Using Cover Crops as Means of Controlling Weeds and Reducing the Applied Quantity of Glyphosate-Based Herbicide in No-Till Glyphosate Tolerant Soybean and Corn
Bibliographic record
Abstract
Weeds represent a serious drawback affecting the productivity of field crops worldwide. While the most common approach to control weeds in no-till practices is the use of glyphosate-based herbicides (GBHs), reducing their use represents a major challenge. This two-year field study aims to evaluate whether the use of cover crops (CC) in transgenic soybean and corn productions can (1) help control weeds and (2) reduce the amount of GBH needed for managing weeds. Sampling was carried out in 32 experimental field plots (four crop managements with four replicates on both crops). Crop managements consisted of GBH applications at rates of 0.84, 1.67, and 3.3 L ha−1 in plots in direct seeding with CC (DSCC) and at rates of 3.3 L ha−1 in plots without CC (DS). Weed cover rates, plant parameters (fresh and dry weights and heights), grain yields, water, and cation contents in soil were considered as indicators of interspecific competition. Results obtained in both years show that it is possible to reduce GBH use by 50% in plots with CC compared to plots without CC using a rate of GBH application of 3.33 L ha−1 (DS 3.3). However, weeds had a large impact on water content in soil, which was reflected by smaller plants and lower yields in plots with only 0.84 L ha−1 of GBH applied. In the context of the study, the use of CCs seems to facilitate the development of more sustainable agriculture while reducing the quantities of GBH generally used.
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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".