Crop Modeling

Community of Practice

Welcome!

The Community of Practice on Crop Modeling (CoPCM) is part of the CGIAR Platform for Big Data in Agriculture and encompasses a wide range of quantitative applications, based around the broad concept of parametrizing interactions within and among the main drivers of cropping system.

The CM-CoP formed in 2017 and is led by Wheat Physiologist Matthew Reynolds at the International Maize and Wheat Improvement Center (CIMMYT) in El Batán, Mexico.

This space can be used as a discussion area, share and request relevant information and contribute towards building the community as a whole.

Crop modeling science encompasses a wide range of quantitative applications based on the broad concept of parametrizing interactions within and among the main drivers of cropping systems.

These are namely: genotype, environment, management and socioeconomic factors (GEMS) to provide information and tools for decision support.

By promoting FAIR (Findable, Accessible, Interoperable, Reusable) data principles, the CM-CoP fosters a culture that values GEMS data and enables its members to solve global grand challenges faster, better and at greater scale.

The CM-CoP has a virtual meeting space where crop modeling enthusiasts and specialists from 15 CGIAR Centers and other industry organizations can share ideas and resources, find collaborators and co-authors and participate in relevant discussions.

Get in touch

CoP Lead

Matthew Reynolds | Email

International Maize and Wheat Improvement Center (CIMMYT) Texcoco, México

CoP Coordinator

Anabel Molero Milan | Email

Consultant at International Maize and Wheat Improvement Center (CIMMYT)

Twitter | Email

Engage with the Community

BE INFORMED

Tune in to the latest CoP updates by subscribing to our newsletter.

ENGAGE

Interact with community members through our LinkedIn Group.

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CONTRIBUTE

Share your expertise in our working groups.

Working Groups

The Crop Modeling CoP hosts a series of different short-life working groups and projects where the real work of the community takes place.

Peer-review Group

The aim of this group is to increase visibility and accessibility of crop modeling research activities through the publication of peer-review papers of interest and highlighting the last crop modeling research outcomes of interest to the crop modeling community. This group is currently working in the elaboration of 4 reviews that will showcase on-going modeling activities and potential impacts from CGIAR and partners among the main drivers of cropping systems:

Breeding (G Group) | Environment (E Group) |Crop Management (M group) | Policy and Socio-Economic (S group)

CMGap

The Crop Model CoP is in a unique position to convene a global consortium to address technology gaps that may otherwise slow the adoption or reduce impacts of recent investments in crop improvement. To identify these gaps, the CMGap group is conducting a wide gap analysis of modeling approaches and capabilities within the CGIAR centers and main partners to determine strengths, weaknesses, opportunities and needs in terms of modeling capacity and expertize.

CM Outreach Group

Increasing visibility and accessibility of crop research data is a key point to improve the global coordination of crop modeling efforts in agricultural research. The CM Outreach working group aims to accelerate dissemination of new crop information and technologies, share crop modeling skills, outcomes, opportunities, impacts and to promote FAIR (Findable, Accessible, Interoperable and Re-usable) crop research data.

Interested in contributing to our working groups?

Community News

Webinar – Potential climate-related impacts on future maize and wheat yields

January 12, 2022

This webinar by the Crop Modeling Community of Practice presents a recently published study showing potential climate-related impacts on maize ...

Webinar – Modeling with 40 years of international wheat data

December 6, 2021

This webinar by the Crop Modeling Community of Practice presents selected outputs from big data analysis of four decades of ...

2020 Convention session – Gridded crop modeling advances & challenges

November 10, 2020

This session on gridded crop modeling advances and challenges aired live at the virtual 2020 CGIAR Convention on Big Data ...

2020 Convention session – Machine Learning and Crop Modeling: A Modern Affair?

November 4, 2020

This session on machine learning and crop modeling aired live at the virtual 2020 CGIAR Convention on Big Data in ...

2020 Convention session – Disease Crop Modeling Advances & Challenges for Large-Scale Simulation Studies

October 28, 2020

This session by the Crop Modeling Community of Practice aired live at the virtual 2020 CGIAR Convention on Big Data ...

2020 Convention session – Phenotyping & Remote Sensing to Facilitate Minimum Data Set Requirements for Crop Simulation Modeling

October 28, 2020

This session by the Crop Modeling Community of Practice aired live at the virtual 2020 CGIAR Convention on Big Data ...

Resources

Documents

Work Plan 2018 – Detailed description of expected outcomes and deliverables for 2018
Work Plan 2019 – Detailed description of expected outcomes and deliverables for 2019
Work Plan 2020 – Detailed description of expected outcomes and deliverables for 2020

CoP Publications

Webinars and presentations

Webinar – Potential climate-related impacts on future maize and wheat yiel...

Webinar – Modeling with 40 years of international wheat data

2020 Convention session – Gridded crop modeling advances & challenges

2020 Convention session – Machine Learning and Crop Modeling: A Modern Aff...

2020 Convention session – Disease Crop Modeling Advances & Challenges...

Newsletters

Community of Practice on Crop Modeling Newsletter 7: November 2020
Community of Practice on Crop Modeling Newsletter 6: October 2020
Community of Practice on Crop Modeling Newsletter 5: June 2020
Community of Practice on Crop Modeling Newsletter 4: March 2020
Community of Practice on Crop Modeling Newsletter 3: January 2020
Community of Practice on Crop Modeling Newsletter 2: May 2019
Community of Practice on Crop Modeling Newsletter 1: September 2018

Crop Modeling publications

The Community of Practice on Crop Modeling has compiled a list of relevant Crop Modeling literature published in the last 5 years. Links to the original papers can be found in each reference.

Go to list (Google Doc) →

Communities

AGRONOMY

CROP MODELING

GEOSPATIAL

INFO & DATA MANAGEMENT

LIVESTOCK

ONTOLOGIES

SOCIO-ECONOMIC

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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

Crop Modeling

Welcome!

Read more

Get in touch

CoP Lead

CoP Coordinator

Engage with the Community

BE INFORMED

ENGAGE

CONTRIBUTE

Working Groups

The Crop Modeling CoP hosts a series of different short-life working groups and projects where the real work of the community takes place.

Peer-review Group

CMGap

CM Outreach Group

Interested in contributing to our working groups?

Community News

Resources

Documents

CoP Publications

Webinars and presentations

Newsletters

Crop Modeling publications

Communities

Interested in joining our community of practice?

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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<img class=" wp-image-93249 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/data-collection-icon.png" alt="" width="100" height="75" />

COLLECTION

<img class="alignnone size-full wp-image-92805 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/tips-icon-orange-100px.png" alt="" width="100" height="100" />

<img class=" wp-image-93476 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/DONT-DO-ICON.png" alt="" width="100" height="100" />

<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

<img class="alignnone size-full wp-image-92805 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/tips-icon-orange-100px.png" alt="" width="100" height="100" />

<img class=" wp-image-93476 aligncenter" src="https://bigdata.cgiar.org/wp-content/uploads/2019/01/DONT-DO-ICON.png" alt="" width="100" height="100" />