Scott Chapman

Optimization of genotype evaluation methods in breeding programs

• By 2005, completed two sugarcane projects that radically changed the priorities and evaluation methods of Australian breeding programs such that the delivery of new varieties now happens 3 to 5 years earlier. The major outcome was a confidential industry report. Supervised similar research for Advanta sunflower breeding in Argentina to reorganise and accelerate preliminary testing program.
• Led the public sector’s most extensive global collaborative study of wheat variety performance (>200 trials). This has assisted the delivery of better spring-wheat varieties into developing countries and into Australia.
• Extended research to use “environment characterization”, which I co-developed in the late 90s. The basic methodology to better identify stable varieties in the face of drought stress, has been adopted by international seed companies and local breeding programs in a range of crops.
• From 2009 to 2017, led the development of applications of ‘Pheno-Copter’ autonomous aerial robot platform at CSIRO based on hardware and software processing systems to allow capture and analysis of high-throughput image information from field crop experiments in wheat, sorghum, sugarcane and cotton.
• Since 2019/2020, have begun to lead two new research projects funded by GRDC involving both UQ and CSIRO. One project (AG-FE-ML) with partners in France (INRAe/ARVALIS) and Japan (U Tokyo) is in the applications of deep learning/feature extraction on agricultural imagery to allow automated segmentation of plant parts from images and to enable counting of reproductive structures (heads/panicles/grains) that are associated with grain yield of crops. The second project (INVITA) is applying a range of technologies (in-field sensors, cameras, satellite imagery, computer simulation) and methods (multi-variate statistics and machine learning) to attempt to improve the prediction of differences in yields among crop genotypes in the National Variety Trials. This research aims to allow the interpolation of results across the national production areas.

Exploiting crop adaptation traits through experiments and simulation studies

• Supervised and co-investigated to demonstrate the adaptive yield and quality value of major wheat genes around the world (dwarfing and disease genes) and across Australia (water soluble carbohydrates, transpiration efficiency and tillering genes)
• As a co-investigator, developed a unique platform (to the public sector) in the simulation modelling of crop growth and plant breeding programs. This platform has attracted >$6 million co-investment (ARC and private company) and provides the full capability to model the breeding systems of major crops. It continues development in the current ARC CoE for Plant Success.
• Co-published pioneering research on the simulation of genetic controls of leaf growth processes within crop models. This original contribution has opened novel opportunities for the high-throughput simulation, testing and improvement of fully-specified physiological, breeding and statistical methodologies that are applied in plant breeding.
• As lead PI (wheat) and co-PI (sorghum), ran experiments and improved models to analyse potential of genetic variation in heat tolerance to cope with current and future climates in Australian environments.