On the different types of interoperability and the concept of agricultural data

I have been involved in various instances where interoperability between different sources of content was the main issue - ranging from the early days of enriching the Organic.Edunet Web portal and collections with content from external sources (such as the OER Commons Green collection) to complex content aggregation workflows of heterogeneous data in the context of the agINFRA FP7 project (the project that gave birth to AGINFRA) and a recent contract with UN FAO regarding the status and potential of various levels of interoperability in the fisheries and aquaculture sector. All these instances allowed me to get to know more about metadata schemas and vocabularies, standards for exchanging metadata, digital repositories and how they communicate with each other, mapping and transformation of metadata schemes to another standard etc.

I was recently invited to complete a questionnaire regarding open data standards and that brought me back to the basics; what are the different types of interoperability that exist out there? I could very quickly think of the following:

• Semantic interoperability, referring to the use of standards for the description (metadata) and the classification (Knowledge Organization Systems such as vocabularies) of data;
• Technical interoperability, referring to the use of standards for the exchange of data, such as the OAI-PMH standard for metadata but also the use of standard file formats for sharing data (e.g. XML, CSV, JSON etc.);
• Legal interoperability, referring to the use of standard licensing schemes, which would facilitate the exchange of data in legal terms.

There may be a couple more out there but time is short for such an academic analysis.

 (Image source: https://xkcd.com/927/)

The same questionnaire referred to “standards for food and agriculture data”; but what exactly are agricultural data? One (including me) could argue that there may not be "food and agriculture data" as different data types. I have been through similar discussions in the past; however, it seems that there is no such thing as "food and agriculture data". This only refers to various types of data that are used in the agrifood sector or are produced in the agricultural context. In this context, “standards for food and agriculture data” are the same as the rest of the data, such as the standards applying to economic, statistical, trade, weather forecasts, soil maps, sensor data, weather forecasts, earth observation data, demographic, bibliographic, metadata etc. It is usually only the context that changes - not the standards or the data themselves.

If we accept this generic point of view, then standards like ISO (see for example the ISO/IEC 11179 standard), metadata standards like Dublin Core, file format standards like ODF, classification standards like OWL and linked data standards like RDF could also be applied in the case of agricultural data. The same goes for technical interoperability standards like OAI-PMH and REST APIs; all these can be (and actually are widely) used in the agricultural sector and are essential tools for those of us working with agricultural information and knowledge management.

So, are there really agricultural data and if so, who could name some examples?

Using social media for increasing awareness at corporate level

Background 

Let’s get things straight from the start: As (at least some of) you know, I am an agronomist (holding a PhD from the Agricultural University of Athens / Dept. of Agricultural Biotechnology), and I have a relatively long experience in the field of agricultural information & knowledge management as well as in the participation of various EU projects (ranging from Leonardo da Vinci to ICT-PSP, FP7 & Horizon 2020), always in the agrifood sector. Through my participation in these projects, I had the opportunity to work on a wide variety of different activities, such as the design and delivery of training courses for agricultural trainers, organization of information and knowledge (e.g. courses, publications and other material) in digital repositories, description of digital resources with metadata and classification with Knowledge Organization Systems), interoperability between different content management systems and digital repositories, metadata management workflows and many-many more. Through this experience I managed to have a different view of agriculture, the one of research outcomes/publications and how these can (and should, in some cases) be freely and openly accessible to anyone interested (this is what Open Access is about) – the same applied to research data (open data). In this context, I recently had the opportunity to combine my background in agriculture/agronomy and my experience in information and knowledge management with social media (which I personally consider extremely useful tools for professional purposes as well). I spent quite some time as the Marketing & Networks Manager of Agroknow, where I had the opportunity to set up existing digital marketing channels and grow the existing ones.

regram @small_ant86 Vassilis is presenting "how to write my first #blog" @agroknow @vprot

A photo posted by Vassilis (@vprot) on Sep 29, 2014 at 5:14am PDT

When I started working for NEUROPUBLIC, a company of a larger scale and different structure compared to the ones that I have worked for in the past, I faced a big challenge: How could I apply my experience in a different (but still not unknown) context, the one of smart farming (or precision farming) and related ones like remote sensing, earth observation, Internet of Things in agriculture etc.? While the means would remain the same, it was obvious that I would need to adapt the approach and the analysis of the specific ecosystem – referring not only to individual stakeholders but also to organizations that are activated in the same field as NEUROPUBLIC. In any case, the main point is to find the best possible way to expose to external stakeholders all this interesting work that takes place in NEUROPUBLIC (but was not communicated at all, during the last years). This was exactly the pain of NEUROPUBLIC and this would be one of my roles in the company.

Activities

The first step in the process was the analysis of the current status of the social media accounts of NEUROPUBLIC (referring to its corporate pages on Facebook and LinkedIn). Then, I took some time to set up a corporate Twitter account, which I consider a necessary communication and outreach mean for a company like NEUROPUBLIC. The last in line was the setup of the corporate blog, which I hope that it will provide an alternative mean of expression and outreach for the company staff. It only became the last in the list due to the fact that it takes a lot of design and planning and we wanted to get things started immediately - at this time, the blog has been set up but we are internally testing content management workflows and roles so it's not public - yet.

