Evidence of Soil Health Benefits of Flooded Rice Compared to Fallow Practice
Bibliographic record
Abstract
Flooded rice (Oryza sativa L.) in south Florida is grown commercially in rotation with sugarcane and vegetables. From 2008 to 2018, rice production has doubled. During the spring-summer, nearly 200 km2 of fallow sugarcane land is available for rice production. In 2017, approximately 113 km2 of rice were planted in the region. The net value of growing rice as a rotation crop far exceeds its monetary return. This study evaluated soil health parameters before and after rice cultivation and compared them against two other common summer farming practices - fallow fields and flooded-fallow. The soil health parameters that were tested as part of this study included soil pH, bulk density, water holding capacity, cation exchange capacity, organic matter, active carbon and nutrient content. Results indicated an increase in soil pH, and a significant reduction in soil bulk density due to rice cultivation. Water holding capacity increased significantly under all flooded land use practices compared to fallow fields. Cation exchange capacity significantly increased when sugarcane fields were cultivated with rice and ratoon rice, nearly doubled from 58 to 101 cmolc kg-1. Small, yet significant 3% increase in organic matter was observed when sugarcane fields were cultivated with ratoon rice. Almost 16 g kg-1 of active C is being generated within fallow soils, whereas less than half that under flooded practices, limiting the amount of soil loss via oxidation. Based on the soil health index, rice cultivation and flooded-fallow improved overall soil quality compared to fallow lands.
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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 | 0.001 |
| 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) | 0.002 | 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".