Cover crops improve some, but not all, soil health indicators in horticultural rotations
Bibliographic record
Abstract
Production of fruits and vegetables provides nutrient dense and high value crops. However, horticulture production is often very intensive and degrading to soil, with high levels of soil disturbance, high use of fertilizer inputs, and with prolonged periods of exposed soil. This can lead to increased soil degradation when compared to other types of cropping. We used a subset of the Atlantic Soil Health Laboratory’s database of on-farm soil samples collected between 2016 and 2018 to assess the impact of cover crop (CC) use in horticulture production in Nova Scotia on soil health. We analyzed 21 soil health parameters on soil collected from 14 fields, seven of which incorporated CCs in their crop rotation and seven of which used no CCs. The inclusion of CCs significantly increased permanganate oxidizable carbon (POX), soil respiration, autoclaved-citrate extractable (ACE) protein, residual soil nitrogen (N), and biologically available N. However, we also found that the variation in these parameters was greater when CCs were part of the rotation. This is likely attributable to the wide range of CC species used and differences in their management, such as the chosen termination method. While cover cropping is seen as a best management practice to improve soil health, simply using a CC in a horticulture rotation does not necessarily lead to improved soil health. Research trials on specific CC species and management to target soil degradation are needed to tailor recommendations to ensure the desired soil health outcomes can be achieved with CC use.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".