Viruses and Phytoparasitic Nematodes of Cicer arietinum L.: Biotechnological Approaches in Interaction Studies and for Sustainable Control
Bibliographic record
Abstract
L. (chickpea) is the world's fourth most widely grown pulse. Chickpea seeds are a primary source of dietary protein for humans, and chickpea cultivation contributes to biological nitrogen fixation in the soil, given its symbiotic relationship with rhizobia. Therefore, chickpea cultivation plays a pivotal role in innovative sustainable models of agro-ecosystems inserted in crop rotation in arid and semi-arid environments for soil improvement and the reduction of chemical inputs. Indeed, the arid and semi-arid tropical zones of Africa and Asia have been primary areas of cultivation and diversification. Yet, nowadays, chickpea is gaining prominence in Canada, Australia, and South America where it constitutes a main ingredient in vegetarian and vegan diets. Viruses and plant parasitic nematodes (PPNs) have been considered to be of minor and local impact in primary areas of cultivation. However, the introduction of chickpea in new environments exposes the crop to these biotic stresses, compromising its yields. The adoption of high-throughput genomic technologies, including genome and transcriptome sequencing projects by the chickpea research community, has provided major insights into genome evolution as well as genomic architecture and domestication. This review summarizes the major viruses and PPNs that affect chickpea cultivation worldwide. We also present an overview of the current state of chickpea genomics. Accordingly, we explore the opportunities that genomics, post-genomics and novel editing biotechnologies are offering in order to understand chickpea diseases and stress tolerance and to design innovative control strategies.
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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 | 0.000 | 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) | 0.001 | 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".