Relationship Between Irrigation Thresholds and Potato Tuber Depth in Sandy Soil
Bibliographic record
Abstract
Soil disturbance resulting from tuber crop harvesting is a major threat to soil health. The depth of soil intervention is a critical factor that further strengthens the effects of such disturbance and makes harvest one of the most harmful cropping operations. In the case of potato, soil moisture is a determining factor for root and stolon development, hence, a deeper soil intervention may be required at harvest. While potato ranks as the fourth most cultivated crop worldwide, the impact of soil moisture on potato tuber vertical and horizontal distribution has received very little attention. The objective of this study was to evaluate the effects of four soil matric potential thresholds (SMPTs; –10, –20, –30, and –45 kPa) on the spatial (vertical and horizontal) distribution of potato tubers grown in plastic containers filled with sandy soil using an X-ray computed tomography scanner. The results of the experiments conducted in a greenhouse environment suggest that the horizontal distribution of the tubers did not differ significantly across the irrigation treatments. However, a linear relationship between SMPT, and therefore irrigation threshold, and potato tuber depth was observed. In addition, the deepest tuber position was observed under the –10 kPa SMPT, while the tubers were closer to the soil surface under the –45 kPa SMPT, which could lead to a greater preponderance of tuber diseases such as late blight or greening. Thus, potato irrigation events implementing a SMPT between –20 and –30 kPa could reduce the harvest depth, hence, decreasing the negative impacts of soil disturbance on soil structural stability and soil organic carbon degradation while mitigating the impacts of disease as well as reducing fuel costs, greenhouse gas emissions, soil loss and erosion.
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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 | 0.000 | 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) | 0.001 | 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".