This report provides a number of recommendations to improve practices around the creation and use of standards that support the collection and use of land and nutrition data. We have drawn on the analyses of gaps in land and nutrition standards reported in our survey of data standards for land and nutrition data (Pesce, Mey, L’Hénaff and Tejo-Alonso, 2018). Land data, like geology, soil and irrigation, and nutrition data, like food consumption and health statistics, are important to inform decision making for interventions to eradicate global hunger and malnutrition. The collection of land and nutrition data varies globally. Like all data, different countries are at different levels of maturity with regards to the way this information is collected, stored, accessed and used. The recommendations draw on our assessments of the existing gaps between current and best practice in two data aggregator use cases. We think the key opportunities around improving use of standards around land and nutrition data should focus on: improving the documentation and description of existing data sources, in particular through the creation of developer documentation and by investing in publishing relevant vocabularies (e.g. indicators, geographic regions, country names and age groups) attempting to address discovery issues relating to land and nutrition data, by improving use of metadata standards to help catalogue and describe data published by existing organisations as well as harmonising existing or developing new standards. working with data publishers to harmonise and publish open standards in a variety of formats, so that different user communities (developers, statisticians, analysts, geospatial specialists) can consume and use data in an efficient way. working with relevant authorities to map and publish sustainable development indicators with definitions and mappings. engage with relevant user communities to promote standards, by working with data providers. These recommendations will be combined with those from previous GODAN Action recommendation reports on agri-food and weather data standards. Together they will inform specifications for standards interoperability services that can help to drive adoption of standards such as tools to publish data into different formats, to support open metadata standards or share standard vocabularies.

The GODAN Action online map of agri-food data standards is a deliverable of the GODAN Action project . GODAN Action supports data users, producers and intermediaries to effectively engage with open data and maximise its potential for impact in the agriculture and nutrition sectors. In particular, we work to strengthen capacity, to promote common standards and best practice and to improve how we measure impact. The project is part of the GODAN programme that promotes the proactive sharing of open data to make information about agriculture and nutrition available, accessible and usable. The project has been initially funded for 3.5 years by the UK Department for International Development. Specifically, the map of data standards supports the GODAN Action task of mapping relevant standards and identifying areas where the lack of standards is inhibiting. The objectives of this task are: • To map currently available open and proprietary standards in use for the exchange of key data on agriculture and nutrition; • To identify where a lack of standards is inhibiting the effective use of agricultural and nutritional data and the best methods for promoting open data standards. The map of standards fulfills the first objective and is also designed to support the gap analysis exercise described in the second objective. The full report on the gap analysis is also available (Pesce, Kayumbi, Tennison, Mey and Zervas 2016). Paraphrasing what the Dublin Core Metadata Initiative says about their DCMI Registry , this map promotes the wider adoption, standardisation and interoperability of agri-food vocabularies by facilitating their discovery and re-use across diverse communities of practice. In addition, it provides a useful overview of what exists and helps to identify overlaps, duplication, gaps and limits to adoption, hopefully encouraging practitioners not to duplicate efforts and to collaborate both to develop and use common standards. The first version of the map is available at http://vest.agrisemantics.org This report is an accompanying document to the map of standards that describes the approach to the implementation of the map, the categorisation of standards, and gives an overview of the initial content. It also gives details on the coverage and organisation of the map, and a summary of how we conducted a call to action to experts to contribute to its improvement. Our approach to creating the map of standards was based on the principles of building on what already exists, providing for sustainability and defining standards in terms of use. As the map is designed to support work on open data, we limited the scope to data standards, and chose not to include other types of standards. It is also important to clarify that while data standards may include the notion of data formats, we do not include mere data formats in the map (such as CSV, MDB, XML). Within the scope of the map, by ‘data standards’ we mean ‘vocabularies’ without addition or qualification. This is the broad sense in which vocabularies are defined by W3C, which includes metadata element sets (schemas or definitions of description models, more in general ‘description vocabularies’) and value vocabularies (sets of controlled values). This is why we use the terms ‘data standards’ and ‘vocabularies’ interchangeably, and they range from description/modelling standards (XML schemas, RDFS schemas, ontologies, application profiles, even UML models) to knowledge organisation systems of different types (classifications, thesauri, even certain types of International Organization of Standardization (ISO) standards as controlled lists of values). The domains of food and agriculture span across several disciplines (including plant sciences, farming systems, natural resources management, forestry, all disciplines involved in the food supply chain), and are also closely interlinked with neighboring disciplines (such as climate, environment, geospatial, biology). We decided to include standards covering all of these disciplines. In the standards map, we also included generic standards that are universally used to describe any resource (such as Dublin Core) or to describe or provide values for generic properties (geographic, ownership, provenance), as these are useful in any domain.

