Evaluation of Conservation Tillage and Cover Crop Systems for Organic Processing Tomato Production
Bibliographic record
Abstract
Field experiments were conducted in 2000 and 2001 in Meridian, Calif. to evaluate the effects of cover crop mixtures and reduced tillage on yield, soil nitrogen (N), weed growth, and soil moisture content in organic processing tomato ( Lycopersicum esculentum ) production. The trial was set up as a randomized complete-block design with eight treatments consisting of a 2 × 3 (cover crop × tillage) factorial design, a fallow control (F) and a single strip-till (ST) treatment. Cover crop mixtures were either legumes (L), common vetch ( Vicia sativa ), field pea ( Pisum sativum ) and bell bean ( Vicia faba ), or those legumes with grasses (GL), annual ryegrass/triticale ( Lolium multiflorum /x Triticosecale ) in 2000; cereal rye ( Secale cereale )/triticale in 2001. Tillage treatments included an incorporation of the cover crop at planting (IP), a delayed incorporation (DI) (17 to 19 days after planting), and no-till (NT). Due to regrowth of the annual ryegrass in 2000, tomato fruit yields in 2000 were reduced by 50% to 97% within all GL treatments. However, regrowth of the cover crop was not a problem in 2001 and yields were not different among treatments. Total percent weed cover was 1.6 to 12.5 times higher in NT than IP treatments in 2000 and 2.4 to 7.4 times higher in 2001 as weed pressure was mainly affected by tillage practices and less by cover crop type. In 2000, available soil N was 1.7 to 9.4 times higher in L than GL treatments and was significantly influenced by tillage, but there were no treatment effects in 2001 due to a 60% reduction in weed pressure and minimal or no cover crop regrowth. Soil moisture content did not differ between treatments in either year. These results demonstrate the importance of appropriate selection and termination of cover crops for their successful adoption in organic conservation tillage systems.
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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 | 0.000 | 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".