Agricultural Data Management: 5 Benefits for Farmers
On today’s farms, data is generated every day, and that involves utilising satellite data, equipment sensors, Internet of Things (IoT) applications, and even the traditional practice of taking handwritten notes. These different types of data have various qualities which determine value for actors on the agrifood value chain. The process of analysing this amount of data is not easy, but proper agricultural data management could enable farmers to make strategic decisions.
Why Farmers Encounter Data Management Challenges?
With ongoing technological advancements, data volumes are rapidly expanding. Research from Statista predicts that the world is set to generate over 180 zettabytes of data by 2025. Data is collected from diverse sources and tools, as mentioned earlier. Each of these tools utilises different formats and terminology to convey information. It resembles receiving weather updates in one language, soil information in another, and crop details in yet another.
To make this data work together seamlessly, an agricultural data management system uses a tool that understands the unique languages of each data source, similar to a translator. It translates weather reports, soil conditions, and crop information into a common language. Moreover, this tool also notices patterns in how weather changes affect crop yield or how certain soil conditions relate to plant growth. It can be compared to having an agricultural assistant who not only translates but also facilitates a profound comprehension of the data.
There are a lot of advantages to using data management such as making predictions that allow farmers to distinguish their product and target premium markets. Still, there is a lot of resistance to adopting data management platforms in agriculture.
In a 2023 study conducted by McKinsey & Company addressing the adoption dilemma for farmers, it was found that farmers might be reluctant to embrace new technologies for various reasons, including:
- Financial uncertainty and risk in investing in new technologies
- Insufficient local support and lack of guidance for successfully utilising data management systems.
- Concerns about data privacy
- Lack of education and training programs tailored to the needs of farmers
- Limited time for learning new software and systems
Furthermore, a discomfort about learning new technologies can deter them from adopting data management systems. One of the main challenges in adopting a data management system in agriculture is lack of infrastructure and technical skills needed to operate complex software programs.
Five Benefits of Implementing Data Management
Automated data processing
Greater reliability
By leveraging data management systems, businesses can enhance the reliability of their data through the reduction of redundancies and the prevention of data tampering. Additionally, Data Management System (DMS) unifies data flows across the organisation, lowering the risk of data tampering, ensuring data consistency, and providing teams with dependable and current information to respond promptly to market changes and customer needs.
Data security and privacy
Data management is crucial for ensuring the security and privacy of sensitive information. Data management software safeguards organisations against data breaches, theft, and losses, employing features like tokenization and encryption. By utilising a DMS, organisations can customise data access levels for employees, providing an extra layer of security and ensuring compliance with data regulations such as General Data Protection Regulation (GDPR). The significance of data privacy becomes even more apparent considering that in 2023, enforcementtracker.com reported approximately €2.1 billion in fines imposed in the EU due to GDPR violations.
Increased data scalability
Scalability is crucial in managing the ever-growing volumes of data that organisations generate and process. Data scalability refers to the ability of a system or software to handle an increasing amount of data without compromising performance or functionality.
Data management software streamlines handling large volumes of data for organisations, automatically adapting to new tools or upgrades. Automating workflows not only saves time and money but also eliminates the need for manual repetitive activities, such as monthly crop yield reports. With automation, systems can efficiently collect, analyse, and generate reports from various sources, reducing the direct involvement of farmers.
Readiness of data assets
Enhancing Agricultural Data Management in STELAR
In the realm of practical data management implementation, an increasing number of projects are working on this topic. When it comes to agriculture, the STELAR project is spearheading the mission. STELAR has the objective to make agrifood data easier to use. This will be done by transforming raw data lakes to knowledge lakes. That will lead to a number of improvements. The team behind STELAR will design data value chains fit for today’s needs and challenges related to data discovery, integration, interoperability, and annotation across various stages of the food supply chain.
Data from different sources and applications have various structures and formats. STELAR is developing a Knowledge Lake Management System (KLMS) that will allow easier connection and understanding of this diverse data by using a tool called GeoTriples to automatically create connections between different types of data.
The KLMS also focuses on ensuring that this process is not only accurate but also works well with large amounts of information.
How STELAR KLMS Enables User-friendly DMS?
In addition to its analytical capabilities, the KLMS puts significant emphasis on design to ensure an optimal user experience (UX). The goal is to make the farmer’s interaction with the tool effortless and intuitive, providing them with a seamless experience as they engage with the data.
To make things simpler for everyday users, STELAR’s agricultural data management platform provides explanations for every decision made, along with recommended settings tailored to the available data. To further facilitate the exploration of its content, the system will be equipped with visualisation tools. This enables users to verify, confirm, and modify data connections. Additionally, the system ensures the persistence of these connections, even if the original data changes.
Furthermore, multiple search functionalities will be provided in order to accommodate different user needs as well as users with different levels of technical and domain expertise. Expert users will be able to pose structured queries (SPARQL), while for novice users it is enabled using keywords or filtering things by specific categories. Sophisticated tools capable of comprehending the user’s intentions will be incorporated, even in instances where the precise terminology is not employed.
Conclusion
In the context of the STELAR project, a three-year initiative under Horizon Europe, significant emphasis is placed not only on the development of the KLMS but also on enhancing User Experience (UX) in Data Management. By working on intuitive search functions for easy access, the team behind STELAR is striving to create a world accommodating to all those who take part in the agrifood value chain
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