Diversified No-Till Crop Rotations: Soil Health Attributes across Multiple Ecozones on the Canadian Prairies
Bibliographic record
Abstract
Traditional cereal-based cropping systems on the Canadian prairies have contributed considerably to soil and environmental degradation, increased production costs, and a threat to agricultural sustainability. To address global food demand, there is a need for sustainable cropping systems that enhance soil health (SH) in current climate conditions while reducing the use of agrochemicals. This dissertation focuses on understanding the impact of diverse crop rotations on SH. Chapter 2 presents a meta-analysis, which revealed that increased crop diversity significantly reduces bulk density, enhances soil aggregation, improves total porosity, and saturated hydraulic conductivity. Although it did not significantly change the infiltration rate, the benefits were more pronounced in medium- and fine-textured soils with >900 mm mean annual precipitation, especially when managed with conservation practices for 5 to 10 years. Chapters 3 and 4 document a multi-year field study conducted at three Canadian prairie sites (Lethbridge, Swift Current, and Scott). Six 4-year crop rotations [denoted as conventional (control), pulse/oilseed intensified, diversified, market-driven, high-risk and high-reward, and soil health-enhanced rotations] were established under no-till in 2018. Chapter 3 explores short-term soil organic matter (SOM) and aggregate stability (AS) dynamics, showing no improvement in SOM fractions but significant changes in AS at two sites (Lethbridge and Swift Current), with the soil health-enhanced and high-risk and high-reward rotations having the highest AS. Chapter 4 investigates how diverse crop rotations can alter soil hydraulic and physical quality, demonstrating moderate improvements in rotations with legumes and increased functional diversity, depending on the site. Chapter 5 assesses overall SH using a minimum dataset, revealing that the diversified rotation at Lethbridge and Swift Current, along with the high-risk and high-reward rotation at Scott exhibited the highest SH index. However, the number of indicators varies across sites, with common indicators such as soil organic carbon, bulk density, macroporosity, and plant-available water capacity. In conclusion, increasing the crop and functional diversity in rotations has the potential to sustain SH and contribute to sustainable agroecosystems but may require a longer period to become more evident.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".