Identifying Key Crop Growth Models for Rain-Fed Potato (Solanum tuberosum L.) Production Systems in Atlantic Canada: A Review with a Working Example
Bibliographic record
Abstract
Abstract The selective use of potato crop models is a key factor in increasing potato production. This requires a better understanding of the synergies and trade-off of crop management while accounting for the controlling effects of potato genetic and agro-climatic factors. Over the years, crop modeling for potato has relied on historical data and traditional management approaches. Improved modeling techniques have recently been exploited to target specific yield goals based on historical climatic records, future climate uncertainties and weather forecasts. However, climate change and new sources of information motivate better modeling strategies that might take advantage of the vast sources of information in the spectrum of actual, optimal and potential yield and potato management methodologies in a more systematic way. In this connection, two questions warrant interest: (i) how to deal with the variability of crop models relevant to their structure, data requirement and crop-soil-environmental factors, (ii) how to provide robustness to the selection process of a model for specific applications under unexpected change of their structure, data requirement and climatic factors. In this review, the different stages of potato model development are described. Thirty-three crop growth models are reviewed and their usage and characteristics are summarized. An overview of the literature is given, and a specific example is worked out for illustration purposes to identity key models suitable for potato management in the Atlantic provinces of Canada. Based on a categorical principal component analysis (CatPCA) procedure three potato models representing three principal components (PCs) were identified which will be useful for future potato production and yield simulation in this geographic area.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".