Cropping systems alter plant volatile emissions in the field through soil legacy effects
Bibliographic record
Abstract
Abstract Crops emit a variety of volatile organic compounds (VOCs) that serve as attractants or repellents for pests and their natural enemies. Crop rotations, off-farm chemical inputs, and mechanical and cultural tactics – collectively called cropping systems – alter soil nutrients, moisture content, and microbial communities, all of which have the potential to alter crop VOC emissions. Soil legacy effects of diversified cropping systems have been shown to enhance crop VOC emissions in greenhouse studies, but how they influence emissions under field conditions remains virtually unknown. To determine the effect of cropping systems on plant VOC emissions in the field, air samples were collected from the headspace of wheat ( Triticum aestivum L. Judee) grown in simplified wheat-fallow rotations or diversified wheat-cover crop rotations where cover crops were terminated by grazing cattle. Across two growing seasons, wheat grown in rotation with fallow emitted greater amounts of Z -3-hexenyl acetate and β-ocimene, key attractants for wheat stem sawfly ( Cephus cinctus Norton), a major pest of wheat. While overall VOC blends were relatively similar among cropping system during the first growing season, emissions varied substantially in the second year of this study where wheat grown in rotation with cover crops emitted substantially greater quantities of volatile compounds characteristic of abiotic stress. Below-average precipitation in the second growing season, in addition to reduced soil water content in cover crop rotations, suggests that cropping system effects on wheat VOCs may have been driven primarily by water availability, a major factor limiting crop growth in dryland agriculture. While the specific mechanisms driving changes in VOC emissions were not explicitly tested, this work shows that agricultural practices applied in one growing season can differentially influence crop VOC emissions in the next through soil legacy effects, illustrating additional avenues through which cropping systems may be leveraged to enhance pest management.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".