Delivering low-cost, high-throughput root phenotyping screens for arable crops

植物的根对于从土壤中吸收水分和养分是必不可少的。因此，根的生长对作物的建立和产量有显著的影响。该项目团队和其他人之前的工作表明，早期根系生长特征与田间可耕作作物的表现之间存在很强的关系。然而，在田间测量根系和选择具有改良的根系的品种是费时、费力和昂贵的。利用实验室技术，可以快速而廉价地测量根的生长--每年数千株植物的根生长。可以在实验室中鉴定出具有更好的根生长和根构型的基因，并可以在田间条件下对选定的植株进行评估，以验证实验室的筛选和评估田间表现。在这项提议中，我们将使用低成本、高通量的方法在实验室中确定1,600个不同的油菜(OSR；)、大麦和小麦的早期根系。单个植物的根部将在两个时间点进行成像。然后对这些图像进行分析，以确定根的数量、根分枝率、根长度、根生长率和根角。为了验证和测试实验室测量的有效性，我们将把它们与(1)在田间进行的根系测量，以及(2)从新的和遗留的田间试验中收集的数据进行比较，以评估国家和推荐名单上的大量新作物品种，以确定与育种的建立和产量相关的根特征。根的生长和结构受基因控制。我们将在OSR、大麦和小麦的大群体中确定影响根生长和结构性状的遗传位点，这些性状与田间资源获取、建立和产量相关。对如何最好地结合有益等位基因的理解将通过建模方法进行评估。为了确定育种的遗传目标，我们将开发数学模型来描述OSR中的根生长和结构，其中包括遗传变异的影响。这些数学模型将被扩展到预测根构型对磷吸收的影响，从而为可持续农业确定具有更好的生根率和更大的磷获取量的潜在基因型。综上所述，这一建议将提供低成本、高通量的根系表型鉴定平台。这些将对养殖业有直接的好处，使他们能够评估与改善建立和产量相关的根生长和结构的种质。将确定影响根系生长和构型的遗传基因座，以加快新品种的选育。数学模型将允许识别与改良的根系相关的基因类型。

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.