The Influence of Bahiagrass, Tillage, and cover crops on Organic Vegetable Production and Soil Quality in the Southern Coastal Plain
Bibliographic record
Abstract
Conventional farming utilizing bahiagrass (Papsalum notatum Flugge) in rotation with crops has been shown to increase yield, improve soil quality, and decrease weed and disease pressure. Organic production systems in the Southern Coastal Plain are challenged with limited soil fertility and a wide array of insect, disease, and weed pests. The purpose of this study was to investigate the influence of sequential years in bahiagrass and tillage (conventional and conservation) on organic vegetable yield and soil indices. After 0-4 years in bahiagrass, a crop rotation of rye and oats (winter cover crop), bush beans (spring vegetable crop), soybean (summer cover crop), and broccoli (fall vegetable crop) was implemented. Vegetable crop yields, plant biomass, plant C and N, and soil C, N, and P were measured for the four crops in the rotation over a three year period. Two years or more of bahiagrass prior to initiating the vegetable crop rotation showed positive effects on vegetable crop yields and soil quality parameters. Tillage treatments did not have a consistent effect on measured parameters. Soil C was not impacted by years in bahiagrass but was influenced by years of crop production. Potential soil N and P mineralization indicated an increase of soil organic fractions with years in bahiagrass. Available N increased after cover crops, and available P decreased with increasing years in bahiagrass.
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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) | 0.000 | 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".