description
- Plant roots are essential for the uptake of water and nutrients from soil. Consequently, root growth has significant effects on crop establishment and yield. Previous work by the project team, and others, has shown strong relationships between early root growth traits and the performance of arable crops in the field. However, measuring roots and selecting varieties with improved root systems in the field is time consuming, laborious and expensive. Using laboratory techniques, root growth can be measured quickly and cheaply - for 1000s of plants a year. Genotypes with better root growth and root architectures can be identified in the laboratory and assessments of selected plants can be made under field conditions to validate laboratory screens and assess field performance. In this proposal we will use low cost, high-throughput methods to define the early root system of >1,600 different oilseed rape (OSR), barley and wheat genotypes in the laboratory. The roots of individual plants will be imaged at two time points. These images will then be analysed to determine the number of roots, root branching rates, root lengths, root growth rates and root angles. To validate and test the utility of measurements made in the laboratory, we will compare them with (1) measurements of root systems made in the field, and (2) data collected from new and legacy field trials assessing large numbers of new crop varieties for National and Recommended Lists to identify root traits correlated with establishment and yield for breeding. Root growth and architecture are genetically controlled. We will identify genetic loci in large populations of OSR, barley and wheat affecting root growth and architecture traits that correlate with resource acquisition, establishment and yield in the field. An understanding of how best to combine beneficial alleles will be assessed through modelling approaches. To identify genetic targets for breeding we will develop mathematical models describing root growth and architecture in OSR that incorporate the effects of genetic variation. These mathematical models will be extended to predict the effects of root architecture on P acquisition and, thereby, identify potential genotypes with improved rooting and greater P acquisition for sustainable agriculture. In summary, this proposal will deliver low cost, high-throughput platforms for root phenotyping. These will be of direct benefit to the breeding industry, allowing them to assess germplasm for root growth and architecture that correlate with improved establishment and yield. Genetic loci affecting root growth and architecture will be identified to accelerate the breeding of new varieties. Mathematical models will allow genotypes associated with improved root systems to be identified.