IGAD Meeting in Philadelphia: Agenda and Venue Published

We are pleased to share the IGAD Philadelphia meeting agenda and venue information.  The  IGAD meeting will take place in Philadelphia on 1 April 2019, before the 13th RDA Plenary Meeting, April 2-4, 2019.

Formed in 2013, since its inception the Interest Group on Agricultural Data (IGAD) has grown in community strength to over 200 members. IGAD is a domain-oriented group working on all issues related to global agriculture data. It represents stakeholders in managing data for agricultural research and innovation, including producing, aggregating and consuming data. IGAD is a forum for sharing experience and providing visibility to research and work in agricultural data.

The new edition of the IGAD Event at the RDA's 13th Plenary Meeting will be held on 1 April 2019 in Philadelphia (United States). Following the IGAD Meeting in Gaborone on Data Collection: State of the Art, Challenges and Solutions, we would like to focus on the next related topic Agricultural Data Interoperability: Opportunities and Lessons Learnt from Sharing and Re-Using Data. We have received contributions from agricultural open data projects and initiatives to support new analyses, modeling, or decision support tools.

The conclusions of the meeting will be shared at the RDA's 13th Plenary Meeting at the Agricultural Data Interest Group Session.

Please note that participation in the RDA conference requires a separate registration.

We are happy to share the agenda and venue information below. In case of changes, the updated agenda can be found here.


1 April 2019

Venue: Stiles Alumni Hall, Room CL-A, Drexel University, Central City Campus,
325 N 15th St, Philadelphia, PA 19102 (US)



08:30 - 09:00

Registration starts

09:00 - 09:15

Welcome addresses. Opening remarks

09:15 - 10:30

Panel Session on Agricultural Data Interoperability Opportunities and Lessons Learnt from Sharing and Re-Using Data moderated by Cynthia Parr (National Agricultural Library. USDA Agricultural Research Service, US)


The Plant Breeding API By Peter Selby (Cornell University, US)

AgMIP Data Interoperability – Lessons Learned from a large multi-disciplinary multi-model project By Cheryl Porter, Chris Villalobos, Gerrit Hoogenboom (Agricultural & Biological Engineering Department, University of Florida) and Jeff White (Arid Land Agricultural Research Center, USDA)

Enabling use of crop experiment trials and agricultural open data through open and community based platforms By Sander Janssen, Hendrik Boogaard, Rob Knapen, Yke van Randen, Steven Hoek, Hugo de Groot (Wageningen Environmental Research), Sven Gilliams (VITO)

10:30 - 11:00

Coffee Break

11:00 - 11:20

The BonaRes Data Centre – A community driven repository for interoperable agricultural data
By Xenia Specka, Nikolai Svoboda, Carsten Hoffmann, Uwe Heinrich (Leibniz Centre for Agricultural Landscape Research (ZALF), Germany)

In Germany, many excellent data are generated and digitally stored in the field of agricultural and soil research. Unfortunately, these data often cannot be reused by other scientists and thus exploit their full potential. The reproducibility of research results and thus good scientific practice hampers. https://datenzentrum.bonares.de/research-data.php
In the BonaRes Data Centre, research data is collected and made available in a standardized form for free reuse via a networked data infrastructure. Once the technical infrastructure is in place, the main task of the Data Centre is to invite soil and agricultural scientists to store their data (for instance long term experiments, field trials, model outcomes) in the Data Centre and provide them for free reuse. Not only the BonaRes project itself, but also the scientific community is showing great interest. The first data are already being made available via the data portal.
The interoperability and thus the quality of our data is increased by the use of AGROVOC and GEMET in the metadata (keywords). The first exploitation right of the authors is guaranteed by an embargo while the legal framework for reuse is defined by a portfolio of different, internationally binding licenses (Creative Commons CC) and a supplemented disclaimer. Research data are provided with the BonaRes DOI for publication (doi.org/10.20387/BonaRes-XXXX-XXXX). An in-depth lecture will highlight our approach of describing research data with metadata and improve its quality. (Specka: The BonaRes Data Centre – Quality assurance measures for metadata).

