Evidence map›Paper›PMID 32336978›Full record

ArticlePlant methods2020

Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials.

Andreas Honecker, Henrik Schumann, Diana Becirevic, Lasse Klingbeil, Kai Volland, Steffi Forberig, Marc Jansen, Hinrich Paulsen, Heiner Kuhlmann, Jens Léon

Abstract read
In one paragraph

Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Andreas Honecker1INRES-Plant Breeding, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany.
Henrik Schumann1INRES-Plant Breeding, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany.
Diana Becirevic2IGG-Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
Lasse Klingbeil2IGG-Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
Kai VollandTerrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany.
Steffi ForberigTerrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany.
Marc JansenTerrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany.
Hinrich PaulsenTerrestris GmbH & Co. KG, Kölnstraße 99, 53111 Bonn, Germany.
Heiner Kuhlmann2IGG-Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany.
Jens Léon1INRES-Plant Breeding, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo ensure further genetic gain, genomic approaches in plant breeding rely on precise phenotypic data, describing plant structure, function and performance. A more precise characterization of the environment will allow a better dealing with genotype-by-environment-by-management interactions. Therefore, space and time dependencies of the crop production processes have to be considered. The use of novel sensor technologies has drastically increased the amount and diversity of phenotypic data from agronomic field trials. Existing data management systems either do not consider space and time, are not customizable to individual needs such as field trial handling, or have restricted availability. Hence, we propose an integrative data management and information system (DMIS) for handling of traditional and novel sensor-based phenotypic, environmental and management data. The DMIS must be customizable, applicable and scalable from individual users to organizations.

resultsKey element of the system is a dynamic PostgreSQL database with GIS-extension, capable of importing, storing and managing all types of data including images. The database references every structural database object and measurement in a threefold approach with semantic, spatial and temporal reference. Timestamps and geo-coordinates allow automated linking of all data. Traits can be precisely defined individually or uploaded as predefined lists. Filtering and selection routines allow compilation of all data for visualization via tables, charts or maps and for export and external statistical analysis. New possibilities of environmental information-based planning of field trials, weather-guided phenotyping and data analysis for outlier or hot-spot detection are demonstrated.

conclusionsThe DMIS supports users in handling experimental field trials with crop plants and modern phenotyping methods. It focuses on linking all space and time dependent processes of plant production. Weather, soil and management, as well as growth and yield formation of the plants can be depicted, thus allowing a more precise interpretation of the results in relation to environment and management. Breeders, extension specialists, official testing agencies and agricultural scientists are assisted in all steps of a typical workflow with planning, designing, conducting, controlling and analyzing field trials to generate new information for decision support in the crop improvement process.

Indexed as

Crop plantsData management systemField trialsGeo-coordinatesOpen sourcePhenotypingSoilTimestampsWeatherWebGIS

Identifiers

PMID32336978
PMCPMC7171732

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.