Management of Cercospora Leaf Spot in Conventional and Organic Table Beet Production

Nighttime Applications of Germicidal UV Light to Suppress Cercospora Leaf Spot in Table Beet

Cercospora leaf spot (CLS), caused by the hemibiotrophic fungus Cercospora beticola, is a destructive disease affecting table beet. Multiple applications of fungicides are needed to reduce epidemic progress to maintain foliar health and enable mechanized harvest. The sustainability of CLS control is threatened by the rapid development of fungicide resistance, the need to grow commercially acceptable yet CLS-susceptible cultivars, and the inability to manipulate agronomic conditions to mitigate disease risk. Nighttime applications of germicidal UV light (UV-C) have recently been used to suppress several plant diseases, notably those caused by ectoparasitic biotrophs such as powdery mildews. We evaluated the efficacy of nighttime applications of UV-C for suppression of CLS in table beet. In vitro lethality of UV-C to germinating conidia increased with increasing dose, with complete suppression at 1,000 J/m2. Greenhouse-grown table beet tolerated relatively high doses of UV-C without lethal effects despite some bronzing on the leaf blade. A UV-C dose >1,500 J/m2 resulted in phytotoxicity severities greater than 50%. UV-C exposure to ≤750 J/m2 resulted in negligible phytotoxicity. Older (6-week-old) greenhouse-grown plants were more susceptible to UV-C damage than younger (2- and 4-week-old) plants. Suppression of CLS by UV-C was greater when applied within 6 days of C. beticola inoculation than if delayed until 13 days after infection in greenhouse-grown plants. In field trials, there were significant linear relationships between UV-C dose and CLS control and phytotoxicity severity, and a significant negative linear relationship between phytotoxicity and CLS severity at the final assessment. Significant differences between UV-C doses on the severity of CLS and phytotoxicity indicated an efficacious dose near 800 J/m2. Collectively, these findings illustrate significant and substantial suppression by nighttime applications of UV-C for CLS control on table beet, with potential for incorporation in both conventional and organic table beet broadacre production systems.

Evaluating Rhizobacterial Antagonists for Controlling Cercospora beticola and Promoting Growth in Beta vulgaris

Cercospora beticola Sacc. is an ascomycete pathogen that causes Cercospora leaf spot in sugar beets (Beta vulgaris L.) and other related crops. It can lead to significant yield losses if not effectively managed. This study aimed to assess rhizosphere bacteria from sugar beet soil as a biological control agent against C. beticola and evaluate their effect on B. vulgaris. Following a dual-culture screening, 18 bacteria exhibiting over 50% inhibition were selected, with 6 of them demonstrating more than 80% control. The bacteria were identified by sequencing the 16S rRNA gene, revealing 12 potential species belonging to 6 genera, including Bacillus, which was represented by 4 species. Additionally, the biochemical and molecular properties of the bacteria were characterized in depth, as well as plant growth promotion. PCR analysis of the genes responsible for producing antifungal metabolites revealed that 83%, 78%, 89%, and 56% of the selected bacteria possessed bacillomycin-, iturin-, fengycin-, and surfactin-encoding genes, respectively. Infrared spectroscopy analysis confirmed the presence of a lipopeptide structure in the bacterial supernatant filtrate. Subsequently, the bacteria were assessed for their effect on sugar beet plants in controlled conditions. The bacteria exhibited notable capabilities, promoting growth in both roots and shoots, resulting in significant increases in root length and weight and shoot length. A field experiment with four bacterial candidates demonstrated good performance against C. beticola compared to the difenoconazole fungicide. These bacteria played a significant role in disease control, achieving a maximum efficacy of 77.42%, slightly below the 88.51% efficacy attained with difenoconazole. Additional field trials are necessary to verify the protective and growth-promoting effects of these candidates, whether applied individually, combined in consortia, or integrated with chemical inputs in sugar beet crop production.

