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Record W4394576924 · doi:10.3390/foods13071119

Climate Change Impacts on Potato Storage

2024· article· en· W4394576924 on OpenAlexafffund
Shu Zhang, Xiuquan Wang, Pelin Kınay, Quan Van Dau

Bibliographic record

VenueFoods · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsUniversity of Prince Edward Island
FundersGouvernement de l'Île-du-Prince-Édouard
KeywordsSproutingDormancyGrowing seasonClimate changeAgronomySowingEnvironmental scienceFood securityBiologyDry seasonGerminationHorticultureAgricultureEcology

Abstract

fetched live from OpenAlex

In this study, we present a comprehensive literature review of the potential impacts of climate change on potato storage. Potato preservation can help reduce food loss and waste while increasing long-term food security, as potatoes are one of the most important crops worldwide. The review's results suggest climate change can negatively affect potato storage, especially tuber sprouting and diseases in storage chambers. Lower Sielianinov coefficient values (indicating dry and hot conditions) during the vegetative season of potato growing can lead to earlier sprouting. For instance, a decrease of 0.05 in the Sielianinov coefficient during the growing season results in tubers stored at 3 °C sprouting 25 days earlier and tubers stored at 5 °C experiencing a 15-day reduction in dormancy. This is due to the fact that the dry and hot climate conditions during the vegetation period of potato planting tend to shorten potato tubers' natural dormancy, which further leads to earlier sprouting during storage. Furthermore, high Sielianinov coefficient values may lead to worse disease situations. The results also suggest that research about the impacts of climate change on potato storage is very limited at the current stage, and further studies are needed to address the key knowledge gaps identified in this study.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.073
GPT teacher head0.305
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2024
Admission routes2
Has abstractyes

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