Spatial Relationships of Landscape Attributes and Wheat Yield Patterns
Bibliographic record
Abstract
Success of precision farming practices requires knowledge of fields such as soil type, topography, soil nutrients, spatial variability effects, yield patterns and their spatial relationships. A three year (2008-09 to 2010-11) field experimental study was conducted at Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the influencing landscape parameters and their spatial distribution, having effects on wheat yield patterns using artificial neural network (ANN) and GIS map overlay techniques. A total of 48 soil samples were collected from top 30 cm of the soil, before sowing, at center of each grid of 24 x 67 m in size along with position data using Global Positioning System receiver (GARMIN, GPS60). Landscape attributes such as elevation, %sand, %silt, %clay, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus were included in the analysis. ANN analysis revealed that urea fertilizer treatments, followed by %sand, %silt, % clay, elevation, soil nitrogen and EC were ranked as the most influencing parameters. The yield data, however, were normalized to remove fertilizer treatments effects and then were used in the subsequent analysis. The map overlay analysis showed that the areas having lower elevation, lower soil EC and higher levels of soil N produced higher yields. Whereas the areas having higher elevation, higher soil EC and moderate soil N produced lower yields, establishing the cause-effect relationships. These results indicated that ANN and GIS techniques were helpful in identifying the influencing parameters affecting wheat yield, which can be managed under precision farming practices.
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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 | 0.001 |
| 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 | 0.000 |
| 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".