claypan Soil
Bibliographic record
Abstract
Use of nitrification inhibitors (NI) in agricultural production systems is considered a risk management strategy for both agricultural and environmental considerations. It can be utilized when risk of reduced nitrogen (N) fertilizer use efficiency or yield, and risk of pollution from mineral N is high which can occur in poorly-drained soils that are vulnerable to waterlogging and runoff. Field research was conducted on corn (Zea mays L.) from 2012 to 2015 in Missouri, USA on a poorly-drained claypan soil. Treatments consisted of two application timings of urea ammonium nitrate (UAN) fertilizer solution [pre-emergence (PRE) and V3 growth stage], two application rates (143 and 168 kg N ha-1) in the presence or absence of nitrapyrin, and a non-treated control. UAN at 143 kg ha-1 with nitrapyrin at the V3 growth stage resulted in the highest yield (8.6 Mg ha-1). Similarly, pre-emergence application of UAN 168 kg ha-1 with nitrapyrin resulted in greater yields (7.7 Mg ha-1). UAN application rates and timings affected soil NO3-N and NH4-N concentrations more than the presence or absence of nitrapyrin during the growing season. A side-dress application of a lower rate of UAN with nitrapyrin at V3 was effective in poorly-drained soils when risk of N losses during the growing season due to unfavorable precipitation events and other environmental variables was high. A pre-emergence application of UAN with nitrapyrin was also effective and it may eliminate the need for split-application of N fertilizer later in the season thereby reducing the workload on growers during the growing season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".