Photo: Anindya Phani

About AgrO

An ontology is a formal representation of a disciplinary domain, representing a semantic standard that can be employed to annotate data where key concepts are defined, as well as the relationships that exist between those concepts (Gruber, 2009). Ontologies provide a common language for different kinds of data to be easily interpretable and interoperable allowing easier aggregation and analysis.

The Agronomy Ontology (AgrO) provides terms from the agronomy domain that are semantically organized and can facilitate the collection, storage and use of agronomic data, enabling easy interpretation and reuse of the data by humans and machines alike.

To fully understand the implications of varying practices within cropping systems and derive insights, it is often necessary to pull together information from data in different disciplinary domains. For example, data on field management, soil, weather and crop phenotypes may need to be aggregated to assess performance of particular crop under different management interventions.

However, agronomic data are often collected, described, and stored in inconsistent ways, impeding data comparison, mining, interpretation reuse. The use of standards for metadata and data annotation play a key role in addressing these challenges. While the CG Core Metadata Schema provides a metadata standard to describe agricultural datasets, the Agronomy Ontology enables the description of agronomic data variables using standard terms.

AgrO is being built from traits and parameters identified by agronomists, the ICASA Data Dictionary, and other existing ontologies such as the Environment Ontology, the Unit Ontology, and the Phenotype and Trait Ontology and enriched with the support of several scientists who bring their domain knowledge.

Contact us

Céline Aubert
Consultant – IFPRI
Email: aubertceline@ymail.com

Marie-Angélique Laporte
Associate Scientist
Alliance Bioversity International-CIAT
Email: m.a.laporte@cgiar.org

Elizabeth Arnaud
Scientist | Email
Alliance Bioversity International-CIAT
Email: e.arnaud@cgiar.org

Medha Devare
Senior research fellow | Module lead Big Data Platform
IFPRI
Email: m.devare@cgiar.org

Core team

AgrO has been developed by a team from several CGIAR Research Centers, with input and support from experts at other institutions.

Medha Devare

Project Lead | Senior Research Fellow - IFPRI, Module Lead - CGIAR Platform for Big Data in Agriculture

m.devare@cgiar.org

Elizabeth Arnaud

Project Co-Lead | Scientist - Alliance Bioversity International-CIAT

e.arnaud@cgiar.org

Marie-Angélique Laporte

Ontology Expert and AgrO Developer | Associate Scientist - Alliance Bioversity International-CIAT

m.a.laporte@cgiar.org

Céline Aubert

AgrO Developer and Curator | Consultant, IFPRI

aubertceline@ymail.com

Key contributors

Pier Luigi Buttigieg

Data Scientist - Alfred Wegener Institute

Léo Valette

Research assistant - Bioversity International (2015-2016)

Cheryl Porter

Coordinator, Computer Applications – University of Florida

Chris Mungall

Department Head, Molecular Ecosystems Biology, Berkeley Lab

Julian Pietragalla

Regional Deployment Manager, Latin America - IBP

Adopters

AgroFIMS

A key use case for AgrO is the Agronomy Field Information Management System (AgroFIMS). AgroFIMS enables digital collection of agronomic data that is semantically described a priori with agronomic terms from AgrO.

It consists of a web application used to design data collection templates for agricultural experiments by selecting variables annotated with AgrO and other relevant ontologies. These variables are sorted into a series of modules representing the typical cycle of operations in agronomic trial management, such as land preparation, irrigation, weeding, soil fertility, weather and soil parameters, and more. The field book application allows the creation of data collection sheets enabling standardization in data collection and description and linkages with breeding and other related data. In addition, AgroFIMS promotes digital collection of data to reduce error and ease data collection via the mobile apps: KDSMart, ODK, and Field Book.

The Agricultural Model Intercomparison and Improvement Project (AgMIP)

AgrO is being used by the University of Florida (UF), and researchers associated with the Agricultural Model Intercomparison and Improvement Project (AgMIP) and IFPRI as a standard reference terminology to enable the generation and reuse of model-ready data.

The goal of this effort is to facilitate data queries in GARDIAN that include a measure of the appropriateness of each dataset for use in quantitative analyses. Each dataset will include metadata that fully describe the terminology used in that dataset with links to AgrO definitions and units.

Learn more: GARDIAN and facilitating data interoperability at CGIAR – Blog post – 12 February 2019

Browse AgrO

Structure

The backbone of AgrO is based on formal categories from the Basic Formal Ontology (BFO) shared by the ontologies of the Open Biological and Biomedical Ontology (OBO) Foundry family.

The mission of the OBO Foundry is to develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate. Therefore, some terms in the AgrO ontology tree are generic, but necessarily so for AgrO to be interoperable with other ontologies. In further efforts towards such interoperability, AgrO reuses and builds on existing terms from other ontologies, thus minimizing term proliferation and duplication across multiple ontologies (c.f. table). This means that some terms in the AgrO ontology tree may be generic, but this is necessary for AgrO to be interoperable with other ontologies.

