Predicted climate conditions and cover crop composition modify weed communities in semiarid agroecosystems
Bibliographic record
Abstract
Abstract The US Northern Great Plains is one of the largest expanses of small grain agriculture, but excessive reliance on off‐farms inputs and predicted warmer and drier conditions hinder its agricultural sustainability. In this region, the use of cover crops represents a promising approach to increase biodiversity and reduce external inputs; however little information exists about how cover crop mixture composition, predicted climate and management systems could impact the performance of cover crops and weed communities. In the 4th cycle of a cover crop‐wheat rotation, we experimentally increased temperature and reduced moisture as expected to occur with climate change, and assessed impacts on the presence and composition of cover crop mixtures and termination methods on weed communities. Under ambient climate conditions, mean total cover crop biomass was 43%–53% greater in a five species early‐season cover crop mixture compared with a seven species mid‐season mixture, and differences were less pronounced in warmer and drier conditions (19%–24%). We observed a total of 18 weed species; 13 occurring in the early‐season mixture, 13 in the mid‐season mixtures and 14 in the fallow treatments. Weed species richness and diversity was lower in warmer and drier treatments, and we observed a shift in weed communities due to the presence and composition of cover crop mixtures as well as climate manipulations. Overall, results suggest that adoption of cover crop mixtures in semiarid agroecosystems requires jointly addressing weed management and soil moisture retention goals, a challenge further complicated by predicted climate conditions.
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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 | 0.001 |
| 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".