Bio-fertilizer and rotten straw amendments alter the rhizosphere bacterial community and increase oat productivity in a saline–alkaline environment
Bibliographic record
Abstract
Saline-alkaline conditions can limit crop productivity and the role of soil microbes in nutrient cycling in arid and semi-arid regions throughout the world. A better understanding of how soil amendments and plant varieties affect rhizosphere microbial communities in saline-alkaline environments is important for the development of sustainable and productive agricultural systems under these challenging conditions. The objective of this study was to determine the effect of organic soil amendments on crop yield, soil physicochemical properties and rhizosphere bacterial communities of two oat cultivars in a saline-alkaline soil. The experiment was conducted in a semi-arid region of Northern China and involved growing two oat cultivars with varying levels of saline-alkaline tolerance under four different amendment treatments: (1) control (no amendments), (2) bio-fertilizer, (3) rotten straw, and (4) combination of bio-fertilizer and rotten straw. The combined bio-fertilizer and rotten straw amendment treatment resulted in the highest oat yields, reduced soil pH, and increased soil salt content for both cultivars. Baiyan2 (tolerant cultivar) had a higher bacterial α-diversity, relative abundance of Proteobacteria and Acidobacteria, and lower relative abundance of Firmicutes compared to Caoyou1 (sensitive cultivar). The rotten straw treatment and combined amendment treatment decreased bacterial α-diversity and the abundance of Proteobacteria, and increased the abundance of Firmicutes, which were positively correlated with soil salt, available nitrogen, phosphorous and potassium for both cultivars. Our study suggested using tolerant oat cultivars with the combined application of rotten straw and bio-fertilizer could be an effective strategy in remediating saline-alkaline soils.
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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 | 0.000 | 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".