Spatial Variability of Potato Tuber Yield and Plant Nitrogen Uptake Related to Soil Properties
Bibliographic record
Abstract
Inferring correlations between yield and soil properties is difficult, because the variables involved vary at different scales. The study goal was to describe the spatial distribution of tuber yield (total yield), plant N accumulation (Nuptake) and soil properties at small and large field scales with a multivariate geostatistical method (Co‐Regionalization Analysis with a Drift), allowing for a correlation analysis of crop and soil properties at the same scale of variability. Two sites with similar growing practices but different pedodiversity were instrumented at 108 sampling points. Total yield was represented mostly by a small‐scale spatial component (<12.4 m) at Site 1, whereas the importance of the small‐scale variability was minor at Site 2. The Nuptake showed a strong spatial structure at small scale at both sites. Large‐scale component of Nuptake was also present at both sites, and correlations with total yield and Nuptake were stronger at this scale. Correlations with soil properties at large scale only indicate a low temperature early in the season and a lack of water later during the season decreased yield whereas lower soil density and higher electrical conductivity increased plant productivity. For high productivity, some irrigation may be needed, even only once during the season. Property measurements should be taken at large scale for precision agriculture by respecting the scale of variability of each property. Small‐scale variability should be used when treatments are of interest, for example, plots no longer than 11 m should be used to compare fertilizer rates at the sites of this study.
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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 | 0.001 | 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) | 0.001 | 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".