There is a considerable variation between species and genera of plants in terms of how photosynthesis functions. In seeking ways to improve photosynthetic performance, understanding the mechanisms underpinning this natural range of functional variation allows the identification of traits, genes and germplasm which can be directly utilised for photosynthetic improvement in crops.

This is becoming an increasingly powerful approach with recent advances in high throughput plant phenomics, DNA and RNA sequencing and genome association mapping.

Program 3 aimed to test the genetic diversity in photosynthetic traits in C3 and C4 crop and model species to determine genes and mechanisms underpinning this diversity. Professor Robert Furbank at ANU and David Jordan at The University of Queensland led this Program.

This program took a suite of approaches to identify functional strategies, mechanisms and genetic variation associated with different aspects of photosynthetic performance.

Research areas and outcomes for this Program included:

Screening existing crop and model plant germplasm for enhanced photosynthetic capacity and efficiency, enhanced stomatal performance and improved photosynthesis and growth at elevated CO2 and building and mining genetic resources to discover new genes responsible for improved photosynthetic performance in sorghum. This project’s leaders are David Jordan and Graeme Hammer from The University of Queensland  and  Robert Furbank and Susanne von Caemmerer from ANU.

Key outcomes: We developed new high throughput phenotyping tools for field screening of photosynthesis,  demonstrated that they can be used in sorghum, wheat, rice and millet and mapped genetic regions in sorghum associated with carbon isotope discrimination, leaf angle, leaf width and canopy size


Dissecting natural variation in canopy light-use efficiency and photosynthetic capacity (rice, wheat and Brachypodium). This project is lead by Robert Furbank from ANU, Tony Condon from CSIRO and Paul Quick from IRRI.

Key outcomes: We developed robust screen for photosynthesis in wheat using leaf reflectance and machine learning.