GODAN Action supports data users, producers and intermediaries to effectively engage with open data and maximise its potential for impact in the agriculture and nutrition sectors. In particular, we work to strengthen capacity, to promote common standards and best practice, and to improve how we measure impact. The first version of the gap analysis on data standards focused primarily on the overall methodology and criteria and gave an initial tentative overview of the partial gap analysis results (based on the content of the map as of 20/11/2016) in particular technical issues of openness and usability. This second version, in line with the 2017 project focus on weather data and related use cases, examines the situation of data standards for weather data (and closely related geospatial data), and in particular weather data for use in farm management services. It also analyses gaps, both from the point of view of the usability and openness of the standards (as we did in the first version) and from the point of view of their adoption, authoritativeness and endorsement. The report starts with a broad review of the relevant types of data, then reviews the standardisation landscape for weather data as a whole, before narrowing down to the use of weather data in farm management information systems (FMIS), specifically. The main conclusions drawn from the report, for weather data standards as well as (and especially) for the use of weather data in FMIS are: Need for application-friendly and possibly harmonised data formats Need for common controlled values, especially variable naming conventions (this might also mean a need for better use of semantic technologies, but feedback from experts hasn’t highlighted this and indeed there doesn’t seem to be much perceived demand for and work on Linked Data in this area) Need for web services/APIs that allow for querying (time and space, but also selected variables) Need for standards to implement ways to clarify and enforce data rights and data ownership at each stage of the data value chain (especially important for FMIS) One strongly felt gap, not strictly related to data standards but worth noting, is the lack of quality (reliable, granular, timely) data from free public services, especially for certain geographic regions.

The presentation will illustrate the findings of a gap analysis study on weather data standards under the lens of the FAIR principles. Data standards and vocabularies can be considered datasets and can be assessed against the FAIR principles. After all, the FAIR principles recommend that “(meta)data use vocabularies that follow FAIR principles”. The GODAN Action project created a map of data standards relevant for food and agriculture and did a first gap analysis focusing on weather data. GODAN Action is a three year project to enable data users, producers and intermediaries to engage effectively with open data and maximize its potential for impact in the agriculture and nutrition sectors. The criteria used for the gap analysis were organized in four categories: (a) fitness for purpose, (b) adoption, c) usability and (d) openness. The criteria in the latter two categories can be used for an assessment of the standards against the FAIR principles, as they all try to measure to what extent the standards are findable (is it available on the web? Is it annotated?), accessible (is it maintained? Is it referenceable?), interoperable (is it available in more formats? Is it machine-readable? Is it semantic, referenceable, linked?) and reusable (does it have a clear license?). However, even more important is the extent to which a data standard or vocabulary contributes to making data that adopts it more FAIR. In some contexts and for some users (data providers, intermediaries) the FAIRness of the standards themselves is very relevant, while for other users (service providers, end users) the FAIRness of the produced data is more important. While it’s implied that the use of FAIR vocabularies and data standards contributes to the “I” in FAIR, certain vocabularies help data also with the “F” (e.g. vocabularies that have properties for identifiers, vocabularies that have dataset metadata), “A” (e.g. vocabularies that have properties for protocols) and “R” (e.g. vocabularies that describe licensing and provenance). The presentation will give some examples of these assessments using a few weather data standards.

Standards are important for data publishing because using them makes it easier for people to publish and use data. Using standards for data increases interoperability for publishers and reusers, the ability to create links between datasets, aggregate them together, compare them and rapidly develop new tools and services using data. Following on from a survey and analysis of the existing standards in food and agriculture (the GODAN Action Gap exploration report), this report looks at how to make standards more usable. Assessing the usability of standards requires us to consider different users of standards. There are publishers who use standards when publishing their data, users who create tools and services that consume data, and standards developers who create standards, often based on existing standards. Good standards are easy to adhere to when publishing data, easy to support in products that consume data, and easy to embed into other standards. There are three sets of activities that could improve the usability of existing standards. These are: advocacy and capacity building to promote good practices amongst standards developers third party contributions to create supplementary materials that fill some of the gaps in provision such as documentation or validators development of tools and services that help standards developers to create good standards and supplementary materials that can drive adoption It may also be that there are missing standards within a particular domain or thematic topic, in which case this report describes the steps that should be taken in filling those gaps. We recommend a combined approach based on a more rigorous examination of the coverage of standards in a particular thematic topic area that includes the following: The development of a curated list of standards relevant in the thematic topic, focused on publishers and users of data, to help them choose which standards they should support in their data publication and the tools that they create. The identification of a small number of key standards within the thematic topic, based on the quantity and utility of data that could be published against those standards, and work with the developers of these standards to increase their utility (which could involve openly licensing it, turning it into linked data, creating documentation and test cases and so on, depending on where those key standards are lacking). The development of tooling only where similar tools do not already exist and it is helpful in increasing the utility of three or more of the key standards.

