A rising tide lifts all the boats: AgGateway’s Collaborative Model for Identification in Field Operations

Berne, Dan; Craker, Ben; Ferreyra, R. Andres; Hillyer, Charles C.; Rhea, Stuart T.; Tevis, Joseph; Wilson, James A.; Wilson, Jeremy

Contemporary farming requires the continuous exchange of information between growers and partners such as agronomists, retailers, custom applicators, insurance agents and customers. A critical part of this interoperability is identification, where a name or code (the “identifier”) is used to reference a particular instance of an object. This enables farm management information systems (FMIS) to distinguish between that unique instance and other instances, and to recognize an instance when encountering it again. There are multiple motivations for uniquely identifying resources in production agriculture data exchange. Examples include unambiguously specifying the products being applied (or planned for application) in a particular field operation; unambiguously specifying the location(s) where these products are applied; and enabling an audit trail (the aspiration of having farm-to-fork traceability) for all the processes of interest[BC1] .

Workflows involving identification often break down when a grower (or other actor) imports data into their FMIS from an external source such as a machine-implement control system (MICS) or other FMIS. Incoming data (that may correspond to objects such as farms, fields, and products in the grower’s own FMIS) might contain externally-created identifiers that the grower’s FMIS does not recognize. The user must then manually match these unknown identifiers with known objects in their system. (This process can be supported by spatial overlap checking, string comparison metrics, and so forth). Users do not like this data mapping (also called record linkage, and object identification): it is time-consuming and error-prone, and is ultimately an obstacle to the broader adoption of precision ag technologies, especially given that users increasingly have an expectation of “frictionless data entry.”

A centralized approach to identification might seem like a solution: supply-chain operations increasingly use GTINs (Global Trade Item Numbers), GLNs (Global Location Numbers), and EANs (International Article Numbers), all codes minted by one or more numbering authorities (such as GS1). This makes clear where the identifier originated (henceforth, its “source”) and what its meaning is. This approach seems ill-suited for the current context in field operations, however; a given user might require thousands of identifiers for their farms, fields and documents; those identifiers may be needed in situations of no Internet connectivity, and paying for identifiers may seem counter-intuitive to the end-user. Moreover, agricultural data exchange happens in a context of scant format standardization: different FMIS and MICS systems use a variety of identifier data types, such as integers, GUIDs (Globally Unique Identifiers), URIs (Universal Resource Identifiers), DOIs (Digital object identifiers), and GLNs (Global Location Numbers). This plethora of different identification schemes makes it even harder to interoperate.

AgGateway’s collaborative model for identification is presented as a potential, distributed, solution. It has a technological component and a social component: The technological component is based on the Compound Identifier, a sort of “basket of identifiers” that associates an object with one or more identifier-source pairs, analogous to what is enabled in ISO 11783 Annex E-mediated Linklist files. The social component consists of encouraging the actors in an exchange process to preserve all the identifiers they receive for a given object, and to pass them all along, together with their own identifier (if it exists) to the next actor in the communication process.

Multiple examples are presented detailing the use of compound identifiers to identify different objects within a farm management information system (e.g., grower / farm / field, equipment, documents).

ASABE keywords: information systems; software; identification; record linkage; standards

Submitted.

Andres made first draft, sent to others. Stuart and Ben gave feedback.

Idea / conclusion for paper:

While we may eventually arrive at some centralized method of assigning identity, that is not where we are today. The CompoundIdentifier-based approach accepts the reality that growers, farms, fields, etc. already have multiple identifiers spread across the systems of various stakeholders.

Observations and measurements are the cornerstone of principled decision-making in agricultural field operations. They are the information captured in crop scouting for crop protection, nutrition and harvest; in postharvest grain weight and quality certificates; in soil and manure lab tests; in asset management and telematics (e.g., observing grain bin / dryer conditions); and are also the multiple environmental inputs used to drive irrigation decisions.

The ISO 19156 standard defines a data model for representing various aspects of observations, such as ultimate and sampling features of interest, observed properties, and the procedures used to derive the observation result. However, the standard does not include vocabularies for capturing the myriad specific combinations of these aspects used in agriculture.

AgGateway’s global effort toward interoperability identified value in implementing an ISO 19156 – based model of observations and measurements for agricultural field operations. This work, centered on the PAIL, SPADE and ADAPT projects, emphasizes the explicit capture of the semantics of the various aspects of an observation. The work, performed by a group of industry and academic AgGateway participants spanning four continents, includes three major parts:

First, defining a componentized model of the properties of an observation, based on an extensible set of orthogonal vocabularies, which includes representing valid combinations of components. Second, deploying infrastructure, in the form of a RESTful API, to make the componentized variable definitions freely available to industry and the research community; this includes putting into place an ISO 19135-based process for stakeholders to request the addition of vocabularies or entries therein. Third, incorporating observations and measurements into AgGateway’s ADAPT common object model and format conversion plug-in architecture, thus enabling widespread interoperability.

