2026 | Project 7 | Agricultural intelligence and freshwater management at regional scale
What becomes possible when information about farms, land, water and regulation is connected?
The Canterbury Freshwater Project demonstrates how trusted farm identity, authoritative public information, geospatial analysis and artificial intelligence can be combined to support freshwater management and agricultural intelligence across Canterbury, New Zealand.
Canterbury contains thousands of farming enterprises operating across extensive landholdings, water resource consents, irrigation systems and diverse environmental conditions.
Understanding the relationships between farming enterprises, land parcels, water allocations, agricultural activities and regulatory obligations is essential for effective freshwater management, agricultural productivity, regulatory assessment and long-term resilience.
Building on earlier collaborative research, Trust Alliance New Zealand (TANZ) members are developing connected agricultural information infrastructure capable of supporting analysis across individual farms, catchments, districts and regions.
Agricultural and environmental information is maintained across multiple organisations and information systems.
Regional councils hold resource consents, water allocations and regulatory conditions. Land information systems maintain cadastral parcels and ownership records. Other organisations hold information about agricultural activities, soils, irrigation infrastructure, environmental conditions and farming enterprises.
Although substantial amounts of this information are publicly available, the relationships between these records are not consistently established.
A single farming enterprise may operate across several land parcels, hold multiple resource consents, draw water from different sources and undertake activities subject to different regulatory requirements.
Traditional databases generally describe individual records without establishing the wider operational and environmental context of a farming enterprise.
This fragmentation creates duplication, increases administrative costs and makes it difficult for farmers, regulators and other organisations to obtain a consolidated understanding of agricultural activity and freshwater obligations.
The Canterbury Freshwater Project applies digital farm identity, authoritative public datasets, geospatial analysis, knowledge graphs and AI-assisted information processing to establish connected information about farming enterprises and their freshwater operating environments.
The project investigates how existing information can be systematically linked across:
By connecting these records to identifiable farming enterprises, the project enables agricultural information to be examined in its operational, geographical and regulatory context.
A central component of the project is the development of a knowledge graph that represents relationships between farms, land, water, activities, regulatory requirements and associated organisations.
Rather than treating a resource consent, land parcel or farming enterprise as an isolated record, the knowledge graph connects them through explicit relationships.
This allows information to be analysed across different geographical and operational levels, including individual farms, catchments, irrigation schemes, districts and the wider Canterbury region.
AI-assisted document processing is used to interpret information contained in regulatory records and help establish relationships that would otherwise require extensive manual investigation.
The resulting infrastructure provides a reusable foundation for agricultural intelligence and the development of specialised analytical services.
The project builds on the farm registry established through the earlier Digital Farm Wallet research, which identified 1,204 farming enterprises in Selwyn District associated with active resource consents by December 2025.
During 2026, the approach was extended across additional Canterbury districts, connecting thousands of farming enterprises with land, consent, water and environmental information.
This work demonstrates how existing authoritative records can be used to establish connected farm identities and regulatory information at regional scale, without requiring every farming enterprise to recreate information already held by public agencies.
The expanding knowledge graph provides a foundation for examining freshwater allocations, regulatory obligations, environmental pressures and agricultural activities across Canterbury.
Canterbury_Freshwater_Data
Establishing relationships between farming enterprises and their operating environments enables information to be analysed in context.
For example, the connected information can support investigation of:
These analytical capabilities can help farmers, industry organisations, financial institutions and public agencies better understand agricultural and freshwater conditions.
The project also provides a foundation for AI agents capable of investigating questions, interpreting relationships and supporting decisions using connected agricultural information.
Connecting agricultural information requires more than technical interoperability. It also requires clear arrangements for identity, authority, information access and governance.
The project builds on TANZ research into decentralised identity, verifiable credentials, permissioned information sharing and agricultural data interoperability.
The objective is to enable authoritative information to remain under the control of its originating organisations while supporting secure and appropriately authorised access to connected information and analytical services.
This federated approach can support the information rights of individuals and enterprises, protect data sovereignty and provide a governance foundation for agricultural AI systems.
AI agents and other digital services must operate within appropriate authorisation, privacy, regulatory and organisational requirements.
The Canterbury Freshwater Project demonstrates the potential to move beyond the digitisation of individual agricultural records towards connected, reusable agricultural intelligence.
It illustrates how common methods can be applied across large numbers of farming enterprises rather than requiring each organisation to develop separate information systems and repeat the collection of the same information.
The work brings together capabilities investigated through earlier TANZ projects, including:
Freshwater management requires reliable information about the relationships between agricultural activities, water resources, environmental conditions and regulatory obligations.
By connecting existing authoritative information to identifiable farming enterprises, the Canterbury Freshwater Project provides a foundation for reducing duplicated data collection, improving regulatory efficiency and enabling more informed agricultural and environmental decisions.
The same approach has potential applications in agricultural finance, environmental risk assessment, sustainability reporting, insurance, supply-chain transparency and market access.
Importantly, the project demonstrates how agricultural digital infrastructure can support both existing information systems and emerging AI-driven services.
TANZ and its members are progressing towards agricultural digital infrastructure capable of supporting both people and intelligent software agents.
This infrastructure must establish trusted identities, connect authoritative information across organisations, interpret agricultural context and govern how information is accessed and used.
Specialised AI agents can then use connected agricultural information to investigate freshwater, environmental, regulatory and operational questions within appropriate governance and authorisation arrangements.
The objective is not to replace existing authoritative systems, but to enable information from those systems to be used together more effectively.
The Canterbury Freshwater Project represents an important stage in the progression from digital farm environment planning and digital identity research towards scalable agricultural intelligence and agentic AI services.
Trust Alliance New Zealand facilitates collaborative research, development and innovation across the agricultural sector.
TANZ does not retain ownership of intellectual property developed through its projects. Intellectual property is either made available through open-source arrangements or remains with participating TANZ members, subject to applicable project agreements.
TANZ’s role is to support collaboration, promote interoperability and contribute to the governance frameworks required for trusted agricultural digital infrastructure.
TANZ does not retain ownership of intellectual property developed through this project. Project intellectual property is either made available under open-source arrangements or remains the property of participating TANZ members, subject to the relevant project agreements.
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