06 July 2015 | Online since 2003

Simulation predicts herbidice resistance in weeds



20 February 2014 04:27:34|Arable,Cereal,Crops,News

Simulation predicts herbidice resistance in weeds


Effective herbicides are essential to rice production in the mid-southern United States. In recent decades, the primary weed that challenges rice fields, barnyardgrass, developed resistance to two widely used herbicides – propanil and quinclorac.

To ensure that current herbicides remain effective, a simulation model is being used to predict the weed’s resistance in future years to guide weed management plans today.

Barnyardgrass can reduce rice yield up to 30 percent, and herbicides are the major component for controlling the weed. Simulation models can be beneficial in understanding the resistance evolution and developing strategies to prevent it.

An article in the journal Weed Technology discusses the development of a model that can analyze the simultaneous evolution of resistance to herbicides that inhibit the acetolactate synthase (ALS) and acetyl-CoA carboxylase (ACCase) enzymes. The model assumes the use of Clearfield rice, a commercial brand of rice cultivars that is resistant to certain ALS-inhibiting herbicides.

Using data from three locations in the rice-growing Mississippi Delta region of eastern Arkansas, the computer simulation model creates weed management scenarios across 1,000 hypothetical rice fields. The model takes into account three stages of growth—dormant seedbank, emerged seedlings, and mature plants—and extends over a 30-year period.

The simulation shows that if an ALS-inhibiting herbicide was used alone in three annual applications, weed resistance would develop within 4 years. Weed management that used both ALS- and ACCase-inhibiting herbicides reduced the risk of barnyardgrass developing a resistance to the ALS-inhibitor, but found a higher risk for the weed developing resistance to the ACCase-inhibitor by year 14. Resistance to both of these herbicide types when used together was predicted by year 16.

Several weed management strategies were revealed through the simulation model. An important insight was that if a grower does not stop using an herbicide soon after resistance to it evolves, resistance can be accelerated for the next herbicide alternative employed even if it provides a different mode of action.

Minimizing the weed seedbank should be the focus of successful weed management. The model shows that diversified management techniques combined with timely applications of herbicide is the best way to achieve positive results.

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