Putting big data to work
Among the topics talked about at exhibits at the 2014 Farm Progress Show at Boone will be a new term, “big data.” What is big data for agriculture? It’s a catch phrase describing the gathering and analysis of the vast amount of digital information generated by farmers.
Data gathering has been the main idea behind precision farming since the mid-1990s. But a recent surge in interest in that data is being driven by the strengthening of data infrastructure. The cost of data storage and handling has declined dramatically. Smartphones now have more processing power than personal computers did a decade ago. And companies want to help farmers harness their data and use it to increase yields and make better input management decisions.
Logically, the more information you have, the more likely you will be able to figure out a better way to do things. But that information needs to be analyzed and interpreted. Even more data will be generated in the near future as unmanned aerial vehicles, or UAVs, start flying over fields recording high-resolution images and field sensors provide real-time information on crop conditions, nutrient use, etc. Data generation has become more than just yield monitors on combines.
The leading application for big data is prescription planting: merging soil, climate and seed data with farmers’ production records for each field. For example, Monsanto’s FieldScripts services, available this year in Iowa, Illinois, Indiana and Minnesota, combines soil and weather data with its own seed data to recommend which hybrid grows best in which field, under what management conditions. The charge for FieldScripts is $10 per acre.
Data-driven precision ag
In 2012, Monsanto bought Precision Planting, which has a system to load planters with data that enables a farmer to plant a field with different varieties at different populations, and vary those according to conditions across the field.
Likewise, DuPont Pioneer’s suite of data and technology services, called Encirca, aims to help farmers more efficiently use seed, fertilizer and other inputs. Encirca Yield is the newest tool in the Encirca services offering. It joins the Encirca View platform launched earlier this year. Users can upgrade to a fee-based Encirca View Premium for field-by-field insight.
Matt Darr, an Iowa State University ag engineering professor, is ISU’s expert on big data. When asked about big data’s return on investment, he advises farmers not to get hung up on returns expressed in dollars or bushels per acre, which vary year to year, depending most notably on weather. He advises taking the longer view. “You have to use the data over time to make it work for you,” says Darr.
Over two-thirds of every dollar spent in agriculture is spent on decisions focused on seed selection, fertility and land access, he notes. “Producers annually compile new information around input selections, farming practices and risk management to implement an improved production plan each year. Recently, advances in data availability, improved climate modeling and new technologies including high-resolution crop imagery have enabled new industries to emerge around the concept of big data, aimed at helping farmers better navigate their annual decision-making process and result in more on-farm productivity and profitability.”
How can more data help?
How can having more data help a farmer? The example Darr cites is nitrogen management. Soil type and how much rain a field receives determine how much N is available for corn at tasseling and grain filling. But it’s difficult to know how many pounds of N per acre are actually there. Big data helps figure that out by using weather models and information on soil types, N application rates and timing.
Having data on the status of nitrogen content in fields will help you understand more about how to manage N efficiently for crop production and profitability, and help address nitrogen timing and water quality issues. You’ll be able to see the data and look at specific field areas in more detail.
Think about it as a fuel gauge for your farm, a nitrogen gauge for what’s in the soil, says Darr. For decision-making, this technology can be very helpful to better understand where to put sidedress nitrogen. It would be particularly useful in springs like this year when rain kept some farmers from getting sidedress N applied.
Should you go ahead and hire a highboy applicator to apply more N on certain parts of fields? Or have granular urea flown on a field with an airplane? “Knowing which fields or areas of fields actually need nitrogen the most would provide risk management, and opportunities to enhance the way we manage nitrogen today,” Darr adds.
Picking the right partner
How can farmers get involved in big data? There are various levels and ways. One part of big data is using your own data yourself or with the aid of a crop consultant — for example, to make better decisions on corn hybrid placement in a field based on yield maps and response.
Another option could be teaming up with a company, such as a seed firm, to tap into additional resources built around weather modeling and environmental factors. Farmers will get into big data in different ways, but the goals should be the same across the board in terms of making incremental improvements in how you farm, says Darr.
“Ag is a decision-heavy industry,” he observes. “If we think about the money farmers spend every year to grow a crop, two-thirds of it goes into decision processes around seed and fertility, renting ground, and input selection, as opposed to the rest, which goes into fuel for machinery and labor to plant, harvest, etc. Until now, precision ag has been focused on reducing costs and improving efficiency mainly on the machinery side. The data side of precision ag is targeting the complex decisions farmers make every year to grow a crop. The goal is to help them use tools to visualize data and make better decisions based on past history of their fields.”
Using big data information can be more environmentally friendly, too. It offers an opportunity to improve both the environment and productivity. Ask farmers if the system they’re using today is the best plan they’ve ever used, and most will say “yes.” Compared to 20 years ago, as far as how they’re now selecting hybrids and managing their soil and the environment, most farmers would say they’ve made progressive increases.
“The idea with big data isn’t to go away from that; it’s to help make even more informed decisions as you move forward,” says Darr. “Looking over a 40-year career as a farmer, maybe big data can enable you to make 50 years of decisions within that 40-year career in the progress of how you farm.”
Make informed decisions
Keeping good records has always been the right idea. The key today is using new ways to make those records really pay for a farmer. Information available now is a lot more detailed than what farmers had in the past. Most of it is a natural progression of technology on the farm, and most of it is now freely available. Yield monitors have been standard equipment on combines for years; planting maps are becoming more readily available. So it doesn’t require a lot of investment to get into this, Darr notes. It’s usually a matter of putting together a plan for “how am I going to integrate data and data-driven decisions into making better choices as a farmer?”
Does the big data investment have good payback? That’s an interesting question. It’s different than, say, an autosteer system, which gives you $5 a year every year, per acre. “The payback from data really is in making progressively improved decisions on how you farm,” says Darr. “There will be years when the payback on data is $100 an acre, and there will be years when the way the weather dictates, there isn’t a payback.”
Weather is always a factor, says Darr. “But it’s still important to develop a philosophy built around how you as a farmer can use data to make better decisions.”
This article published in the August, 2014 edition of WALLACES FARMER.
All rights reserved. Copyright Farm Progress Cos. 2014.
Precision Farming Technology (Equipment)
Precision Farming Management