Examining and Modelling the Relationship Between Local Topographic Variation and Crop Yield Potential
Bibliographic record
Abstract
Local topographic variation directly influences crop yields in unirrigated agricultural fields. Topography-driven surface processes affect soil moisture and nutrient distributions throughout a field, thereby introducing spatial heterogeneity in soil fertility where crops are grown. This research used traditional and secondary terrain attributes derived from fine-resolution topographic surface data to model estimated crop yield for a row crop field in southern Ontario. A moving-window Pearson’s correlation technique was used to assess individual influence between topographic attributes and crop yield. Influential variables were then selected as input variables for a geographic weighted regression (GWR) model where crop yields were estimated and compared to observed values. Slope and relative topographic position were consistently selected as effective explanatory variables for the predictive GWR models regardless of crop type. Yields were sufficiently predicted for each crop type, with calculated coefficient of determination values equaling R2 = 0.80 for corn, R2 = 0.73 for wheat and R2 = 0.71 for soybeans. The model performed better in areas of the field with greater variation, suggesting that this method works best in variable terrain. These results indicate that local topographic variation plays a significant role in crop yield and various topographic attributes should be included in crop yield estimation models.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".