The terms Decision Support Tools (DST), or Decision Support Systems (DSS), refer to a wide range of computer-based tools (simulation models, and/or techniques and methods) developed to support decision analysis and participatory processes. A DSS consists of a database and different tools coupled with models, and it is provided with a dedicated interface in order to be directly and more easily accessible by non-specialists (e.g.that is, decision- makers). DSS have specific simulation and prediction capabilities andbut are also used as a vehicle of communication, training, and experimentation. Principally, DSS can facilitate dialogue and exchange of information, thus providing insights to non-experts and supporting them in the exploration of management and policy options.
Digital Certification
Certifications can be used to demonstrate green provenance, net-zero and biosecurity claims to enable value creation and market access across supply chains. Most certifications require the creation of a written plan, recordkeeping, and on-site inspections to verify compliance. Managing records and written plans for certification digitally can streamline the recordkeeping process for producers, allowing them to use data across a range of certifications and export data in the format needed for any particular certification. Digital and technical infrastructure can enable secure exchange, management, and organization of data across a range of certification needs, including organic, grass-fed, regenerative, food safety, animal welfare, etc.
Digital Certification standards
Certifications can be used to demonstrate green provenance, net-zero and biosecurity claims to enable value creation and market access across supply chains. Standards for certifications are to be met by producers to achieve a particular certification. In this case, the data necessary to meet those standards is tracked digitally.
Ecosystem Service Markets
Ecosystem services are the many and varied benefits that humans freely gain from the natural environment and from properly-functioning ecosystems such as air and water quality, habitat, esthetics, and recreation. A marketplace quantifies and creates markets based on the change over time in those services.
Electronic Authorization (E-Auth)
The process of establishing confidence in user identities electronically presented to an information system. An example of electric authorization could include verifying the identity of a computer system user.
Environmental Asset Claims
Environmental assets are defined as naturally occurring living and non-living entities of the Earth, together comprising the bio-physical environment, that jointly deliver ecosystem services to the benefit of current and future generation. An environmental asset claim refers to the process of an individual submitting documentation that demonstrates environmental benefit as it relates to that environmental asset, ensuring proper MMRV using tools acceptable to various methodologies.
Environmental Claims Clearinghouse
A clearinghouse of environmental claims enables the flexible development of new and diverse environmental claim assets classes while providing a trusted methodology for claim identification and assurance of uniqueness. An ECC enables claims searches by boundary, claimant, duration and type and a common format to enable registered claims to avoid conflicts related to additionality or double counting.
Environmental Product Declarations (EPD)
An Environmental Product Declaration (EPD) is a comprehensive, internationally harmonized report created by a product manufacturer that documents the ways in which a product, throughout its lifecycle, affects the environment. This report quantifies environmental information on the life cycle of that product to enable comparisons between products fulfilling the same function.
ex-ante power analysis
The calculations used to estimate the smallest sample size needed for an experiment or research question, given a required significance level, statistical power, and effect size. This ensures adequate sampling densities and that data collected has sufficient power to detect changes over time.
FAIR
Guidelines for ‘good data management,’ which improve the discovery and (re)use of scholarly data by humans and computers (e.g., machine learning, algorithms). These principles include:
– Findable: Data (or any digital object), metadata (i.e., information about that digital object), and infrastructure (e.g., data registered or indexed in a searchable resource) should be easy to find for both humans and computers
– Accessible: Once found, there should be clear means of accessing data, metadata, or infrastructure of interest
– Interoperable: Data should interoperate with applications or workflows for analysis, storage, and processing
– Reusable: Metadata and data should be well-described so that they can be (re)used, replicated, or combined in different settings.