Agricultural Data Interest Group (IGAD) Pre-Meeting Agenda Research Data Alliance 21st to 22nd September 2015 INRA, Paris (France)

1. The Crop Ontology
harmonizing semantics for
agricultural field data
www.cropontology.org
Elizabeth Arnaud (Bioversity International)
Co-authors: Leo Valette, Marie Angelique Laporte (Bioversity), Julian Pietragalla (Integrated
Breeding Platform), Medha Devare (CGIAR)
And all crop curators and breeders
IGAD pre-meeting to 6th Research Data Alliance Conference, 21-22 September 2015

2. A common structured language for
multidisciplinary agricultural research
– Molecular geneticists, breeders,
agronomists, physiologists,
highthrouput phenotyping, and crop
modelers
– Enabling farmers to access
information and exchange their
preferences
– Calls for
• a Common Terminology for annotating
data
• A mediation language that supports data
interpretation
• Ontology for knowledge inference
Photos : courtesy of IRRI

3. Semantic Barriers to data
interpretation
• No naming convention for variables and methods of
measurement which are heterogeneous
• Confusion between traits and variables
• No semantic coherence
 Same trait given different names or abbreviations
 One trait named the same way for various species but
refers to different plant structures
• Definitions and measurements are different between
farmers, breeders, agronomists, modelers
• No ontology on methods of measurement for formal
description

4. The Integrated Breeding Platform
www.integratedbreeding.net
Crop Ontology provides most frequently measured traits and their
standard variables for the Breeding fieldbook and for data
annotation in the crop databases
• Crop Traits (agronomy, morphology, phenology, physiology,
quality, stress)
• Experimental Design, trial management
• Environmental factors

5. Crop Ontology
www.cropontology.org
• Banana
• Barley
• Cassava
• Chickpea
• Common bean
• Cowpea
• Groundnut
• Lentil
• Maize
• Oat (Global Triticeae )
• Pearl millet
• Pigeon Pea
• Potato
• Soybean (USDA & IITA)
• Sweet Potato
• Rice
• Sorghum
• Vitis (INRA)
• Wheat
• Yam

6. Extracting standard variables
Trait Dictionary Template 5.0
Trait = Entity + Quality
(Flower) (colour)
Trait ID CO_341:0000090
Trait Flower color
Entity Flower
Attribute Colour
Trait synonyms Flower pigmentation
Trait abbreviation FCL
Trait abbreviation
synonyms
FlwCol
Trait description Color of the flower
Trait class Morphological traits
Trait status Recommended
Trait Xref TO:0000537
• A Trait can group several variables
Grain Weight
– Weight of 100 grains expressed in g
– Average weight of a grain, expressed in g
– Weight of 100 grains expressed on a
categorical scale: 1=low (50-100g), 2=
medium (100-150g), 3=high (150-200g)
Julian Pietragalla, IBP,
Agronomist - Based in CIMMYT
Léo Valette, Bioversity, Agronomist

7. Standard Variable
Method and scales are important information to capture for data comparison &
interpretation (e.g. crop models). Current ontologies provide sometimes brief
information on Methods but as a text in the attribute information
A Variable is described by the assembly:
Property (Trait) + Method + Scales/units
 Unique name
 Annotate the real value of the measurement (for fieldbook, for
databases)
 Proposed convention for a standard variable naming : P_M_S
•Measurement
•Counting
•Estimation
•Computation
•Nominal
•Ordinal
•Numerical
•Time
•Duration
•Text
•Code
Methods types Scales/Units

8. Online vizualization
Trait, Methods , Scales & Standard
Variables

9. Naming convention
for standard variables
Property (Trait) Method of measurement Scale or Unit
Applicable to any type of measurement & indicators for
survey, monitoring

10. Google Cloud & API
EU-SOL - Solanaceae Breeding DB
Wageningen.
International cassava DB – Boyce
Thompson Institute/IITA
USERS
Global Repository of Evaluation
trials – Agtrials
1,410 agronomic variables are
mapped to Crop Ontology traits for
29,633 trials out of 34,329 trials
description
Phenomics Ontology Driven DB
(PODD)
Luca Matteis,
Web developer

