Perennial and Annual Cropping Sequences Differentially Influence Soil Functionality in Luvisolic Soils
Bibliographic record
Abstract
Abstract Cropping system diversification with perennial herbage seed crops has been proposed as a promising strategy for sustaining soil health and functional agroecosystems. We evaluated the impact of diversified cropping systems on key soil properties important for crop production over a decade of experimentation. A field experiment was conducted from 2013 to 2024 with eight cropping sequences under three nitrogen levels (0, 45, and 90 kg N ha − 1 ) on a dark gray Luvisolic soil in the Peace River region of western Canada. The cropping sequences included two perennial legumes (red clover and alsike clover), three perennial grasses (creeping red fescue, meadow bromegrass, and timothy), and four annual field crops (wheat, canola, pea, and barley). Cropping sequences influenced soil functionality parameters regardless of nitrogen fertility levels. The creeping red fescue-dominated cropping sequence showed significant improvements in soil structure measured as stability and mean weight diameter of soil aggregates, soil organic carbon, microbial biomass, and active carbon in the top soil (0–15 cm). Soil enzymatic activities, particularly β-glucosidase and β-N-acetyl-glucosaminidase, which mediate C and N cycling, were also higher in the top soil under fescue-and legume-integrated sequences. However, the bulk density and water characteristics remained stable with a similar degree of soil penetration resistance across cropping sequences. Similarly, soil pH remained consistent across cropping sequences, while plant-available nitrogen (NH₄⁺-N and NO₃⁻ -N) and total nitrogen levels varied with crop-specific influences. Overall, the inclusion of perennial herbage seed crops in conventional annual crop-based cropping systems improved soil health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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".