Project : USDA ARS

ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Sugarbeet Research » Research » Research Project #444181

Research Project: Improving Sugarbeet Productivity and Sustainability through Genetic, Genomic, Physiological, and Phytopathological Approaches

Location: Sugarbeet Research

2024 Annual Report

Objectives
Objective 1. Develop new strategies, identify molecular markers, and release germplasm for the genetic enhancement of pest and disease resistance in sugarbeet. Sub-objective 1A: Identify new genetic sources for improving sugarbeet resistance to Cercospora leaf spot and sugarbeet root maggot. Sub-objective 1B: Conduct genetic mapping and marker development for genes conferring sugarbeet root maggot resistance. Sub-objective 1C: Develop new pre-breeding germplasm with improved resistance to Cercospora leaf spot and sugarbeet root maggot. Objective 2. Develop strategies to limit sucrose losses due to disease, such as Cercospora leaf spot, and postharvest metabolism through a fundamental understanding of pathogen diversity, pathogen/host/environment interactions, and storage physiology in sugarbeet. Sub-objective 1C: Develop new pre-breeding germplasm with improved resistance to Cercospora leaf spot and sugarbeet root maggot. Sub-objective 2B: Identify genomic loci in the C. beticola genome associated with adaptation to management practices including fungicides and new tolerant sugarbeet varieties. Sub-objective 2C: Identify rhizomania resistance-breaking variants of Beet necrotic yellow vein virus using high-throughput sequencing technologies. Sub-objective 2D: Investigate virus-host interactions to characterize virulence factors and host-responsive elements that can be used to develop new tools for rhizomania disease management. Sub-objective 2E: Identify and characterize genes and metabolic pathways responsible for sucrose loss and quality deterioration during storage and determine genetic and environmental factors that regulate them. Sub-objective 2F: Identify and characterize sugarbeet storage pathogens. Sub-objective 2G: Assess the effect of in-season fungicides on soil communities of postharvest pathogens in the sugarbeet rhizosphere. Objective 3. Develop integrative agronomic tools and strategies for managing the emerging threat of herbicide-resistant weeds that threaten sugarbeet production in the United States.

Approach
The U.S. sugarbeet industry is valued at $1.7 billion and ensures a domestic supply of a staple in the American diet. Productivity and sustainability of the industry, however, is imperiled by the diseases Cercospora leaf spot (CLS) and rhizomania, the sugarbeet root maggot (SBRM), herbicide-resistant weeds, and storage deterioration. Collectively, these agricultural challenges cost the industry more than $350 million annually. Losses to these challenges are expected to intensify in future years since the pathogens responsible for CLS and rhizomania are increasingly tolerant to contemporary and conventional chemical and genetic controls, pesticides for SBRM control are increasingly use-restricted, herbicide tolerance in weed species and populations is growing, and climate change is fueling storage losses. New management strategies, therefore, are needed to maintain industry productivity and sustainability. However, the development of new management strategies requires a greater understanding of the fundamental biology underlying these challenges and new genetic tools. Research is proposed to generate the knowledge and tools needed to reduce losses to CLS, rhizomania, SBRM, herbicide-resistant weeds and storage deterioration by (1) identifying new sources of genetic resistance for CLS and SBRM control, (2) developing new CLS and SBRM resistant germplasm, (3) developing molecular markers for CLS and SBRM genetic resistance, (4) generating new understanding of how pathogens responsible for CLS and rhizomania overcome host genetic resistance and pesticide toxicity, (5) determining and characterizing population diversity and gene flow for CLS- and rhizomania-causing pathogens, (6) identifying and functionally characterizing genes responsible for postharvest sucrose loss and quality deterioration, (7) identifying and characterizing the microbes causing storage diseases, and (8) determining the effect of in-season fungicides on postharvest pathogen diversity. Success in this research will provide knowledge and tools to reduce losses to CLS, rhizomania, SBRM, herbicide resistant weeds, and storage deterioration for improved productivity and sustainability of the sugarbeet industry.