Table: Ontologies used in AgrO

Ontology acronym	Ontology full name	Ontology content used in AgrO	Example of terms
BFO	Basic Formal Ontology	Foundation of the ontology	entity, continuant, occurrent
ChEBI	Chemical Entities of Biological Interest	Chemical entities along with their biochemical role	role fertilizer, chemical entity
ENVO	Environment Ontology	Environments and entities	animal manure, soil, field
IAO	Information Artifact Ontology	Information entities. Entends OBI	assay, has measurement datum has time stamp
OBI	Ontology for Biomedical Investigations	Study design, protocols and instrumentation	achieves_planned_objective objective_achieved_by
PATO	Phenotype And Trait Ontology	Qualities of entities	adjacent to, contributes to, concentration of,
PECO	Plant Experimental Condition Ontology	Abiotic treatments, growing conditions and study types	silt content exposure, clay content exposure
PO	Plant Ontology	Plant anatomy, morphology and growth	leaf, bud, seed
TO	Plant Trait Ontology	Phenotypic traits in plants	yield trait
UO	Unit Ontology	Units	m, kg, L

Searching terms within AgrO

The Ontology Lookup Service of the European Biological Institute (OLS-EBI) allows an online search of the ontology.

To find a term, simply type it in the search box of the OLS tool below. You can also navigate into the tree using the + sign on the left side of the terms.

You can also query the OLS API directly by modifying the R script available here.

Download AgrO

AgrO developments are openly available on the Agricultural Semantics GitHub repository.

AgrO can be downloaded here. This file is readable using ontology editors like the software Protegé (using the ‘open from URL’ option for instance).

R users may download and parse AgrO as needed by modifying the script available here.

navigate AgrO

Main categories in AgrO

The ontology has been categorized in different classes, following the backbone of the OBO foundry ontologies. Below are the details of the hierarchy of the main AgrO classes to help you navigate the ontology tree.

Entity	Process (Entity>occurrent>process>planned process) every planned and unexpected “event” happening in the field	Experimental conditions Management practices (irrigation process, harvesting process, planting process)
	Material and immaterial entity (Entity>continuant>independent continuant) Every object/organism involved in an agricultural experiment	Organisms (plants, animals, insects, bacteria, virus, …) Agricultural implements Organic fertilizers Chemical entities (pesticides, fertilizers) Plant structure Field environmental context (environmental zones such as sites, plot, environmental materials such as soil and soil types, water sources, ecosystems) Organization (for metadata)
	Quality (entity>continuant>specifically dependent continuant) characteristic of an entity or a process	Size Number of Shape Concentration Amount
	Plan specification (entity>continuant>generically dependent continuant>information content entity>directive information entity) method or protocol realized during a process	Application method (aerial application, drilling, seed dressing, band application) Irrigation method Planting distribution Measurement protocol Agronomic experimental design
Role behavior of a material entity	Pesticide Fertilizer
Unit unit of measurement

All these classes interact via relations described in the ontology.

For example:

The term: tillage process is linked to the terms soil, tractor and tillage implement with the relation has participant.
The term animal manure is linked to the term organic fertilizer, with the relation has role.
Agricultural experimental Plot contains process agricultural experiment

Citation

We request that users cite the ontology using the following citation:

Aubert C., Buttigieg P.L., Laporte M.A., Devare M., Arnaud E., (2017) CGIAR Agronomy Ontology, http://purl.obolibrary.org/obo/agro.owl, licensed under CC BY 4.0

This Creative Commons Attribution 4.0 International License allows you to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Reference

Data-driven Agricultural Research for Development: A Need for Data Harmonization Via Semantics. Devare M., Aubert C., Laporte M.A., Valette L., Arnaud E., Buttigieg P.L.. Proceedings of the Joint International Conference on Biological Ontology and BioCreative, Corvallis, Oregon, United States, August 1-4, 2016. http://ceur-ws.org/Vol-1747/IT205_ICBO2016.pdf

Participate

If you would like to ask for a new term or synonym, suggest enhancements, or report an error please open a ‘’New Issue’’ in our GitHub issue tracker.

In the title of the issue, add the term you wish to add or modify. In the comment of the issue, add the terms’ definition, its reference and its location in the ontology’s tree.

For any other request, feel free to contact us and register to the agronomy ontology mailing list: agronomyOntology@googlegroups.com.

