The Impact of Marker-Assisted Selection on Soybean Yield and Disease Resistance
Bibliographic record
Abstract
Soybean ( Glycine max ) is a crucial crop for global food security and agricultural sustainability, with breeding efforts focusing on improving yield and disease resistance. This study explores the role of Marker-Assisted Selection (MAS) in accelerating genetic improvement for these traits in soybean. We systematically studythe principles and types of genetic markers used in MAS, including simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), and quantitative trait loci (QTLs), and highlight recent technological advancements such as high-throughput genotyping platforms and the integration of genomic selection (GS). Two case studies illustrate the practical impact of MAS: one on the development of high-yielding soybean varieties and another on enhancing resistance against soybean cyst nematode (SCN). While MAS has led to substantial gains in yield and resistance, its application is not without challenges, including technical, genetic, and economic constraints. This studyconcludes with a discussion on future perspectives for MAS, emphasizing the integration of emerging technologies like CRISPR/Cas9 and omics approaches. The findings indicate that MAS will continue to play a pivotal role in soybean breeding, with prospects for enhancing both yield and resilience against biotic stresses.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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 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".