Date Palm Waste Compost Application Increases Soil Microbial Community Diversity in a Cropping Barley (Hordeum vulgare L.) Field
Bibliographic record
Abstract
Application of date palm waste compost is quite beneficial in improving soil properties and crop growth. However, the effect of its application on soil microbial communities is less understood. High-throughput sequencing and quantitative real-time PCR (qPCR) were used to evaluate the effect of compost application on the soil microbial composition in a barley field during the tillering, booting and ripening stages. The results showed that compost treatment had the highest bacterial and fungal abundance, and its application significantly altered the richness (Chao1 index) and α-diversity (Shannon index) of fungal and bacterial communities. The dominant bacterial phyla found in the samples were Proteobacteria and Actinobacteria while the dominant fungal orders were Ascomycota and Mortierellomycota. Interestingly, compost enriched the relative abundance of beneficial microorganisms such as Chaetomium, Actinobacteriota, Talaromyces and Mortierella and reduced those of harmful microorganisms such as Alternaria, Aspergillus and Neocosmospora. Functional prediction based on Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) showed that amplicon sequence variant (ASV) sequences related to energy metabolism, amino acid metabolism and carbohydrate metabolism were associated with compost-treated soil. Based on Fungi Functional Guild (FUNGuild), identified fungi community metabolic functions such as wood saprotroph, pathotroph, symbiotroph and endophyte were associated with compost-treated soil. Overall, compost addition could be considered as a sustainable practice for establishing a healthy soil microbiome and subsequently improving the soil quality and barley crop production.
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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) | 0.000 | 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) | 0.001 | 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".