Productivity and profitability of straw-tillage and nitrogen treatments on irrigation in southern Alberta
Bibliographic record
Abstract
Straw-tillage treatment and N fertilizer rate were assessed for their impact on plant growth, N uptake, and profitability of irrigated cereals and oilseeds on a Dark Brown Chernozemic Lethbridge loam in southern Alberta. Straw was either retained or removed by baling. Tillage treatments were fall plowing, spring plowing, and direct seeding. Four N fertilizer rates (0, 50, 100, and 200 kg ha-1) were imposed on the straw-tillage treatments. The data were analyzed by crop using analysis of covariance with year and replication as random factors, straw-tillage treatment as a fixed effect, and fertilizer and fertilizer squared as covariates. Grain and straw yields were higher for fall plowing than for spring plowing or direct seeding at zero N rate, but were not different at the high N rate (200 kg ha-1). Grain and straw N concentrations were higher for fall plowing than spring plowing or direct seeding at the low N rate (50 kg ha-1), but similar at the high N rate. Total N uptake was higher for fall plowing or spring plowing without retaining straw than direct seeding without retaining straw or spring plowing with retaining straw at the low N rate, but similar at the high N rate. Net returns were higher for fall plowing and treatments that sold the straw, and were maximized at a N rate of about 100 kg ha-1. Key words: Crop residue, fertilizer nitrogen, economics, tillage timing, irrigation
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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".