University of Arkansas to begin breeding new Provisia rice lines

University of Arkansas rice plot harvester
A combine cuts breeder seed at the University of Arkansas’ Rice Research and Extension Center near Stuttgart — photo courtesy University of Arkansas

The University of Arkansas System Division of Agriculture will begin breeding new lines of Provisia rice varieties, which use non-GMO herbicide-resistant traits developed by BASF.

The Division of Agriculture and BASF signed the breeding development agreement earlier this year. Bob Scott, director of the Rice Research and Extension Center in Stuttgart, Arkansas, says the timing will allow Division of Agriculture researchers to take advantage of the 2019 growing season to begin what will likely be a four- to seven-year process to develop and release commercially viable Provisia rice varieties.

“We are very excited to begin our breeding efforts on BASF’s new Provisia technology,” Scott says. “The Provisia fields I looked at last year were put into some very bad resistant and weedy situations and were still some of the cleanest fields out there.”

Through the new breeding agreement, Division of Agriculture researchers will focus on developing varieties of Provisia that are primarily suited to Arkansas growing conditions.

Donnarie Hales, rice portfolio manager with BASF, says that one of Provisia’s primary strengths is that it allows growers to keep more of their rice acreage in rice production by adding an additional herbicide rotation to their operation.

“For growers who lost a lot of acres to volunteer rice and grass pressure, we saw Provisia provide excellent control last season, which really cleaned up their fields,” Hales says. “We’re excited about the potential of this collaboration and the continued advancement of this herbicide-tolerant technology to help growers continue to grow quality rice.”

John Carlin, director of the Arkansas Crop Variety Improvement Program, says he hopes the cooperative endeavor with BASF will provide Arkansas growers with better tools to achieve maximum yield while minimizing inputs.

The University of Arkansas contributed this article.


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