11:20 - 11:40

Opportunities and Lessons Learnt from AgGateway’s field operations interoperability effort
By Andres Ferreyra (Syngenta)

Data sharing and reuse in the industrial field-operations space has the potential to unlock huge value for farmers and their business partners, but it has been limited by two fundamental interoperability challenges: Lack of common data formats, lack of common semantics (meanings)
An collaborative international effort (reflected in the SPADE, PAIL and ADAPT projects) has been taking place since 2011 in the context of the AgGateway organization, which brought together machine and implement manufacturers (OEMs), farm management information system (FMIS) companies and service providers to implement existing standards, and work with standards organizations to create new ones where appropriate standards didn’t fit or exist.
Key lessons from the process include:
* Implementation of these solutions by OEMs and FMIS companies requires significant allocation of resources, which is difficult in the context of “lean” organizations. A chicken-and-egg problem ensues, where the various actors until hedge they see others make the commitment.
* This pattern can be broken when a major industry player commits, and reaps early-adopter benefits accordingly. Others soon follow, and soon a participation & investment become normal.
* A succession of small, limited scope projects can continue to make progress when there is no longer momentum for large, open-ended commitments.
* Face-to-face trust-building is an effective tool for competitors to find, and come together to collaborate in, a pre-competitive space.
Opportunities are numerous, and include:
* It becomes easier for software companies to provide farmers with friction-less tools that can support principled agronomic and farm management decisions (e.g., by leveraging soil test and crop protection label data, calculating nutrient and active ingredient loads, restricted-entry and pre-harvest interval notifications, and so forth).
* It becomes easier for farmers to share data with partners in valuable ways (e.g., custom application, accurate seed, crop nutrition and crop protection purchase orders, cost and revenue-sharing with landlords, information exchange with insurers and financiers, etc.).
* Many of these applications can be implemented using mobile technology, and are applicable both a) by small companies and b) in a smallholder context, leveraging interoperability investments made by major industry players.

11:40 - 12:00

Using AgMIP tools to FAIR-ify CGIAR data (describes active project with CGIAR Big Data Platform)
By Cheryl Porter, Chris Villalobos,, Gerrit Hoogenboom (Agricultural & Biological Engineering Department, University of Florida, US), Medha Devare, Jawoo Koo (International Food Policy Research Institute)

The CGIAR International Research Centers collect large amounts of data through on-station and on-farm experiments, surveys and others means of data collection. These data are extremely valuable and application of the FAIR (Findable, Accessible, Interoperable, Reusable) principles to these data would increase the value, particularly for data which are suitable for quantitative analyses. Through proper metadata description and data annotation, it is possible to allow datasets of interest to researchers to be made interoperable. The Big Data Platform initiative of CGIAR is committed to this effort and the AgMIP data interoperability tools will be a useful tool in the process. The Global Agricultural Research Data Innovation & Acceleration Network (GARDIAN; http://gardian.bigdata.cgiar.org) web interface already provides discovery and access to selected CGIAR data and publications.  
Two main efforts are taking place now to address these issues of data interoperability of CGIAR data. The first deals with collection of new data. The electronic Field Book, currently under development by the CGIAR, will allow new data collected in field experiments to be automatically annotated with descriptive metadata which will allow data to be discovered and reused.  
The second focus for CGIAR data interoperability is on capturing old datasets, which are in distributed databases, diverse formats, and do not use a consistent vocabulary. A project is underway to allow data to be annotated with standardized metadata to allow automated discovery and translation to standardized formats useful in models and other types of quantitative analyses. The first phase of this project included the design of the dataset annotations and manual implementation of these annotations for a limited number of datasets as a proof of concept. Annotation of datasets was accomplished with three “Sidecar files” which contain additional metadata beyond the core GARDIAN metadata. Regardless of the physical location and format of the raw data in this distributed system, these metadata files will be readily available for rapid data searches for specific analytics. Phase 2 will allow these sidecar files to be created and used in data translation in a more automated way using an expanded set of AgMIP data translators.

