Scale‐Dependent Relationship between Wheat Yield and Topographic Indices
Bibliographic record
Abstract
Topography can have a significant influence on crop yield, thus a better understanding of the effects of topographical parameters on crop yield is important—especially for site‐specific soil management. The objective of this study was to determine whether topographical indices developed for hydrological studies could be used as indicators of crop yield using a wavelet approach. The effects of soil curvature, upslope length, and a wetness index on wheat grain yields in a hummocky terrain were investigated along a transect on a clay loam Black soil (Blaine Lake Association) in Saskatchewan, Canada. A wavelet approach was used to elucidate the processes underlying the relationships between crop yield and topographical parameters. Wheat grain yields had significant correlations with upslope length ( R 2 = 0.60) and the wetness index ( R 2 = 0.46), whereas soil surface curvature explained only 15% of the variations in grain yield. Wavelet analyses revealed that significant variations occurred at scales <160 m for wheat grain yield, 280 m for upslope lengths, and 110 m for the wetness index. The cross wavelet analysis indicated that significant covariance existed at scales <180 m between wheat grain yield and upslope lengths and at scales <140 m between wheat yield and the wetness index, at a 99% confidence level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".