Spatial Variation of Soil Health Indices in a Commercial Potato Field in Eastern Canada
Bibliographic record
Abstract
There is increasing interest in using various indices to assess soil health; however, the nature of the within‐field variation in such indices, and their relationship with soil properties, are generally unknown. This study examined the spatial variation of 15 soil health indices in a 21‐ha commercial potato field in New Brunswick, Canada. Soil samples (0–15 cm depth) were collected in spring of 2016 at 154 geo‐referenced locations within the field. With the exception of CaCl 2 extractable NH 4 –N, all soil parameters demonstrated strong or moderate spatial dependence. Several soil properties were significantly correlated, for example, soil organic carbon was strongly positively correlated with indices of soil C availability, soil N availability and soil physical properties. Principal Component Analysis suggested that the parameters fell into three major groups: PC1 (39.9% of total variance) was associated primarily with parameters related to the quantity of soil organic matter; PC2 (15.3% of total variance) with parameters related to soil organic matter quality; and PC3 (10.3% of total variance) with parameters related to soil structure. In comparison, the spatial pattern of total tuber yield was related to soil texture and soil drainage and was most strongly correlated with indices of soil organic matter quality (PC2). Soil management zones and mapped soil series were both generally effective in capturing the spatial variation in soil health indices and can be used to stratify the sampling of soil health indices in spatially variable fields.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".