12:00 - 13:00

Lunch Break

13:00 - 13:20

Semantic Interoperability of Agricultural Data: the Output of the Agrisemantics WG
By Caterina Caracciolo (FAO of the UN, Italy), Sophie Aubin, (INRA, France) Brandon Whitehead (CABI, United Kingdom)

We can find research fronts by continuously tracking the most important scientific research papers in the world, and analyzing the cited patterns and clustering of papers, especially the frequent co-citation of highly cited papers into clusters. A research front is formed when a cluster of highly cited papers are cited together to a certain degree. Base on the bibliometrics method, we selected 14 research fronts in 2017 from the ESI database of agricultural science, animal and plant science in Web of Science relying on the related experts. We also analyzed the structure and layout of the 2017 global agricultural science research fronts and etc.


13:20 - 13:40

The AGROVOC multilingual thesaurus in 2019, and multischeme management
By Imma Subirats (FAO of the UN, Italy)
The AGROVOC multilingual thesaurus was created by the Food and Agriculture Organization of the UN (FAO)  in 1980. AGROVOC is a controlled vocabulary covering all areas of interest of the FAO, including food, nutrition, agriculture, fisheries, forestry, environment etc. Since 2010, AGROVOC has been expressed as a Semantic Web concept scheme using Simple Knowledge Organization System (SKOS). AGROVOC is a collaborative effort, coordinated by FAO, and maintained by an international community of experts and institutions active in the area of agriculture and related domains.
AGROVOC is widely used in specialized libraries as well as digital libraries and repositories to index content and for text mining. It is also used as a specialized tagging resource for knowledge and content organization by FAO and other third-party stakeholders. Since April 2017, AGROVOC has had monthly releases, and since March 2018 AGROVOC has been available for re-use as a Linked Open Data under the international CC-BY IGO 3.0. License, A number of AGROVOC concepts have been aligned with concepts in other multilingual knowledge organization systems related to agriculture and related domains. The general public can explore over 36,000 AGROVOC concepts in up to 33 languages in a Web-based browsing environment, SKOSMOS.
The technical infrastructure for AGROVOC has been managed in collaboration with FAO for the past decade by the Artificial Intelligence Research group, University of Tor Vergata in Rome.  AGROVOC is edited through the web-based platform VocBench 3, - an advanced collaboration environment for maintaining thesauri, ontologies, code lists and authority resources, providing features such as history, validation, a publication workflow, and multi-user management with role-based access control.
Specialized concept schemes are now possible within AGROVOC. VocBench 3 supports the use of hierarchical relation properties that are specific to a scheme. The multischeme hierarchy approach means that a controlled vocabulary can be viewed flexibly and edited with its customized relations, or exported with a generic SKOS hierarchy of broader and narrower relations, without changing the hierarchy of AGROVOC itself. The LandVoc thesaurus is an example of a specialized concept scheme within AGROVOC: a set of 270 terms about land governance created and maintained by the Land Portal organization, which is also a distinct concept scheme within AGROVOC. LandVoc pulls concepts scattered throughout the AGROVOC hierarchy and re-structures them into a hierarchy designed for people working on land tenure, land management and land governance.
This opens for some very interesting potential collaborations with specialized communities: their vocabulary could be hosted by AGROVOC,  and would enrich AGROVOC, while maintaining the possibility for separate hierarchy, exports and display.

13:40 - 14:00

Maize [Zea Mays (L.)] Crop-Nutrient Response functions in Sub-Saharan Africa: Data extrapolation from field level to regional-scale
By Gebreyesus Brhane Tesfahunegn (Soil and Watershed Management and Dean, College of Agriculture, Aksum University, Ethopia)

