Weed management strategies effect on glyphosate‐tolerant maize and soybean yields and quality
Bibliographic record
Abstract
Abstract Weed management (WM) is necessary to prevent crop losses through competition with weeds and maintain high yields. However, in the case of glyphosate‐tolerant (GT) crops, phytotoxic effects can occur after glyphosate‐based herbicide (GBH) applications, which could impact yields and quality. In order to assess the agronomic performance of six WM strategies on GT soybean [Glycine max (L.) Merr.] and maize (Zea mays L.), field experiments were conducted in randomized blocks replicated four times (T1: Mechanical weeding; T2: Other herbicide application [Soybean: Chlorimuron ethyl + Imazethapyr] [Corn: Saflufenacil + Dimethenamid‐P]; T3: One GBH application; T4: One GBH + other herbicide application [Soybean: Imazethapyr] [Corn: S‐metolachlor + Mesotrione]; T5: Two GBH applications; T6: Two GBH applications + other herbicide application [Soybean: Chlorimuron ethyl + Imazethapyr] [Corn: S‐metolachlor + Mesotrione]). In soybean, T1 was the least productive treatment with an average yield of 2,652 kg ha−1, while T4, T5, and T6 produced significantly higher yields (4,315, 4,646, and 4,248 kg ha−1 respectively). However, the protein content was higher in T1 (42%) than in T3‐6 (40.85, 40.55, 40.68, and 40.65%), as well as the linolenic acid content whereas the total oil content was significantly lower. For maize, there were no significant differences in yields nor in nutritional content for all treatments. These findings question the systemic usage of GBHs in GT crops. If unnecessary, GBH applications could be reduced, which would relieve the selection pressure for glyphosate‐resistant weeds, especially in the case of GT soybean and maize crop rotation.
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 | 0.000 |
| 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".