Agronomic responses and economic returns from wheat–canola rotation under Humalite and urea applications
Bibliographic record
Abstract
Abstract Humalite is a humic acid‐rich biostimulant known for its ability to improve plant agronomic parameters and increase crop nitrogen use. Limited field research exists on Humalite effect, its application rate, and its interaction with urea, especially at reduced rates on grain agronomic parameters. Therefore, a field study was conducted from 2021 to 2023 at three Alberta sites—Battle River Research Group (BRRG), Gateway Research Organization (GRO), and St. Albert Research Station (St. Albert), in a split‐plot design with four replications, three urea levels (i.e., recommended, half‐recommend, and zero urea) combined with five Humalite rates (0, 56 (or 112), 224, 448, and 896 kg ha−1). In 2021, the highest wheat yields were observed at half urea rates plus 224 kg ha−1 at BRRG (35% yield increase), GRO (8.4% yield increase), and St. Albert (33.5% yield increase). In 2022, canola yields were unaffected by Humalite application rates. In 2023, wheat yields from half‐recommended and recommended urea rates plots outperformed zero urea plots across all sites, regardless of Humalite rates. The highest wheat grain protein content values were observed at 224–448 kg ha−1 of Humalite plus half‐recommended or recommended urea rate. Depending on the site, the highest net revenue resulted from half urea rates plus Humalite at application rates between 112 and 448 kg ha−1 in wheat, that is, optimal Humalite rate for increased profitability. Therefore, the incorporation of biostimulants such as Humalite can reduce urea use and contribute to the sustainability of wheat cropping systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".