Evidence map›Paper›PMID 36512609›Full record

ArticlePloS one2022

Consumer acceptance of new plant-breeding technologies: An application to the use of gene editing in fresh table grapes.

Azhar Uddin, R Karina Gallardo, Bradley Rickard, Julian Alston, Olena Sambucci

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Azhar UddinPostdoctoral Research Associate, Institute for Research and Education to Advance Community Health (IREACH), Washington State University, Spokane, Washington, United States of America.
R Karina GallardoSchool of Economic Sciences, Puyallup Research and Extension Center, Washington State University, Puyallup, Washington, United States of America.ORCID 0000-0002-6609-4711
Bradley RickardCharles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, New York, United States of America.
Julian AlstonDepartment of Agricultural and Resource Economics, University of California, Davis, Davis, California, United States of America.
Olena SambucciDepartment of Agricultural and Resource Economics, University of California, Davis, Davis, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study estimates consumers' willingness to pay for specific product (quality) and process (agronomic) attributes of table grapes, including taste, texture, external appearance, and the expected number of chemical applications, and for the breeding technology used to develop the plant. Considering varietal traits, on average our survey respondents were willing to pay the highest price premiums for specific offers of improvements in table grape taste and texture, followed by external appearance and expected number of chemical applications. Considering breeding methods, on average our respondents were willing to pay a small premium for table grapes developed using conventional breeding rather than gene editing (e.g., CRISPR). Results from a latent class model identify four different groups of consumers with distinct preferences for grape quality attributes and breeding technologies. The group of consumers most likely to reject gene editing considers both genetic engineering and gene editing to be breeding technologies that produce foods that are morally unacceptable and not safe to eat.

Indexed as

Consumer BehaviorVitisGene EditingPlant BreedingTaste

Identifiers

PMID36512609
PMCPMC9747045

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.