Suppressing maize stalk rot through promoted rhizosphere microbial recruitment in cultivar mixtures
Bibliographic record
Abstract
Introduction Crop diversification is a critical strategy for improving resource use efficiency and suppressing disease. Mixing cultivars from the same crop is one approach to diversifying planting patterns. However, it remains unknown how mixing cultivars in maize affects the soil microbial community, stalk rot incidence, and grain yield. Methods A 2-year field experiment was performed, which included one stalk rot-resistance cultivar, DH662; one stalk rot-susceptible cultivar, DH701; and a mixture of DH662 and DH701. Results Cultivar mixtures and DH662 monocrop had lower disease incidence and higher grain yield than DH701 monocrop. The microbial community structure in both rhizosphere and bulk soil was notably impacted by the resistant cultivar as well as the cultivar mixtures. Distinct modules within the rhizosphere microbial community co-occurrence network were identified, differentiating cultivar mixtures from DH701 monocrops. The network structure of the rhizosphere in cultivar mixtures closely resembled that observed in DH662 monocrops. Keystone taxa were higher in cultivar mixtures compared to their abundance in DH701 monocrops. The keystone taxa ( Adhaeribacter and Gemmatimonas ) were positively related to grain yield and negatively correlated with disease incidence. Discussion Overall, our results demonstrated that cultivar mixtures had a substantial impact on the assembly and keystone taxa of microbial communities in the rhizosphere and bulk soil, leading to a reduction in stalk rot occurrence and an increase in grain yield. This study demonstrated the potential for maize yield improvement through cultivar mixtures. These findings improved insights into how beneficial microbial communities in the rhizosphere contributed to the positive effects observed in cultivar mixtures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".