Glyphosate Tolerant Soybean Response to Different Management Systems
Bibliographic record
Abstract
The benefits of glyphosate tolerant crops technology are well-known, and its acceptance by farmers is undeniable. However, results of recent research indicate that, in some situations, glyphosate applied to herbicide-tolerant soybean crops may have phytotoxic effects affecting nutritional balance, photosynthesis and others biochemical process in plants. Despite the increasing information available on this subject, there are still scientific and technical issues that need to be clarified. Therefore, the present study aimed to assess the impact of applying different rates, management systems, and formulations of glyphosate to glyphosate-tolerant soybean trough different regions of Brazil in different environmental conditions. Two experiments were conducted over two crop seasons. A 2 × 2 × 5 (formulations × stage of application × doses) factorial design was used in each of them, for a total of 20 treatments with four replications. The study assessed a series of variables related to agronomic performance such as total chlorophyll and yield. The results suggest some problems associated with post-emergent use of glyphosate in tolerant soybean crop as 5% total yield reduction even without phytotoxicity symptoms dependent of season. There was not found any formulation interaction with yield decrease.
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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".