CONVENING AWARDS

CONVENING AWARDS

Data collection, sharing, and use at the farm level

Agricultural Data Coalition
in partnership with Crop IMS and Ag Technologies Greenpoint Ag

This project aims to address privacy concerns among stakeholders, by assisting agricultural producers, context data vendors, and data consumers in navigating their way toward the exchange and trade of anonymized data collected from producer

fields. Outcomes include a quantitative assessment of producer data privacy protection risks and evaluation of potential data value after anonymization. Additionally, the critical issue of producer trust will be addressed by offering education on the risks and benefits associated with data collection and exchange, and introducing anonymization of standardized context enriched data as a tool to mitigate both producer privacy and business risks. As a multitrack initiative, the project is poised to significantly enhance data literacy and technology education among producers.

Coalescing Robust Data Access for U.S. Specialty Crop Producers

University of Nebraska-Lincoln in partnership with Clemson University and the National Food MarketMaker Program

Enabling Cyberinfrastructure: the OATS Data Frameworks

Purdue University and members of the OATS Center

This project aims to provide exposure for the NAPDC to diverse communities, access and education to enable the use of Trellis and OADA frameworks, and input on questions of national scope for an agricultural producers data framework. Expected outputs include: 1) results of a survey to the OATS community regarding a national agricultural producers data framework, 2) presentations to the OATS community around features, considerations, and solutions for a national agriculture producers data framework, and 3) exposure for the NAPDC among many potential agricultural industrial partners.

This project aims to document the barriers to developing and utilizing data frameworks from the perspectives of crop, livestock, and aquaculture producers of Virginia. The team will focus on challenges pertinent to three critical framework components: (1) cyberinfrastructure, (2) data analytics and decision tools, and (3) education, training and workforce development for commercial commodities produced in VA. Expected outputs include (1) summarized surveys for a diverse range of cropping systems, livestock, and shellfish production on current data usage and management at producer level, (2) analyzed survey insights on current status, challenges, and needs pertinent to data framework components in the form of reports and publications, (3) documented breakout session reports with key highlights on future action steps to accomplish the identified framework components, (4) detailed answers and recommendations to each critical question outlined by NAPDC, (5) summarized use cases for the inter and intra-domain data frameworks, and (6) de-identified datasets from the producers

Nave Analytics Inc and South Dakota State University

Data Anonymization: promoting producer data exchange

This project aims to develop a national data and modeling framework that will enable agricultural producers (particularly dairy farmers) to optimize profitability and sustainability through data-driven informed decisions and management. The team will adapt the existing proof-of-concept DairyBrain Agricultural Data Hub (AgDH) to a scalable open-source implementation using the RuFaS framework to enable gathering of data from a number of dairies. This framework will facilitate precision on-farm management by leveraging advanced data capture, collection, and analysis techniques. Expected outcomes include producing influential design documents in collaboration with a diverse group of stakeholders, transitioning DairyBrain's data server to a cloud-based infrastructure, and showcasing implementation of DairyBrain<>RuFaS Digital Twin in Wisconsin dairy operations.

FAIR in Practice toolchain to support data collection and management

Our Sci LLC in collaboration with OpenTEAM, Pasa Sustainable Ag, Fibershed, and Iowa State University

FAIR in Practice: Connecting Values to Reality for Managing Farm Record Keeping Data

This project aims to identify common needs and features for implementing the “FAIR in Practice” toolchain by leveraging multi-organizational partners, including partners where producers are directly involved. All software in the toolchain is open source with the goal to create new interfaces for the farmer across agroecosystem types, and includes features on farm onboarding and management, farm data intake and storage, interoperable farm data formats, farm data aggregation and permissions, and front-end applications. Outcomes from this project will include a gap analysis that addresses the data system needs determined through the needs assessment created by collaborating partners.

RuFaS: uniting dairy farmers with a data & modeling framework

University of Wisconsin-Madison & Cornell University

This project aims to identify and document the information needs and gaps in the specialty crops sector, with an aim to develop a data framework and a standardized data collection process to improve strategic decision making across the food chain. Expected outputs include an overall project report, a set of elements based on our work to be included in a more comprehensive strategic plan for the larger project, and a commitment to continue to engage with the overall project effort based upon mutual interests.

This project aims to develop open-source tools and integrate them into user-friendly online platforms, thereby democratizing geospatial data science for agricultural producers. Outcomes include making code open source and preparing and delivering educational materials at multiple training workshops during professional meetings. Integrating the developed open-source tools into the Data to Science Engine (D2SE) framework will enable users to easily access and use tools without installing complicated software on their machines. This will benefit agricultural producers by making publicly available geospatial data products FAIR (Findable, Accessible, Interoperable, and Reproducible).

Data to Science Engine: Democratizing geospatial data science for agricultural producers

Purdue University & University of Tennessee

This project aims to advance data exchange in the agricultural industry by developing an open-source plugin to link the existing ADAPT framework to a new ADAPT standard. This will be a key resource for data interoperability within the precision agriculture industry by facilitating cloud-to-cloud data transfer from on-farm equipment that will enhance the technical capabilities of producers. Development of an ADAPT Standard Plugin will help support data transfer from the thousands of machines in use today that generate data in proprietary formats with every pass across the field.

ADAPT: enabling efficient field operations data exchange

Ag Gateway

This project aims to improve soil moisture monitoring by advancing gamma-ray sensing and cosmic-ray neutron sensing (CRNS) techniques. Given that the gamma-ray sensor (sub-field scale) and CRNS (field scale) monitor soil moisture at complementary scales based upon similar theory, CRNS developments can provide a framework for future gamma-ray sensor research, with hydrological applications, including irrigation management at lower soil depths. Outcomes from this research will impact the development trajectory of gamma-ray soil moisture sensing and the global availability of scale-appropriate technology to improve agricultural water use.

University of Nebraska-Lincoln (Dr. Trenton Franz)

Investigating proximal gamma-ray sensor for soil moisture monitoring

Developing local infrastructure for beef genomics as proof-of-principle for national engagement

University of Nebraska-Lincoln (Dr. Matthew Spangler)

This project aims to develop infrastructure to facilitate the routine genotyping, phenotypic data recording, and curation of data and tissue samples of all beef animals in the UNL system. Animal genetics are a strategic resource that underpins livestock sector production and profitability, making access to data and information sharing crucial for supporting the livestock industry and the research community. Development of data support tools is intended to facilitate the routine genotyping, phenotypic data recording, and curation of data and tissue samples of beef cattle populations in the UNL system. Outcomes from this research include building and maintaining a database of “routine” phenotypes and array (100K SNP) genotypes. Continued efforts are working to ensure the sustainability/maintainability of the infrastructure to prioritize, facilitate and guide collection and information gathering, and serve as a key proof-of-principle that may scale to engagements among ARS, industry, and university collaborators.