Control of Phoma Leaf Spot and Root Decay of Table Beet in New York

Disease caused by Neocamarosporium betae (syn. Phoma betae, Pleospora betae) results in reductions in plant populations, foliar disease (Phoma leaf spot [PLS]), and root disease and decay in table beet. Disease caused by N. betae has reemerged as prevalent in organic table beet production in New York. The disease can also cause substantial issues in conventional table beet production. To evaluate in-field control options for conventional and organic table beet production, small-plot, replicated trials were conducted in each of two years (2019 and 2021). The fungicides, propiconazole and difenoconazole, and premixtures, pydiflumetofen + fludioxonil or pydiflumetofen + difenoconazole, provided excellent PLS and root decay control. Azoxystrobin provided excellent (69.9%) control of PLS in 2019 and lesser (40%) control in 2021. Field trial results complemented in vitro sensitivity testing of 30 New York N. betae isolates that were all highly sensitive to azoxystrobin (mean effective concentration to reduce mycelial growth by 50%, EC₅₀ = 0.0205 µg/ml) and propiconazole (mean EC₅₀ = 0.0638 µg/ml). Copper octanoate and microbial biopesticides containing either Bacillus amyloliquefaciens D747 or B. mycoides strain J provided moderate (68.5 to 74.6%) PLS control as reflected in epidemic progress. The Gompertz model provided the best fit to PLS epidemics reflecting a polycyclic epidemic. Reductions in PLS severity were associated with significant decreases in Phoma root decay and increases in canopy health and the time-to-death of leaves compared with nontreated control plots. Prolonging leaf survival is critical for mechanical harvest of roots. These findings underpin the design of programs for foliar disease control in conventional and organic table beet production. Assessment of PLS severity in the field will better inform postharvest management decisions.

Development of a Sequential Sampling Plan using Spatial Attributes of Cercospora Leaf Spot Epidemics of Table Beet in New York

Sampling strategies that effectively assess disease intensity in the field are important to underpin management decisions. To develop a sequential sampling plan for the incidence of Cercospora leaf spot (CLS), caused by Cercospora beticola, 31 table beet fields were assessed in the state of New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of three to six linear transects per field. Spatial pattern analyses were performed, and results were used to develop sequential sampling estimation and classification models. CLS incidence (p) ranged from 0.13 to 0.92 with a median of 0.31, and beta-binomial distribution, which is reflective of aggregation, best described the spatial patterns observed. Aggregation was commonly detected (>95%) by methods using the point-process approach, runs analyses, and autocorrelation up to the fourth spatial lag. For Spatial Analysis by Distance Indices, or SADIE, 45% of the datasets were classified as a random pattern. In the sequential sampling estimation and classification models, disease units are sampled until a prespecified target is achieved. For estimation, the goal was sampling CLS incidence with a preselected coefficient of variation (C). Achieving the C = 0.1 was challenging with <51 sampling units, and only observed on datasets with incidence >0.3. Reducing the level of precision, i.e., increasing C to 0.2, allowed the preselected C to be achieved with a lower number of sampling units and with an estimated incidence ([Formula: see text]) close to the true value of p. For classification, the goal was to classify the datasets above or below prespecified thresholds (p_t) used for CLS management. The average sample number, or ASN, was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to p_t, and approximately 11 when far from p_t. Correct decisions occurred in >76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.

Optimizing Cercospora Leaf Spot Control in Table Beet Using Action Thresholds and Disease Forecasting

