Bibliographic record
Abstract
Chickpeas ( Cicer arietinum L.) are a vital legume crop, contributing significantly to global food security and nutrition. However, chickpea cultivation faces numerous challenges, including yield instability, susceptibility to biotic and abiotic stresses, and the need for improved nutritional quality. This study explores key traits for chickpea improvement, focusing on enhancing yield, resistance to diseases and pests, tolerance to environmental stresses, and nutritional enhancement. It further reviews molecular and genomic approaches such as Marker-Assisted Selection (MAS), Genomic Selection (GS), Genetic Mapping, Quantitative Trait Loci (QTL) analysis, and CRISPR/Cas9 genome editing, highlighting their application in chickpea breeding programs. A case study on improving drought tolerance is presented, illustrating genetic, genomic, and breeding strategies to develop drought-resilient varieties. Emerging technologies like high-throughput phenotyping, multi-omics, and artificial intelligence are discussed for their potential to revolutionize chickpea breeding. The study also addresses challenges and opportunities, emphasizing the need for diverse germplasm utilization, effective policy support, and bridging the gap between research and farmer adoption. The study concludes by underscoring the pivotal role of integrative breeding technologies in shaping the future of chickpea improvement programs and ensuring sustainable agricultural practices.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".