Long-term tillage, straw management, and N fertilizer rate effects on crop yield, N uptake, and N balance sheet in a Gray Luvisol
Bibliographic record
Abstract
A field experiment (established in autumn 1979, with monoculture barley [1980-1990] and barley/wheat-canola-triticale-pea rotation [1991-2008] was conducted on a Gray Luvisol [Typic Haplocryalf] loam soil at Breton, Alberta, to determine the influence of tillage (zero tillage [ZT] and conventional tillage [CT]), straw management (straw removed [SRem] and straw retained [SRet]) and N fertilizer rate (0, 50 and 100 kg N ha-1 in SRet, and only 0 kg N ha-1 in SRem plots) on seed yield, straw yield, total N uptake in seed + straw (1991-2008), and N balance sheet (1980- 2008). The N fertilizer urea was midrow-banded under both tillage systems in the 1991-2008 period. There was a considerable increase in yield and total N uptake up to 100 kg N ha-1 under both tillage systems. On the average, CT produced greater seed yield (by 223 kg ha-1), straw yield (by 177 kg ha-1) and total N uptake (by 5.6 kg N ha-1) than ZT. Compared to SRem treatment, seed yield, straw yield and total N uptake tended to be greater with SRet at the zero-N rate used in the study. The amounts of applied N unaccounted for over the 1980-2008 period ranged from 845 to 1665 kg N ha-1, suggesting a great potential for N loss from the soil-plant system through denitrification, and N immobilization from the soil mineral N pool. In conclusion, crop yield and N uptake were lower under ZT than CT, and long-term retention of straw suggests some gradual improvement in soil productivity.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".