This means that we need to step up our crop production significantly to cater to the growing number of people. Unfortunately, rapid urbanization and climate changes have claimed a major share of farmlands. In the United States alone, there has been a dip in the total area of farmlands from 913 million acres in 2014 to 899 million acres in 2018.
Today there is an urgent need to produce more food for the growing population - with less land to grow it on. In this article, let’s take a closer look into how big data and agtech (or agricultural technology) can help tackle this challenge.
Agriculture technology is a fascinating subject, but when you're living it it's probably a little more tedious. Capturing data, transferring data and interpreting the information are challenges the industry faces for the future.
Last week during the John Deere launch event, John Fulton, extension specialist, Auburn University, was on hand to talk about precision technology and and where the industry is headed. He noted that farmers are striving for efficiency and accuracy; input stewardship and managing data. And he made an observation that our colleagues at Corn+Soybean Digest have noted in the past too - we're close to managing information about each plant in the field.
"On the academic and research side we're looking at individual plants and we're learning a lot," he says. For example, how is a corn seed planted, oriented, placed at the proper depth and how does it singulate from the row unit.
Today it's possible on the research level, and more likely at the farm level, to look at individual seed performance. The key is the value of that information to the farmer, but with rising seed costs maximizing every seed that goes into the ground is important.
Major drivers of agricultural big data – implications for suppliers
As part of his talk Fulton gave some interesting stats that we sometimes overlook - just how much information are we gathering on a per-plant basis each year? Turns out if you gather up soil type, location, inputs applied, growing conditions, irrigation water used and weather, you could be gathering about .85 kilobytes per plant. Doesn't sound like much but it adds up to about 26 megabytes per acre per year.
That's a lot of data to collect, manage and preserve. And that gives rise to the notion that the future of agriculture is Big Data. It's from that collected information that crop modeling software can look at all those factors and help you make big decisions. Fulton notes it's that kind of data that can show "how tire impressions impact plant performance," he notes.
Where can this big data go? Part of the challenge is managing larger equipment and understanding just how those tools are working. For example, a Deere DB-120 planter is often viewed as a large planter, but Fulton says in the future that should be thought of as "48 planters on a single toolbar," he notes.
Changing your perspective on data capture and management starts at the machine, but the idea of managing individual row units as "planters" will challenge your thinking. It's something to ponder as you consider all that data you're collecting.
Top 4 use cases for big data on the farm
The scope for big data applications is large, and we’ve only just begun to explore the tip of the iceberg. The ability to track physical items, collect real-time data, and forecast scenarios can be a real game changer in farming practices. Let’s take a look at a few use cases where big data can make a difference.
1. Feeding a growing population
This is one of the key challenges that even governments are putting their heads together to solve. One way to achieve this is to increase the yield from existing farmlands.
Big data provides farmers granular data on rainfall patterns, water cycles, fertilizer requirements, and more. This enables them to make smart decisions, such as what crops to plant for better profitability and when to harvest. The right decisions ultimately improve farm yields.
2. Using pesticides ethically
Administration of pesticides has been a contentious issue due to its side effects on the ecosystem. Big data allows farmers to manage this better by recommending what pesticides to apply, when, and by how much.
By monitoring it closely, farmers can adhere to government regulations and avoid overuse of chemicals in food production. Moreover, this leads to increased profitability because crops don’t get destroyed by weeds and insects.
3. Optimizing farm equipment
Companies like John Deere have integrated sensors in their farming equipment and deployed big data applications that will help better manage their fleet. For large farms, this level of monitoring can be a lifesaver as it lets users know of tractor availability, service due dates, and fuel refill alerts. In essence, this optimizes usage and ensure the long-term health of farm equipment.
4. Managing supply chain issues
McKinsey reports that a third of food produced for human consumption is lost or wasted every year. A devastating fact since the industry struggles to bridge the gap between supply and demand. To address this, food delivery cycles from producer to the market need to be reduced. Big data can help achieve supply chain efficiencies by tracking and optimizing delivery truck routes.
