Effect of Dry and Flooded Rice as Cover Crops on Soil Health and Microbial Community on Histosols
Bibliographic record
Abstract
Soil loss due to subsidence is a major concern in the Everglades Agricultural Area (EAA) of South Florida. Summer is typically the fallow season in the EAA, and soil loss due to oxidation and erosion is significant. Flooding and cover cropping are common practices being adopted to conserve soil, reduce weed pressure, and enhance soil health in the EAA. Cover crops also increase the microbial biomass which are the key drivers of soil function. The objective of this study was to determine the effect of (i) fallow, (ii) dry rice as a cover crop, (iii) flooded fallow, and (iv) flooded rice as a cover crop on soil health indicators and microbial community and diversity within the EAA. Baseline (pre-planting) soil samples were collected from all fields before the application of different treatments and post-harvest soil samples were collected after rice was cut and tilled into the soil surface. Microbial community composition was determined using 16S rRNA gene amplicon and fungal ITS gene amplicon sequencing. Soil bulk density decreased, and cation exchange capacity (CEC) increased in all farming practices including fallow fields. Results showed flooded fallow, flooded rice, and rice planting increased maximum water holding capacity (MWHC) and soil protein and decreased total potassium (TK). Bulk soil microbial communities responded surprisingly quickly to the applied treatments. Taxonomic composition of prokaryotic and fungal communities at the phylum level revealed visible shifts in microbial communities in response to the treatments. Instead of leaving field fallow, planting rice or flooding is a better strategy to improve soil health.
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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".