On-farm Soil Health Assessment of Cover-cropping in Florida
Bibliographic record
Abstract
Conventional cropping systems on sandy soils require continuous application of large amounts of external nutrients and irrigation water yet remain vulnerable to loses of these inputs. Within the state of Florida, need exists to provide farmers with economically viable alternatives that harness ecological processes and improve soil health and biodiversity. Cover crops are proving to be vital in the development of soil health. As part of this study we conducted a comprehensive on-farm assessment involving nine collaborative growers (ten farms) across the state; with each individual farm following its unique cover-cropping practice. Our goal was to shadow their practice and determine its effect on soil health indicators such as soil pH, bulk density (BD), maximum water holding capacity (MWHC), organic matter (OM), active carbon, cation exchange capacity, soil protein, Total Kjeldahl nitrogen (TKN), total phosphorus (TP), Mehlich-3 P (M3P) and potassium (M3K). Compared to fallow, soil OM, MWHC, and soil protein showed increases in cover crop fields for most farms, which presented a positive change towards building up soil health. Although soil TKN level was significantly decreased due to cover crops, soil protein level building up over time was the most positive change for soil health. M3K decreased in cover-crop fields, which indicated that supplementary K would be necessary prior to planting subsequent cash crops.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".