Agronomic benefits of alfalfa mulch applied to organically managed spring wheat
Bibliographic record
Abstract
Field experiments were established at two locations in Manitoba in 2002 and 2003 to determine N contribution, moisture conservation, and weed suppression by alfalfa mulch applied to spring wheat (Triticum aestivum L). Mulch treatments included mulch rate (amount harvested from an area 0.5×, 1× and 2× the wheat plot area), and mulch application timing (at wheat emergence or at three-leaf stage). Positive relationships were observed between mulch rate and wheat N uptake, grain yield, and grain protein concentration. At Winnipeg, the 2× mulch rates (3.9 to 5.2 t ha-1) produced grain yields equivalent to where 20 and 60 kg ha-1 of ammonium nitrate-N was applied in 2002 and 2003, respectively. Where mulch and ammonium nitrate produced equivalent grain yield, grain protein in mulch treatments was often higher than where chemical fertilizer was used. N uptake was also observed in the following oat (Avena sativa L.) crop. The highest mulch rate (2×) produced higher N uptake and grain yield of second-year oat compared with ammonium nitrate treatments. N use efficiency of mulch-supplied N by two crops over 2 yr [calculated as (treatment N uptake – control N uptake)/total N added] was between 11 and 68%. Mulch usually suppressed annual weeds, with greater suppression with late- than early-applied mulch. Increased soil moisture conservation was observed with high mulch rates (≥ 4.3 t ha-1) at three sites. Alfalfa mulch holds promise for low-input cropping systems when used on wheat at the 2× rates. Key words: Legume N, low-input farming, integrated weed management, wheat protein
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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) | 0.000 | 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".