"This method allows us to measure improvements we have engineered in a plant's photosynthesis machinery in about ten seconds, compared to the traditional method that takes up 30 minutes. That's a major advance because it allows our team to analyze an enormous amount of genetic material to efficiently pinpoint traits that could greatly improve crop performance," said lead researcher, Katherine Meacham-Hensold.
The traditional method for assessing photosynthesis analyzes the exchange of gases through the leaf, it provides a huge amount of information, but it takes 30 minutes to measure each leaf.
A faster, or "higher-throughput" method, called spectral analysis, analysed the light that is reflected back from leaves to predict photosynthetic capacity in as little as 10 seconds.
"The question we set out to answer is can we apply spectral techniques to predict photosynthetic capacity when we have genetically altered the photosynthetic machinery. Before this study, we didn't know if changing the plant's photosynthetic pathways would change the signal that is detected by spectral measurements," said research leader Carl Bernacchi.
"Spectral analysis requires custom-built models to translate spectral data into measurements of photosynthetic capacity that must be recreated each year. Our next challenge is to figure out what we are measuring so that we can build predictive models that can be used year after year to compare results over time," Meacham said.
"While there are still hurdles ahead, spectral analysis is a game-changing technique that can be used to assess a variety of photosynthetic improvements to single out the changes that are most likely to substantially, and sustainably, increase crop yields. These tools can help us speed up our efforts to develop high-yielding crops for farmers working to help feed the world," said one of the researchers, Christine Raines.