Soil disinfestation and optimized nutrient management reduces nitrogen leaching and shapes soil microbial community composition in greenhouse cucumber production systems
Bibliographic record
Abstract
High nutrient input leads to problems such as excessive accumulation of soil nutrients, and imbalance of microbial communities. However, there remains significant gaps in the impact of soil disinfestation and optimized nutrient management measures on the growth and soil conditions of greenhouse cucumber. This study investigated the effects of non-disinfected soil with conventional nutrient treatment (CK), after disinfection with conventional chicken manure application (FP), after disinfection with bio-organic fertilizer application (BF), and after disinfection with organic materials application (OM) on cucumber growth performance, soil nutrients, and microbial communities. The results indicated that compared to the CK, FP, BF, and OM could significantly increase cucumber yield, dry weight, nitrogen uptake, and the nutritional yield of potassium, calcium, and magnesium, with BF showing the most pronounced effect. BF effectively alleviated the migration of dissolved inorganic nitrogen in the 0–200 cm soil layer, thereby reducing its loss. In addition, BF reduced the accumulation of nutrients such as total nitrogen, available phosphorus, available potassium, and available magnesium in the 0–20 cm soil layer. The optimization measures altered the species diversity of the soil microbial community. In the BF group, microorganisms showed a negative correlation with soil properties, while OM was positively correlated with pH and C/N, with norank_c__Subgroup_6 and Gaiella being the dominant bacteria. Functional analysis revealed that carbon and nitrogen metabolism functions were significantly enriched in BF-associated bacteria compared to all other groups. These findings provide new insights and strategies for saving resources, improving crop quality, and reducing nutrient accumulation and waste.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".