GODAN Action supports data users, producers and intermediaries to effectively engage with open data and maximise its potential for impact in the agriculture and nutrition sectors. In particular, we work to strengthen capacity, to promote common standards and best practice, and to improve how we measure impact. This gap analysis report is the third in a series which has examined gaps in data standards. The first version of the report examined gaps in agriculture and food data (Pesce, Kayumbi, Tennison, Mey, and Zervas: 2016). A second version (Pesce, Tennison, Dodds and Zervas: 2017) examined the situation in the area of data standards for weather data (and closely related geospatial data), and particularly focused on weather data for use in farm management services. This third version focuses on data standardisation gaps in specific use cases of aggregation of land data and nutrition data around indicators: the Land Portal and the Global Nutrition Report. The report starts with a review of the relevant types of data for these use cases, then illustrates similarities between the two projects and similar standardisation gaps, and then moves to more specific challenges for the two individual projects. The Land Portal (LP) gathers information from a broad range of land-related data and information providers. It is organised and visualised in ways that are intuitive and usable for researchers, private sector actors and policy makers at global and local levels. The information provided can strengthen research, advocacy, and policy making efforts by enabling a better understanding of land governance issues affecting various countries and regions. The Global Nutrition Report (GNR) is a comprehensive narrative on global and country-level nutrition. GNR produces the Report annually and aggregates a wealth of nutrition and nutrition-related data from a wide range of sources. This data underpins the report itself as well as being used to produce a range of supplementary materials, including country, regional, and sub-regional profiles and data visualisation tools. The main conclusions drawn from the report are that The two use cases present many similarities. They both aggregate data from secondary sources, already partly normalised by global agencies; they both aggregate data around specific indicators; they aggregate from datasets with a similar structure (indicator, country, year, value). The identified gaps in data standardisation are very similar. The names of countries and regions in data sources are not standardised or they are standardised according to different conventions; the names of the variables do not follow any convention; indicators are represented by strings and may change over the years (both their names and the measurement methods). With reference to our data standard assessment criteria, the few standards used by the data sources (country naming conventions, value ranges, units of measurement) in both cases are not open and not very usable. The situation is different when it comes to the way the two projects re-publish the data: the GNR normalises values around some conventions, while the LP re-publishes everything according to Linked Data principles, and uses published vocabularies.

