Project Number: 2050-21000-034-040-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2021
End Date: Aug 31, 2022
1. Identify regions of the oat genome that influence seedling ability to maintain optimum growth under water stress, including both drought and saturation. 2. Develop computer algorithms for high-throughput seedling recognition from overhead imaging.
Oat grain yield can be negatively impacted by lack of moisture during critical growth stages. Seedling emergence and early growth is one-such critical stage. In the early stages of oat growth, seedling emergence can be reduced when soil moisture is below normal, but also when soil is fully saturated. Similarly, the rate of seedling growth per day can be negatively impacted by drought or saturation. Not all oat varieties respond to an excess or dearth of water in the same way. For example, the cultivar Osmo shows little decrease in seedling vigor with an increase in water from normal to fully-saturated. In contrast, the cultivar Svea shows very poor seedling emergence from saturated soil, but seedling emergence and growth under water-limited conditions are close to normal. Although vigorous seedling growth is important to early crop establishment, very little is known about its genetic control in oat. This project will utilize a state of the art phenomics facility to identify regions of the genome that influence oat seedling response to water stress. The sample will consist of 750 oat lines selected from the United States National Small Grains Collection to represent the widest possible diversity among cultivated oat. This panel of lines will be evaluated for seedling growth under 4 levels of gravimetrically-determined water availability. Seedling emergence varies with soil water content for oats with efficiency falling off when the relative soil moisture falls below 40%. Additionally, tillering is particularly sensitive to both high and low soil moisture content. Therefore, emergence and tillering will be scored using a small plant phenotyping system. This involves compost filled pots being placed on the small dynamic phenotyping platform for daily watering and weighing until they reach target weight (a measure of soil water content). Once target weight is achieved, water is added daily to maintain the soil water level. When all pots have reached their desired water content, seeds (4 per pot) will be sown into the different soil moisture levels. Emergence and formation of the first tiller will be scored on a daily basis for the subsequent 3 weeks. Environmental parameters will be collected as per standard practice. High-throughput phenotyping of cereal seedling characteristics will be advanced by incorporating overhead imaging. Based on these images, computer algorithms will be developed for seedling recognition (either standard PSI computer vision or deep learning) to automate the current manual scoring. This project will identify germplasm tolerant to specific soil conditions and may also reveal genotypes resilient to a range of conditions. Previously obtained genotype data and statistical association mapping methods will be used to determine genomic regions that influence variation in seedling growth at specific water availability levels, and also to find genomic regions that influence oat seedling resilience to water availability. The information gained will be useful in breeding for improved crop performance in water-stress environments.