“Software is Feeding the World” is a weekly newsletter about technology trends for Food/AgTech leaders.

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Interoperability is often on people’s minds when it comes to agriculture data. I have written about it over the past three years, and it is time to do a refresher again.

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Potential problems with interoperability in agriculture data

Interoperability in agriculture data refers to the ability of different agricultural systems and software to exchange, understand, and use data with each other. The lack of interoperability in agriculture data can cause several problems.

• Data fragmentation: Without interoperability, data generated from different sources such as farm machinery, weather sensors, soil sensors, and crop models may be stored in different formats or siloed in different systems, leading to data fragmentation. This fragmentation makes it challenging to integrate data and generate insights that could drive value creation.
• Data duplication and inconsistency: When data is not interoperable, it may be duplicated or stored in different formats, leading to inconsistencies and errors in data analysis. This can lead to decisions based on incomplete or inaccurate data, which can impact value creation and value capture.
• Limited data sharing and collaboration: The lack of interoperability in agriculture data can limit data sharing and collaboration between different stakeholders, such as farmers, agribusinesses, and researchers. This can lead to missed opportunities for innovation, efficiency gains, and improved outcomes for farmers and consumers.
• Limited innovation: The lack of interoperability can limit innovation in the agriculture industry. When data is not easily shareable or accessible, it can be challenging for stakeholders to develop and implement new solutions that drive value creation and capture.
• Increased costs: Without interoperability, it may be more challenging to integrate and manage data across different systems, leading to increased costs for stakeholders in the agriculture industry. This can limit investment in data-driven solutions that drive value creation and capture.

Causes of lack of interoperability in agriculture data

There are many reasons which lead to lack of interoperability in agriculture data. The following four are the most salient from a technology standpoint.

1. Data silos: Agriculture data is often stored in proprietary formats or siloed in different systems, making it difficult to access and use for different purposes. This leads to data duplication, inconsistency, and inefficiency.
2. Incompatible data standards: Agriculture data is generated from various sources such as farm machinery, weather sensors, soil sensors, and crop models. These data sources often use different data standards and formats, which makes it challenging to integrate them into a common data platform. There is a lack of standardization in agriculture data, which makes it difficult to compare data from different sources, regions, or time periods. This limits the ability to make informed decisions based on data and hinders innovation in agriculture.
3. Limited data sharing: Farmers, agricultural researchers, and other stakeholders have limited opportunities to share data with each other due to data privacy concerns, intellectual property rights, and regulatory restrictions. This limits the potential benefits of data-driven agriculture and slows down innovation.
4. Complex data integration: Agriculture data integration requires expertise in various fields such as data management, data analysis, and data visualization. This complexity increases with the number of data sources and the size of data sets, which can make it challenging for farmers and other stakeholders to manage and use the data.

Addressing these problems requires a concerted effort from stakeholders in the agriculture industry to develop and adopt common data standards, open data sharing policies, and interoperable data systems.

Let us dive a bit deeper into these problems.

1. Data Silos

Data silos are a real problem in agriculture when it comes to interoperability. Agriculture data is often generated from different sources such as farm machinery, weather sensors, soil sensors, and crop models, and it is frequently stored in proprietary formats or siloed in different systems.

As a result, it is challenging to access and use this data across different systems, which limits the potential benefits of data-driven agriculture.

Breaking down data silos can be difficult for several reasons:

• Proprietary data formats: Agriculture data is often stored in proprietary formats that are specific to particular systems, making it difficult to access and use the data across different systems. This can be challenging to address because different companies or organizations may have vested interests in maintaining their proprietary formats.
• Data privacy concerns: Agriculture data often contains sensitive information such as crop yields, soil quality, and irrigation practices. Farmers and other stakeholders may be reluctant to share this data due to privacy concerns, which can create data silos and limit data interoperability.
• Regulatory restrictions: Regulations can add friction when it comes to sharing data in agriculture in several ways. Some regulations restrict data sharing between different organizations, which can create data silos. For example, regulations such as the General Data Protection Regulation (GDPR) in Europe require that personal data be collected, processed, and stored securely and with informed consent from the data subject. These regulations can create challenges for sharing data that contains personal information, such as farmer's names or contact information.
• Limited technical capacity: Some stakeholders in agriculture may not have the technical capacity or expertise to manage and integrate data across different systems. This can make it challenging to break down data silos and promote data interoperability.