Improved soil fertility management is key to increasing crop  production and reducing food insecurity in Sub Saharan Africa (SSA). The  investment in fertilizer use must be highly profitable to justify much investment by typically very poor smallholder farmers. The profitability  of fertilizer use can only be well determined once crop nutrient response functions are determined for a production environment. Recommendations made for all diverse soil types and climate conditions must be thus generalized from research results obtained from relatively few situations. Methods are needed for adapting field research results to other growing conditions. However, there is no generally adopted and completely reliable and easy method for prediction of crop-nutrient response functions based on field trial yield data at regional scale (e.g., SSA) from biophysical variables. The objectives of this research were designed to 1) establish relationships between maize crop-nutrient response functions and biophysical variables; 2) determine prediction equations for maize nutrient response functions; and 3) extrapolate maize nutrient response functions using the predictor equations to areas with inadequate measured crop-nutrient response and then evaluate the goodness-of-fit of the extrapolated data by the model. This study was based on maize growing areas in SSA using geo-referenced crop-nutrient response functions determined from past and recent research by the Optimizing Fertilizer Recommendations in Africa (OFRA). The available asymptotic maize nutrient response functions for N, and P were 736, and 488, respectively. The independent variables considered were elevation, absolute value of latitude, three each of soil and climate properties. Data were subjected to statistical analysis of Generalized Linear Model (GLM) univariate using SPSS.20 using a probability level ≤ 0.05. The relationship of each independent variable to each response function coefficient was evaluated using GLM based on the size of the model coefficient, effect size measure (Eta), standard error, significance level (F-test), and coefficient of determination (R2). The effect of multi-colinearity among the independent variables that significantly influenced the dependent variables was tested using partial correlation analysis. A total of 4646 geo-referenced new points were identified across the maize potential areas in the SSA for the extrapolation purpose. The model goodness-of-fit between the actual and predicted values were evaluated using R2 and standard error. The average R2 for the predicted compared with the actual values of the coefficients a, b, and c of the maize crop-nutrient response functions were 69, 68, and 66%, respectively. The prediction fitting-lines between calculated and extrapolated maize crop-nutrient response functions are also described by R2 > 65%, slope near 1 and intercept near 0, with acceptable lower standard errors. In conclusion, these predictive equations can be used to estimate maize nutrient response functions for important maize growing areas throughout SSA.

Keywords: Soil fertility management, Biophysical variable, General Linear Model Abbreviations: N, P refers to Nitrogen, phosphorus fertilizer, respectively

14:00 - 14:20

Evaluation of the distribution of fertilizer to Nigeria farmers in the past and Present
By Michael Adedotun Oke(Founder Michael Adedotun Oke Foundation, Nigeria)

The effect of past government policy in stimulating fertilizer demand an d improving fertilizer access , through the subsidy  fertilizer reform program is  being recognized. Nigeria procured fertilizer independently and distributed the fertilizer through sales  agents and the extension system (ADPs) (Nagy & Edun 2002 ) .  T he  subsidy reform program was introduce by  the previous  Minister  o f Agriculture, Dr Akin Adesina in the year  2011  called the  Fertilizer subsidy reform ( Growth Enhancement Support  GES)  , the  Pilot voucher schemes, which  was planned to target 5 million farmers per year, reaching al l 20 million farmers in 4  year . . T hi s paper therefore review  the new distribution of fertilizer in the past   the problems associated.  T he  Old Subsidy  scheme which was  untimely distributed i n efficient targeting, poor Leakages ,  in the  year 1976 .  T here   were  inadequate  establishment of  distribution channels, promotion activities (Banful 2011) . And the  s low response of private fertilizer sector . And also the  recent registration of farmers was done earlier by filling a registration form were, it contains all the detailed  information, using the personal pictures of the farmers at the end of the day, right finger too signature . A production of  Identification card in other to access  farm inputs. Both another policy came that the farmers needs  to register with the use of  Tablet computer , different equipment of Solar charging panel , Spare Batteries , Enumerators and Extension Agent capture the eligible farmers, who seeks to be registered, within the guidelines of the GES - TAP, Some Enumerator, extension Agent and  Supervisors selected from Federal Capital Territory Agricultural Development and Individual from different organization in  collaboration with the International Fertilizer Development Center and Federal Ministry of Agriculture .Ges - tap cards were  produce which gives all the vital information about the farmers and used to access farm inputs. Thus this paper evaluate all  the  different procedures, the problems encountered by the Extension Agent Supervisors ,the impact it will have in the distribution of  farming input , Questionnaires were distributed analyzed and Pictures were taken to support the different methods been use . With this new reform of the distribution ,  there was several risk , with the different  uncertainty on voucher redemption problem’s  from the famers couple with the  different problems from the  input dealer, the  timelines s, delay in the distribution of fertilizers, rejection   of   vouchers  entitlements  – defect   of   mobile   phones,   phone   signal Information   asymmetry   Fertilizer   quality  (Adulteration)  and  the    Market  structure , Insufficient  number  , density  of  redemption  centers  (only  900  in  2012). Monopoly  by  certain  dealers ,  the  high  entry  cost  for  new  dealers ,  few voucher  redemption center,  whereby  is  not  close  to  the  farmers.  The study recommended t he increase in the size of redemption center and timely release of the fertilizer to the farmers by the agro - input dealers and effective program development on the advisory deliver by the agro - input dealers.