At the same time, I had to find the sources from which I could mine content related to the company that I could then promote through the aforementioned social media channels – the corporate website was a great start as it already contained a wealth of information about the company and its work along with great visual content. On top of that, I did some research regarding quality sources of news related to the thematic areas that NEUROPUBLIC is activated in; in this way, we would not only share our corporate news but we would also draw the attention of those activated in the same context.

Before I were in the position to promote NEUROPUBLIC’s work to external stakeholders, I had to achieve a deep understanding of them: this translates to countless hours of studying related documents, slides and other types of publications for better understanding concepts like precision agriculture, remote sensing, earth observation, NDVI, IACS, LPIS, even CAP etc. and see how they are integrated in the Smart Farming outcomes of NEUROPUBLIC, such as the GAIA Sense network of the GAIATron telemetric stations, GAIA Agronomy, GAIA Irrigation, GAIA Shield etc.

Last but not least, I keep studying the networks in which NEUROPUBLIC is a member of, such as the European Association of Remote Sensing Companies, the Big Data Value Association, the Alliance for Internet of Things Innovation and the Global Open Data for Agriculture and Nutrition (GODAN) and follow their activities, trying to establish and maintain contact, as well as to provide feedback when needed. I also check out further networking opportunities - social media are extremely useful in this case, too.

Next steps

The next steps are more or less typical for this type of work: (i) Keep social media frequently updated, (ii) establish connections and (iii) make the most out of them - opportunities exist even in social media. :-)

I have 26 new followers from UK., USA, and more last week. See https://t.co/SVXfnVNr4D pic.twitter.com/ZB4kUJtQGO

— NEUROPUBLIC (@Neuropublic) August 5, 2016

The progress can be evaluated (partially, from my point of view) through the analytics tools provided by each channel. I believe that there is no need for complex reports and focusing on these metrics (vanity metrics as many call them). An increasing number of followers, likes and shares is an indication that what you are doing is good and draws the attention of people but since the company does not sell its products through social media there’s no point in focusing on them. Instead, my approach is that social media in cases like NEUROPUBLIC should be used for keeping people informed about the activities of the company and show a constant activity on social media. My experience says that the rest will follow.

Challenges in the digital transformation of Greek agriculture (and the role of Smart Farming)

I have to admit that despite my agronomic studies (PhD in Agricultural Biotechnology), I was not familiar with the concept of digital farming and I was only recently introduced to smart farming. On the other hand, I had a pretty long experience in agricultural data - referring to data produced in an agricultural context or used for improving agriculture. I recently found myself working for NEUROPUBLIC, a Greek SME that is designing and developing smart farming services - and smart farming makes use of various types of data for providing informed advice to farmers. Bingo!

Image source: http://www.nesta.org.uk/blog/precision-agriculture-almost-20-increase-income-possible-smart-farming

But what are some of the benefits of smart farming (in general)? Here’s an indicative list:

1. Minimizing the production costs through the calculated and rationalized application of inputs (irrigation, fertilization and crop protection) as well as the necessary labour cost (through better management and reduction of specific agricultural activities).
2. Improved quality and increased production yields through the optimization of the inputs.Improved competitiveness of the agricultural products in the global market through the reduced production cost, the improved quality and the standardization of the processes (which allow for traceability).
3. The minimization of inputs’ application allow the produce to meet the strict criteria defined not only at EC level, but also from specific wholesale buyers, such as large retailers (who usually set even higher standards).
4. Contribution to the improvement of the local ecosystem’s and environment’s quality through the rationalized application of inputs as well as water conservation (referring to the water needed for irrigation - which may amount up to 70% of total water consumption). 

Agriculture is probably the most important sector in Greece in terms of exports' value; at the same time, Greek farmers do not seem to be eager to adopt modern farming approaches - for various reasons:

1. Low income: The high cost of the investment required for introducing technological advances in farming (e.g. for smart farming) is prohibitive for Greek farmers, most of which are smallholders.
2. High fragmentation of agricultural land: Agricultural parcels in Greece are highly fragmented and farmers usually own remotely located fields, a fact that does not favor the application of traditional smart farming approaches which show their real potential in huge, homogeneous farms.
3. Low adaptation rate: The majority of Greek farmers (as the rest of the European ones) are elderly and therefore tend to follow traditional approaches in farming, inherited from father to son. They are hard to follow technological advances and they trust their own, proven cultivation practices. (however, recent reports show that a new wave of educated people is engaged in farming, therefore this situations seems to be changing for the better).
4. Lack of education: Greek farmers are not fully aware of the advantages of the adoption of smart farming and they directly make the connection between the smart farming concept and huge, hi-tech tractors and other agricultural machinery equipped with GPS sensors, screens and antennas, running up and down huge, monotonous fields (a view that is far from the typical Greek farm).
5. Lack of motivation: Greek farmers do not feel the urge to adopt new approaches. For example, policies developed for the application of the Common Agricultural Policy (CAP) in Greece seem to be on the “safe side” and promote traditional farming approaches instead of “pushing” farmers to explore and adopt modern ones.

Image source: https://www.theguardian.com/world/greek-election-blog-2012/2012/jun/14/greek-farmers-rent-land-crisis

NEUROPUBLIC is currently working on changing this situation and revolutionizing Greek farming, by introducing smart farming services that are adapted to the specific needs of Greek farmers and making them accessible to them in terms of cost.