Developing novel and high throughput field trial evaluation technologies
University Investigators: Owen Atkin, Caitlin Byrt, Robert Furbank, Matthew Gilliham, Barry Pogson, Stuart Roy, Matthew Tucker, Dabing Zhang
Potential Partners and Institutions for Placements: Advanta, AGT, APPF, DART, InterGrain, LongReach PB, NuSeed, NSW-DPI, SARDI
Contact: Deputy Director – Industry, Stuart Roy
We will develop innovative trait and field evaluation pipelines that will optimize efficiencies in GM and non-GM field evaluation.
With GM field trials typically 100× more expensive than those for non-GM crops, our aim is to develop cost effective innovative tools, methodologies and training approaches for enhancing our evaluation of the phenotypic performance of GM plants in the field
PhD projects in this program will interface with, and benefit from placements at, OGTR to ensure compliance and communication on optimal regulatory strategies.
Program 4.1 Innovative trait and field evaluation to accelerate the selection of material for sowing and develop technologies for accurately genotyping genome edited plants (UA/ANU)
OGTR has indicated that plants generated through site-directed mutagenesis, with no residual inserted DNA (SDN-1) may be considered non-GM . However, with no protocols to guarantee this, all gene-edited plants must currently be considered GM until proven otherwise.
We urgently need to reduce trial costs by: I) preselecting lines from greenhouse-grown plants via predictive technologies; and II) developing robust techniques to clearly demonstrate a gene-edited plant is free of any modifications that would define it as a GMO.
I) Enhanced and predictive phenotyping of biotechnology plants in the greenhouse (UA/ANU)
Working with APPF, PhD projects will develop fast, and efficient methods to select the best material in greenhouse trials for field trials.
Traits to monitor are growth, biomass, yield, grain quality, abiotic stress tolerance, and nutrient and water distribution. Phenotypic traits will be assayed by developing predictive tools using machine learning by using hyperspectral cameras, non-destructive phenotyping and image analysis, Cropatron and lidar buggy, X-ray computer tomography and the Drought Spotter. Machine learning algorithms will be developed to predict the performance of greenhouse grown plants in the field.
II) Robust techniques to genotype genome edited plants (ANU/UA)
The Centre will train personnel in the characterisation of novel genotyping gene-edited plants in partnership with DART. PhD projects will develop industry-standard protocols to confirm presence/absence of remnant foreign DNA in SDN-1 genome edited lines.
Engaging with the OGTR the projects will define a minimum insert size for a GMO, compared to random insertions and mutations that occur in a genome naturally. This new technology will enable accurate screening of SDN-1 genome edited lines to confirm whether their offspring can be treated as non-GM plants.
Program 4.2 Create novel analytical technologies for use in a GM field site and develop technology to reduce the administrative load of running GM field trials (UA/ANU)
While the industry is proficient at evaluating non-GM material, most players have little experience in the characterisation of GMO plants or the complex regulatory framework involved with handling them. Until demonstrated to be non-GMO, gene-edited crops are also classified as GMO and must be grown under that framework.
This presents a clear role for the Centre to develop industry capacity for GM and gene-editing trials, and develop innovative approaches for assessing plant growth and OGTR compliance via novel analytical technologies and Standard Operating Procedures (SOP).
Innovations to ensure OGTR compliance and ease field trial logistics (UA)
In collaboration with NSW-DPI and SARDI our PhD students will work on projects using static cameras, remote, non-destructive imaging, image analysis and machine learning, to develop protocols for predicting developmental stage, floral initiation and yield, thus making OGTR compliance faster and easier.
PhD projects will develop machine learning image analysis protocols to monitor weeds and sexually compatible species at GMO sites. Innovative use of image recognition and tracking technology will ensure compliance with licence conditions and monitor pests and diseases. All HDRs and Partners will have the option for training in these technologies.
Development new ML phenotyping tools for GM field trials (ANU/UA)
Efficient and timely field characterisation of GM plants under optimal or imposed stress conditions requires innovative approaches due to the constraints of test sites.
PhD projects will design systems for using static cameras, remote, non-destructive imaging, image analysis and ML for the early discarding of off-type plants with poor general performance (disease, idotype, etc.).
Using ML and working with APPF’s FieldExplorer, which combines LiDAR with visible-near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging, LICORs and PhotosynQ MultispeQ for photosynthesis and respiration, projects will measure key plant forms and functions that determine yield and resilience, and, develop industry-standard protocols for rapid measures of key plant parameters using ML.
SOCIALLY RESPONSIBLE GENETIC & FIELD TECHNOLOGIES FOR FUTURE CROPS
The ARC Training Centre for Accelerated Future Crops Development is funded by the Australian Research Council under its Industrial Transformation Training Hubs Program to run from 2022 to 2027.
It is a collaboration of universities, government research agencies and the Australian grains sector’s key stakeholders in training, R&D, social engagement, responsible innovation, breeding, marketing and delivery.
It also has international partners in gene-editing, SynBio, crop breeding, and, other partnerships for co-developing deep technologies to transform the agriculture industry and global food security.