Scientists Leverage Computer Modeling to Help Farmers Hedge Their Bets Against Weeds
Just like the weeds they target, herbicides are also sensitive to the weather—before, during, and after application. Now, with the aid of computer modeling and one of the largest databases yet, a team of scientists is working to refine the outlook for herbicide use in corn and soybean crops amid extreme weather events. The resulting data, in turn, will help inform farmers decisions on where, when, and how best to apply the chemicals and safeguard crop yields.
Led by Christopher Landau, a postdoctoral research agronomist with the Agricultural Research Service (ARS) in Urbana, Il, the computer models leverage historical data gleaned from more than 10,000 herbicide evaluation trials conducted by land-grant universities across the United States.
Weeds outrank insects and disease as top threats to corn and soybean crops nationwide, inflicting yield losses of up to 50% if left unchecked. Post-emergent (POST) herbicides, which target above-ground weeds, constitute a major portion of the total herbicides applied to corn and soybeans, which supply the lion’s share of America’s grain and edible oil, respectively.
However, mindful of shifts in seasonal rainfall patterns and temperature, the scientists set out to better understand the portent of those shifts on herbicide effectiveness for the weeds they target.
“The seasonal distribution of precipitation in the Corn Belt is expected to shift toward increased winter and spring precipitation and reduced summer precipitation,” Landau and his co-authors wrote in a paper published in the online version of Weed Science. “Greater spring precipitation reduces the number of field-working days, which can delay planting and herbicide application.”
Refining the outlook for POST herbicide use in corn and soybean crops amid extreme weather events could help farmers optimize their control of common weeds like giant foxtail, shown infesting the corn plot at right. (Photo credit: Doug Buhler, USDA ARS)
To analyze potential impacts on herbicide efficacy, however, the researchers first had to gather more data than had been previously collected. More specifically, they wanted data spanning longer time periods and a broader range of crop production environments in the U.S. Midwest.
Towards that objective, they created a relational database and populated it with 30 years of historical weather data and 10 million visual assessments of herbicide efficacy derived from field trials conducted by land-grant universities in 14 states and one Canadian province.
Using filters built into the database, the researchers homed in on specific datasets for use in computer models simulating the interplay between weeds and POST herbicides for hundreds—and sometimes thousands—of different weather scenarios. With the filters, for example, the researchers could adjust for the number of days before or after application, as well as the herbicide product and rate used.
For modeling purposes, the team focused attention on three commonly used POST herbicide treatments (fomesafen, glyphosate, mesotrione, and fomesafen plus glyphosate) and three common weeds that are especially problematic in corn and soybean crops—namely, waterhemp, giant foxtail, and several species of morning-glory.
The results, in some cases, affirm prior findings and in others, offer new insights.
For example, the models indicate the best predictor of herbicide efficacy falls within a 10-day window after application—not just the first few hours as previously believed. During this 10-day window, daily average air temperatures that fall below 66 degrees Fahrenheit or rise past 77 degrees can diminish an herbicide’s prospects for successful weed control (meaning weed control of 85% or greater).
“Planting earlier has the potential for reducing the probability that a POST herbicide would be exposed to daily average temperatures in excess of 77 degrees Fahrenheit after application even under predicted future temperature increases,” according to the research team.
However, there’s a trade-off: Earlier planting can also expose weeds and POST herbicide treatments alike to more extreme rainfall events, which, in the model, generally translated to poorer weed control.
Although the models determined 10 days after application was often the best predictor, “in some cases weather 10 days before application was noteworthy as well,” added Martin Williams, an ARS ecologist and paper co-author. “Both time periods have biological importance.”
For example, physiological responses in weeds to insufficient rainfall can induce thickening cuticle waxes on leaves that make it harder for POST herbicides to penetrate and kill them. Additionally, in some weed species, heatwaves can ratchet up their ability to metabolize (break down) certain herbicides.
Reflecting the results of prior research, the models also indicate that herbicide combinations like fomesafen plus glyphosate offer more consistent weed control under variable weather conditions than when either herbicide is used alone.
That said, integrating the chemicals’ use with cultural, mechanical, or biological weed control methods will become increasingly important in helping minimize the risk of crop yield losses amid the effects of extreme weather events, the researchers noted.
“The take-home message is that more variable weather is poised to make these POST treatments less effective,” Williams said. “So, we need innovation in weed management; more support tools and diverse systems.” – by Jan Suszkiw, ARS Office of Communications
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