Effect of previous crop and herbicides on weed growth and wheat yield
Bibliographic record
Abstract
Field experiments were established in 1998, 1999 and 2000 at the Lethbridge Research Centre to examine the effect of preceding annual crops and accompanying recommended herbicides on weed infestations in following winter and spring wheat (Triticum aestivum L.). Seven crops and fallow were established in year 1 with and without herbicides for weed control. In year 2, spring and winter wheat were grown without herbicides and with minimal fertilizer inputs to measure the effect of the previous crop and herbicides on weed and crop growth, available N and P, and available soil moisture. Lentil (Lens culinaris Medic) as a previous crop provided an environment that promoted wheat growth but not weed growth. In contrast, mustard (Brassica juncea L.) as a previous crop inhibited spring and winter wheat growth, but did not inhibit total weed biomass production in year 2. The beneficial effects of lentil and the detrimental effects of mustard were partially explained by available soil moisture and soil N for the second crop but additional factors, such as allelopathic chemicals or changes in soil biology, must be involved to cause the large differences in weed and wheat growth that were observed in year 2. The lack of herbicides with some crops led to dense weed populations in year 2. In addition, N, P, moisture utilization, and possible additional changes in soil biology or chemistry caused by uncontrolled weeds affected wheat yield after lentil and fallow. Key words: Available soil moisture, available soil nitrogen, crop rotation, lentil, mustard, weed density
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".