Isaac Newton, one of the most influential scientists in history, once said “If I have seen further, it is by standing on the shoulders of giants.”
This holds true in agriculture and agritech analytics. Through collaboration and open-access data, scientists are able to continue building on the achievements made before them to better our world. In agriculture, building on these achievements allows scientists and researchers to address critical problems with data – including food scarcity, feeding a growing population, reducing agricultural greenhouse gasses, and adapting to a changing climate.
Big data has a lot of power in agriculture. Big data can be viewed as the combination of technology and analytics that can collect and compile novel data and then process it to support decision-making. Big data can provide farmers with information on factors that impact crop yields, such as soil moisture or weather changes. However, big data requires standardization, harmonization, and analysis to be valuable – and it takes a lot of resources to accomplish this due to the massive size and complexity of big data.
Alongside big data comes the need for FAIR data. The FAIR Guiding Principles were published in 2016 to address scientific data management and stewardship. FAIR stands for Findability, Accessibility, Interoperability, and Reuse of digital assets, according to the FAIR principles. They call out that data and metadata should be easy to find for humans and computers, and the finder should know how to access the data. Then, the data needs to be integrated with other data, including workflows and applications for storage and analysis. And ultimately, FAIR promotes the reuse of data, requiring data to be well-described for replication or combination in alternate settings.
For agriculture research, big data is the fuel that can be successfully used when the FAIR data principles are in play. Ag data analytics is a powerful tool to solve major challenges in the world, but only when data is interoperable and reusable. This can be a challenge given the size and complexity of agricultural big data. Luckily, Agmatix, an agro-informatics company, is focused on turning big data into powerful insights.
How Agmatix’s Axiom Platform Can Help
When using unfamiliar data for modeling efforts or meta-analysis, it is all about findability, accessibility, and interoperability – the F, A, and I of the FAIR principles. For researchers embarking on the agritech analytics journey, it is essential to get acquainted with the data producers, time frames, relevant metadata, and relevant methods and protocols. This information is the basis for finding and accessing the right data.
Agmatix’s Growing Universal Agronomic Data Standard (GUARDS) protocol is based on the FAIR data principles. It uses ontologies and hierarchy definitions to classify data components. This reflects findability and interoperability. Datasets are automatically screened and categorized to create direct links between metadata, as well as independent and dependent parameters.
By using the Insights module, data scientists access a user-friendly interface to visualize big datasets, descriptive statistics, and graphic content visualization. They can access standardized datasets without the time consumption and effort of individual dataset standardization. It’s even possible to share notions and conclusions between users. There are several statistical tests available for data analysis, and users can create a knowledge graph based on the ontologies and analyzed data. Ultimately, the Insights module supports data scientists in accessing diverse datasets.
Agmatix’s Axiom platform is different from other ag data analytics platforms and agronomic analysis tools. Agmatix is the first company to apply data standardization according to the FAIR principles over agronomic legacy data and ongoing trial data collection processes. The Axiom database is the first multi-disciplinary agronomic database that allows meta-analysis alongside modeling and data sharing. Sophisticated data validations and integrity tests are performed to ensure the highest data quality according to the FAIR standards. In the heart of Axiom, an ontology codebook is used to understand and define relations between data points.
Case Study: Corn Prediction Model Using Axiom Platform
Big and FAIR data through the Axiom platform was the basis for a recent corn yield prediction model. The model unified data from many sources across various production environments in the U.S.
Data included farm management and experimental data from collaborators and open repositories of publicly available data. Common parameters were identified between these datasets. They included total Nitrogen applied, previous crop, soil texture, soil organic matter, and irrigation data. The base data was enriched with weather data, including precipitation and temperature at specific corn stages. The key factors in any model will change as inputs change.
In this corn prediction model example, using unified data from multiple data sources, corn yield can be estimated at an accuracy of 16 bushels per acre – and higher if looking specifically at a single region.
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Agmatix Enables Collaboration
Agmatix drives agronomic innovation by moving research and experimental data into real-life action. The power of data is core to Agmatix’s beliefs, driving innovation in data access and decision support through agronomic analysis tools. Agmatix encourages and supports the implementation of the FAIR principles in a few different ways.
The GUARDS protocol which governs data standardization is based on the FAIR data principles. Of particular focus are the F and I in FAIR: the representation of the metadata (Findability) and ontology allocation (Interoperability).
The INSIGHTS and MODELS platform completes the FAIR acronym. It allows access to diverse datasets (Accessibility) and sharing between projects and users (Reusability).
Agmatix’s TMP platform for planning and executing new and ongoing trials preserves the data according to the GUARDS protocol on the go. This platform unlocks the power of agronomic trials and big data.
Agmatix is dedicated to growing data for impact – a fertile ground, rife with possibilities, when with big data and the FAIR principles.