To address data silos in agriculture, it is essential to establish common data standards, open data sharing policies, and interoperable data systems. This requires a concerted effort from stakeholders in the agriculture industry, including farmers, researchers, policymakers, and technology companies.

2. Incompatible data formats

The prevalence of proprietary and incompatible data formats in agriculture can be attributed to several factors:

• Competition among technology providers: The agriculture technology market is highly competitive, and companies often differentiate themselves based on the data they collect and the services they provide. Proprietary data formats can be seen as a way for companies to protect their intellectual property and gain a competitive advantage.
• Rapidly evolving technology: Agriculture technology is rapidly evolving, and companies may have different approaches to data collection and analysis. Proprietary data formats can be seen as a way to maintain flexibility and adapt to changing technology and market conditions.

The prevalence of proprietary data formats can create challenges for the agriculture industry. It can limit data interoperability and prevent stakeholders from accessing and using data effectively.

There are several companies and organizations that are working to solve the problem of interoperability in agriculture data, especially working on from a technical standpoint.

1. ​AgGateway: AgGateway is a non-profit organization that develops and promotes data standards and guidelines for agriculture. They work with a broad range of stakeholders in the agriculture industry, including farmers, retailers, manufacturers, and technology providers, to promote data interoperability and collaboration.
2. ​The Open Ag Data Alliance (OADA): The OADA is an industry-led initiative that aims to create a common platform for sharing and accessing data in agriculture. They develop open-source software tools and data standards that enable farmers and other stakeholders to share data across different systems and platforms.
3. Ag Data Coalition: The Ag Data Coalition is a non-profit organization that promotes data ownership, security, and portability in agriculture. They provide a framework for farmers to control their data, including the ability to access, share, and delete their data at any time.

3. Limited Data Sharing

One of the key reasons for limited data sharing in agriculture is a zero-sum game mentality prevalent within agriculture. There are many things for tech to learn from agriculture (resilience, humility, patience among other things) though I have found the increase-the-pie mentality more prevalent in tech. This leads to many data privacy concerns (and they are valid, especially when it creates information asymmetries which put farmers and other actors at a disadvantage)

Data privacy is a critical concern in agriculture, as data generated by farming operations may contain sensitive information such as crop yields, soil quality, and irrigation practices. Some of the issues around data privacy can be addressed with some of the following steps.

• Develop clear data ownership policies: Stakeholders should develop clear policies around data ownership and usage rights. Farmers should retain ownership of their data and be informed about how their data will be used and who will have access to it.
• Use data anonymization techniques: Data anonymization techniques can be used to remove personal identifying information from datasets, while still preserving their utility for analysis.
• Implement data security measures: Robust data security measures, such as encryption and access controls, should be put in place to protect data from unauthorized access or use.
• Adopt industry-wide data standards: Industry-wide data standards can promote data interoperability and collaboration, while establishing common guidelines for data privacy and security.
• Promote transparency: Transparency is critical to building trust among stakeholders. Data users should be transparent about how data will be used, who will have access to it, and how it will be protected.
• Provide education and training: Stakeholders should provide education and training programs to promote data literacy and enable stakeholders to use data more effectively while understanding data privacy and security best practices.
• Work with regulatory frameworks: Regulations and frameworks such as GDPR can provide a framework for data privacy and data protection that can be followed by agriculture data stakeholders.

4. Complex data integration

Organizations often lack the skill sets and the resources to execute on complex data integrations between two systems. They have to understand the different formats, dig deep into the different use cases, have the experience and technology skills to execute on complex data integrations, and have the resources to actually do it. Data integration is often not considered a core strategic skill by most ag and Agtech companies, as they want to focus on their strengths..

It has created a space for companies like Telus Agriculture’s AgIntegrated, and Leaf Agriculture. Other services available include Google Earth Engine, Data Manager for Agriculture from Microsoft and Bayer, OneSoil for integration with basic remote sensing based data layers, and companies like Geopard which make it easier to use their tools to do data integration, and data analysis.

It is fairly tricky, time consuming, and often on the margins for most large agribusinesses from their priorities standpoint. (Though I disagree with their prioritization). It often makes sense to outsource the work to a 3rd party player, who is dedicated to solving the problem.

What do you think?

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About me

My name is Rhishi Pethe. I lead the product management and technology delivery teams at Mineral, an Alphabet company. The views expressed in this newsletter are my personal opinions.