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Record W3190221921 · doi:10.3389/fpls.2021.698060

Potato Response to Drought Stress: Physiological and Growth Basis

2021· article· en· W3190221921 on OpenAlexafffund
Taylor Gervais, Alexa Creelman, Xiu‐Qing Li, Benoît Bizimungu, David De Koeyer, Keshav Dahal

Bibliographic record

VenueFrontiers in Plant Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaGovernment of Canada
KeywordsCultivarBiologyDrought toleranceAgronomyDrought stressHorticultureCropYield (engineering)GreenhouseCrop yieldPlant breeding

Abstract

fetched live from OpenAlex

Drought poses a major challenge to the production of potatoes worldwide. Climate change is predicted to further aggravate this challenge by intensifying potato crop exposure to increased drought severity and frequency. There is an ongoing effort to adapt our production systems of potatoes through the development of drought-tolerant cultivars that are appropriately engineered for the changing environment. The breeding of drought-tolerant cultivars can be approached through the identification of drought-related physiological and biochemical traits and their deployment in new potato cultivars. Thus, the main objective of this study was to develop a method to identify and characterize the drought-tolerant potato genotypes and the related key traits. To achieve this objective, first we studied 56 potato genotypes including 54 cultivars and 2 advanced breeding lines to assess drought tolerance in terms of tuber yield in the greenhouse experiment. Drought differentially reduced tuber yield in all genotypes. Based on their capacity to maintain percent tuber yield under drought relative to their well-watered controls, potato genotypes differed in their ability to tolerate drought. We then selected six genotypes, Bannock Russet, Nipigon, Onaway, Denali, Fundy, and Russet Norkotah, with distinct yield responses to drought to further examine the physiological and biochemical traits governing drought tolerance. The drought-induced reduction in tuber yield was only 15-20% for Bannock Russet and Nipigon, 44-47% for Onaway and Denali, and 83-91% for Fundy and Russet Norkotah. The tolerant genotypes, Bannock Russet and Nipigon, exhibited about a 2-3-fold increase in instantaneous water-use efficiency (WUE) under drought as compared with their well-watered controls. This stimulation was about 1.8-2-fold for moderately tolerant genotypes, Onaway and Denali, and only 1.5-fold for sensitive genotypes, Fundy, and Russet Norkotah. The differential stimulation of instantaneous WUE of tolerant and moderately tolerant genotypes vs. sensitive genotypes was accounted for by the differential suppression of the rates of photosynthesis, stomatal conductance, and transpiration rates across genotypes. Potato genotypes varied in their response to leaf protein content under drought. We suggest that the rates of photosynthesis, instantaneous WUE, and leaf protein content can be used as the selection criteria for the drought-tolerant potato genotypes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.239
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations108
Published2021
Admission routes2
Has abstractyes

Explore more

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