The diversity of agricultural sources of observations and measurements makes it challenging to implement a system that is both comprehensive and consistent. The work described here enables growers and their advisers to not only gather the data but to integrate them into their decision-making.ASABE keywords: measurement; information systems; data collection; ISO standard; software

• Could include:
• Glossary
• ContextItems
• Representations
• OM codes
• ISO 19135 Governance
• ISO 25964

Helping machines and people agree on what things mean in production agriculture: AgGateway’s Glossary and Semantic Infrastructure

AgGateway is a nonprofit consortium of 240+ companies dedicated to advancing implementation of standards for interoperability in agriculture. Its work originally targeted supply chain processes, but was expanded in 2010 to include production agriculture field operations such as planting, crop protection, crop nutrition, irrigation, and harvest. An important deliverable of AgGateway’s field operations projects (SPADE, PAIL) has been to identify the data variables that support the growers’ field operations processes, document their meaning, and establish the various contexts in which they are used. It subsequently became necessary to create machine-readable variable type registries, controlled vocabularies, and other semantic resources to facilitate interoperability, thus enabling all participants in a data exchange process (or their software, rather) to unambiguously understand the meaning of the data being exchanged. These resources will be made available to users primarily through one or more application programming interfaces (APIs) for use by farm management information systems (FMIS) and other software.

There are so far three fundamental types of variable registries in this semantic infrastructure: a) The Representation System, which describes “universal” variables and their units of measure (e.g., yield as mass per unit area), derived from an internal representation system John Deere donated to the ADAPT project; b) The ContextItem System, which describes geopolitical-content-dependent variables (e.g., EPA numbers, FSA tract Ids) and complements the Representation system; c) Observation Codes, which are a specialized form of representation system built using the ContextItem system’s sophisticated architecture.

This infrastructure will require ongoing additions and maintenance, but the resources’ and APIs’ originating projects (e.g., SPADE, PAIL) have a finite duration. AgGateway’s Standards and Guidelines Committee consequently saw fit to create a Controlled Vocabularies Working Group (CVWG) to provide a permanent home for the semantic resources’ management processes. The overarching intent is to create resources that will be used extensively by the industry. An important precondition for that usage is transparency in governance; as a result, the CVWG derived its governance model from the ISO 19135 standard for register management.

AgGateway has also invested in a human-readable Glossary, hosted at www.agglossary.org and designed to bring together agricultural terminology from different sources (e.g., ASABE) as an educational resource and discussion-support tool. This resource includes a taxonomy for representing the origin and context of each term, and was initially hosted using the MediaWiki platform. The Glossary Team is now working toward upgrading the architecture of the glossary, seeking machine-readabiity and compatibility with the ISO 25964 data model, in pursuit of maximizing the value of the resource to contributors and users of the data, especially for concepts and terms that may be contained within it and not in international efforts such as GACS (the Global Agricultural Concept Scheme); e.g., legal terms and terms derived from ASABE (and other) standards.

ASABE keywords: information systems; software; semantics; infrastructure; API

Submitted.

Andres made first draft, sent to others.

At v4.

Enabling Interoperabiity in the Irrigation Sector: PAIL

Adhikari, Diganta; Andrus, Richard; Berger, Aaron; Berne, Dan; Charling, Kurtis; Ferreyra, R. Andres; Hillyer, Charles C.; Nef, Bart; Russo, Joseph

AgGateway's PAIL project emerged from an initiative by the Northwestern Energy Efficiency Alliance (NEEA) to optimize the use of energy (and consequently, water) in irrigation. It became clear that a major obstacle to the scalability of this pursuit was the lack of interoperability among the manufacturers of irrigation equipment, environmental sensors, farm management information systems (FMIS) and service providers. NEEA identified developing an industry-wide agreement on data standards as the first step needed to overcome that obstacle; PAIL was created for that purpose.

There have been three major phases in the PAIL project:

1) Requirements-gathering: this included capturing user stories, modeling irrigation processes, and capturing data requirements for exchange.

2) Alignment: Much work went into aligning PAIL's data requirements with the Core Documents model for field operations captured in the SPADE project (Plan, Observations & Measurements, Recommendation, Work Order, Work Record). A second line of alignment work included harmonizing with the ISO 19156 model for Observations and Measurements, in the context of a strong need expressed by some project participants to develop a compact schema that would minimize the transmission of redundant data, and seeking to enable bandwidth-limited data loggers to directly source data from the field. A third set of alignment activities was centered around harmonizing with the ADAPT Common Object Model.

