Evaluation of Alternative Nitrogen Fertilizers for Corn and Winter Wheat Production
Bibliographic record
Abstract
As natural gas, and thus N fertilizer, prices increase, farmers are looking for ways to decrease N costs in farming operations. To potentially alleviate this cost burden, alternative synthetic N fertilizers are available as potential management tools for increasing crop yields and N use efficiency, and decreasing volatilization. In North Carolina specifically, little data exists on these new, synthetic N fertilizer products being marketed to farmers. Therefore, we undertook a study to compare them with aqueous urea ammonium nitrate (UAN) [(NH2)2CO, NH4NO3] during a 2‐yr field experiment. Corn (Zea mays L.) and wheat (Triticum aestivum L.) were grown in the three physiographic regions of North Carolina with four fertilizer sources (NutriSphere [Specialty Fertilizer Products, Leawood, KS], Environmentally Smart Nitrogen Polymer Coated Urea or ESN [Agrium Inc., Alberta, Canada] UCAN‐23 [Yara, Tampa, FL], and UAN) at up to six fertilizer rates. The use of the alternative products did not regularly produce more corn or wheat grain compared to UAN, while wheat straw yield was greater with NutriSphere, UCAN, and UAN compared to ESN in three of four site years. Also, an aerobic incubation experiment was performed to evaluated N release profiles of the fertilizers at 25°C. The study found that NutriSphere and UCAN release time was similarly to UAN, while ESN showed a slower release profile. However, any difference in release did not affect yields of spring planted corn, NutriSphere and ESN increased corn stover yields in 3 of 6 site‐years. In determining whether to use these alternative N fertilizer products, farmers should consider location, climatic conditions, and fertilizer costs in comparison to UAN.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".