Agriculture relies on actual farm data to develop the industry. This raises the matter of how farm data should pooled.
The issue of farm data pooling relates to how data users and analytics combine to create incentives for data pools.
There are three stakeholder groups relying on farm data: farmers, service providers and policy makers. Farmers need real-time data to support analytics used for operational decisions. Farmers may collect this data themselves or it may be sourced from public or private service providers. Policy makers (both government and peak industry bodies) also use analytics (at a higher scale) to direct industry development programmes, regulation and education.
Analytics to benefit users
There are a range of situations where data pools are important in the development of analytics for the benefit of users:
- industry trends – used by policy makers, farmers and service providers to make market based decisions
- benchmarking – practices and technologies can be compared and copied to improve performance, in particular with heterogeneous groups or when a new technology or practice is available
- predictions – where large amount of data is needed to develop predictions (e.g. genetics and seasonal forecasting).
All three face challenges from the emergence of lower cost or more tailored analytics, greater deregulation and competition and increasing market-climate variability. These factors have reduced participation and support for public data pools (and possibly the emergence of new ones) but have also allowed private data pools to arise (e.g. Birchip and Climate Corporation).
Balancing the efficiencies
There is also the issue of balancing the efficiencies of larger multi-use data pools against actually capturing enough data and the ability to tailor value adding analytics at the right scale (subsidiarity principle). Or to put it another way:
- farmer + farmer data pools works if they don’t compete and they have the appropriate capability and incentive
- farmer + service provider data pools work if farmers can’t do it on their own and are willing to pay (through industry levies or service charges)
- farmer + policy data pools have slower feedback time frames and require multiple data sources
- policy + service provider data pools have private-public benefit trade-offs
- policy + farmers + service provider pools have many objectives that can be hard to reconcile.
Networked data pools
One way to think about how to approach these considerations is through the concept of networked data pools rather than a single data pool. A networked approach allows each pool to be structured so that it meets users’ needs, can be financed (fully or partly) by users and can manage public-private benefit trade-offs appropriately. Each pool should be based on the following principles:
- farmers own the data they provide
- data should be publically available including to other pools (in line with privacy considerations)
- collaboration/competition should focus on innovative analytical services
- over time pools can be combined where there are efficiencies.
To make this work there needs to be some sense of what the pools are and how they are performing. This can then form the basis of identifying gaps and opportunities at regional / industry / technology / national / international scales as well as looking at linking them through standards, governance and operational efficiencies (economies of scale).