Prediction of Soil Nitrogen Supply in Potato Fields using Soil Temperature and Water Content Information
Bibliographic record
Abstract
This study evaluated different strategies for use of a simple first‐order kinetic model (N min = N 0 [1– e − k t ] where N 0 is potentially mineralizable nitrogen and k is the mineralization rate constant) to predict growing season soil nitrogen supply (SNS) in potato ( Solanum tuberosum L.) fields under cool humid climatic conditions. All strategies considered spring soil mineral nitrogen (SMN) and the labile mineralizable N pool (Pool I), and correction of the value of k was evaluated based on temperature (T) only, or based on both T and water content (θ). The strategies examined: (i) the depth of the soil used; (ii) the choice of k value used for Pool I; and (iii) the replenishment of the mineralizable N pools. Predicted SNS was compared with a field‐based estimate of plant available soil nitrogen supply (PASNS) measured as plant (vine plus tuber) N uptake plus residual nitrate at harvest in unfertilized plots. When k was corrected using T only, the strategies generally overestimated the PASNS. When k was corrected using both T and θ, predicted SNS was not significantly different from PASNS in most cases. The most promising strategy used a depth of 0 to 20 cm, the common depth for tillage, rather than 0 to 15 cm, which represents the actual depth used for soil sampling. This study demonstrated that SNS can be adequately predicted using a simple kinetic model, and that consideration of soil water content was important in predicting SNS even in humid soil moisture regimes.
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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) | 0.000 | 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".