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AgroFIMS: Your new companion for easy standardization of data collection and description

The Agronomy Field Information Management System (AgroFIMS) allows users to create fieldbooks to collect agronomic data that is already tied to a metadata standard (the CG Core Metadata Schema, aligned with the standard Dublin Core), and semantic standards like the Agronomy Ontology (AgrO), generating data that is Findable, Accessible, Interoperable, and Reusable (FAIR) at collection. AgroFIMS therefore standardizes data collection and description for easy aggregation and inter-linking across disparate datasets. The fieldbooks you create can be exported to the Android-based KDSmart data collection application, and collected data imported back to AgroFIMS for statistical analysis and reports. In 2021 AgroFIMS will allow you to set up agronomic survey questionnaires, for data collection via ODK. It will also allow easy upload of your “born FAIR” data to Dataverse repository platforms with Dublin Core-compliant metadata schemas. Funding for AgroFIMS was provided by the Bill and Melinda Gates Foundation’s Open Access, Open Data Initiative, and the CGIAR Platform for Big Data in Agriculture. AgroFIMS is under GPL license. Go to AGROFIMS →

Responsible Data Management Guidelines to protect privacy

CGIAR Platform for Big Data in Agriculture advocates open data for agricultural research for development. It considers that opening up research data for scrutiny and reuse confers significant benefits to society.

However, the Platform appreciates that not all research data can be open and that a broad range of legitimate circumstances may require data to be restricted.

As an integral component of its advocacy for open data, the Platform promotes responsible data management through the entire research data lifecycle from planning, collecting, storing, disclosing or publishing, transferring, discovery and archiving.

These guidelines were created from information collected from: review on best and emerging practices across various sectors in the fast changing landscape of privacy and ethics (130 external resources); privacy and ethic materials sourced from seven CGIAR centers; first draft was circulated for input and feedback across CGIAR and incorporated into this edition. It’s important to note that this is an evolving document, the next stage is to consult externally for further input.

These Guidelines are intended to assist agricultural researchers handle privacy and personally identifiable information (PII) in the research project data lifecycle.

Check the guidelines →

REUSE / TRANSFER

Ensure consistency with the DMP-PII and the purpose for which prior informed consent has been obtained
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII (as above)
Ensure PII is stored securely to protect privacy (as above)
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools (as above)

Don’t transfer data containing PII unless have explicit consent
Don’t transfer data containing PII in the absence of a data sharing agreement identifying aspects such as purpose and scope of use, privacy protections measures, confidentiality and any limitations)
Don’t reuse or transfer PII until any inconsistencies with the DMP-PII and/or purpose compatibility have been resolved (e.g. through updated ethics review or consent from participant)

ARCHIVING / DISCARDING

Plan for archiving or data destruction early in the process. Destroying data can be more secure, however, archiving can be beneficial if the data has ongoing evidentiary, scientific or cultural value. If archiving, identify where and how, the budget require
Ensure DMP-PII and purpose compatibility (as above)
Ensure adequate security measures to protect privacy (as above)

Don’t wait until the end of the project to assess archiving needs when time and resources may be limited
Don’t assume the longevity of a particular format, future-proof your archives data
Don’t forget to budget for archiving data, this should be done as part of your Data Management Plan

PUBLISHING AND DISCOVERY

Ensure DMP-PII and purpose compatibility (as above)
Revaluate likelihood of (re-)identification and risk of harm, particularly if it involves a public data-set containing PII
Indicate in metadata the availability of raw data or minimized data containing PII, if available bilaterally
Minimize use of PII and risk of disclosure through pro-privacy access controls and analytical tools

Don’t include PII in public datasets unless absolutely necessary to preserve the data’s analytic potential, scientific utility or benefit to the participant (and subject to participants informed consent and a rigorous risk assessment)

STORAGE AND ANALYSIS

Ensure compatibility with the DMP-PII (as above) and also the purpose for which prior informed consent has been obtained

Ensure PII is stored securely to protect privacy, through organizational or project specific safeguards to prevent unauthorized access, accidental disclosure or breach of data (physical & technical)

encryption for the storage and transmission of PII
access control measures to limited access to PII
two-factor or multifactor authentication
cloud services & back-end security

Don’t store data in unsecured locations or on unsecured devices or servers

Don’t store encrypted data and encryption keys in locations where they can be easily accessed simultaneously

Don’t underestimate the importance and value of administrative safeguards to standardize practices (i.e. organizational policies, procedures and maintenance of security measures that are designed to protect private information, data and access)

