Genotype-Specific Variation in the Structure of Root Fungal Communities Is Related to Chickpea Plant Productivity
Bibliographic record
Abstract
Increasing evidence supports the existence of variations in the association of plant roots with symbiotic fungi that can improve plant growth and inhibit pathogens. However, it is unclear whether intraspecific variations in the symbiosis exist among plant cultivars and if they can be used to improve crop productivity. In this study, we determined genotype-specific variations in the association of chickpea roots with soil fungal communities and evaluated the effect of root mycota on crop productivity. A 2-year field experiment was conducted in southwestern Saskatchewan, the central zone of the chickpea growing region of the Canadian prairie. The effects of 13 cultivars of chickpea, comprising a wide range of phenotypes and genotypes, were tested on the structure of root-associated fungal communities based on internal transcribed spacer (ITS) and 18S rRNA gene markers using 454 amplicon pyrosequencing. Chickpea cultivar significantly influenced the structure of the root fungal community. The magnitude of the effect varied with the genotypes evaluated, and effects were consistent across years. For example, the roots of CDC Corrine, CDC Cory, and CDC Anna hosted the highest fungal diversity and CDC Alma and CDC Xena the lowest. Fusarium sp. was dominant in chickpea roots but was less abundant in CDC Corrine than the other cultivars. A bioassay showed that certain of these fungal taxa, including Fusarium species, can reduce the productivity of chickpea, whereas Trichoderma harzianum can increase chickpea productivity. The large variation in the profile of chickpea root mycota, which included growth-promoting and -inhibiting species, supports the possibility of improving the productivity of chickpea by improving its root mycota in chickpea genetic improvement programs using traditional breeding techniques.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".