The world population is predicted to reach 9.8 billion by 2050. In order to feed this number of people, we need to dramatically increase cereal yields, above what is already being achieved by plant breeders. Wheat is one of the oldest and most important cereal crops. Without it we wouldn’t have tasty food like bread, pasta, cake, and pizza. (I get hungry just thinking about wheat. So delicious). Some of the factors that can be selected for to improve wheat yield include nitrogen uptake, leaf mass and photosynthetic efficiency. However, in order to select the best plants for breeding, all of these factors need to be measured. Traditionally these measurements are time consuming, destructive, and require special facilities, which is not practical on the large scale required for plant breeding. Ideally, researchers want to be able to tease out genetic markers they can look for that will tell them if a plant, or its offspring, will be high-yielding. However, considering the wheat genome has 6 times more base pairs than humans, this is no easy task!
Recently a group of researchers at the ARC Centre for Excellence for Translational Photosynthesis sought to speed up the wheat-analysis process. They wanted to see if hyperspectral reflectance– that is reflecting light in colours beyond the visible spectrum, like UV and infrared – could be linked to photosynthetic- and biomass-related traits. To test this they grew a lot of wheat plants in a greenhouse and took measurements of traits like carbon dioxide conversion, leaf mass, nitrogen content and chlorophyll content, as well as the leaf reflectance over a full range of wavelengths (350 – 2500 nm, or UV-Vis-Infrared). They then put these measurements together to construct a statistical model of how well hyperspectral reflectance can be linked to the measured traits. Their model is able to provide robust estimates for 6 traits that are linked to wheat yield, biomass and photosynthetic ability. The importance of this technique is that it is very fast compared to traditional methods. It used to take researchers 20 minutes per leaf to obtain this kind of information, but using hyperspectral reflectance approximately 100 plants can be analysed within an hour. Higher-throughput will make it easier to screen large populations for breeding and to identify genetic markers that will increase wheat yield. The researchers hope to expand their model by adding more leaf properties to measure, and the technique is starting to be used for other crops including corn, rice and sorghum. Down the track, using hyperspectral reflectance will hopefully lead to higher grain and cereal yields to meet the demands of a growing population.
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Emi Schutz Archives
March 2018
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