Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada
Why this work is in the frame
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Bibliographic record
Abstract
Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 < r < 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it