Scroll through agenda below for links to PDF version of presentations and links to video recordings of plenary sessions
The 2020 LD4D Community of Practice meeting was hosted by the UN Food and Agriculture Organisation (FAO) in Rome from the 4-6th of February. There was a special focus on “making the most of FAO’s livestock data”.
Meeting objectives
Identify strategies to improve data generation and use in our livestock initiatives with a view to strengthening impact. This will be done by addressing four key questions:
How do we use data to plan future livestock initiatives?
How do we track changes in the livestock sector over time?
How can technology applications support livestock health and productivity in LMICs?
How do we measure our impact?
Share progress made by LD4D working groups, gather feedback, develop future workplans for the community.
Offer LD4D members a platform to share new concepts and innovations, and to network.
Mr Berhe G. Tekola, Director. Animal Production and Animal Health Division, FAO Prof Andy Peters, Director, SEBI Belinda Richardson, BMGF Karen Smyth, SEBI
Speakers will present their insights into future livestock trends (increased demand for meat, climate change impacts on food production, etc) and present key opportunities and challenges for the livestock sector.
A review of global livestock models and predicted trends – Mario Herrero (CSIRO) African Sustainable Livestock 2050 – Ugo Pica Ciamarra (FAO) Forecasting market potential for the animal health markets in Africa Luc Dumand (consultant)/Obai Khalifa (BMGF) Emerging and future challenges for livestock disease Claudia Pittiglio (FAO)
Session facilitator: Di Mayberry (CSIRO) Session reporter: Lisa Boden (GAFFS)
12:30
Lunch
13:30
Chat show – introducing new members of LD4D
13:45
Demonstration of livestockdata.org
14:45
Breakout discussions: LD4D Working group and related initiatives
Longitudinal datasets provides valuable insights to changes over time at a national level. This session will consider which longitudinal datasets are currently available, how they contribute to knowledge in the sector, and their limitation. We will discuss how these datasets can be used to help provide the LD4D community with insights.
FAOSTAT Irina Kovrova and Salar Tayyib (FAO)) Data to end hunger: the 50×2030 initiative using LSMS and AGRIS Neli Georgieva (FAO) RuLIS – Rural Livelihoods Information System Piero Conforti (FAO) EMPRES-I – Focus on African Swine Fever Fairouz Larfaoui (FAO) The World Animal Health Information System (WAHIS) Paolo Tizzani (OIE)
Session facilitator: Tim Robinson (FAO) Session reporter: Esther Kamau (CTLGH)
10:30
Session 3a: Data solutions and innovationsintroduction Watch session video| Download session slides IoT and the dairy value chain in India – Venkatesh Seshasayee, Chief Architect, Stellapps
10:50
Coffee
11:15
Session 3b: Data solutions and innovationscattle mart No video available from cattle mart session How are technology applications supporting livestock health and productivity in LMICs? This dynamic and interactive session will showcase well-established solutions as well as new and emerging tech that support value chains and research in the livestock sector.
12:30
Lunch
Session 3b: Data solutions and innovations cattle mart
Herd 1
Herd 2
Herd 3
MPA – the Market Profiling Application – Ryan Aguanno (FAO)
EMA-i – The Event Mobile Application (EMA-i) to strengthen animal diseases reporting, surveillance and early warning – Asma Saidouni (FAO)
Tech Transformation of Livestock Industry in Nigeria – Adebayo Sopeju (Livestock 24/7)
Guidelines for the Enumeration of Nomadic Livestock – Ugo Pica-Ciamarra (FAO)
New methods to downscale livestock census data: from country to farm – Marius Gilbert, (ULB)
Dtreo: Farm-level data recording in LMICs – Bruno Santos (AbacusBio)
Natural language processing: using informatics to address livestock data challenges – Seraphina Goldfarb Tarrant and Alexander Robertson (Bayes Centre)
Blockchain for pig sector in Vietnam – Erik Árokszállási (TE-Food)
LabCards application for veterinarians – Tetiana Miroshnychenko (Zoetis)
Real-time data collection on artificial insemination interventions – Yuvraj Gaundare (BAIF)
Digital innovations for livestock enterprise in Africa – Odede Ochieng (SIDAI)
Producers and other actors are making significant investments in livestock farming systems and value chains. How should we plan investments that safeguard sustainable livestock development now and in the future? How should targets be set for this sector, and how do we balance future needs?
Reflections on sectors progress in bringing about necessary changes – Mario Herrero (CSIRO) Scaling up: How do we bring about meaningful change in this sector? – Tim Byrne (Abacus Bio) Target setting for Livestock Master Plans – challenges and opportunities – Sirak Bahta (ILRI) Tools for future livestock planning – Tim Robinson (FAO) Reflections from the donor perspective – BMGF, DFID, USAID, Abdul Latif Jameel Foundation
Session facilitator: Michael Victor (ILRI) Session reporter: Anni McLeod (LTSi)
15:00
Coffee
15:15
Breakout discussions: LD4D Working group and related initiatives
Natural language processing – using informatics to address livestock data challenges
Evidence for advocacy GLAD and Livestock Facts Setting targets Data quality Pastoralist data
17:00
Wrap up
Day 3 – Thursday 6 February
08:30
Coffee
09:00
Welcome, overnight thoughts and reporting back from Day 2
Presenters will share experiences measuring impact at different scales, including what methods and data they use to measure impact, and the robustness of those measurements. The community will be invited to provide feedback and propose areas for improvement.
Project level – measuring impact of activities on the ground for livestock health – Neil Gammon (GALVmed) Portfolio level – measuring impact of investments – Andrew Bisson (USAID) and Belinda Richardson (BMGF) National level – Measuring Livestock Mortality Rates – Giles Innocent (BioSS) Sector level – Tracking progress towards achieving the Sustainable Development Goals (SDGs) – Roswitha Baumung (FAO)
Session facilitator: Andy Peters (SEBI) Session reporter: Julie Ojango (ILRI)
12:00
Actions from the meeting, next steps and evaluation
12:30
Lunch & Close
This article was originally posted on the Livestock Data for Decisions website | Read article
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 Platformfor 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.
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-privacyaccess 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)
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
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-privacyaccess 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)
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)
De-identify data to anonymizeby 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
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 checklocal 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
