Does Handling Physically Alter the Coating Integrity of ESN Urea Fertilizer?
Bibliographic record
Abstract
Environmentally smart nitrogen (ESN) is a polymer‐coated form of N that provides controlled‐release, allowing higher seed‐placed safe rates of urea fertilizer. The influence of coating integrity on the rate of N release in field conditions is unknown. Field studies were conducted from 2008 to 2011 near Lethbridge, AB, Canada, to determine the impact of handling methods on the polymer coating of ESN when seed‐placed with canola (Brassica napus L.), wheat (Triticum aestivum L.), or triticale (X Triticosecale Wittmack). Abrasion levels were created through laboratory simulation (0–80% N release after 7d in 23°C water, calibrated in increments of 10%; Exp. 1), or from handling by collecting ESN from exit points on nine implements, which was subjected to two methods of loading and unloading at the retail point and farm (9 × 2 × 2 factorial; Exp. 2). Nitrogen release data was related to plant responses in the field by seed placing the ESN at rates of 45 kg N ha−1 with canola and 90 kg N ha−1 for cereals. At the highest N release treatment in Exp. 1, winter cereal and canola stands were reduced by ∼30% and spring cereals by 18%. Grain yield was unaffected in winter wheat but reduced in canola and spring cereals by 20 and 10%, respectively. In Exp. 2, abrasion from transferring ESN in equipment containing scaly deposits or seeders configured with header‐manifold systems operating at high fan speeds corresponded to higher N release treatments, which reduced winter wheat and canola stands. Crop injury and grain yield, however, was usually mitigated through proper equipment maintenance and settings.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".