Evidence map›Paper›PMID 29555431›Full record

ReviewTrends in plant science2018

Translating High-Throughput Phenotyping into Genetic Gain.

José Luis Araus, Shawn C Kefauver, Mainassara Zaman-Allah, Mike S Olsen, Jill E Cairns

Abstract readReview
In one paragraph

Review in Trends in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 283 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
283citing papers in PubMed, 2 pooled it
–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

283 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  18. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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223 more citing papers are in PubMed but not listed here.

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.

José Luis ArausUnit of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain. Electronic address: jaraus@ub.edu.
Shawn C KefauverUnit of Plant Physiology, Faculty of Biology, University of Barcelona, Barcelona, Spain.
Mainassara Zaman-AllahGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT) Southern Africa Regional Office, Harare, Zimbabwe.
Mike S OlsenGlobal Maize Program, CIMMYT, Nairobi, Kenya.
Jill E CairnsGlobal Maize Program, International Maize and Wheat Improvement Center (CIMMYT) Southern Africa Regional Office, Harare, Zimbabwe.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inability to efficiently implement high-throughput field phenotyping is increasingly perceived as a key component that limits genetic gain in breeding programs. Field phenotyping must be integrated into a wider context than just choosing the correct selection traits, deployment tools, evaluation platforms, or basic data-management methods. Phenotyping means more than conducting such activities in a resource-efficient manner; it also requires appropriate trial management and spatial variability handling, definition of key constraining conditions prevalent in the target population of environments, and the development of more comprehensive data management, including crop modeling. This review will provide a wide perspective on how field phenotyping is best implemented. It will also outline how to bridge the gap between breeders and 'phenotypers' in an effective manner.

Indexed as

Crops, AgriculturalGenomicsHybrid VigorPhenotypePlant BreedingPlant DevelopmentPlantsQuantitative Trait Locifield phenotypinggenetic gainhigh-throughputremote sensing

Identifiers

PMID29555431
PMCPMC5931794

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.