Impact of weed management practices on soil biological activity in corn and soybean field crops in Québec (Canada)
Bibliographic record
Abstract
Repeated applications and combination of glyphosate-containing herbicides (GCH) with other herbicides are two weed management practices (WMP) used to compensate for GCH decreasing efficiency impacts in field crops. These practices may have serious impacts on soil functions because GCH affect soil biota and soil biological activity (SBA). Two field experiments, one with corn and one with soybean crops, were conducted during one growing season. SBA indicators, soil respiration (SR) and fluorescein diacetate (FDA) hydrolysis, were measured at two sampling times following six WMP. These WMP included one or two GCH applications (GCH alone or combined with other herbicides), applications of other herbicides only and mechanical weeding. WMP did not affect FDA neither for corn or soybean at either sampling times. In contrast, WMP affected SR in corn fields at both sampling times and SR in soybean field at the first sampling time. Repeating GCH applications and combining different herbicides led to lower SR, suggesting that these practices decreased SBA, whilst one single GCH application presented higher SR, suggesting that this practice stimulated SBA. Our study demonstrates that using GCH in combination with other herbicides or in multiple applications affects SBA in field conditions. Affecting soil functions and carbon cycle do bear serious weed management implications, and the choice of WMP should be taken into consideration to minimize their impacts on SBA for field crops sustainability.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".