Progress Report
Research on all project plan Objectives and Subobjectives advanced in FY2024. In Objective 1A research, the genetic diversity of 1,936 publicly available germplasm lines were characterized using SNPs (single nucleotide polymorphisms) covering the entire sugarbeet genome. Results of this research confirmed the narrow genetic base of sugarbeet and identified germplasm accessions with inherently greater genetic diversity. These inherently diverse lines, which were predominantly accessions of wild sea beet (B. vulgaris ssp. maritima (L.) Arcang.), displayed distinct genetic dissimilarity from cultivated sugarbeet lines, indicating their high potential for sugarbeet improvement when introgressed with cultivated sugarbeet germplasm. Objective 1B research progressed with the development of two populations that derive sugarbeet root maggot (SBRM) resistance from parental lines F1024 and PI 179180, respectively, which will be used to map sugarbeet root maggot (SBRM) resistant genes. Each population contains 300 lines that will be self-pollinated to produce seeds and will be used to obtain a subsequent generation of plants. The development of pre-breeding lines with improved disease and pest resistance (Objective 1C) progressed with a yield trial of elite sugarbeet lines. Results from this trial led to the selection of three lines (F1024, L-LTM and R376-43) that will be used as cultivated parents in future crosses with genetically diverse wild sea beet accessions identified in Objective 1A. The development of genomic resources for Cercospora beticola (Objective 2A) was initiated with the collection of leaf samples from inoculated and uninoculated sugarbeet at 1, 3, 7, 11, 17, and 25 days post-inoculation. Subsequent sequencing of these samples will provide the raw data necessary to identify C. beticola genes associated with the biotrophic and necrotrophic phases of the disease interaction for both host and pathogen. Genomic resource development for the global collection of C. beticola isolates was also initiated with 326 whole genomes sequenced to date, representing ten of the twelve countries in the collection (Objective 2A). Sequencing of the global C. beticola collection will produce the raw data necessary to assess genetic diversity on a global scale and facilitate the analysis of global gene flow (Objective 2A). Additionally, a project examining C. beticola adaptation to triazole fungicides and the recently deployed sugarbeet resistance trait, CR+, has been initiated with 384 isolates collected in 2021 which were sent for whole genome sequencing. This project is ongoing and will provide the data needed to identify C. beticola genomic loci associated with adaption to these important management tools (Objective 2B). Identification of beet necrotic yellow vein virus (BNYVV) variants that overcome genetic resistance and cause rhizomania disease symptoms in infected sugarbeet plants progressed in FY24 (Objective 2C). Rhizomania-infested soil samples were obtained from all U.S. sugarbeet growing areas and evaluated for the presence of resistance-breaking variants using soil-baiting assays with susceptible and resistant sugarbeet varieties and enzyme-linked immunosorbent assays (ELISA). Studies to identify gene expression and small RNA changes in sugarbeet in response to BNYVV infection (Objective 2D) were initiated by inoculation of sugarbeet seedlings with BNYVV, collection of root samples at two-week intervals in the eight weeks following inoculation, and isolation of RNA from collected samples. Research into the causes of postharvest sucrose loss (Objective 2E) advanced with the identification of genes that potentially regulate postharvest respiration rate and identification of environmental and biological factors that increase sucrose conversion to ethanol during storage. Three sugar transporter genes were identified that are likely responsible for the remobilization of sucrose from storage into active metabolism and directly contribute to postharvest sucrose loss. A glycolytic gene was also identified as a potential controller of the rate of postharvest respiration and the sucrose consumed by this metabolic process. Several factors that promote sucrose catabolism to ethanol during storage were identified and include the fungal storage pathogens, Botrytis cinerea and Penicillium vulpinum, root injuries from harvest and piling operations, and prolonged storage durations. Sucrose catabolism to ethanol, however, was unaffected by a common bacterial pathogen, Leuconostoc mesenteroides, or inadequate ventilation of storage piles. Objective 2F research identified Botrytis cinerea, species of Penicillium and Mucor, and Leuconostoc mesenteroides as the major fungal and bacterial pathogens present in sugarbeet storage piles. Among Penicillium species isolated from commercial storage piles, P. expansum and P. italicum were found to cause the greatest tissue damage and the most extensive areas of rot when inoculated on harvested sugarbeet roots. Additionally, a field experiment to identify the effect of in-season fungicides on soil communities of postharvest pathogens in the sugarbeet rhizosphere (Objective 2G) was initiated. Under Objective 3, research towards the development of a robotic system of thermal weed control was initiated in collaboration with partners in the Departments of Plant Science, Electrical & Computer Engineering, and Agricultural & Biosystems Engineering at North Dakota State University. The temperature and quantity of hot water needed for effective control of waterhemp and foxtail, two common noxious weed species, were evaluated in greenhouse studies. Motor control and GPS-based autonomous navigation of a four-wheel drive robotic vehicle were field-tested as a potential weed management platform. Weed identification using AI algorithms was investigated and validated for 3424 images of four weed types using Segment Anything Model (SAM) and U-Net, while drone images of a sugarbeet field were collected for weed mapping and processed for image stitching.

Accomplishments
1. Identification of wild sea beet subpopulations facilities their use for sugarbeet crop improvement. Sea beet is the wild ancestor of cultivated beets and is found along coastal regions surrounding the Mediterranean Sea and European North Atlantic Ocean. Sea beet is widely recognized as an important source of new genes for improved sugarbeet resistance against disease, insect pests and environmental stresses since sea beet thrives in diverse and often demanding environments. Moreover, these genes can be easily incorporated into elite sugarbeet lines through traditional breeding techniques. Efficient use of sea beet for sugarbeet improvement and the conservation of this important genetic resource, however, requires understanding of sea beet evolution. ARS scientists in Fargo, North Dakota, characterized the genetic material of all publicly available sea beet lines using DNA markers and classified these lines into distinct, evolutionarily-related sub-populations. The information gained from this research will increase the efficiency of sugarbeet improvement efforts that utilize sea beet as a source for new resistance genes and guide collection and conservation efforts for this important genetic resource.

