Can a geographical-perspective help make precision-ag pay?
Precision-ag technologies continue to be adopted by farmers worldwide, though not as rapidly as many would like. Indeed, many farmers are still unsure if precision-ag technologies can actually better their bottom line.
According to researchers at the University of Guelph – one of Canada’s major agricultural schools – apprehension is often warranted when it comes to variable rate application and yield mapping. This is because neither technology offers a true understanding of why yields and fertilizer demands vary in the first place.
John Sulik, a geographer by trade and assistant professor of precision agriculture in the university’s department of Plant Agriculture, says a wide range of biological data is needed to make precision agriculture actually pay for farmers. More specifically, he says knowledge of an area’s unique geography and other biophysical factors are critical for farmers to make more accurate in-season decisions, hopefully improving economic and environmental efficiency in the process.
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Precision mapping answers what, not why
As a professor of precision agriculture, Sulik says he is interested in changing the way people think about technologies like yield mapping and field prescription maps. While such technologies can be useful, he says they fail to reveal a lot of important agronomic information that producers require to make profitable decisions during the growing season.
It’s easy to generate a prescription map. The problem is it’s too easy
But by measuring other plant and soil characteristics – through what Sulik calls “biophysical remote sensing” – and comparing that data to yield and prescription maps, farmers can better evaluate whether the information presented by precision-ag technologies is worth acting on.
“It’s easy to generate a prescription map. The problem is it’s too easy […] anyone can make a map but they may not be able to tell you anything about the agronomy,” says Sulik. “We need to use data differently in different conditions and geographies.”
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If a map showing nitrogen levels indicates the need for more fertilizer in a given area, for example, but fails to show why that nitrogen is needed – such as a lack of in-soil availability as a result of ongoing water stress issues – then such agronomic information is not useful. Indeed, acting on it may be economically and environmentally detrimental. “The advertising for precision-ag technologies treats everything as uniform. There’s a lot more [context] that needs to be considered,” he says.
Growing condition variability & “flat payoff function”
Approaching precision-ag by looking at different data-streams in comparison to one another is also important given how growing conditions can vary considerably between regions, as well as between individual fields. This includes, most notably, regional climates and soil types.
Data, Sulik says, needs to be used differently in different conditions. This need is illustrated with what he refers to as the often “unproven” return on investment in variable rate techniques.
Also read: Sensor tested for measuring nitrate levels in soil
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The problem, he says, is variable rate fertilizer application works as a “flat payoff function” where the level of fertilizer applied can vary, at times considerably, yet generate the same yield profitability; soil type, he adds, is a particularly significant factor here. Applying 60 kg of nitrogen per hectare, for example, might bring the same returns as applying 120 kg per hectare, though with a considerably reduced cost – something, too, not communicated in standard nutrient maps.
I think the caution people have towards precision-ag is valid
“At some point there’s no benefit to applying more,” says Sulik. “Variable rate doesn’t always pay off. We want to know when it does and how we can act on it […] “I think the caution people have towards precision-ag is valid.”
Future research
Sulik has already begun working on a number of projects with agricultural specialists at university, within government, and in the private sector. Currently, these projects involve looking at corn, soybeans, and edible bean yields, and seeing what other types of data can be used to explain yield differences within each crop.
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As these projects only recently got off the ground – Sulik just started his professorship in June, 2018 – Sulik says he is working to collect as much archival yield data as possible. The more data they have to work with, that is, the more rigorous their analysis will be.
He adds any strategies developed from such research will “not help everyone every year,” but reiterates that determining more relevant information will certainly help farmers decide whether acting on yield and nutrient mapping makes good business sense.
Want to learn more? Als watch this video describing the issue
