Bucciarelli Bruna, Xu Zhanyou, Ao Samadangla, Cao Yuanyuan, Monteros Maria J, Topp Christopher N, Samac Deborah A
Department of Agronomy and Plant Genetics, University of Minnesota, 1991 Upper Buford Circle, St. Paul, MN, 55108, USA.
USDA-ARS, Plant Science Research Unit, 1991 Upper Buford Circle, St. Paul, MN, 55108, USA.
Plant Methods. 2021 Dec 7;17(1):125. doi: 10.1186/s13007-021-00825-3.
The root system architecture (RSA) of alfalfa (Medicago sativa L.) affects biomass production by influencing water and nutrient uptake, including nitrogen fixation. Further, roots are important for storing carbohydrates that are needed for regrowth in spring and after each harvest. Previous selection for a greater number of branched and fibrous roots significantly increased alfalfa biomass yield. However, phenotyping root systems of mature alfalfa plant is labor-intensive, time-consuming, and subject to environmental variability and human error. High-throughput and detailed phenotyping methods are needed to accelerate the development of alfalfa germplasm with distinct RSAs adapted to specific environmental conditions and for enhancing productivity in elite germplasm. In this study methods were developed for phenotyping 14-day-old alfalfa seedlings to identify measurable root traits that are highly heritable and can differentiate plants with either a branched or a tap rooted phenotype. Plants were grown in a soil-free mixture under controlled conditions, then the root systems were imaged with a flatbed scanner and measured using WinRhizo software.
The branched root plants had a significantly greater number of tertiary roots and significantly longer tertiary roots relative to the tap rooted plants. Additionally, the branch rooted population had significantly more secondary roots > 2.5 cm relative to the tap rooted population. These two parameters distinguishing phenotypes were confirmed using two machine learning algorithms, Random Forest and Gradient Boosting Machines. Plants selected as seedlings for the branch rooted or tap rooted phenotypes were used in crossing blocks that resulted in a genetic gain of 10%, consistent with the previous selection strategy that utilized manual root scoring to phenotype 22-week-old-plants. Heritability analysis of various root architecture parameters from selected seedlings showed tertiary root length and number are highly heritable with values of 0.74 and 0.79, respectively.
The results show that seedling root phenotyping is a reliable tool that can be used for alfalfa germplasm selection and breeding. Phenotypic selection of RSA in seedlings reduced time for selection by 20 weeks, significantly accelerating the breeding cycle.
紫花苜蓿（Medicago sativa L.）的根系结构（RSA）通过影响水分和养分吸收（包括固氮）来影响生物量生产。此外，根系对于储存春季和每次收获后再生所需的碳水化合物很重要。先前对更多分支和须根的选择显著提高了紫花苜蓿的生物量产量。然而，对成熟紫花苜蓿植株的根系进行表型分析既费力又耗时，且易受环境变异性和人为误差的影响。需要高通量和详细的表型分析方法来加速具有适应特定环境条件的独特根系结构的紫花苜蓿种质的开发，并提高优良种质的生产力。在本研究中，开发了用于对14日龄紫花苜蓿幼苗进行表型分析的方法，以识别可测量的、高度可遗传的根系性状，并能够区分具有分支根或直根表型的植株。植株在可控条件下的无土混合物中生长，然后用平板扫描仪对根系进行成像，并使用WinRhizo软件进行测量。
相对于直根植株，分支根植株的三级根数量显著更多，三级根也显著更长。此外，相对于直根群体，分支根群体中长度大于2.5厘米的二级根显著更多。使用两种机器学习算法（随机森林和梯度提升机）确认了这两个区分表型的参数。被选为具有分支根或直根表型的幼苗用于杂交区组，产生了10%的遗传增益，这与之前利用人工根系评分对22周龄植株进行表型分析的选择策略一致。对所选幼苗的各种根系结构参数进行遗传力分析表明，三级根长度和数量的遗传力很高，分别为0.74和0.79。
结果表明，幼苗根系表型分析是一种可用于紫花苜蓿种质选择和育种的可靠工具。在幼苗中对根系结构进行表型选择将选择时间缩短了20周，显著加速了育种周期。
