Impact of cover crops and crop residue removal on soil quality, N dynamics, and processing tomato (Solanum lycopersicum L.) yield and quality
Bibliographic record
Abstract
Crop residue removal negatively impacts the soil physical, chemical, and biological properties. Therefore, inclusion of cover crops (CC) in the cropping systems offers an opportunity for maintaining agroecosystem functionality and counterbalancing the negative effects of crop residue removal on soil quality. Despite the multifunctional role of CC to agroecosystems, the benefits to soil quality have not been well investigated. Therefore, a medium-term experiment, established in 2007 and repeated at an adjacent site in 2008, at University of Guelph, Ridgetown Campus was used to evaluate effects of CC (6-yr) and crop residue removal (3-yr) on soil quality (chemical, physical, and biological properties), nutrient cycling, and subsequent tomato (Solanum lycopersicum L.)-winter wheat (Triticum aestivum L.) yields in a horticultural system in 2015 and 2016. This study is the first evaluation of comparisons between soil quality tests in a CC-based horticultural system in a temperate climate. Overall, our results indicated the positive influences of CC on soil quality where CCs had greater soil quality scores using comprehensive assessment of soil health (CASH), weighted soil quality test (WSQI), and Haney soil health test (HSHT) than the no CC control (no-CC). Among the three tested soil quality tests (CASH, HSHT, and WSQI), we recommend the WSQI as a more suitable and practical method for soil quality evaluation. An increase in the soil organic C (SOC) concentration with CC compared with no-CC indicates the potential of CCs to build stable pools of soil C. Cover crop induced temporal effects on labile pools of C and N were detected in our production system indicating the potential role of CC on nutrient cycling and microbial activity. Increases (15 to 28%) in tomato yields with CC than without CC further confirms the suitability of the tested CCs for increasing crop productivity in otherwise similar cropping systems. Study results indicate the long-term implications of CC on increasing soil and crop productivity.
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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) | 0.001 | 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".