This report describes the methodology and results of our gap analysis on the availability and usability of data standards for food and agriculture. This is the first of three analysis reports, which form part of the GODAN Action project which aims to enable data users, producers and intermediaries to engage effectively with open data in the agriculture and nutrition sectors. This initial report is an attempt to provide a baseline gap analysis, against which subsequent analyses will be conducted. It is based on the content of the GODAN Action Map of agri-food data standards as of 20 November 2016. However, since this is the first gap analysis report and is based on an early and presumably incomplete version of the database, it focuses primarily on the overall methodology and criteria, and only gives an initial tentative overview of the partial gap analysis results. The methodology includes the design criteria behind the online database where all the metadata about the data standards were collected – the online map of data standards, described in Pesce, Kayumbi, Tennison, Mey and Zervas (2016) – as well as the assessment process and the elaboration of results. The metadata model, besides following existing standards for describing vocabularies, includes additional assessment metadata, drawn from two existing assessment practices (the assessment process used by the UK government’s Open Standards Board and the Open Data Certificates). These assessment criteria were organised in four categories: fitness for purpose, adoption, usability and openness. Although the whole set of assessment criteria must be considered for a full gap analysis, the fitness/adoption criteria are very difficult to assess. Such an assessment requires the participation of domain data experts who can evaluate the scientific soundness, the completeness and the level of adoption and authoritativeness of a standard. These domain-specific analyses will be conducted in a second phase, when the GODAN Action project has selected its thematic topics and domain experts can be brought on board. In this first version of the gap analysis, we are limiting ourselves to the usability and openness criteria, which can be more objectively evaluated by open data experts. The first part of the report illustrates the metadata used in the global map to describe data standards, in particular the specific assessment metadata (is it machine-readable? Is it served by application programming interfaces (APIs)? Is it clearly licensed?). The second part provides an initial analysis of the results of the assessment. The key findings of the assessment exercise are: In terms of openness and usability, a certain number of standards are barely usable because they are not even available on the web (16 per cent). Only 55 per cent of the standards are presented in machine-readable formats. Most standards fail to present a clear license (only 21 per cent) though where they do they are generally open (13 per cent). There is a gap between the information presented on the web and the documentation on the standard, with only 31 per cent of the standards having documentation, only 5 per cent having tests and only 40 per cent being supported.Doing an analysis by domain, it appears that certain domains are better covered than others (plant sciences above all, followed by natural resources), but the type and quality of standards used in domains that are apparently similarly covered varies greatly. Most of the standards used in plant sciences are in the form of ontologies and are highly open and usable, while the level of openness and usability of standards in sub-domains of natural resources is lower and still varies. The soil domain is covered by good standards (widely adopted models, thesauri) though in most cases are not yet formalised as open standards. In the land sector, classifications are still fragmented and on paper (with a subsequently low level of openness and usability) and only one open standard has been developed. More generally, we noted that in certain domains (e.g. plants) research institutions play a key role in developing ontologies, while government and standardisation bodies provide basic normative descriptors or syntactic standards (like the Multicrop Passport descriptors) with little use of common semantics. For other types of data (like soil, plant products, animal products), international bodies provide models and normative classifications (like INSPIRE for soils or the FAO/UN commodities and product classifications or the ISO Animal Identification standard) while the development of ontologies and common semantics seems to be still slow. Finally, although in the area of value chain data, standards are being increasingly used (messaging standards, product classifications) and semantics are slowly starting to be used (e.g. food ontologies), government and legislation data is an area where standardisation seems to be low – besides the use of basic statistical standards – with little development or re-use of semantics.

GODAN Action supports data users, producers and intermediaries to effectively engage with open data and maximise its potential for impact in the agriculture and nutrition sectors. In particular, we work to strengthen capacity, to promote common standards and best practice, and to improve how we measure impact. This report is a short accompanying document to the GODAN Action Map of Data Standards (http://vest. agrisemantics.org) which now includes two new sections dedicated to data standards relevant for the two thematic topics, which are the GODAN Action project focuses in its second year: land data and nutrition data. What we describe in detail here is the process followed to identify relevant data standards for the two new thematic topics. As we did for weather data in the first year of the project, we framed our survey of data standards around: the types of data commonly used for land and nutrition; the standardisation practices that are currently in use for these types of data (from statistical data formats to code lists and classification schemes); the corresponding authoritative bodies to which all experts look to, as well as important projects and initiatives. An initial overview of the data standards identified shows that the data formats most widely used for these types of data are the typical statistical formats (from tabular to SDMX). Indeed, malnutrition, land tenure and land use are socio-economic dimensions, mainly measured through statistics and surveys and elaborated through projections. On the other hand, the truly topic-specific data standards are the data dictionaries, code lists and the classification schemes used for each specific type of data. This explains why the majority of the data standards identified for these two thematic topics are value vocabularies, like code lists and classification schemes. However, beyond a few formalised classifications, most of the standardisation work in these areas is done through recommendations and guidelines issued by authoritative bodies. A more in-depth analysis of the data standards and standardisation gaps in the areas of land data and nutrition data is provided in our full gap exploration report (Pesce et al.; 2018).

