Cropping diversity is a main driver of soil health under intensive organic cropping systems
Bibliographic record
Abstract
The ability of organic cropping systems to sustain soil health may vary with management intensity. Little research has examined the impact of varying management practices on soil health within intensive organic field crop production systems. A field survey was conducted in the fall of 2019, 2020, and 2021 on 10 certified organic farms in Québec, Canada. Their cropping systems comprised an intensive three-year maize ( Zea mays L.)-soybean ( Glycine max [L.] Merr.) – small grain (i.e., winter or spring cereals) rotation. On each farm, soil health was measured on the three rotated crop fields in the fall of 2019, 2020, and 2021 (n = 90). The relationships between soil health indicators and indices of management practices were assessed. The 3-year Crop Diversity Index (CDI) ranged from 2 to 16 across the sampled fields, with the highest values observed where cover crops were used annually, and winter cereals were included in the rotation. Soil physical health indicators were positively influenced by higher CDI values. In contrast, higher Soil Tillage Intensity Ratings for tillage (STIR tillage ) had a negative effect on soil organic carbon (SOC) concentrations. Soil health indicators did not vary among crop phases, except for water-stable aggregates (WSA) which was greater in small grain fields (43.5 %) than in soybean fields (33.9 %). The results from this study demonstrated that soil health was positively influenced by increased crop diversity and reduced tillage intensity. These findings will help organic growers choose and refine best management practices to maintain soil health when cropping intensively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".