Spatial variation of soil quality indicators as a function of land use and topography
Bibliographic record
Abstract
Soil quality (SQ) indicators such as plant available water (PAW), soil organic carbon (SOC), and microbial biomass carbon (MBC) can reveal agroecological functions; however, their spatial variabilities across contrasting land uses need to be better understood. This study examined the spatial variation of these key SQ indicators as a function of two land-use systems and using topography covariates. We sampled a total of 116 point locations in a native grassland (NG) site and an irrigated cultivated (IC) site located near Brooks, Alberta. Compared with NG, cultivation altered soil pore-size distribution by sharply reducing macroporosity by 25%. However, conditions in the IC soil supported greater accrual of microbial growth (MBC of 601 vs. 812 nmol phospholipid fatty acids g −1 soil) probably due to more availability of water and nutrients. Focusing on the effects of topography on SQ indicators, terrain elevation (by light detection and ranging) and estimated depth-to-water were found to be key controllers of SQ at the two land-use systems. Also, there were gradual increases in both SOC and MBC where estimated water table was deeper, and higher SOC also associated with lower elevation. A comparison of ordinary kriging and cokriging (coK) geostatistical mapping indicated that the coK method performed better as demonstrated by improvements in the accuracies of spatial estimations of PAW, SOC concentration, and MBC. Thus, implementing coK using the aforementioned topography covariates enhances the capability for predictive mapping of SQ, which is particularly useful when spatial data for key SQ indicators are sparse and challenging to measure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".