GODAN Action supports data users, producers and intermediaries to effectively engage with open data and maximise its potential for impact in the agriculture and nutrition sectors. In particular, we work to strengthen capacity, to promote common standards and best practice, and to improve how we measure impact. This report presents a number of recommendations aimed at improving current practices around the creation and use of standards that support the collection and use of weather data. We draw on both the assessments of existing gaps between current and best practice reported in our ‘Gap analysis on weather data standards’ (Pesce, Dodds, Tennison & Zervas 2017) and the general recommendations from ‘Recommendations for filling identified gaps in data standards for food and agriculture’ (Tennison, Dodds, Pesce & Zervas 2017). To better understand the needs of data users we also conducted a brief “developer experience” review based on the farm management use case described in ‘Agrifood data standards: a gap exploration report’ (Pesce, Kayumbi,Tennison, Mey & Zervas 2016) . Weather data infrastructure has developed over many years, through a number of international efforts to support the work of meteorological services around the world. In some cases the formats and data structures used to report and share data either pre-date the internet or are used in other media, for example in radio and satellite broadcasts. This has left its mark on the current state of the data and standards landscape. Many of the existing formats, while widely used by some organisations, are not accessible or well-documented for use by non-specialists. But the increasing use of weather data, as an important component in new products and services means that it must be more accessible to a wider audience. We think that the key opportunities around improving use of standards around weather data should focus on: attempting to address discovery issues relating to weather data, by improving use of metadata standards to help catalogue and describe data published by existing organisation, in existing formats improving the documentation and description of existing data sources, in particular through the creation of developer documentation and by investing in publishing existing vocabularies in new ways exploring options to build a community of practice around weather and agricultural data, for example through a community-lead Q&A “help desk”.

Information and Communication Technologies (ICT) are being used across the world to generate efficiency gains for farmers. This has led to an information and data explosion with an associated boom in new applications, tools, actors, business models, and entire industries. Agri-food systems are being transformed. Beyond the technological developments, data for and from farmers has become a growth area, driving expectations and investments in big data, blockchain technology, precision agriculture, farmer profiling and e-extension. Investing in data-driven agriculture is expected to increase agricultural production and productivity, help adapt to or mitigate the effects of climate change, bring about more economic and efficient use of natural resources, reduce risk and improve resilience in farming, and make agri-food market chains much more efficient. Ultimately, it will contribute to worldwide food and nutrition security. Smallholders in particular have much to gain from data – small improvements in their operations are likely to provide larger gains at household level, proportionally, and, if the improvements are widely adopted, the whole agricultural sector in many countries that depend on smallholder agri-food systems can be transformed. However, for smallholders to benefit from data-driven agriculture, tools and applications need to be designed for their specific situations and capacities; they – and the organizations that support them – need to grow their capacities to become smart data users and managers; measures are needed to ensure that farmer-generated data is not exploited or misused; and smallholders, usually the least powerful parts of a value chain, must grasp every opportunity to be included in the collective data flows within agri-food systems This white paper discusses the huge opportunities and the main challenges of data-driven agriculture for smallholder farmers, illustrates some data and agri-food system drivers that can help make data-driven agriculture more smallholder-friendly and proposes a few institutional and policy approaches to develop a data ecosystem that can enable farmers to fully harness the power of data. The paper is a contribution towards the Collective Action on Farmers’ Data Rights being developed through the Global Forum on Agricultural Research and Innovation (GFAR), with the Global Open Data for Agriculture and Nutrition (GODAN) initiative and the Technical Centre for Agricultural and Rural Cooperation (CTA). It is co-published by the three organizations. With a preface by Mark Holderness, Executive Secretary, GFAR, and special thanks to: Martin Parr (GODAN), André Laperrière (GODAN), Hugo Besemer (Wageningen University).

Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain’s semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform’s content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain.

Semantics refers to the description of the meaning of data, made possible by “semantic resources” (aka “semantic structures”) aiming at making explicit the information that may help to find, understand, and reuse data(sets). Semantics may also make explicit the entities and relations the data embody. Granted that no data is ever produced or distributed without some attempts to describe its meaning (all databases have column names, all documents have a title and often some ways to indicate what topics they are about), the level of semantic richness, accuracy, shareability and reusability of the resources used vary greatly.

Standards to describe soil properties are well established, with many ISO specifications and a few international thesauri available for specific applications. Besides, in recent years, the European directive on “Infrastructure for Spatial Information in the European Community (INSPIRE)” has brought together most of the existing standards into a well defined model. However, the adoption of these standards so far has not reached the level of semantic interoperability, defined in the paper, which would facilitate the building of data services that reuse and combine data from different sources.

