Tackling the physiological phenotyping bottleneck with low-cost, enhanced-throughput gas exchange and ceptometry
- School of Life and Environmental Sciences, Sydney Institute of Agriculture, The University of Sydney.
- Department of Plant Sciences, University of California, Davis.
High throughput phenotyping platforms (HTPPs) are increasingly adopted in plant breeding research due to developments in sensor technology, unmanned aeronautics and computing infrastructure. Most of these platforms rely on indirect measurement techniques therefore some physiological traits may be inaccurately estimated whilst others cannot be estimated at all. Unfortunately, existing methods of directly measuring plant physiological traits, such as photosynthetic capacity (Amax), have low throughput and can be prohibitively expensive, creating a bottleneck in the breeding pipeline. We have addressed this issue by developing new low-cost enhanced-throughput phenotyping tools to directly measure physiological traits of wheat (Triticum aestivum). Our eight-chamber multiplexed gas exchange system, OCTOflux, can directly measure Amax with 5-10 times the throughput of conventional instruments, whilst our handmade ceptometers, PARbars, allow us to monitor the canopy light environment of many plots simultaneously and continuously across a diurnal cycle. By custom-building and optimizing these systems for throughput we have kept costs to a minimum, with OCTOflux costing roughly half that of commercially available single-chamber gas exchange systems and PARbars costing approximately 95% less than commercial ceptometers. We recently used these tools to identify variation in the distribution of Amax relative to light availability in 160 diverse wheat genotypes grown in the field. In a two-week measurement campaign we measured Amax in over 1300 leaves with OCTOflux and phenotyped the diurnal light environment of 418 plots using 68 PARbars. These tools could be readily modified for use with any plant functional type and also be useful in validating emerging HTPPs that rely on remotely sensed data to estimate photosynthetic parameters.