Washington DC (SPX) May 07, 2014
 A geospatial crop model interface was designed and implemented for simulating the potential production of potato across the Eastern Seaboard Region. |
Farmers are used to optimizing crop production on their own lands. They
do soil tests to choose the right amount of fertilizers to apply, and
they sometimes plant row crops on some fields while keeping others in
pasture.
But is it possible to optimize production across a much bigger area-say, the whole East Coast of the United States? That's the question a team of USDA-ARS scientists in Beltsville, MD, has begun to tackle by developing a sophisticated new modeling tool.
Known as the Geospatial Agricultural Management and Crop Assessment Framework (GAMCAF), the tool brings together crop models that estimate plant growth and crop yield at scales as fine as 30 meters (90 feet), with spatial sources of information on soils, water, land use, and other factors. Crop models aren't normally designed to work automatically with spatial data, explains Jonathan Resop, who led the platform's development as a USDA-ARS postdoc. Now, the new interface-published in the Jan.-Feb. 2014 issue of Agronomy Journal-allows exactly that.
"This way, we can make much larger predictions [of yields] across the entire regional scale," Resop says. But, he adds, "The real power of this framework is that it lets you look at different scenarios of land use change, water, and climate change." For example, what will happen to production if more farmland is lost to development, or if average growing season temperatures rise substantially in coming decades?
But is it possible to optimize production across a much bigger area-say, the whole East Coast of the United States? That's the question a team of USDA-ARS scientists in Beltsville, MD, has begun to tackle by developing a sophisticated new modeling tool.
Known as the Geospatial Agricultural Management and Crop Assessment Framework (GAMCAF), the tool brings together crop models that estimate plant growth and crop yield at scales as fine as 30 meters (90 feet), with spatial sources of information on soils, water, land use, and other factors. Crop models aren't normally designed to work automatically with spatial data, explains Jonathan Resop, who led the platform's development as a USDA-ARS postdoc. Now, the new interface-published in the Jan.-Feb. 2014 issue of Agronomy Journal-allows exactly that.
"This way, we can make much larger predictions [of yields] across the entire regional scale," Resop says. But, he adds, "The real power of this framework is that it lets you look at different scenarios of land use change, water, and climate change." For example, what will happen to production if more farmland is lost to development, or if average growing season temperatures rise substantially in coming decades?
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