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

植物根系是从土壤中吸收水分和养分的关键。因此，根系生长对作物的生长发育和产量有重要影响。项目团队和其他人之前的工作已经表明，早期根系生长性状与田间可耕地作物的表现之间存在密切关系。然而，在田间测量根系和选择具有改良根系的品种是费时、费力和昂贵的。使用实验室技术，可以快速而廉价地测量根系生长-每年可测量1000株植物。具有更好的根系生长和根系构型的基因型可以在实验室中鉴定，并且可以在田间条件下对所选择的植物进行评估，以验证实验室筛选并评估田间表现。在该提案中，我们将使用低成本、高通量的方法在实验室中确定超过1，600种不同油菜（OSR）、大麦和小麦基因型的早期根系。将在两个时间点对单个植物的根进行成像。然后将分析这些图像以确定根的数量、根分枝率、根长度、根生长率和根角度。为了验证和测试在实验室中进行的测量的效用，我们将它们与（1）在田间进行的根系测量进行比较，以及（2）从新的和传统的田间试验中收集的数据，这些试验评估了大量国家和推荐清单的新作物品种，以确定与育种建立和产量相关的根系性状。我们将在OSR、大麦和小麦的大种群中确定影响根系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。将通过建模方法评估如何最好地组合联合收割机有益等位基因的理解。为了确定育种的遗传目标，我们将建立数学模型，描述OSR中的根生长和结构，并将遗传变异的影响。这些数学模型将被扩展到预测根系构型对磷吸收的影响，从而确定潜在的基因型，改善生根和更大的磷吸收可持续农业。总之，这一建议将提供低成本，高通量平台的根表型。这些将直接有益于育种业，使他们能够评估种质的根生长和结构，与改善建立和产量相关。将确定影响根系生长和结构的遗传位点，以加速新品种的培育。数学模型将允许与改进的根系相关的基因型被识别。

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.