Keywords: fertilizer, growth enhancement support, farmers, registration, Ges - Tap and Manual, Niger

14:20 - 14:40

Current efforts to develop ethiopian agriculture sector comprehensive management information system, its challenges and way forward 
By Tsehayu Gardachew Wotie (Ethiopian Ministry of Agriculture, Ethopia)

Defining objectives for Ethiopia’s  agricultural development and transformation, evidence based planning,  measuring progress of results, assessing sectoral developments, and  learning from achievements is critical to achieve the goal of  transforming the Ethiopian agriculture sector, as it allows for informed  decision-making and mid-course adjustments based on evidence. It also  helps to promote the efficiency of resource utilization by monitoring  spending vis-à-vis results achieved. The agriculture sector is  complex; its development therefore needs systematic follow up, both in  terms of monitoring day-to-day progress and evaluating results, to  ensure that the transformation process is on track and that related  government support is optimal. In order to inform the planning process  with evidence, to monitor progress of an intervention and to understand  sectoral resources, an efficient data management system is  indispensable.  A diagnostic study has been conducted to assess  the current practices of data management and critical constraints were  identified. A finding of the diagnosis shows that the current data  systems as they relate to the agriculture sector are inadequate,  fragmented and doesn’t read each other. Roadmap to address the  bottlenecks was developed and implementation is underway to address the  bottlenecks since 2016. Based on the recommendation in the road map  major use cases has been developed as per the information need of  decision makers, strategic data needs to support the use cases were  identified, and system to support the data collection, organization and  analysis is under development. Hence the presentation will focus  on how the initiative to improve the Ethiopian agriculture sector data  system was developed; proposed use cases and related data bases;  existing database landscaping, key findings and constraints; efforts to  address the constraints in the existing data system, challenges and way  forward.

14:40 - 15:00

Coffee Break

15:00 - 15:20

Ethical approach of open data benefiting smallholder farmers
By Foteini Zampati (Kuratorium für Technik und Bauwesen in der Landwirtschaft (KTBL), Germany)

Currently, nearly 800 million people struggle with debilitating hunger and malnutrition and can be found in every corner of the globe. That's one in every nine people, with the majority being women and children. The solution to Zero Hunger lies within existing, but often unavailable, agriculture and nutrition data. Open Data offers a great potential for innovations from which the agricultural sector can benefit decisively due to a wide range of possibilities for further use. However, the use of open data is associated with some technical, ethical and legal challenges.   The technical challenges are associated with the need to create and develop new standards, platforms and infrastructures to allow access and better use of the data according to FAIR principles.  In the last couple of years, the use of open data has raised also some ethical and legal issues as more and more stakeholders have entered into the agricultural sector developing new technologies that focus mainly on the collection, analysis and management of agricultural data.   Ethics is about choices, and agricultural ethics is about choices for people engaged in agriculture, from farmers, industries, researchers, governments, policymakers, technology developers to consumers. There is no doubt that data driven agriculture has a lot of potential and benefits. Nevertheless farmers raise concerns about data ownership, intellectual property, privacy and security.
 Mostly in developing countries smallholder farmers are not harnessing the power of data and many times must overcome challenges and risks to ensure that these investments benefit them.  The lack of awareness about farmers ́ rights on how the data is used, will likely lead to the unfair distribution of wealth in the agricultural sector which will increase due to Data-driven  knowledge.  Farmers need to feel and be engaged of their privacy and  control, they seek trust and transparency in their interaction with  providers and of course they would like to receive also benefits of  their data, and to have access to all data.   Although all  of us make choices, few of us actively engage in an ethical analysis of  our actions or can provide reasons for the choices we make. Our scope is to address some of these ethical issues and questions.  

