Influence of cover crops on invertebrate diversity in Niagara organic vineyards
Bibliographic record
Abstract
Cover crops can play different functions in vineyards, such as preventing soil erosion, improving soil health, reducing the need for pesticides, and increasing nitrogen fixation in soils. Despite the potential of cover cropping, little research has been done on the influence of diverse vegetation on invertebrate functional groups. This study aimed to examine the influence of different cover crop species in the diversity of pests, neutral, and beneficial invertebrate species. The first objective of this study was to assess diversity in the invertebrate community and their functional groups in six different cover crop treatments (Trifolium incarnatum, Vicia villosa, Pennisetum glaucum, mixture of T. incarnatum, V. villosa, P. glaucum, Lolium perenne, and spontaneous vegetation) across two different organic vineyards in the Niagara region. The second objective was to test the use of sentinel plants to identify direct interactions between cover crops and invertebrates. These sentinel plants were grown in greenhouse conditions before being introduced into the field. To access the invertebrate communities involved, the deployment of sticky traps, vine sweeps and cover sweeps in June, August and September 2022 was used to obtain a comprehensive idea of invertebrate communities. The results showed that cover crop species, whether grown in monoculture or mixture, did not significantly influence invertebrate diversity and relative proportion of functional groups. Instead, factors such as vineyard management, environmental conditions, and landscape complexity may play a key role in shaping invertebrate community diversity. Additionally, temporal variation played an integral role in influencing the abundance of pest and beneficial morphospecies in both vineyards. Invertebrate interactions on sentinel plants were nonconclusive, leaving potential interactions between cover crops and invertebrates uncertain in terms of sentinel plant deployment. Also, cover crops grown in the interrow of the grapevines were not the sole vegetation species present in the area, making it challenging to attribute performance solely to a singular species in the study. The results of this study indicate how complex and intricate invertebrate community diversity can be in operating applications. Further investigation on cover crop performance in the field is warranted to better understand how they may influence invertebrate community structure dynamics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".