Farming and Its Tech Future

Farming and Its Tech Future

Amy A* is 24, a Millennial in every sense of the word, a graduate of the Community College of Cantril Iowa, and she is a farmer. Yes, you read that right. She is a 24 year old farmer in a country where there are 2.1 million farms, 88% are family owned and the average age of a farmer is 58.5 yrs. Industry consolidation had suggested that Amy, who had grown up on the farm her father ran, was not going to become a farmer. But she ended up at Cantril College and realized that the proliferation of technology across other heavy asset-based industries presented an opportunity for her family to maintain ownership of the farm and land.

Amy realized technology was going to be the savior of her family’s well-being and way of life…

Data and image courtesy of USDA.

Downward Trends

Issues came to a head for Amy’s family when the convergence of several negative factors left them with very few options as:

USDA Long-term Cash Income projections 2016

Amy’s father, one of the 97% of smallholder farmers running family owned farms in the US, had worked on his 5,000 acre plot for as long as Amy could remember. He replaced his equipment every 5 years, with an 8yr replacement cycle for the heavier machinery. Squeezed on cash and running out of options something drastic had to happen…

Gross cash farm income (GCFI) includes income from cash receipts, farm-related income, and government farm program payments. GCFI is forecast at $357 billion (inflation-adjusted 2009 dollars) in 2016, versus $277 billion in 2000, with the increase largely due to higher cash receipts.

Convergence of Technologies

That was 2 years ago. Now, Amy runs the farm with the same number of employees while seeing yields double and profits triple as a result of 50% reduction in equipment expenses. Amy convinced her family to invest in a combination of hardware and software that, combined, has become known as precision farming.

While not new?—?Farmers have used GPS soil and yield mapping tools to increase yield for the last 6 years and have used variable-rate input applications (VRT) to increase planting efficiency for even longer?—?precision farming methods have benefited from the technological advances in self driving tractors, drones and big data software. All these enabled the ability to draw insights from the data gathered through the sensors embedded in said tractors and drones. While GIS is the highest revenue generating market segment in precision farming technologies, it is the 3rd element (Big Data and Machine Learning for predictions) that has finally brought exponential benefits to the smallholder farmer.

A convergence has created a modern day farmer out of Amy.

Amy, and the millions of US smallholder farmers, now live in a world where a drone can capture information on the soil quality on (say) 40 acres of a farm and relay that information to the Big Data/Machine Learning platform in the cloud in real-time. The analytics platform, in real-time?—?combines the drone data with 50 years of weather, yield, crop DNA sequence and market price information and makes a determination of the fertilizer type and nutrient infusions that is required. This information, including the consequent soil fertility as a result of the fertilization, is provided to Amy, through her mobile phone, and she taps or swipes to initiate self-driving fertilizer dispensers to that particular area of the 40 acres of the farm that needs it most. Dispensing starts even before the drone has landed in its dock. Yield saved. Revenue assured.

RIPPA (Robot for Intelligent Perception and Precision Application) Image courtesy Engineers Australia

Amy’s father was skeptical of the benefits the new technology would bring. The life of a smallholder farmer can be turned upside down in a year where the harvest is lower than expected, but he’s come around to the value that precision farming enables. Never has the industry had the benefit of solutions founded on technical insights to help farmers make real-time decisions with agronomic precision. Amy has found the most success in the augmentation of her team; without having to increase the number of farm employees, she has been able to outsource the work of yield prediction to a company like Descartes Lab which uses Machine Learning algorithms that learn from 4 petabytes of satellite imaging data to let Amy know what yield she should expect across the whole farm. The data, combined with market analytics, ensures that Amy will never lose visibility into how much she stands to make from her farm at anytime. All in real-time. The benefits of this extend into her ability to get loans from banks and defer non-essential capital expenses. Consequently, the stability that comes from precise predictability of both revenue, funds and expenses provides her something her father never had in his days running the farm; peace of mind.

Image courtesy of Drone Deploy.

ROI Concerns

Questions remain about the Return on Investment (ROI) on the hardware elements of Precision farming equipment but there is no doubt as to the value of the software. Where precision farming equipment is applied to precision weed removal (for e.g.) it is still hard to justify the cost of the equipment to the smallholder farmer. The self driving thresher (image below) would cost a farmer over a million dollars, an impossible expense to justify.

Autonomous Concept Thresher by CashIH

To counter the issues of hardware expenses, companies are developing business models that provide the hardware on a subscription basis and the farmer pays a subscription for just the software and analytics. This ensures that the real value, obtained through the big data software, does not get lost in the final estimation. This business model takes the work of managing equipment out of the hands of Amy/the farmer and leaves them focused on their area of expertise and the one thing they always wanted to do; Farm.

*Amy is fictional. The technology is not.

Originally published here.

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