Soil properties and topographic features influence within‐field variation in potato tuber yield in New Brunswick, Canada
Bibliographic record
Abstract
Abstract Reduced within‐field potato ( Solanum tuberosum L.) yield variation may lead to increased productivity and reduced environmental impact. Using soil samples collected from 88 site‐years in commercial fields in New Brunswick, Canada from 2013–2017, this study examined how within‐field variation in potato tuber yield was related to soil properties and topographic features. At each of 774 sampling locations, a wide range of soil physical and chemical properties was measured in the lab and topographic features were assessed using a regional digital elevation model. Principal component (PC) analysis identified three PCs, which accounted for 79.1% of the total variation. The PC1 (41.3% of total variance) was dominated by soil texture (i.e., sand, silt) and the quantity and quality of soil organic matter (i.e., soil organic C, particulate organic matter C, and soil C/N ratio). Under rain‐fed potato production in New Brunswick, finer soil texture and increased soil organic matter pools are expected to enhance soil water availability and thereby improve yield. The PC2 (22.7% of total variance) was related primarily to parameters associated with soil fertility, and PC3 (15.1% of total variance) was related primarily with concave or convex landforms, which may influence yield through drought or excess water. This study demonstrated the value in using multivariate approaches to identify the factors that control within‐field yield variability in the presence of significant regional variation in soil properties and environmental conditions. The findings point to the value of enhancing the quantity and quality of soil organic matter as a key strategy to overcome yield limitations under rain‐fed production.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".