Photo: Lindi Botha
Why does Syngenta need farmers’ data?
To be clear, Syngenta doesn’t need farmers’ data. Farmers use tools developed by Syngenta, feed this data into these tools and get insights for their own use. All the data they feed in remains for their own insights.
Unless explicitly shared by the farmer with anonymisation, Syngenta doesn’t need this data, doesn’t use it for any other purposes, and doesn’t share it with anyone else.
However, when farming data is shared with the informed consent of farmers, it can benefit them by creating collective intelligence or tap into innovation and services from third parties.
For example, when data about a certain disease is shared, all farmers nearby could get an alert and even ways to avoid being affected. It is a give-and-take approach: the more people share, the more accurate insights you’ll get.
Syngenta has built tools and ML/AI [machine learning/artificial intelligence] algorithms that combine computational agronomy to offer decision support to the farmers.
These algorithms, when fed with macro/on-farm/micro data, give increasingly better and precise recommendations to farmers, such as variable rate application of inputs, site-specific crop management, and yield mapping.
By leveraging this data, Syngenta can help farmers optimise input use, reduce waste, and improve resource efficiency, leading to higher profitability and environmental sustainability. So, it is farmers’ data used for offering services and insights to farmers.
For example, in Argentina, through our Cropwise ecosystem, we provide seed product recommendations on what type of hybrids to plant and at what density. With climate change becoming prevalent, we are able to ingest weather data and run scenarios to help growers with decisions for dry and then normal cycles.
How would the data be used if it were shared?
Farmers are in control and they choose to share their data based on the value they get back. When they grant consent, the data is aggregated, anonymised, and then made available to the entity they chose to share with, which in turn provides additional products, services, and subsidies. These are some examples:
- Sharing with Syngenta to improve product development and innovation. Aggregated and anonymised farm data complements Syngenta’s research and development efforts, enabling us to develop innovative products, such as new seed varieties, crop-protection solutions, and digital tools tailored to specific farmer needs.
- Sharing with community to create a macro awareness of in-season threats. Anonymised data about in-season threats, such as insects and diseases spotted by other farmers, can help everyone take preventative action. This is enabled by all participating farmers sharing their data in the collective intelligence pool. Everyone then benefits from aggregated insights.
- Sharing with the regulators in exchange for benefits. Farmers can choose to share their on-farm data from the farm-management systems with regulators. This exchange is between the farmer and the entity they share the data with, and Syngenta has no access to the data.
- Sharing with other service providers or digital tools. Farmers almost always use services from multiple parties – it could include machinery from different manufactures, digital tools from different innovators, or even advisory services from different experts.
- Sharing their data with these parties can allow them to receive the innovation, advice or services and, once again, this exchange is directly between the farmer and the third party, with Syngenta only acting as a facilitator of the data exchange. We want to bring down any walled gardens that prevent a farmer from sharing their data with third parties like these.
Anonymised data about in-season threats, such as insects and diseases spotted by other farmers, can help everyone take preventative action.
Generally, people, not just farmers, have concerns about sharing their data. How are you addressing these concerns to ensure an enabling environment for data sharing?
We’re educating our customers and farmers about this mindset. However, we also recognise their concerns and take them seriously. We have implemented various measures to ensure an enabling environment for responsible and secure data sharing.
Key tenets of our data manifesto include transparency and consent, data privacy and security, and data governance and stewardship.
Syngenta engages with farmer advisory boards and industry associations to gather feedback, address concerns, and co-create data-sharing practices that benefit farmers while respecting their privacy and autonomy.
Syngenta’s data-management practices are regularly audited by independent third-party organisations to ensure compliance with industry standards and regulations, providing an additional layer of accountability and trust.
What guidelines should farmers have in place to ensure that data sharing is always beneficial to them and the industry at large?
In Syngenta, we strictly follow the Digital Agriculture Association’s Guiding Principles. We recommend farmers reference them when needed.
On a practical level, farmers should clearly understand their rights and ownership over the data they generate and review data-sharing agreements carefully to ensure that their rights are protected and that they maintain control over how their data is used and shared.
Data-sharing partners must be thoroughly evaluated to assess the partner’s data privacy and security practices, reputation, and track record in responsible data stewardship. It is important to have a clear idea of the data farmers are willing to share, with whom, and for what purposes.
Make sure to have some mechanisms in place to revoke access or terminate agreements if data is misused or mishandled.
