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GL: Aglytix has been in the Agricultural Digital Ag business since 2011. The original company, Superior Edge, was re-branded Aglytix to better encapsulate the ideas of incorporating analytics into Agriculture. It is based in Mankato, Minnesota, pretty much in the heart of the American corn belt. It was founded by Jerry Johnson; a Minnesota farmer turned farm equipment and implement dealer turned software developer.
GL: Aglytix was founded to solve the key issue of how to assure growth in the Agriculture business, knowing that over the next two generations, the world will need to double the amount of food it grows. So, a singular focus on developing solutions for improved yield and reduced inputs application and cost was the driving force in the development of Aglytix’ portfolio of tools that we call Solvers.
After the healthcare business, there is probably no other industry that generates as much data as the ag business. Agronomic data, spatial data, multispectral imagery, equipment data, meteorological data and many other data layers are available for a given singular square meter of land. What to do with this information?
To determine why a particular crop yields a specific amount at harvest is a multidimensional challenge to which we, at Aglytix, have applied our best thinking. By looking at multidimensional causation, we are narrowing in on how to improve yield levels.
We address several basic issues. How does stand influence final yields? How does tillage affect stand? How does equipment performance influence residue spread during tillage? How does implement selection influence equipment performance?
GL: Well, Agriculture is not going away, but it will be changing!! With a need to double output over the next 20 plus years, Agriculture has a positive future.
Prediction #1: The drive towards reducing or eliminating the use of fossil fuel powered vehicles will have a disruptive effect on farming within the next generation.
By 2030 many countries in Europe will have banned fossil fuel cars and while the USA has still to develop a national strategy, some states such as California, have already started to think of fossil-free transportation.
Currently, over 40% of all USA corn produced is destined for ethanol production. So, the potential to significantly change the agricultural landscape is certainly there.
The average Iowa cornfield has the potential to deliver more than 15 million calories per acre each year. This is enough to sustain 14 people per acre, with a 3,000 calorie-per-day diet. But since so much corn is destined for ethanol production or animal production, we end up with around 3 million calories of food per acre per year, mainly as dairy and meat products, enough to sustain only three people per acre.
That is lower than the average delivery of food calories per acre from farms in Bangladesh, Egypt and Vietnam.
Prediction #2: Consumer demand will drive reduced inputs and improved food quality resulting in increased demand at the field level for analytics that will pinpoint where and how much nutrients, fertilizer, pesticide, fungicide, and herbicide will be applied to the crop. Farmers will need to become certified and adhere to input management targets to supply the larger food processing companies and supply chain transparency will drive in-field analytics for food traceability.
GL: Trends in the agricultural business are hard to see. It is a business where the “proof of concept” timeline is between 9 to 12 months. In the technology business, this is an eternity. In an era where test- fail – pivot- test – succeed is based on speed of execution and validation, many companies cannot survive such an extended validation process.
Additionally, Ag technology solutions are simply too “hard” to gain adoption. Unless we can make it really easy, digital ag will remain a nice idea but with limited adoption.
The farmer does not need a big easy button. The farmer needs NO BUTTON. Technology needs to be unseen and seamless.
Technologies that can help achieve this are:
1. AI and machine learning
A perfect application for these technologies that are now trending in this space. Like the healthcare business, AI is admirably suited to take vast datasets and extract focused decision ready outcomes that are based on solid science. We see AI as the key differentiator going forward for agricultural analytics
2. Machine translation
Like the tower of Babel, agriculture is full of machines speaking foreign tongues. Each manufacturer has felt the need to make their equipment “proprietary”. The emergence of translation software is a much-needed trend that will reap benefits going forward.
3. Geospatial analytics
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