"This kind of numerical approach using realistic models of plant canopies can provide a method for trying many more trait combinations than are possible through field breeding," Drewry said. "This approach then can help guide field programs by pointing to plants with particular combinations of traits, already tested in the computer, which may have the biggest payoff in the field."
The researchers hope their modeling approach will not only improve soybean yields, but also benefit agriculture worldwide as the population continues to rise.
According to Long, "The Food and Agriculture Organization of the United Nations predict that by 2050 we will need 70 percent more primary foodstuffs to feed the world than we are producing today and yet will have to do that with probably no more water while at the same time dealing with climate change."
"We need new innovations to achieve the yield jump," Long said. "We've shown that by altering leaf arrangement we could have a yield increase, without using more water and also providing an offset to global warming."
Next, the researchers plan to use their model to analyze other crops for their structural traits. As part of a project supported by the Bill and Melinda Gates Foundation, Long is leading an international effort to improve rice, soybean and cassava guided by similar computational approaches, with the end goal of making more productive and sustainable crops.
"By examining plants using detailed computer models and optimization, we have the potential to greatly expedite the development of new types of agricultural plants that can tackle some of the greatest challenges facing society today, related to the need to produce more food in a more variable and uncertain climate system," Drewry said.
|Contact: Liz Ahlberg|
University of Illinois at Urbana-Champaign