Challenges and Prospects of Wild Soybean as a Resistance Source Against Soybean Aphid (Hemiptera: Aphididae)
Bibliographic record
Abstract
Abstract Crop wild relatives (CWRs) have high levels of genetic diversity compared to their domesticated descendants. Soybean (Glycine max) has over 20 species of CWRs, most of which are in secondary and tertiary gene pools. Glycine soja, hereafter ‘soja,’ is the only wild relative in the primary gene pool, i.e., species that readily cross with soybean. Soja has many advantageous traits that may be transferrable to soybean, including resistance to insect pests, with particularly strong sources of resistance to the soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae). Soybean aphid has been a major soybean pest in the United States and Canada since 2000 and a longstanding pest in East Asia. This paper reviews the challenges of developing soybean with durable resistance to soybean aphid in light of multiple, virulent biotypes in North America and China. It also examines particular challenges in evaluating soja germplasm for soybean aphid resistance and resultant solutions to those challenges. Soja germplasm is widely available, but from our experience, the logistics associated with reliably procuring high-quality soja seed has posed the main challenge in working with this CWR. This review highlights soja accessions identified with strong resistance to soybean aphid and their genetic bases, and it discusses possible strategies for exploiting aphid-resistant soja accessions to improve soybean pest management.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".