Delineation of Management Zones Based on the Agricultural Potential Concept for Potato Production Using Optical Satellite Images
Bibliographic record
Abstract
Management zones (MZs) are a key precision agriculture strategy for managing spatial variability in crops, but conventional delineation methods are costly, time-consuming, and rely on specialized equipment. Previous studies in potato production have primarily relied on single-year NDVI or proximal soil sensor data analyses, limiting their ability to capture temporal stability and variability across multiple fields. This study addresses this gap by applying multi-year, multi-source NDVI composites to characterize spatial and temporal patterns of agricultural potential across 17 commercial potato fields at McCain’s Farm of the Future, Florenceville-Bristol, New Brunswick. A total of 230 NDVI images from Sentinel-2 and Landsat 8 (2015–2023) were processed into composite metrics (mean, standard deviation, skewness) to delineate three agricultural potential (AP) MZs. Validation was conducted using 2023 potato tuber yield and soil physicochemical properties. The results showed statistically significant correlations between NDVI metrics and key soil nutrients (total carbon: |r| < 0.19; total nitrogen: |r| < 0.28), with tuber yield (|r| < 0.41). Spatial patterns of total carbon and nitrogen corresponded with delineated MZs, and tuber yield variability partially aligned with these zones. These findings demonstrate that multi-year NDVI composites provide a cost-effective and scalable approach for mapping agricultural potential, capturing both spatial and temporal variability, and supporting data-driven management decisions in potato production systems.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".