* What ethical principles need to be in place for the handling of data? 
* Who owns data and who has really control of the use of data? 
* What is responsible data?

* Are farmers reluctant to share their data? If so why?
* How do we make information accessible to all actors involved in agriculture? 
* What about data protection? What do we mean by the farmers rights to data?
-What is the state of recognition of these rights in national and international level ? 
* What ́s the role of GDPR or other legislation in the agricultural sector?
* How should these rights be implemented in local and international laws,  guidelines and policies and how can they be protected?
*  What should be done to include and give more voice to  farmers in the mechanisms of data? What role can governments play in this?
* How can we choose a path to a more equitable and innovative future for all? 

This topic is a good opportunity to start a conversation with different stakeholders about open data benefiting the smallholder farmers, acknowledging their importance in the data value chain.


15:20 - 15:40

On-farm Agronomic Research, Data Generation, and Modeling in the Data-Intensive Farm Management Project
By David Bullock (University of Illinois Dept. of Agricultural and Consumer Economics, US)

The Data-Intensive Farm Management Project runs large-scale, on-farm randomized field trials to estimate relationships among crop yields, managed input application rates, field characteristics, and weather variables.  DIFM is developing cloud-based farm management decision tools, that help farmers make management decisions on their own farm fields, based on data generated on those same fields.  In 2019, DIFM will run approximately seventy trials in ten U.S. states, Argentina, Brazil, and Colombia.  DIFM is developing software that will allow it to “scale up” its data management, processing, and analysis activities, and provide a farmer-consultant decision tool that will allow the practical implications of the data analysis to positively affect the efficiency of farmers’ input management decision.  DIFM is interested in exploring possibilities of working with other groups to develop a cloud-based research cyber-infrastructure that will aid researchers worldwide who conduct run on-farm agronomic research.

15:40 - 16:00

GO FAIR Food Systems Implementation Network Manifesto - To advance a global data ecosystem for agriculture and food by implementing FAIR data and services
By Ben Schaap (Wageningen UR, The Netherlands), Sophie Aubin (INRA, France)

Agri-food systems are facing unprecedented societal, economical, and environmental challenges to feed 2 billion more people by 2050. In order to effectively address these pressing global challenges we need better availability of data. The heterogeneous nature of many data sources force us to think about interoperability of the data for better reuse, both by humans and machines. For better reuse of data, we need to achieve a shared understanding of how we describe and publish data, in a semantically grounded manner.

The GO FAIR initiative has initiated a global movement of implementation networks (IN’s) for the FAIR data principles. Collectively implementation choices of the IN’s from various sectors will showcase how effective implementation of the FAIR data principles can be done without duplications efforts.  
Purpose of the Food Systems Implementation Network
The purpose of the Food Systems IN is to support the implementation of FAIR principles in agri-food sciences, in providing guidelines, tools, methods, etc. with specific efforts towards achieving semantic interoperability. To this end, the Food Systems IN will boost the adoption and implementation of recommendations from existing initiatives such as RDA working groups and interest groups, GODAN working groups, and W3C. All IGAD members are welcome to join the Food Systems IN.

Overarching Principle of Operation
The IN will leverage the ecosystem of current organizations and projects willing to commit to guiding principles for implementing the FAIR data principles in Food Systems as described in the Food Systems IN manifesto.
This manifesto is signed by a broad range of organizations who came together in different data related engagement structures to implement FAIR data and services in Food Systems research and to work towards a global data ecosystem for agriculture and food.
Though we appreciate diversity, especially in the research field, we consider this joint statement a way to speak with one voice on a number of critical issues that are of generic importance and on which we feel we have reached consensus. We will therefore coordinate our investments in and support of the technological and social developments in the distributed management and analysis of food systems data. We also commit to comply with the Rules of Engagement of GO FAIR Implementation Networks.

