OGC Agriculture Summit Web
Few activities are more tied to location, geography, and the geospatial landscape than agriculture. Agricultural science, business, and policy are themselves increasingly tied to quantitative data about crops, soils, water, weather, markets, energy, and biotechnology. Matching precision agricultural machinery with precision agricultural knowledge and promoting evidence-based sustainable agricultural practices are increasingly global concerns. Access to such knowledge involves sensing, validating, integrating, and analyzing ever larger scale geospatial data streams. Sharing and interoperability of agricultural data consistent with privacy, security, and business concerns is a challenge at every scale from local farm operations to regional markets, national policy development, and global resilience projects. The participants in the Summit intend to consider the sources and roles of critical agricultural data collections, starting with soils as the common medium that connects nutrients, water, crops, weather, and arable land to agricultural sustainability. The goal of the Summit is to identify geospatial contributions and collaborations on international agricultural data standards that will be critical to successful data sharing and integration practices.
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Introduction -- Josh Lieberman, Chair OGC Agriculture Working Group
There are many types and sources of agricultural geodata, many stakeholders, many geographic models, scales, and units for information, and many sometimes complex scenarios for the interchange, harmonization, and integration of such data that might benefit from common standards. Identifying the critical use cases for agricultural data standards will be necessary in order to focus, establish effective collaborations, and make significant progress in standards development and adoption.
Soil for starters -- David Medyckyj-Scott, Land Care Research New Zealand
Soil is one of the building blocks for life. Farmers, foresters and other land managers manage the majority of our soils while research by scientists provides knowledge on how best to sustainably use soils. Observed and modelled data provides the critical evidence for all involved to assess, use and monitor soils but data is widely distributed, patchy and difficult to access. There is an urgent need for a recognized standard for the exchange and collation of consistent harmonized soils data worldwide to help the sharing and aggregation of data. Work is taking place but is fragmented. Collaboration is essential in establishing this standard and OGC working groups and standards provide a pivotal foundation.
Water is a key resource for agricultural production, and growers increasingly find themselves having to record and justify its usage in irrigation. A lack of interoperability among different irrigation equipment, sensor, and software manufacturers, however, makes scalable, principled irrigation decision-making quite difficult. There is a recognized standard, ISO 11783-10, for the exchange of data between agricultural machinery and farm management software, but 11783 does not fit irrigation operations well, and it does not include other relevant aspects of the grower's decision-making cycle (e.g., recommendations). AgGateway 's PAIL project represents an industry effort to propose a standard data exchange format that can fill this gap. Its operations component seeks to be as compatible as possible with ISO 11783 and other AgGateway field operations work; its observations component is an implementation of the ISO 19156 standard. Future development will include data exchange APIs, for which the team is considering OGC standards such as SOS and SensorThings. Upon completion, PAIL deliverables will be handed off to ASABE for consideration as a US National Standard, and subsequently to ISO TC23/SC18.
The agriculture sector comprises a network of interacting organizations with a unique strategic importance for both citizens (consumers) and economies (rural, regional, national, and global). With new seed-to-store crop knowledge management opportunities and mandates comes the need for farmers to manage a lot of diverse information in order to make sound economic and environmental decisions. The management tasks are still largely manual and labor intensive, including monitoring field operations, managing finances, complying with environmental protection rules, and applying for subsidies. They rely on a variety of separate software applications that deal with data in proprietary structures and formats specific to an organization, vendor, or crop type. If we would like to integrate data from these disparate sources, we will need to establish a data model that is unified but also capable of representing the nuances of existing fragmented data models. The conceptual model of the FOODIE platform targets this need, relying upon data and service models based on ISO/OGC geospatial standards and best-practices as well as specific agriculture standards and best-practices such as the INSPIRE data model for Agricultural and Aquaculture Facilities, Transport, and Monitoring.
Agriculture field operations vary greatly between crops, regions and even growers. AgGateway 's SPADE and PAIL projects have endeavored to document user stories and provide some common ground for other projects primarily in the form of use cases and data requirements. We will review the SPADE/PAIL core documents as well as provide a high-level overview of different standards efforts related to telematics that are focused on different aspects of efficiently transferring data between the field to the back office. These efforts range from identifying data requirements for specific use cases, to identifying protocols and techniques for structuring API's such as Protobufs and MQTT. There is potential here for collaboration / adoption of OGC standards such as SensorThings.
Agricultural science faces diverse and growing data on the source side; new big data techniques on the analytical side; and great economic, climatic, and political challenges on the application side. Sources range from remote, in situ, and on-machine sensors to plant genetic characteristics and quantification of farming knowledge and experience. Analytics draw from new techniques in machine learning as well as enhanced functional yield models that are robust and scalable but still require quality data. Applications range from precision variable rate field operations to environmental compliance, economic sustainability, and climate resilience. All of these depend on a scale of data sharing and experimentation that is a technical as well as cultural and economic challenge. This segment addresses both the scientific data sharing needs and what science can do to help meet them.
All of the data and models in the world will not be able to improve the practice of agriculture unless viable business and regulatory models can be found to make such advances part of a profitable and sustainable agricultural sector. Issues include costs versus returns of data-intensive farming methods, data ownership, privacy, and security, mechanisms for data sharing, and balancing the value of agricultural data at local, national, industry, and regional scales. A critical issue will be translating all of the scientific, technological, and political imperatives into farm operations where it makes sense for farmers and field workers to collect good data, participate in data analyses, and act on the results.