COLLECTION

Ensure compatibility with the DMP-PII
De-identify data to anonymize by default unless it will impair the data’s analytic potential, scientific utility or benefit to the participant,
If you cannot anonymize, minimize the PII and pseudonymize to reduce the disclosure risk
Provide research participants sufficient information to use reasoned judgment to decide whether or not they wish to participate in the project
Ensure informed consent is designed to address the following elements:
- competence, comprehension, full disclosure, voluntariness
- legitimate scientific purpose for which the PII is collected and scope of use (e.g. stored, transferred, published and whether as anonymized, minimized or raw data)
- foreseeable risk of privacy loss and consequences
- meaningful alternatives including opt-in protection/anonymization
- safeguards to protect privacy, conditions on which PII may be shared and any limitations on reuse or third- party access and use of PII
- permission to follow-up or contact the participant and for what purpose (including by third- parties)
- participant’s right to withdraw and rights regarding their data (e.g. to be informed; to access; to rectify; to object; to erase)
- inclusion of physical, phone and/or electronic contact (at least two forms of contact) that participant can reach to exert her/rights
- explicit consent and participant’s acknowledgement of understanding
- if written, provide the participant a copy of processed informed consent
Use plain language and adapt informed consent to meet the needs of vulnerable populations (e.g. obtain orally or in local language)

Don’t collect PII unless you have a Data Management Plan and any necessary approvals in place, including the recorded approval of the potential participant
Don’t collect PII unless you absolutely need it
Don’t assume that removal of direct identifiers is sufficient to anonymize data or that all de-identification techniques will result in anonymized data. Consider the risk of re-identification of a research participant, particularly if datasets are combined. If there is a reasonable risk of re-identification the information should be handled as PII (i.e. undertake risk analysis, evaluate stronger anonymization techniques, seek informed consent for the disclosure of data and explain its possible consequences)
Don’t include vulnerable participants or communities if their ability or capacity to provide voluntary informed consent is genuinely in question
Don’t underestimate the potential of quasi or indirect identifiers to identify an individual, particularly the inherent ability of location-based data to identify participants and their communities, and the increased risk of harm this may pose to potentially vulnerable individuals/communities
Avoid seeking overly broad consent that may call into question transparency or a research participant’s understanding regarding the use of their PII, be specific regarding the activities, purpose and limitations associated with PII so that the participant can make a genuinely informed decision and downstream users can evaluate purpose compatibility and seek fresh consent if needed

PLANNING AND APPROVAL

Develop a Data Management Plan which governs the handling of PII in the research project and beyond (DMP-PII). It should address:
- the type and nature of PII
- compliance requirements (including necessary forms for obtaining consent, and ethics clearance, if applicable)
- legitimate research objectives that will be advanced by the PII
- foreseeable risks and consequences if participants are identified from the data
- privacy protection measures (or lack thereof) for collection, storage, transfer and publishing
- process for obtaining informed consent
- timeframe or trigger for archiving or deletion of PII
Employ stricter standards for research involving vulnerable populations such as children or illiterate participants or sensitive data such as ethnicity or religious beliefs
Undertake due-diligence of datasets previously collected by you or third parties to ensure you are entitled/permitted to use for your research project
Consult the legal, IRB or ethics clearance committee or any other relevant institutional group for specific institutional, local, regional or national policies and regulatory frameworks that may apply to PII in the context of your work

Don’t leave the handling of PII and privacy protection as an after-thought, plan ahead!
Don’t forget to check local laws and donor or third-party requirements in addition to institutional policies governing research ethics and privacy protection (seek expert support if unsure!)
Don’t ignore ethical practices/standards, if your institution does not have an ethics framework or clearance process in place self-assess!
In assessing whether information is capable of identifying someone (i.e. PII) don’t limit your focus to direct identifiers, also consider indirect/quasi identifiers. Appreciate this will depend on the context of the research project, the data in question and external data which is or may become otherwise available (i.e. there is no exhaustive list).
In assessing risk of harm don’t forget to consider potential harm to the participant’s community or groups of individuals that can otherwise be identified or associated with the participant

About AgrO

Contact us

Core team

Medha Devare

Elizabeth Arnaud

Marie-Angélique Laporte

Céline Aubert

Key contributors

Pier Luigi Buttigieg

Léo Valette

Cheryl Porter

Chris Mungall

Julian Pietragalla

Adopters

AgroFIMS

The Agricultural Model Intercomparison and Improvement Project (AgMIP)

Browse AgrO

Structure

Searching terms within AgrO

Download AgrO

navigate AgrO

Main categories in AgrO

Citation

Reference

Participate

Search the website

Discover agricultural data and publications

Powered by GARDIAN

Become a youth in data partner

Submit an initiative!

AgroFIMS: Your new companion for easy standardization of data collection and description

Responsible Data Management Guidelines to protect privacy

<img class="wp-image-93311 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/REUSE_arrow.png" alt="" width="100" height="100" />

REUSE / TRANSFER

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ARCHIVING / DISCARDING

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PUBLISHING AND DISCOVERY

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STORAGE AND ANALYSIS

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COLLECTION

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<img class=" wp-image-93217 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/planning-icon.png" alt="" width="100" height="114" />

PLANNING AND APPROVAL

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