Scaling Properties of Topographic Indices and Crop Yield: Multifractal and Joint Multifractal Approaches
Bibliographic record
Abstract
Topography controls soil water distribution in semiarid environments where water is the major growth‐limiting factor. Identification of the topographic index that best represents the spatial variability and scaling properties of crop yield is important for precision farming. Our objective was to characterize the scaling properties of four topographic indices [relative elevation (RE), wetness index (WI), upslope length (USL), and curvature (CR)] and their relationships to wheat (Triticum aestivum L.) grain yield and biomass using multifractal and joint multifractal approaches. Wheat grain yield and terrain data were collected at 6‐m intervals along a 576‐m‐long transect on a nonlevel landscape with dominant soil type of Aridic Ustoll, under the semiarid environment of Saskatchewan, Canada. Results indicated that CR and RE had a fractal type of scaling only for a narrow range of moment orders. Wetness index showed a monofractal scaling with fractal dimension of 0.98; whereas yield, biomass, and USL showed a multifractal scaling. Joint multifractal analyses showed a high correlation coefficient between the scaling indices of grain yield and USL (r = 0.93). Wetness index appeared to be effective as a yield covariate only at low slope areas and depressions where it has similar scaling to that of USL. Results from this study suggested that USL was the best indicator of grain yield and biomass at any scale. The implication for precision farming is that USL can be used as a guideline for varying production inputs such as fertilizer as well as for yield prediction.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".