New global initiative promises to shake-up agriculture – reducing risks, boosting resilience and improving incomes for the world’s most vulnerable farmers

Cali, Colombia (15^th May 2017) – A new multimillion-dollar initiative plans to disrupt food production across the developing world, with the aim of making it more productive, efficient and resilient – all through the power of information.

The CGIAR Platform for Big Data in Agriculture is jointly led by the International Center for Tropical Agriculture (CIAT) and the International Food Policy Research Institute (IFPRI), with tech giants IBM and Amazon among a list of high-level partners.

It brings together thousands of experts, from crop scientists to computer programmers to collect, process and analyze vast amounts of data on crops, weather, soils and more, with the aim of producing some of the most precise and reliable recommendations for farmers, governments and policymakers in developing countries.

The USD$30 million initiative will be officially launched today at the Information and Communications Technology for Development (ICT4D) conference in Hyderabad, India.

“It’s time for smallholder farmers to stop looking at the sky and praying for rain. With enough data and enough analysts we’ll be able to say if the rains will be late or on-time. We’ll be able to say which crops to plant, when to plant and how much fertilizer or water to use. We’ll be able to anticipate shocks, reduce risks and maximize opportunities for profitable, sustainable agriculture.”

Andy Jarvis

Decision and Policy Analysis Area Director, and Flagship Leader for Climate Smart Agriculture, CIAT, CCAFS

Early efforts by CIAT to apply so-called big data approaches to agriculture in Colombia have proven successful. In 2013 a team of analysts ran studies using decades of data from the country’s meteorological office and rice growers’ federation. The resulting recommendations on sowing times are estimated to have saved farmers in the country’s Córdoba Department around USD$3.6m in input costs in a single season. CIAT and its partners have also used big data approaches to track deforestation in the Amazon in near-real time.

“There’s no reason for precision farming to be the preserve of the fortunate few any more,” continued Jarvis. “While the data revolution has been a boon for farmers in richer countries, it needs to be democratized so that the world’s 500 million smallholders can benefit too – after all, they produce 70% of the world’s food.”

Other Platform partners in the include the Bill & Melinda Gates Foundation, the World Bank, the universities of Penn State and Michigan State, Kings College London, and PepsiCo, which has pioneered the use of big data to manage supply chains for consumer goods.

Organize

Convene

Inspire

The Platform will focus on three priority areas:

1. Organize – data on soils, climate, crops and more will be organized, standardized and made publicly available by the organizations that generate it. The Platform will begin by prioritizing the free and open sharing of data held by researchers at CGIAR – a global network of agricultural research organizations.

2. Convene – foster new partnerships between the agricultural science and technology sectors in order to bring together the best minds, and accelerate progress towards achieving the United Nations’ Sustainable Development Goals.

3. Inspire – put the data and partnerships into practice via a USD$4m fund to support innovative projects with big data approaches at their core, such as real-time monitoring of pest outbreaks, or site-specific recommendations for farmers on water and fertilizer use

“With enormous expertise and processing power now at our disposal, this is the next frontier in agricultural research-for-development, better use of data can help drive better policy decisions, helping solve development problems more quickly, cheaply, and at a greater scale than before.

“If we’re going to achieve the United Nations’ Sustainable Development Goals of increasing food production, reducing poverty and tackling climate change, one of the quickest ways will be to close the digital divide between rich and poor farmers. This will help ensure the world’s farmers and policymakers are making informed choices that produce the biggest impacts. The CGIAR Platform for Big Data in Agriculture aims to do exactly that.”

Jawoo Koo

Senior Research Fellow, Environment and Production Technology , IFPRI

***

Additional information

The CGIAR Platform for Big Data in Agriculture aims to harness the capabilities of big data to accelerate and enhance the impact of international agricultural research. The six-year initiative will provide global leadership in organizing open data, convening partners to develop innovative solutions, and demonstrating the power of big data analytics through inspiring projects that focus on improving agriculture in developing countries, and informing policymakers. bigdata.cgiar.org.

The International Center for Tropical Agriculture (CIAT) is a scientific research organization committed to sustainable food production and improving rural livelihoods in Africa, Asia and Latin America. As well as developing new techniques and approaches to make agriculture more profitable, competitive and sustainable, for 50 years CIAT has been a trusted provider of impartial advice on agricultural and environmental issues to governments and policymakers all over the world. www.ciat.cgiar.org

The International Food Policy Research Institute (IFPRI) seeks sustainable solutions for ending hunger and poverty. IFPRI was established in 1975 to identify and analyze alternative national and international strategies and policies for meeting the food needs of the developing world, with particular emphasis on low-income countries and on the poorer groups in those countries. www.ifpri.org.

Both CIAT and IFPRI are part of CGIAR, a global agriculture research partnership for a food secure future. Its research is carried out by 15 centers in close collaboration with hundreds of partner organizations. www.cgiar.org

For media enquiries:

Neil Palmer
Senior Communicator
International Center for Tropical Agriculture (CIAT)
n.palmer@cgiar.org

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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