Picture an acre of farmland. About the size of a football field, with rich topsoil and a waterway streaking across it. It's winter, and there's a stillness about it. Much work is yet to come before that acre gets put to work come spring planting.
It's easy to envision what comes next: maybe a soil finisher or strip-till bar to prepare the seedbed. A planter, dropping seeds precisely in the trench. A farmer, checking for uniform emergence. A side dress bar, applying nutrients. A drone, looking from high up for any disease pressure. And eventually, a harvester - reaping what was sown.
It's not as easy to picture what's happening all season long as that crop grows. Each pass and every check is collecting data. That data is generated all year by farmers, agronomists, or sensors on machinery, and it's a powerful tool.
Whether that acre was a research plot, an on-farm experiment, a field test, or a production ground, the data that was collected is useful in many ways for the farmer and others in the industry. It can help determine if a product is effective and marketable; it can show whether a production practice improved yield; it can show if the risk was minimized.
But this is only possible if data isn't siloed and can be shared. Data without standardization is like a field without a crop - not reaching its true potential.
Standardization for Data Utility
Standardizing data means converting it into a common format for processing, analyzing, and sharing. This process enables collaborators to work together efficiently because the data is "apples to apples." When everyone has access to the dataset and can use it in its entirety, it's possible to address agronomic knowledge gaps efficiently. That's the process of agronomic data sharing.
Through this collaboration, it's possible to validate agronomic practices to ensure they deliver the intended result. Ultimately, this helps promote practices that increase efficiency or productivity year over year.
The supporting data helps tell the story of the value of that production practice or product, especially to farmers - who need to know how much risk their business takes on by adopting it. Having return on investment numbers or information on efficacy helps farmers to understand and decide if that product or practice is right for them.
Data Separation Challenges
Data silos or fragments are a result of data being collected separately, without standards, or coming from multiple sources. This happens a lot in agriculture - where data from multiple sites or multiple sources is compared. Think about our acre of land; data is being collected manually by the farmer, but also automatically by sensors and a drone. This can also happen when multiple people are collecting data but using different protocols or standard operating procedures.
This creates data silos, which prevent collaboration that enhances the value of the data. Fragmented data forces researchers and farmers to spend valuable time trying to tie the datasets together into a singular structure - trying to make it standard.
For farmers, especially in season when every hour and day count, this really chips away at the value of the data and makes it less likely to be used when making a decision. Even if users are willing to standardize the data, the process can waste time and resources. In industry and research, standardization can make validation of data challenging - if not impossible - for researchers and peer reviewers.
It's easy for this separation and fragmentation to occur. Without agronomic data standardization, everything from naming systems to protocols to data collection methods presents an opportunity for separation to occur as data collection gets out of sync.
Sharing Agronomic Data: A Slam-Dunk
Sharing agronomic data has benefits for farmers, agronomists, researchers, and companies alike. A common language enables collaboration across the industry, unlocking innovation. An agronomic database can support data sharing, while data standardization enables collaboration.
Innovation
Agriculture has always been an industry built on innovation. From the self-scouring plow to the green revolution, innovation has changed the way farmers feed the world for centuries. In today's world, technological innovation is changing how farmers make decisions, raise their crops, and impact the environment.
A perfect example is Agmatix, an agro informatics company that develops data-driven solutions for Ag professionals worldwide. With Agmatix's cutting-edge platform that uses agronomy data science and advanced AI technology, agronomic data is converted into actionable insights at field level. With this revolutionary approach the lack of data standardization can be solved to dramatically increase crop yield, quality, and promote sustainable agriculture.
An agronomic database allows the brightest minds in agriculture to view the same data and put their heads together to solve problems with it. From each professional in the industry - farmer, agronomist, researcher - comes a unique perspective and objective. Together, maximum context and expertise on the ground truth and science come together. Add in data in a common language and the sky is the limit for innovation.
Digital precision ag technologies are just one example of data-driven innovation. With digital precision ag technology, farmers can use data to precisely apply nutrients right where the crop was planted or adjust seeding populations or nutrient rates at different places across the field. Ultimately, they have the potential to improve crop productivity while reducing environmental impact.
Agronomic data companies, researchers, and farmers need access to the foundational data to expose trends, correlation, and causation. They must be able to ingest and use the data. This mandates a singular, standard format rather than data that's siloed or fragmented.
Training and calibrating models are also possible using agronomic data. These models drive innovation in agriculture in many ways. Modeling helps with understanding crop, environmental, climate, and production practice complexities. Through modeling, tools can be developed to recommend improved pest and disease control protocols or recommend the best crop variety for a specific site.
Visibility
Data can provide a different perspective on a situation. While farmers often have decades-long, ground-up perspectives, data can supplement this and support decision-making. Agronomic data sharing helps teach people about agronomy and crop production. It can also be used at high levels to advocate for a data-based decision with leadership entities.
More and more, global government and industry leaders are becoming concerned with food security, soil health, and environmental sustainability. Data can help researchers, agronomists, and farmers identify sustainable practices even as the climate changes the weather and environment the crops are grown in. This will be critical for managing food security throughout the next several decades.
Agronomic data standardization can also provide a new view of information to farmers that previously may not have had access to or use for it. Smallholder farmers around the world may be able to analyze the benefits of production practices that were previously difficult to consider. Empowered with an agronomic database, they can adopt practices like proper fertilizer application or different tillage practices.
With data, it's possible to provide data-based recommendations on crop nutrition to support yield and profit while taking human, soil, and environmental health into consideration. This data is the key to unlocking value and sustainability at the farm level.
Market penetration
Farmers collect a lot of data on the farm, and have the ability to manage who has visibility to it. Collaboration with companies is often beneficial because it allows a product or practice to be evaluated at scale, across differing environments.
Farmers may feel that collected data is more valuable to companies than it is when used strictly on-farm. A best practice for companies that collaborate with farmers is ensuring they understand their rights and have access to relevant data. This ensures insights will have good market penetration.
Consultants may collaborate with farmers for agronomic data. This supports clients that are developing new products like fertilizers and bio-stimulants.
Sharing and standardizing for sustainability
Advancements in the agriculture industry will be driven by data. As agriculture is challenged with a growing population to feed and a changing climate that's shifting the crop production environment, these advancements are needed more than ever. Thankfully, efficient innovation driven by data is possible.
This starts with unlocking data's true potential through standardization. When data is no longer siloed and fragmented, companies, researchers, and farmers can use this data to inform critical decisions and improve our world.
Author Bio
Ron Baruchi is the president and CEO of Agmatix. With over 20 years of experience in the technology sphere, Ron has taken this experience to the agricultural sector. Passionate about using data to solve complex problems, he has used his expertise in technology with Agmatix to improve crop yields and quality while limiting environmental impact.
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