Long-term cover crop impacts on soil health indicators and processing tomato yield and quality in a temperate humid climate
Bibliographic record
Abstract
Abstract Cover crops (CC) play a critical role in developing and maintaining agroecosystem resiliency. However, current research indicates an inconsistent effect of CC on soil health indicators and the relationship of soil health with crop yield and quality parameters. Hence, a long-term CC experiment established in 2007 at Ridgetown, Ontario, Canada was used to evaluate the CC effects on soil health indicators (56 indicators collected from 0 to 15 cm depth) and tomato fruit marketable yield and quality in 2019. To determine the association of soil functionality with tomato fruit yield and quality (i.e., plant compounds associated with human health), soil health indicator(s) were grouped into six critical soil functions. The CC treatments used to assess the soil health indicators and associated soil functions were winter cereal rye, radish, a mixture of radish and rye (radish + rye), and a no cover crop control (no-CC). Cover crops significantly enhanced 22 indicators by 2–35% than the no-CC treatment with the majority associated with nutrient supply. Fruit yield was greater with long-term cover crop, but there was no evidence that CC adoption would influence phytochemical contents and antioxidant activities of processing tomato. Among the tested CCs, greater values for most of the soil health indicators were observed for radish + rye ≥ radish > rye. Principal component analysis (PCA) demonstrated a clear separation of no-CC plots from the long-term CC species for the soil functions of erosion control, nutrient supply, and climate regulation; thus, confirming the implications of long-term CCing on increasing soil functioning and building resilient production systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".