A Global Data Ecosystem for Agriculture and Food
Agriculture would benefit hugely from a common data ecosystem. Produced and used by diverse stakeholders,from smallholders to multinational conglomerates, a shared global data space would help build the infrastructures that will propel the industry forward.
In light of growing concern that there was no single entity that could make the industry-wide change needed to acquire and manage the necessary data, this paper was commissioned by Syngenta with GODAN’s assistance to catalyse consensus around what form a global data ecosystem might take, how it could bring value
to key players, what cultural changes might be needed to make it a reality and finally what technology might be needed to support it.
This paper looks at the challenges and principles that must be addressed in in building a global data ecosystem for agriculture. These begin with building incentives and trust – amongst both data providers and consumers – in sharing, opening and using data. Key to achieving this will be developing a broad awareness of, and making efforts to improve, data quality, provenance, timeliness and accessibility. We set out the key global standards and data publishing principles that can be followed in supporting this, including the ‘Five stars of open data’ and the ‘FAIR principles’ and offer several recommendations for stakeholders in the industry to follow.
- Finding business models that provide incentives for various entities to collect and share data. If these models provide business value directly to the data providers, the quality of the collected data will be higher.
- Leading by example by providing open data sources. Syngenta has already done this by publishing data about the results of its Good Growth Plan.
- Encouraging data standards that make it easier to produce and share data. In doing so, stakeholders will need to have reasonable expectations of how these standards will be used.
- Automating data collection. Automatically collected data is more likely to be accurate and precise than data collected by hand.
- Annotating datasets. Even automatically collected data cannot be used if it is not described in a consistent and understandable way.
- Following data sharing principles. The five-star maturity model and the FAIR principles provide guidelines for creating and sharing data.
- Using the data. All of the best data sharing efforts have little impact if the data is not used in a
- productive way. Stakeholders must encourage a cottage industry of data-backed apps that get the most value from datasets.
In response to the paper, four key organisations engaged in developing data infrastructures.