Genomics-Assisted Breeding: A Powerful Breeding Approach for Improving Plant Growth and Stress Resilience
Bibliographic record
Abstract
Climate change biotic and abiotic stressors lead to unpredictable crop yield losses, threatening global food and nutritional security. In the past, traditional breeding has been instrumental in fulfilling food demand; however, owing to its low efficiency, dependence on environmental conditions, labor intensity, and time consumption, it fails to maintain global food demand in the face of a rapidly changing environment and an expanding population. In this regard, plant breeders need to integrate multiple disciplines and technologies, such as genotyping, phenotyping, and envirotyping, in order to produce stress-resilient and high-yielding crops in a shorter time. With the technological revolution, plant breeding has undergone various reformations, for example, artificial selection breeding, hybrid breeding, molecular breeding, and precise breeding, which have been instrumental in developing high-yielding and stress-resilient crops in modern agriculture. Marker-assisted selection, also known as marker-assisted breeding, emerged as a game changer in modern breeding and has evolved over time into genomics-assisted breeding (GAB). It involves genomic information of crops to speed up plant breeding in order to develop stress-resilient and high-yielding crops. The combination of speed breeding with genomic and phenomic resources enabled the identification of quantitative trait loci (QTLs)/genes quickly, thereby accelerating crop improvement efforts. In this review, we provided an update on rapid advancement in molecular plant breeding, mainly GAB, for efficient crop improvements. We also highlighted the importance of GAB for improving biotic and abiotic stress tolerance as well as crop productivity in different crop systems. Finally, we discussed how the expansion of GAB to omics-assisted breeding (OAB) will contribute to the development of future resilient crops.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".