Responses of spring-seeded cover crop roots by herbicide residues and short-term influence in soil aggregate stability and N cycling
Bibliographic record
Abstract
Cover crop (CC) prevalence between cash crop systems is contributing to long-term sustainable crop production systems. However, herbicide residue impact on CC root growth and its subsequent effect on soil aggregation and fertility are unknown. An untreated control plus preemergence (PRE) application of saflufenacil (at 67 and 134 g a.i. ha−1)/dimethenamid-P (at 668 and 1336 g a.i. ha−1) as well as S-metolachlor (at 1600 and 3200 g a.i. ha−1)/atrazine (at 1280 and 2560 g a.i. ha−1) + mesotrione at 140 and 280 g a.i. ha−1 to sweet corn (Zea mays L. var. rugosa Bonaf.) and imazethapyr (100 and 200 g a.i. ha−1) to pea (Pisum sativum L.) were made in spring 2011 and spring 2012. One year later, ryegrass (Lolium multiflorum Lam.), buckwheat (Fagopyrum esculentum Moench), sorghum–sudangrass [Sorghum bicolor (L.) Moench × S. bicolor var. sudanese (L.)], and spring wheat (Triticum aestivum L.) were seeded. Root biomass and N content, wet aggregate stability (WAS), and aggregate size plus soil mineral N (SMN) were determined. Buckwheat root biomass declined in all herbicide treatments but root N declined in imazethapyr and S-metolachlor/atrazine + mesotrione at the 2× and 1× rates, respectively. Ryegrass root biomass decreased in S-metolachlor/atrazine + mesotrione but root N content declined only at the 2× rate. Sorghum–sudangrass and spring wheat roots decreased in imazethapyr and S-metolachlor/atrazine + mesotrione at the 2× rate, respectively. Despite the impact of herbicide residues on roots, differences in aggregate size, WAS, and SMN were not detected. Findings from this study add knowledge on herbicide residue effect on CC root growth and provide useful CC seeding guidelines for growers.
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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) | 0.001 | 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".