Building better soybeans for a hot, dry, hungry world
A new study shows that soybean plants can be redesigned to increase crop yields while requiring less water and helping to offset greenhouse gas warming. The study is the first to demonstrate that a major food crop can be modified to meet multiple goals at the same time.
The study, led by Darren Drewry of NASA’s Jet Propulsion Laboratory, Pasadena, Calif., used an advanced vegetation model and high-performance computer optimization techniques. It found that by redesigning soybean plants in various ways, it was possible to increase soybean productivity by seven percent without using more water. Soybean plants also could be redesigned either to use 13 percent less water or to reflect 34 percent more light back to space without a loss of crop yield. The research was funded by the National Science Foundation with support from JPL and the Bill and Melinda Gates Foundation.
"My intuition would have told me that some of these goals are mutually exclusive -- that there is a fundamental tradeoff between increasing productivity and conserving water," said Drewry. "We are now able to say that there actually is a combination of traits that will make progress toward all of these goals simultaneously." The study by Drewry and coauthors Praveen Kumar and Stephen Long (both of the University of Illinois) was published April 4 in the journal Global Change Biology.
The research comes at a time when global food security is threatened by population growth and climate change. The United Nations estimates that food production will need to increase 70 percent by 2050 to meet the world's food needs. Today, yields of major crops are increasing slowly or not at all. Soybeans are the world's most important protein crop.
Drewry developed the model that he used for this study (called MLCan, for multi-layer canopy model) to study U.S. Midwest agricultural systems, but it can be modified for research on other types of vegetation. It captures exchanges of carbon dioxide, water and energy between vegetation and the atmosphere in great detail.
The research used numerical optimization, a mathematical way to decide which choice among a range of options will create the closest match to a desired outcome. Drewry chose five structural characteristics of a plant, such as the total leaf area (the number and size of the leaves) and the angle at which leaves are set on the plant stems. The MLCan model varied one or a combination of the five traits for each experiment, discarding less successful solutions and refining those that made progress toward the goals.
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