Farmers should advocate for transparency and accountability from data-sharing partners, demanding clear communication about data practices, regular audits, and mechanisms for addressing concerns or grievances.
Is the technology already there to collect and analyse this data and make the kind of recommendations that will take the industry forward?
The technology for collecting and analysing farm data is rapidly evolving, and while significant progress has been made, there is still room for further advancements to fully leverage the insights and drive the industry forward. In many cases, it ultimately comes down to cost and scalability of the data-collection technologies.
Current technologies in use include precision agriculture tools – sensors and cameras installed on tractors or drones, farm-management software that allows for manual record-keeping and integration with machinery, Internet of Things, and sensors like weather stations, soil sensors, insects traps and disease spore sensors.
Then there are data analytics and machine learning, digital platforms and marketplaces that allow technology to store the data and securely make it available to algorithms.
Technologies in the pipeline or on our wish list include advanced sensing and imaging, such as hyperspectral imaging, infrared sensors, and other advanced sensing technologies to gather more detailed and comprehensive data on crop health, nutrient status, and stress factors.
We require more work on AI and deep learning to unlock new insights from complex and unstructured data, such as images, videos, and sensor data streams.
Currently under development are improved large language models to help farmers understand the data in their native languages and lower the barrier of tech-savviness needed for the farmers.
With the advent of 5G and edge computing, real-time data processing and decision-making could be brought closer to the field.
What key insights do you hope to gain and what problems do you aim to solve through mass data collection?
- Yield optimisation. Yield insights are valuable for farmers, but macro-level yield data can also help policymakers, regulators and other companies like insurance and fintech providers to fine-tune their offers for growers.
- Pest and disease management. Syngenta is offering the world’s first commercial digital solution to diagnose infestations of plant-parasitic nematodes in soya bean crops by analysing photographs taken from satellites.
- Water management and sustainable farming practices. With the Cropwise sustainability app, farmers can compare themselves to industry averages and identify opportunities to improve using the Sustainable Outcomes in Agriculture Standard.
- Supply chain optimisation.
Data on crop production, logistics, market demand, and pricing can be leveraged to optimise supply chain operations, reduce waste, and improve profitability for farmers and other stakeholders.
Is the current operating environment conducive to creating enabling cycles for this kind of technology?
The current legislative environment and public perceptions present opportunities and challenges for data-driven technologies in agriculture. While regulations aim to protect data privacy and rights, they can also create complexities.
Some companies cite the data protection regulations to create walled gardens. The European Data Governance Act is a good example of regulation that promotes data sharing.
The other key issue is a lack of standardisation that makes it difficult to connect systems or interpret data that is shared. Multilateral initiatives like the International Organization for Standardization and AgGateway are examples where companies and academic institutions as well as government agencies are coming together to define data standards and reduce the friction for data sharing.
Public concerns around data ownership, privacy, and ethical implications need to be addressed through transparent governance frameworks, education, and inclusive policies.
Collaborative efforts involving all stakeholders are crucial to foster trust, ensure equitable access, and create an enabling environment. For example, in China, strawberries are tracked from farm to supermarket.
Consumers can scan barcodes on the packaging and know exactly where the strawberries were grown, when were they harvested, and track logistics movements to know when were they made available to the supermarket (how fresh they are)
It is important to create incentives for farmers that are linked to data sharing. For example, carbon marketplaces or sustainably grown commodities that get premiums are good examples of such incentives.
For a farmer to monetise carbon credits or sustainability practices, they would need to share data with third parties such as verification agencies and with value chain companies. Where there are incentives, actions will follow.
Actions that would enable technology include establishing multi-stakeholder governance frameworks that enable compliant and safe data sharing, building public trust and transparency in data-driven technologies, investing in educational programmes and capacity building initiatives to increase digital literacy, data management skills and awareness of the benefits and risks of data-driven technologies.
We also need to promote standardisation of data and integration protocols to remove technical barriers for data sharing.
We need to look at the entire ecosystem around the farmer, which includes retailers and distributors, value chain companies, input providers, machinery manufacturers, fintech providers, marketplaces and regulators and policymakers.
Each of these players has an opportunity to promote and create enabling frameworks for data sharing that will ultimately create a self-sustaining data sharing virtuous cycle.
Contact Feroz Sheikh by emailing Michelle Ng at [email protected].