3) Synthesis: Working PAIL data requirements back into ADAPT-compatible objects, and developing schemas to serialize the objects to XML, JSON, etc.

It was agreed between AgGateway and ASABE that PAIL deliverables would be presented to ASABE as a proposed national standard, and the X632 project was created for that purpose.. The proposed standard is divided into three parts: definition of the core concepts and data structures common to subsequent parts, pertaining to matters such as identification, time, space and data pedigree; Observations & Measurements, which represents the irrigation domain - specific implementation of ISO 19156 in an ADAPT-compatible context; and Operations, which represents the irrigation-specific implementation of the Core Documents model. This paper describes the three parts of the proposed standard, along with pertinent background information regarding the PAIL development process.

PAIL provides an information technology foundation for effective irrigation management.  The proposed standard will facilitate integration of disparate sources of irrigation data, and will enable a new generation of FMIS functionality that makes precision irrigation more practical, and thus more practiced.

KEYWORDS: irrigation, irrigation technology, precision irrigation, standards, information management. 

Andres intiated; got Charles feedback; now on v3.

Distributed to the rest of the group for input.

Application of the Precision Ag Irrigation Language (PAIL)

Over the past six years, a team of industry professionals and Extension researchers has developed a standard that supports data exchange among irrigation technologies.  The standard is currently in the process of balloting to become an ASABE standard.

The North Plains Groundwater Conservation District (NPGCD) in the Texas panhandle has funded development of an integrated irrigation management system.The motivation for this system stems from the disparate sources of information needed for precise management of irrigation.  The Texas panhandle region has critical water shortages because of declining aquifer levels.  Producers in the region have a reputation as progressive adopters of new technology when those technologies provide real benefit to their operations.  Irrigation management technologies maximize their benefit when used in concert with tools that focus on particular elements of the water management process.  Thusly, technology integration becomes an important of a management system.

The system is “integrated” in that it combines information from multiple sources into a single web application.  The PAIL standard is the key enabling element of this integrated system.  Each of the data sources (weather stations, soil moisture sensors, and pivot control system) sends or receives information in the PAIL format.  The development of NPGCD’s integrated system began in December of 2016 and is undergoing preliminary testing during the 2017 irrigation season.  In this paper, we present an overview of the PAIL standard and basic examples of PAIL’s core documents from each of the data sources used in the NPGCD project.  Additionally, we will present initial results from the development and application of the NPGCD’s system and observations relating to how the PAIL standard reduced cost and complexity for the system’s software.

Keywords: PAIL, irrigation management, data exchange, standards, systems integration

Charles intiated; got Andres feedback; now on v3.

Distributed to the rest of the group for input.

• Shannon
• Marty Matlock
• Brandon? 
• Andres*

• Andres
• Stuart*
• Simon / Nick?

Where did this data come from and how was it made? Introducing a provenance model into ADAPT

The agricultural industry is increasingly interconnected, and the volume of data being exchanged among growers and other actors is rapidly escalating.  It is also becoming critically important to document the provenance of the digital objects being exchanged; i.e., information about the entities, activities, and people involved in producing, delivering, or modifying a piece of data or thing. This provenance data can be used to form assessments about the object of interest’s quality, reliability or trustworthiness, and can be crucially important for mitigating liability.

The World-Wide Web Consortium (W3C) has published a provenance standard, PROV. This standard accommodates agent-centered provenance (e.g., Who created this data set? Who collected or tested this soil sample?), object-centered provenance (by tracing the origins of a document, or parts thereof, to other documents; e.g., What work order motivated this field operation? What set of field observations motivated this recommendation to irrigate?), and process-centered provenance (describing what steps were taken to generate a particular piece of information; e.g., what corrections have been applied to this yield dataset? What geometric and/or radiometric corrections have been applied to this drone-collected remote sensing dataset?)

AgGateway’s field operations projects (SPADE, PAIL, ADAPT) have produced a common object model of agricultural field operations that provides an opportunity for the industry to transcend long-standing interoperability limitations and exchange data in a common format. The existence of this platform provides a valuable opportunity to introduce a provenance framework that can be used to support traceability and principled decision-making in agriculture. This paper describes how the PROV model can be added to ADAPT, and provides specific examples of its application to some fundamental provenance use cases in field operations: documenting change in reference data and variable type registries;  documenting the lifecycle of machine configuration data; documenting relationships among core documents in field operations (i.e., Plan, Observations and Measurements, Recommendations, Work Orders, and Work Records); and supporting traceability.

Superseded by Semantic Infrastructure paper

Four ideas pitched to AgGateway-Europe for Agritechnica?

• CompoundIdentifier "basket of Ids"
• Reference data as an API-enabled distributed system
• Enabling principled decision-making: CP recommendation-centric case study

• Full, data-driven support for any geopolitical context