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I grew up in upstate New York, not far from New York City. That hometown is less of an upstate community these days. It is more of a suburb, bleeding into the iconic metroplex. It is quite different than the town I recall and most identify with from my childhood.
I decided to study agronomy at Iowa State, what I like to call the Harvard of agriculture. I graduated in the mid-1990s, a time when precision agriculture was taking the industry by storm. Handheld GPS units changed field surveying techniques, and lightbar herbicide sprayers guided systems followed soon thereafter. We also saw tremendous improvements in seed genetics and herbicide formulations. This progress brought us to today, a time where auto-steer machinery can plant and combine fields. It was an amazing transformation, and marked big strides for innovation in the agricultural space.
Technology remains at the forefront of agriculture. However, the use case changed. Rather than simplifying tasks, agriculture asks how to make better decisions using technology. These decisions rely on a simple denominator - data.
The incorporation of this data is in its infancy. We are scratching the surface of machine learning algorithms and remote sensing systems - tool that will revolutionize how we approach decisions in agriculture. My company, Agrograph, focuses on field-level data solutions that embrace this change. We set the stage for systems that direct what, how and when machines operate in the field using data and such technology. We see a future where technology allows a farmer to plant, plow, spray and harvest, crops automatically not from the seat of a tractor, but from anywhere in the entire globe.
The Agrograph platform combines field observations weather forecasts, soil data, satellite imagery and historic performance for companies. It's the foundation of what we offer. Meanwhile, our tools create solutions. No, we are not a satellite company. No, we are not a precision agricultural company. We are different - a resource that looks at the big picture and the small picture. We are a company that provides answers. We focus on benchmarking data and managing risk on a granular level, ensuring folks making billion dollar decisions can benchmark data and manage risk.
Our latest focus is a tool to assess risk of farms. We think of it as the credit score of agriculture. This idea came from a desire to make sense of information in new ways. I saw other data-reliant industries that were more mature than agriculture as examples of what we can achieve. Consider the finance industry that developed credit scores in the 1950s to standardize the lending process for banks. With credit scores, lenders made objective investments in financially secure individuals and businesses using a standardized process. Such formula, like today's FICOⓇ score, it used with in-house methods from lending institutions to determining creditworthiness.
Traditional assessments of risk for agriculture leverage credit scores as well as softer data to form a ‘management score’, something that has not changed over the years. This softer data considers farm quality and an individual's relationship with a lender or insurance agent. It is difficult to benchmark the softer data objectively, but we realized we can.
To achieve this, the Agrograph credit score approaches data that informs these decisions. This spans from historic field production, crop diversity, yield history, weather patterns, soil types -- all data that is readily available. This information is standardized for a management score. It's part of an overall drive to be more competitive lenders, to expand portfolios, and operate with confidence. With our tools, we can see a GEICO of ag insurance or Capital One for ag banks that can approve farmers instantly online and provide quotes within minutes.
We are also focused on automation. Agrograph technology can be lifted to assess various decisions in the field like readiness for planting or harvest, an area we already began exploring. Machinery companies can lift this decision-making technology into tractors to automate tasks in the field, while the marketing department in the same company can leverage the technology to guide geographic campaigns and customer-driven sales. Even grain merchandisers and ethanol plants can use the platform to set basis prices and better manage their supply chain. This is just the tip of the iceberg.
You may have caught on that our solutions do not focus on the front end of the value chain. Farmers already have a number of tools and resources competing for market share. I wanted to focus on the back end, developing solutions for the industries that support farmers. This included segments such as crop insurance, financial lending institutions, service providers, and even grain merchandising companies.
We are not naive. This data we leverage has been around for a long time, and, frankly, simply applying it to risk management is not a novel idea. However, simplifying the application of this data through aggregation allows for better visualization and assessment, the first stride toward providing better solutions for the industry. By training our program with the data we know, machine learning takes hold and paints a complete picture for every field across the globe in real-time. We believe in agriculture, and our solutions ensure its longevity by allowing companies to better support farmers.
We are leading the way in data in agriculture - something I find very exciting. From building the credit score of agriculture, to benchmarking crop yield, land valuation, and historical volatility, we guide data-driven decisions that accurately match price to risk. Businesses can scale across millions of acres and multiple states -- scalability that couldn’t be achieved today without sending hundreds of workers to manually survey fields and digitize their observations.