In 1995 the University of Arizona (UA) Libraries, partnering with UA College of Agriculture & Life Sciences (CALS) rangelands specialists, joined as a charter member of the AgNIC initiative coordinated by the National Agriculture Library (NAL). The topic of rangelands was chosen by Arizona, and early on, the team recognized the challenge of representing the knowledge universe related to that topic. As a result, in 2001, the Deans of the UA Libraries and CALS invited their counterparts in the Western Land Grant Universities (LGUs) to join in a Western Rangelands Partnership. Today this collaborative effort, known simply as the Rangelands Partnership, has grown to include nineteen LGUs, with international participation from Australia, Mexico, and the United Nations’ Food and Agriculture Organization (FAO). This collaborative partnership culminated in the release in December 2012 of a suite of websites – Global Rangelands, Rangelands West and hosted state Rangelands sites - with more than 13,000 resources. Resume: En 1995, les bibliotheques de l'universite de l'Arizona (UA), en partenariat avec des specialistes en parcours, du college de l'agriculture & des sciences de la vie (CALS) de l’UA, sont devenus membres fondateurs de l’initiative AgNIC, coordonnee par la bibliotheque nationale agricole (NAL). Le theme des parcours a ete choisi par l'Arizona, et au debut, l'equipe a reconnu le defi de representer l’univers des connaissances liees a cette rubrique. En consequence, en 2001, les doyens des bibliotheques de l'UA et de la CALS ont invite leurs homologues des universites de l'Ouest (LGU) a se joindre au Partenariat Western Rangelands. Aujourd'hui, cet effort de collaboration, connue simplement comme le Rangelands Partnership, s‘est developpe pour inclure dix-neuf LGU, avec une participation internationale de l'Australie, du Mexique, et de l'Organisation des Nations Unies pour l'alimentation et l'agriculture (FAO). Ce partenariat collaboratif a abouti a la publication en decembre 2012 d'une suite de sites web - Global Rangelands, Rangelands West et autres sites etatiques sur les parcours - avec plus de 13 000 ressources. Resumen: En 1995, las bibliotecas de la Universidad de Arizona (UA), en alianza con los especialistas en pasturas de la Facultad de Agricultura y Ciencias de la Vida (CALS, sus siglas en ingles) de dicha Universidad, se unieron como miembros fundadores de la iniciativa AgNIC coordinada por la Biblioteca Agricola Nacional (NAL, sus siglas en ingles). El tema de pasturas fue escogido por Arizona, y desde el principio, el equipo reconocio el reto de representar el universo de conocimientos relacionados con ese tema. Como resultado, en el 2001, los decanos de las bibliotecas de la UA y CALS invitaron a sus homologos de las universidades publicas del Oeste (conocidos como “land-grant universities”) a unirse en una Alianza de Pasturas de la Zona Oeste. Hoy en dia este esfuerzo de colaboracion, conocido simplemente como la Alianza de Pasturas, ha crecido hasta incluir a 19 universidades publicas, con participacion internacional de Australia, Mexico y la Organizacion de las Naciones Unidas para la Alimentacion y la Agricultura (FAO). Esta alianza colaborativa culmino con la liberacion en diciembre del 2012 de una serie de sitios web - Global Rangelands, Rangelands West y otros sitios patrocinados sobre pasturas a nivel estatal, con mas de 13.000 recursos.

The movement to share data has been on the rise in the last decade and lately in the agricultural domain. Similarly platforms for publishing scientific and statistical datasets have sprouted and have improved visibility and availability of datasets. Yet there are still constraints in making datasets discoverable and re-usable. Commonly agreed semantics, authority lists to index datasets and standard formats and protocols to expose data are now essential. This paper explains how the CIARD RING provides a global linked data catalog of datasets for agriculture. The first part of this paper will describe the Linked Data layer of the CIARD RING focusing on the data model, semantics used and the CIARD RING LOD publication. The second part will provide examples of re-use of data from the RING. The paper concludes by describing the future steps in the development of the CIARD RING.

The agINFRA project focuses on the production of interoperable data in agriculture, starting from the vocabularies and KOS used to classify and an-notate them. In this paper we report on our first steps in the direction of con-tributing to a LOD of agricultural data. In particular we look at germplasm data and soil data, which are still widely missing from the LOD landscape, seeming-ly because information managers in this field are still not very familiar with LOD practices. This is why this paper also recaps the basics of LOD publishing, which will be applied in the agINFRA project.

The agINFRA project focuses on the production of interoperable data in agriculture, starting from the vocabularies and Knowledge Organization Systems (KOSs) used to describe and classify them. In this paper we report on our first steps in the direction of publishing agricultural Linked Open Data (LOD), focusing in particular on germplasm data and soil data, which are still widely missing from the LOD landscape, seemingly because information managers in this field are still not very familiar with LOD practices.