Soil capability as a predictor of cropland change in <scp>A</scp> lberta, <scp>C</scp> anada from 1988 to 2010
Bibliographic record
Abstract
Abstract Understanding the mutual influences between cropland use and soil characteristics is important in anticipating and planning for food production, environmental protection and resource sustainability. Numerous studies focus on the relationship between crop rotations and soil characteristics at a microscale, but fewer studies focus on the relationships between soil capability and cropland use and change at a medium scale. We explore how soil capability has influenced cropland changes over 22 yr, using statistical and land use transition analysis. Landsat images from the years 1988, 2002 and 2010 were used to map cropland changes by soil capability class within a pilot site in Alberta, Canada. Between the late 1980s and 2010: (i) the area of annual crops increased substantially while that of forest and summer‐fallow decreased; (ii) changes in cropland use among annual crops, perennial crops and summer‐fallow differed substantially depending on soil capability; and (iii) a transition from annual crops and summer‐fallow to perennial crops was more likely on land of poorer soil capability, whereas the transition from perennial crops and summer‐fallow to annual crops was more likely on land with higher soil capability. The changes in land use practices identified in this study indicate that producers are actively intensifying production on their best land to optimize profitability while simultaneously reducing the intensity of production on poorer land for either financial or environmental reasons, or both.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".