Management zone delineation: utilizing multiple data sources to minimize soil spatial variability in commercial potato fields under Prince Edward Island pedoclimatic conditions
Bibliographic record
Abstract
Spatial variability in soil physicochemical properties within agricultural fields significantly influences crop management practices and results in uneven resource utilization, yield reduction, and environmental issues stemming from excessive fertilizer use. This study investigates the use of apparent soil electrical conductivity (ECa), field elevation, and tuber yield data obtained from yield monitors to delineate subfield regions and reduce variability across the field. Four commercial potato fields were selected in Kensington and Souris, Prince Edward Island: The Oyster Cove fields (OC 1 and OC 2) and the Black Pond fields (BP 1 and BP 2). In 2019, these fields underwent commercial soil proximal sensor (Veris 3100) operations to measure soil ECa at two different depths: shallow (0–30 cm, ECa_S) and deep (0–100 cm, ECa_D), as well as elevation. Soil samples (0.0–0.15 m) were collected from each field to measure soil physicochemical properties that can be used to delineate management zones (MZ). Study results revealed significant correlations between delineating, soil texture, and soil chemical properties. The variance reduction indicated that three MZ were optimal for representing the spatial variability of soil physicochemical properties. Multiple comparisons revealed that higher values were obtained for most of the soil properties in MZ3 compared to MZ1. The significant between-zone differences in soil properties indicated that soil ECa and elevation data, along with tuber yield, can be used to develop MZ. Additionally, this approach is highly effective in capturing site-specific variability and can guide management practices, such as fertilization in potato fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".