Targeted Objectives
In order to address the global challenges related to Food Systems with FAIR data we will work on the following objectives that adhere to the above guiding principles:
To  advocate for FAIR data principles in data sharing policies.
To  foster the continued implementation of FAIR principles based on existing      recommendations and if needed support to create new ones. Facilitate      agreement on the use of vocabularies, standards and protocols.
To disseminate best practices to a large community of practitioners.

16:00 - 16:20

Global Agricultural Science Research Fronts Analysis based on Bibliometrics
By Zhang Xuefu, Sun Wei and Xu Qian (CAAS)

We can find research fronts by continuously tracking the most important scientific research papers in the world, and analyzing the cited patterns and clustering of papers, especially the frequent co-citation of highly cited papers into clusters. A research front is formed when a cluster of highly cited papers are cited together to a certain degree.
Base on the bibliometrics method, we selected 14 research fronts from the ESI database of agricultural science, animal and plant science in Web of Science (2013-2017) with the help of the related domain experts. We also analyzed the structure and layout of the global agricultural science research fronts and etc.


16:20 - 16:40

The role of the Ag Data Commons as a catalog and repository for sharing USDA data for re-use
By Cynthia Parr (National Agricultural Library. USDA Agricultural Research Service, US)

The National Agricultural Library’s Ag Data Commons (https://data.nal.usda.gov) serves as the central catalog for USDA’s publicly available research data. It is primarily designed to promote discovery of the widely distributed large and small agricultural data products resulting from USDA intramural and extramural funding.
We expect it to also support re-use of that data in order to accelerate new science and applications. However, this need to support re-use increases the burden on both our platform and our data submitters, especially considering the wide diversity of communities who might re-use the data.
In this talk we describe several current and planned features of DKAN Science, the open source, open government platform we use for Ag Data Commons, that were developed specifically to ease interoperability and re-use. We explore the challenges and opportunities in using these features, such as basic visualization tools, several Application Programming Interfaces, support for machine-readable shared data dictionaries, and impact metrics.
We describe ongoing data science projects that leverage Ag Data Commons content and features, and what we are learning from them. Finally, we make recommendations for future enhancements and invite participants to suggest partnerships and technical directions that will further reduce data silos and promote innovative re-use.

16:40 - 17:00

Mechanisms to support small holder agriculture with open weather, land & nutrition data
By GODAN Action partnership (WUR, CTA, ODI, FAO (incl. GFAR), LandPortal, IDS, AgroKnow)

Open data for development has received a lot of attention over the last years. However, many open data applications still focus on informing about transparency and accountability, on national governmental level and on the level of funding programmes. At the same time, cases where open data directly benefit farmers are scarce.
As an example, with weather patterns changing and growing seasons becoming less predictable in the face of climate change, access to weather data is a strategic asset for farmers. GODAN Action (Global Open Data for Agriculture and Nutrition) has examined the landscape of open weather, nutrition and land data, the drivers and barriers for achieving impact for smallholder farmers, and particularly the mechanisms that allow creating added value to deliver actionable information for smallholder farm management.
The outcomes emphasize among others the role of information and service intermediaries and how increased private sector involvement and a strong ecosystem of intermediaries can lead to improved, localized climate services for climate smart farm management. To support such mechanisms, GODAN Action developed guidelines for data interoperability and training modules on open data publication and use across weather, land and nutrition. The relevance of an ecosystem of added value climate service providers was underpinned by impact narratives of successful open data initiatives, that ultimately lead to a methodology framework to assess ex-ante the likely impact of open data interventions.

17:00 - 17:30

Wrap up and Closure of the day
Details about the Plenary Meeting



We are looking foward to the IGAD meeting and fruitful discussions!



IGAD Pre-Meeting is supported by:

Global Open Data in Agriculture and Nutrition (GODAN)


Add comment

Log in or register to post comments