11. Breeding Management System
Annotation of breeders data

12. Agronomy Ontology
• Agronomic trial data are often collected, described and/or formatted in
inconsistent ways
• An Agronomy ontology will support the integration of pre-breeding,
breeding and agronomy data
• Combining results of field management practices x crop traits
measurements leads to fully understand how factors vary within a cropping
system
• First step: Aligning with the International Consortium for Agricultural
System Applications
(ICASA)(http://research.agmip.org/display/dev/ICASA+Master+Variable
+List ) - 600 standard variables – used by Crop Models of AGMIP and Crop
Research Ontology
©Cimmyt
CROP - PLANTING
SEED TREATMENT
IRRIGATION
FERTILIZER
PESTICIDE
SOIL
BIOTIC STRESS
ABIOTIC STRESS
HARVEST-YIELD
Medha Devare
Data and Knowledge Manager
CGIAR Consortium Office

13. ICASA Variables for Crop Models

14. 14
Common Reference Ontologies for Plants (cROP) and Tools for Integrative Plant
GenomicsCommon Reference Ontologies for Plants (cROP) and Tools for Integrative
Plant Genomics
Planteome pilot project
• Centralized platform for reference ontologies for plants
• Online informatics portal for ontology-based, annotated data for plant germplasm, gene
expression, and non-model genomes
• Data query, analysis, visualization and community-based annotation and curation tools
• Plant Ontology (PO)
• Plant Trait Ontology (TO)
• Plant Stress Ontology (PSO)
• Plant Experimental Conditions
Ontology (PECO/EO)
• Gene Ontology (plants)
• Phenotypic Qualities Ontology (PATO)
• Cell Type Ontology (CL)
• Chemicals (ChEBI)
• Protein Ontology (PRO)
Common Reference Ontologies for Plants and
Tools for Integrative Plant Genomics
• Lead PI : Pankaj Jaiswal,
• Sinisa Todorovic, Eugene Zhang Oregon State University, USA
• Dennis W. Stevenson New York Botanical Garden, NY, USA,
• Elizabeth Arnaud sity International, Montpellier, France;
• Christopher Mungall . Lawrence Berkeley National Laboratory,
Berkeley CA, USA,
• Georgios V. Gkoutos, John Doonan ; University of Aberystwyth, UK
• Barry Smith, University of Buffalo, NY, USA

15. PATO:0000122
Length
CO_321:0000056
Spike Length
TO:0000271
Inflorescence Length
?
PO:0009049
Inflorescence
Narrow
Synonym: spike
CO_321:0000056
Spike Length
TO:0000271
Inflorescence Length
PO:0009049
Inflorescence
Narrow
Synonym: spike
CompoundmappingsInference
PATO:0000122
Length
Mapping Crop Ontology terms across
species and to the Reference Ontologies
• Mappings performed by Marie Angélique Laporte
• Automatic mapping generated by AML tool developed by Catia Pesquita cpesquita@di.fc.ul.pt ,
Daniela Oliveira doliveira@lasige.di.fc.ul.pt from LaSIGE - Large-Scale Informatics Systems Laboratory
((https://github.com/AgreementMakerLight/AML-Project )
• This mapping tool is used for thesaurus alignment of FAO, CABI, NAL in the Global Agricultural
Concept Server (GACS) project

16. Future Activities
• Content Expansion:
– Farmers’ preferences of Participatory Variety Selection (PVS)
– Functional traits for Agroecology and Ecosystem Services restoration
– Hosting Agricultural and Nutrition Technology ontology (ANT) of IFPRI
– Aligning with Agrovoc, CABI, NAL thesauri for literature mining
• Community Use Expansion
– Through Planteome and Divseek initiative
– International Wheat Initiative
– Collaborative, Open Plant Omics (COPO): a community-driven
bioinformatics platform for plant science (BBSRC)
– The ISA-Tools group at Oxford and test their Statistical Method Ontology
:http://www.stato-ontology.org
April 2016: Workshop on Crop Ontology for scientists to discuss
their data and the definitions of traits, present our project results –
Hands on sessions; Vocamp
Sponsors, sessions conveners ?

17. CGIAR Crop Lead Centers and
partners
Since 2008
Community workshop in 2014, Montpellier : http://tiny.cc/rw51ax