Once AgGateway Working Groups produce a tested and implementable message, tool, or standard, they are made available to the agriculture industry. The list below shows the most current and ongoing outputs that have emerged from the Working Group process, along with links for additional information.
If you have any questions, please reach out to Ben Craker, Portfolio Manager, at ben.craker@aggateway.org.
https://agw24.org/Resources/Data-Ethics-White-Paper
The rapid advancement of data-driven technologies in agriculture offers immense potential to enhance productivity, sustainability, and economic resilience. However, this increasing reliance on data also brings ethical, legal, and stewardship challenges that must be addressed to ensure responsible use and equitable benefits for all stakeholders. This document explores key principles of data ethics and stewardship within agriculture, emphasizing the importance of privacy, ownership, transparency, fairness, and responsible governance in agricultural data management.
Members Only: https://github.com/AgGateway/DatasetMetadata
The Dataset Metadata definition is AgGateway's implementation guideline (aka 'BIE profile') of OAGi's Dataset Metadata noun found in the connectSpec (OAGIS) Component Library. OAGi's Dataset Metadata was developed in collaboration with the Aerospace industry, AgGateway and the National Institute of Standards and Technology supporting their additive manufacturing research and advanced manufacturing data infrastructure and analytics programs.
https://github.com/aggateway/dealer-lookup
From time to time dealers need to determine the identifier of another dealer to support various supply chain operations, notably product transfers. Furthermore, they may need to know the whether the manufacturer regards the identified dealer as elligible to recieve product transfers. The following UML sequence diagram shown in the linked page describes the process of a pair of messages to support these requirements.
https://github.com/AgGateway/Dispensing
Several AgGateway member companies in North America that produce fertilizer blending equipment have developed a first version of a standard for dispensing work orders and work records. The end goal is to speed up data exchange between blending equipment and a company’s back office accounting processes. The work order specifies amounts and sources of inputs into a blending operation as well as the output locations. The group is working on refinements to the standard.
https://github.com/AgGateway/In-FieldProductID
This proof of concept provided the Azure resources to enable the transfer of unique identifiers from Farm Management Information Systems (FMIS) to in-cab displays. The core work flow is the item identifiers for products on a delivery document are transfered along with work orders/prescriptions to clearly document the inputs used in a field operation without relying on the operator of the maching to manually record the product names or IDs.
https://ModusStandard.org https://github.com/AgGateway/Modus
Current trends in sustainability, traceability, and compliance reporting demand that growers report ever-increasing amounts of data to justify their operations. Soil test data is the most commonly used data layer in digital agriculture and drives much agricultural decision-making. Modus provides a list of test methods for labratory analysis for soil, manure, plant tissue and other materials. The primary purpose of these lists is to clearly document the exact analysis method use to ensure results can be reliably compared.
No current links available due to current ISO stage for standard drafting/review. Will be for purchase through National Standards Body/ISO
Technology has come a long way in helping growers irrigate their land in smarter ways. Producers can invest in soil mapping, install various types of pumping plants and flow meters, use soil sensors, put a VSI (Variable Speed Irrigation) or VRI (Variable Rate Irrigation) system on center pivots, and tie these together through a few software applications. However, none of these tools actually talk effectively or efficiently to each other. This lack of integration remains a key barrier to the use of precision irrigation technologies. Precision irrigation relies upon the capture and analysis of multiple sets of data from various sensors and databases. The purpose of the PAIL (Precision Agriculture Irrigation Language) project is to enable this communication by providing a common set of data standards. These standards will represent weather, soil moisture, irrigation system and other relevant data, currently stored in a variety of Original Equipment Manufacturer (OEM) formats, in an industry-wide format that can be used by irrigation data analysis and prescription programs. The PAIL data standards exist to support integrated irrigation solutions by making it easier to request and receive relevant data from a variety of equipment, manufacturers and data servers.
Members only: https://github.com/AgGateway/ProductCatalog
Some cooperatives in North America are offering branded online store services to their retail members and customers. Crop protection and seed manufacturers wanted to ensure that such online stores have complete, accurate and up-to-date information in their product catalogs. To address this issue, an AgGateway team has completed version one of an API (Application Programming Interface) definition for the management of product catalog data. Several businesses are currently implementing catalogs using the API, which focuses on data for crop protection products. As a next step, AgGateway team participants plan to onboard additional crop protection members and expand the API to include seed and nutrition products. The API is an AgGateway members-only resource. AgGateway members can access it on AgGateway GitHub repository. Others interested can contact AgGateway Member Services at Member.Services@AgGateway.org learn more about AgGateway membership. (See press release here.)
Members only: https://github.com/AgGateway/ScaleTicket
With the growing interest in traceability and tracking carbon and other greenhouse gas footprints of commodity production, a more modern and standardized interface is required to share the data that traditional exists on a paper scale ticket among systems. Additionally the paper tickets often result in a time consuming, manual process to link the tickets to the farms and fields they came from and contracts the commodity was sold under. Farmers need this information to post-calibrate yield maps to drive accurate profit maps as well as reporting actual production for insurance or governmental requirements.
The same is true for managing inputs that are delivered to the farm. Knowing the quantity and quality of feed delivered is important for inventory management as well as tracking herd performance. Similarly bulk fertilizer or other inputs need to be tracked for inventory purposes but also for accurately allocating costs to different fields.
https://github.com/AgGateway/TraceabilityAPI
There is increasing demand for food traceability — from consumers wanting to know where their food comes from (GMO, organic, climate-smart) to input manufacturers wanting to know product effectiveness on-farm.
Existing traceability rules, driven by the FDA's Food Safety Modernization Act (FSMA), cover packaged goods from origin to retail, using Critical Tracking Events (CTE) and Key Data Elements (KDE). Commingled Raw Agricultural Commodities (RACs) like grains and milk are excluded from FSMA's Food Traceability List, seen as too complex to manage.
This OpenAPI specification implements CTE and TRU concepts with explicit naming to leverage code generation, simplifying implementation and helping farmers meet growing traceability demands.
Members only: https://aggateway.atlassian.net/wiki/x/A4CV1g
AgGateway projects often generate use cases, BPMN or sequence diagrams, data elements and definitions and other resources. A library of these artifacts I maintained in Confluence (aka the AgGateway Wiki) for use by members and subsequent project teams.
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