Effect of herbicide residues on fall-seeded cover crops influence soil aggregate stability and mineral N
Bibliographic record
Abstract
With increasing public emphasis on sustainable food production, cover crops (CC) integration into conventional production systems is gaining growers’ interest. However, herbicide residue effects on CC on soil fertility, aggregate stability, and size are poorly understood. In the spring of 2012 and 2013, an untreated check plus pre-emergence (PRE) application of saflufenacil/dimethenamid-p (735 and 1470 g a.i. ha−1) and s-metolachlor/atrazine+mesotrione (2880, 5760, and 140, 280 g a.i. ha−1) to sweet corn, and imazethapyr (100, 200 g a.i. ha−1) to pea were set. Post-harvest, rye (Secale cereale L.), hairy vetch (Vicia villosa Roth), oilseed radish (Raphanus sativus L. var. oleiferus), and oat (Avena sativa L.) were planted vertically into herbicide treatments and untreated check. Biomass and N content in CC roots, wet aggregate stability (WAS), aggregate size, and soil mineral N (SMN) in the soil were determined before CC seeding (BCCS) and before main crop seeding (BMCS). Root biomass in vetch and radish was reduced by imazethapyr and 2× rates of saflufenacil/dimethenamid-p and s-metolachlor/atrazine+mesotrione. Greater aggregate size in winterkilled CC (oat, radish) plots and WAS in oat-plots was likely due to availability of decomposable residues. SMN was unaffected by CC root N content. This preliminary study demonstrates herbicide residue effects on CC reduction and potential impact on soil quality parameters.
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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".