Beneficial effects of solar <scp>UV</scp>‐B radiation on soybean yield mediated by reduced insect herbivory under field conditions
Bibliographic record
Abstract
Ultraviolet-B radiation (UV-B: 280-315 nm) has damaging effects on cellular components and macromolecules. In plants, natural levels of UV-B can reduce leaf area expansion and growth, which can lead to reduced productivity and yield. UV-B can also have important effects on herbivorous insects. Owing to the successful implementation of the Montreal Protocol, current models predict that clear-sky levels of UV-B radiation will decline during this century in response to ozone recovery. However, because of climate change and changes in land use practices, future trends in UV doses are difficult to predict. In the experiments reported here, we used an exclusion approach to study the effects of solar UV-B radiation on soybean crops, which are extensively grown in many areas of the world that may be affected by future variations in UV-B radiation. In a first experiment, performed under normal management practices (which included chemical pest control), we found that natural levels of UV-B radiation reduced soybean yield. In a second experiment, where no pesticides were applied, we found that solar UV-B significantly reduced insect herbivory and, surprisingly, caused a concomitant increase in crop yield. Our data support the idea that UV-B effects on agroecosystems are the result of complex interactions involving multiple trophic levels. A better understanding of the mechanisms that mediate the anti-herbivore effect of UV-B radiation may be used to design crop varieties with improved adaptation to the cropping systems that are likely to prevail in the coming decades in response to agricultural intensification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".