Response of oat morphologies, root exudates, and rhizosphere fungal communities to amendments in a saline-alkaline environment
Bibliographic record
Abstract
The application of organic amendments to saline-alkaline soil has been recommended as an agricultural strategy to improve crop productivity and soil health. However, there has been limited research on how organic soil amendment strategies affect the health of oats and their associated rhizosphere fungal communities in saline-alkaline conditions. Thus, the objectives of this study were to understand the effects of oat cultivars with contrasting saline-alkaline tolerances and different amendments on plant morphologies, root exudates (soluble sugars and organic acids), and rhizosphere fungal communities in a saline-alkaline environment. Experiments were conducted on a saline-alkaline tolerant cultivar, Baiyan2, and a saline-alkaline sensitive cultivar, Caoyou1, under four different organic amendment strategies: 1. control (no amendment application), 2. bio-fertilizer application, 3. rotten straw application, and 4. a co-application of bio-fertilizer and rotten straw. Results showed that plant morphological characters of Baiyan2 were better than Caoyou1, and that soluble sugar and organic acid levels in the rhizosphere of Baiyan2 were significantly lower than Caoyou1. Compared to the control, oat root and plant development was significantly improved by the combined bio-fertilizer and rotten straw amendment. Bio-fertilizer application promoted malic and citric acid levels, contributing to a higher total organic acid level, and significantly increased the abundance of Rhizopus arrhizus and decreased the abundance of the fungal pathogens Alternaria, Cladosporium, Sarocladium and Heydenia of Ascomycota in both oat cultivars. All amendment treatments containing rotten straw, except the combined amendment in Baiyan2, significantly increased the relative abundance of Ascomycota (specifically Gibberella, Talaromyces, Fusarium, and Bipolaris) and decreased the relative abundance of R. arrhizus by reducing soluble sugar and organic acid levels. For the combined amendment in Baiyan2, there were no significant changes in Gibberella and Rhizopus between the control and amendment treatment. Our results suggest that co-application of bio-fertilizer and rotten straw, combined with a tolerant oat cultivar, is an effective method to increase crop productivity and enhance soil health in a saline-alkaline environment.
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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) | 0.001 | 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 | 0.000 |
| 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".