Climate change impacts on global potato yields: a review
Bibliographic record
Abstract
Abstract Potatoes as a food crop contribute to zero hunger: Sustainable Development Goal 2. Over the years, the global potato supply has increased by more than double consumption. Changing climatic conditions are a significant determinant of crop growth and development due to the impacts of meteorological conditions, such as temperature, precipitation, and solar radiation, on yields, placing nations under the threat of food insecurity. Potatoes are prone to climatic variables such as heat, precipitation, atmospheric carbon dioxide (CO 2 ), droughts, and unexpected frosts. A crop simulation model (CSM) is useful for assessing the effects of climate and various cultivation environments on potato growth and yields. This article aims to review recent literature on known and potential effects of climate change on global potato yields and further highlights tools and methods for assessing those effects. In particular, this review will explore (1) global potato production, growth and varieties; (2) a review of the mechanisms by which changing climates impact potato yields; (3) a review of CSMs as tools for assessing the impacts of climate change on potato yields, and (4) most importantly, this review identifies critical gaps in data availability, modeling tools, and adaptation measures, that lays a foundation for future research toward sustainable potato production under the changing climate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".