Big Data Analytics in Agriculture: the Key to Unlocking the Potential of Field Trials
In the last decade, “big data” has taken over many facets of our life – from financial decisions to social media sites, music applications, R&D, and even agriculture. These large, complex sets of information and measurements allow agriculturists to improve decision-making on when to harvest crops, fertilizer application amounts and timing, and so much more.
With the world population expected to rise close to 10 billion by 2050, the FAO estimates that overall food production will need to increase by 70%. Agriculture big data analytics is one way to help unlock the potential of agriculture by improving yields and profitability for farmers.
The entire field of agriculture, from sowing the seeds to harvesting and then marketing conditions, has shown potential to be positively impacted by insights from big data analytics.
Big data analytics in agriculture
While farming has always been driven by data, the scale of it has increased exponentially over the last 200 years. The range of big data applications in agriculture is vast, as just about every aspect of agriculture has been touched by technology and data science.
Big data not only requires access to large datasets, but powerful enough systems to process these data, and the capacity to extract valuable insights. Big data can be characterized by the following 4 V’s: volume, velocity, variety, and veracity.
- Volume: The amount of data that is available is extremely large and continues to grow. Farmers can collect data manually on their cell phones or tablets using applications or automatically through machines and sensors on their farms, for example.
- Velocity: The speed at which data is collected, sorted, processed, and stored has increased immensely and can occur in real-time. This has only increased over the years and lends itself to be very useful for in-the-field evaluations.
- Variety: The types of data that are being collected range just as deep as the volume as it is. For example, farmers can use drones to image fields and estimate growth rates or disease, and sensors in the soil can help estimate the saturation level and specific fertilizer needs. Data can be captured for weather patterns, crop growth and productivity, economic trends, and soil measurements, among other things.
- Veracity: The accuracy of the data is very important to make meaningful conclusions from large datasets. Without high-quality data, the trustworthiness and value for scientific meaning are minimal.
Big datasets can be generated independently, or combined from multiple data streams. Being able to standardize these datasets is extremely important to ensure that each data point has the same format, is consistent (with what makes sense for what is being measured), and is labeled correctly.
This standardization, combined with the 4 V’s mentioned above, is what gives big data value. Focusing on efficient and streamlined processes for the generation and management of these datasets is a good investment now and in the future.
Unlocking the potential of big data from agronomic field trials
Big data applications in agriculture are especially helpful for stakeholders who are involved with agronomic field trials. Agronomic field trials are a way for farmers and researchers to evaluate how practices, products, and equipment will work in the desired cropping systems.
Agriculture big data analytics can provide a range of insights from field trials through the acquisition of data on crop growth, weather, topography, etc. These data are then used in statistical models or machine learning to help inform best practice decisions to increase yield or minimize risk or loss.
Different members of the agriculture ecosystem use the data for various evaluations, depending on the area that they’re interested in. The agriculture ecosystem is composed of agronomy, economics, natural resources, food science, and systems and technology to name a few.
Through collaboration, data can be collected once and shared with other members to be used for different evaluations. Some of the stakeholders that benefit from big data are agriculture input companies, food and beverage companies, farmers, and researchers.
- Agriculture Input Companies: Companies that focus on the development and production of seed, fertilizer, crop protection, and irrigation tools utilize agronomic field trials in the commercialization pipeline to evaluate the efficacy, safety, and marketability of new products.
- Food and Beverage Companies: These companies focus on increasing the quality and yield of their raw ingredients through crop nutrient planning and ensuring the crops are produced sustainably.
- Farmers for On-farm experiments: Farmers rely on agriculture big data analytics to increase their crop production and improve the sustainability of their production for long-term success.
- Researchers in Universities, NGOs, and Government Research Centers: Researchers across the board rely on agronomic field trials for hypothesis testing and experimenting with potential solutions to problems that farmers face on a local and global scale.
The research questions and experiments for these stakeholder groups focus on a variety of topics from drought-tolerant crops to pesticide application timing to crop rotations, but all require large amounts of data for statistical analyses.
With an increase in quality data comes an increase in “power,” or probability of a true effect (i.e., it’s not just random pure luck). The power of agriculture big data analytics is what allows people to parse out meaningful results to inform better agricultural decisions.
Agmatix and agriculture big data analytics solutions
For data-driven ag solutions, Agmatix has tools to assist with field-level decisions using a revolutionary platform that turns big data into powerful models and meaningful insights.
The Agronomic Trial Management system allows you to standardize collected data for comparison, evaluation, and data loss prevention. With a user-friendly interface that supports multiple devices, all of the agronomic field data is standardized through a set protocol. To match whatever observations or sampling you’re conducting, customized forms can be created and used for data preservation. This tool from Agmatix can assist farmers and researchers in agriculture big data analytics.
With the need for bigger yields in coming years, it’s essential that farmers harness bigger data. The Agronomic Trial Management system provides agronomy big data standardization to make field trial management easier and more accessible.