Implications of cover crop management decisions on <i>Amaranthus</i> species density and biomass in temperate cropping systems: a meta-analysis
Bibliographic record
Abstract
Abstract Weed-suppression benefits of cover crops (CCs) have long been recognized; however, the specific ability of CCs to suppress highly epidemic Amaranthus spp. (Palmer amaranth (Amaranthus palmeri S. Watson), redroot pigweed (Amaranthus retroflexus L.), smooth pigweed (Amaranthus hybridus L.), and waterhemp [Amaranthus tuberculatus (Moq.) Sauer]) has not been widely discussed. The objective of this meta-analysis was to evaluate the implications of CC management decisions (CC type, planting and termination methods, residue fate after termination, and in-season weed management plan) on Amaranthus spp. weed density (ASWD) and Amaranthus spp. weed biomass (ASWB) compared with no CC (NCC) in temperate regions, including the United States and Canada. We found 41 studies conducted across the United States and Canada and extracted 595 paired observations. The results indicate that CCs reduced the ASWD by 58% in the early season (0 to 4 wk after crop planting [WAP]), by 48% in the midseason (5 to 8 WAP), and by 44% in the late season (>8 WAP). Similarly, CCs reduced ASWB by 59%, 55%, and 37% in the early, mid-, and late season, respectively. Meta-regression analysis showed CCs terminated within 2.5 wk of crop planting reduced ASWD by ≥50%. CC biomass required to reduce ASWD and ASWB by 50% was 4,079 kg ha−1 for ASWD and 5,352 kg ha−1 for ASWB. Among CC types, grasses and mixtures reduced ASWD by 60% and 77% in early season, 53% and 59% in midseason, and 44% and 47% in late season. Legume CCs were effective only during the early season (47% ASWD reduction), while brassicas did not affect ASWD. CC residues remaining on the soil surface were more effective for reducing ASWD than incorporation. CCs did not affect ASWD or ASWB compared with NCC when herbicides were used for in-season weed management. In general, CCs were found to reduce ASWD and ASWB and therefore can be used as an effective tool for integrated management of Amaranthus spp.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.042 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".