Barley Productivity Response to Polymer‐Coated Urea in a No‐Till Production System
Bibliographic record
Abstract
Farmers are interested in more cost‐efficient and environmentally sound fertilization programs in field crops. A multi‐site study on the Canadian prairies was conducted to determine the effect of polymer‐coated urea (Environmentally Smart Nitrogen, ESN) compared with urea on weed management and barley (Hordeum vulgare L.) yield and quality. Treatments included a semi‐dwarf and tall barley cultivar, polymer‐coated urea (ESN) and urea, 100 and 150% of soil test N fertilizer rates, and 50 and 100% of registered herbicide rates. Treatments were applied to the same plots in four consecutive years. Barley yield was greater with semi‐dwarf compared with tall barley in 13 of 20 site‐years but weed biomass was greater in 7 of 18 site‐years with the semi‐dwarf cultivar. The 150% N fertilizer rate increased yield of both cultivars in 9 of 20 site‐years and of the semi‐dwarf cultivar in four additional site‐years. Barley yield was often similar with ESN and urea but ESN increased barley yield in three site‐years at both N rates, two additional site‐years at the 150% N rate, and one further site‐year with semi‐dwarf barley. Barley grain protein concentration was greater with ESN than with urea in 8 of 20 site‐years. Information gained in this study will be used to advise growers on improved barley production practices.
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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 | 0.000 |
| 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) | 0.001 | 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".