Land Use Effects On Soil Biological Properties In Two Topographically Variable Agroecosystems In Saskatchewan
Bibliographic record
Abstract
Soil biological properties tend to be under utilized as indicators in soil health tests. Novel measures - those seldom used in such metrics - such as microbial abundance and biomass, community structure, and enzyme activity are directly related to soil resource availability, organic matter decomposition, and nutrient cycling, which are affected by agricultural land use. The inclusion of biological properties in soil health tests may allow land managers to compare the effects of management practices faster than if they relied solely upon changes in chemical and physical properties over time. The objective of this project was to measure how enzyme activity, microbial abundance, and community structure are affected by annual and perennial cropping systems at different landscape positions, over different depths, and across the growing season at two different locations within the province, [St. Denis National Wildlife Area (SDNWA), and the Conservation Learning Centre (CLC)]. The SDNWA and CLC were chosen due to their topographic variation and presence of adjacent perennial and annual land use. Samples were taken at different landscape positions at different time points within the growing season to account for both location and seasonal effects on soil function. Time within the growing season influenced enzyme activity differently between perennial vs. annual cropping systems at both sites. However, seasonal dynamics in the annual system differed between the two sites due to differences in crop rotation. For example, SDNWA had elevated enzyme activity in the annual cropping system early in the growing season, a trend that did not exist at CLC. The effects of perennial cover were found to often buffer the effects of topography in the perennial agroecosystems, which affected not only enzyme activity but also PLFA abundance, particularly fungal signatures. Finally land use effects were moderated by both topographic and depth effects at CLC in a complex interaction which affected enzyme activity and PLFA abundance, while the same interaction was not seen at SDNWA. These findings indicate that novel soil biological properties respond to changes in management factors often interconnectedly with land use, and environmental factors such as topography, depth, and season, and that these biological properties could be valuable additions to soil health indices.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".