2. Guidelines for the storage of drought-stressed sugarbeet roots. Although drought stress impacts sugarbeet root yield and sucrose content at harvest, its effect on the ability of roots to maintain sucrose content and processing quality during storage is unknown. Knowledge of the effects of preharvest drought stress on storage deterioration, however, is critical for determining whether drought-stressed roots should be excluded from storage piles, segregated from non-stressed roots for early processing, or incorporated into storage piles without precaution. ARS scientists in Fargo, North Dakota, quantified the effect of varying levels of preharvest drought stress on sugarbeet root storage respiration rate and susceptibility to storage rots, two storage traits that are largely responsible for postharvest sucrose loss and root quality deterioration. The research demonstrated the devasting effect of severe preharvest drought stress on sugarbeet root storage, quantified the likelihood of storage losses as a function of drought severity, and demonstrated how storage duration exacerbates storage loss of drought-stressed roots. The information gained from this research provides the sugarbeet industry with valuable information for developing best storage practices for drought-stressed roots to minimize postharvest loss.

3. Identification and geographical expansion of new viruses in sugarbeet. Sugarbeet is susceptible to several viruses that often coexist under field conditions, making it difficult to identify the viral pathogen(s) responsible for disease symptoms. Identity of the viruses causing disease symptoms in plants, however, is critically important for developing strategies to limit crop losses from current and emerging diseases during sugarbeet production. ARS scientists in Fargo, North Dakota, identified the viruses present in sugarbeet plants that exhibited virus disease symptoms from fields in California, Colorado, Idaho, Minnesota, and North Dakota. The research identified coexisting viruses present in diseased plants, revealed the geographical expansion of individual viruses to new locations, and identified the widespread occurrence of a novel virus, Erysiphe necator-associated abispo virus, in sugarbeet. This research identifies viruses that are new to sugarbeet, documents the expanded geographic range of some sugarbeet-infecting viruses, and indicates the potential threat to sugarbeet production of new diseases due to mixed viral infections.

4. Identification of bacterial contaminants present in sugarbeet processing factories. Sugarbeet roots are processed into sugar by sequential slicing of roots, extraction of soluble sucrose from root slices, concentration of the resulting raw juice, and crystallization of sugar from concentrated juice. During this process, microbes consume some of the sucrose present in these factory processes and produce impurity compounds that cause additional sucrose loss by reducing the efficiency of sucrose recovery. Reducing microbes present on the roots and soil that enter the factory and controlling microbial populations in the factory, therefore, would increase sugarbeet industry profitability, but requires knowledge of the microbes present to develop effective mitigation strategies. ARS scientists in Fargo, North Dakota, analyzed the bacteria present in raw juice extracts obtained from factories across the U.S. and Canada in roots that were stored for progressively longer times between harvest and processing. Lactic acid bacteria, including Leuconostoc and Lactobacillus species, were the major bacterial contaminants identified in raw juice extracts regardless of factory location or time between harvest and processing. This research indicates that strategies to limit factory sucrose loss caused by microbes should focus on reducing the presence and growth of species of lactic acid bacteria.

5. Improvements in the use of artificial intelligence to identify weeds in commercial fields. Site-specific weed management was introduced in the 1990s and relies on images captured by robotic vehicles and drones to detect weeds. The use of artificial intelligence (AI) for detecting objects has made outstanding improvements in weed identification, yet its ability to efficiently detect weeds requires enhanced sensitivity in the partitioning of the digital images taken of large-scale crop production systems into individual weed and non-weed segments. ARS scientists in Fargo, North Dakota, improved the accuracy of weed identification using a new AI-driven tool, Segment Anything Model (SAM). Computer programs were written that improve the identification of detected objects as weeds using SAM. This development improves the detection accuracy of weeds in sugarbeet fields and allows AI researchers new insights in ways to apply SAM to their AI models for improved detection.

6. New software utilizes satellite imagery to monitor plant health. Timely detection of plant diseases and environmental stresses allows growers the ability to rapidly employ intervention measures to limit their losses. Satellite images are collected daily and can be used to monitor the development and progression of diseases or environmental stresses in crop production fields, although the software available to download and analyze satellite images is limited. ARS scientists in Fargo, North Dakota, developed an analytical software program, iCalendar, that automatically generates a time-based description, or calendar, of the vegetation in each field using satellite images. A filtering tool was developed that removes unusable images and prepares output for the detection of crop health status using artificial intelligence. The software displays the status of the vegetation in a field on a calendar and allows farmers to manage their fields proactively before severe damage occurs. This new software was made publicly available and can be obtained from the National Ag Library.

U.S. DEPARTMENT OF AGRICULTURE

Sugarbeet Research: Fargo, ND