Cercospora leaf spot (CLS), caused by the fungus Cercospora beticola, is the dominant foliar disease affecting table-beet production in New York. CLS epidemics occur annually and, if uncontrolled, will rapidly lead to defoliation. In broad-acre production, season-long maintenance of healthy leaves is important to facilitate harvest by top-pulling. Fungicides are the dominant means of CLS control and applications are initiated at an action threshold of 1 CLS lesion/leaf. Regular fungicide application occurs thereafter without regard for scheduling based on weather-based risk. The current action threshold was evaluated with selected fungicides in two replicated field trials. Copper oxychloride + copper hydroxide and propiconazole significantly improved CLS control if initiated prior to infection. Pydiflumetofen + difenoconazole significantly reduced area under the disease progress stairs compared with other fungicides tested and was most efficacious when applications began at 1 CLS lesion/leaf. Six replicated field trials also evaluated the utility of scheduling fungicides on weather-based risk rather than a calendar approach. Two risk thresholds (moderate and high) integrating the accumulation of daily infection values based on temperature and relative humidity from a forecaster for CLS in sugar beet were evaluated. Applications of pydiflumetofen + difenoconazole were reduced from three to two by using the forecaster at either risk threshold compared with calendar applications without affecting CLS control. For propiconazole, the moderate risk threshold provided CLS control equivalent to calendar applications and saved one spray per season. Thus, there was substantial scope to reduce spray frequency by scheduling based on weather-based risk rather than calendar applications. The optimal risk thresholds for pydiflumetofen + difenoconazole and propiconazole were high and moderate, respectively. In these trials, periods of high risk occurred less frequently than moderate risk, increasing the reapplication intervals and, hence, represented a less conservative approach to disease management.

Detection of Cercospora beticola and Phoma betae on Table Beet Seed using Quantitative PCR

Cercospora beticola and Phoma betae are important pathogens of table beet, sugar beet, and Swiss chard (Beta vulgaris subsp. vulgaris), causing Cercospora leaf spot (CLS) and Phoma leaf spot, root rot, and damping-off, respectively. Both pathogens may be seedborne; however, limited evidence is available for seed infestation by C. beticola. Due to the limitations of culture-based seed assessment methods, detection of these pathogens was investigated using PCR. A P. betae-specific quantitative PCR assay was developed and used in conjunction with a C. beticola-specific assay to assess the presence of pathogen DNA in 12 table beet seed lots. DNA of C. beticola and P. betae was detected in four and eight seed lots, respectively. Plate tests and BIO-PCR confirmed the viability of each pathogen; however, competitive growth of other microbes and low incidence limited the frequency and sensitivity of detection in some seed lots. The results for P. betae support previously described infestation of seed. Further investigation of C. beticola-infested seed lots indicated the ability of seedborne C. beticola to cause CLS on plants grown from infested seed. Detection of viable C. beticola on table beet seed demonstrates the potential for pathogen dispersal and disease initiation via infested seed, and provides valuable insight into the epidemiology of CLS. Surveys of commercial table beet seed are required to determine the frequency and source of C. beticola seed infestation and its role as primary inoculum for epidemics, and to evaluate the effectiveness of seed treatments.

Genetic Diversity and Structure in Regional Cercospora beticola Populations from Beta vulgaris subsp. vulgaris Suggest Two Clusters of Separate Origin

Cercospora leaf spot, caused by Cercospora beticola, is a highly destructive disease of Beta vulgaris subsp. vulgaris worldwide. C. beticola populations are usually characterized by high genetic diversity, but little is known of the relationships among populations from different production regions around the world. This information would be informative of population origin and potential pathways for pathogen movement. For the current study, the genetic diversity, differentiation, and relationships among 948 C. beticola isolates in 28 populations across eight geographic regions were investigated using 12 microsatellite markers. Genotypic diversity, as measured by Simpson's complement index, ranged from 0.18 to 1.00, while pairwise index of differentiation values ranged from 0.02 to 0.42, with the greatest differentiation detected between two New York populations. In these populations, evidence for recent expansion was detected. Assessment of population structure identified two major clusters: the first associated with New York, and the second with Canada, Chile, Eurasia, Hawaii, Michigan, North Dakota, and one population from New York. Inferences of gene flow among these regions suggested that the source for one cluster likely is Eurasia, whereas the source for the other cluster is not known. These results suggest a shared origin of C. beticola populations across regions, except for part of New York, where population divergence has occurred. These findings support the hypothesis that dispersal of C. beticola occurs over long distances.

Evaluation of App-Embedded Disease Scales for Aiding Visual Severity Estimation of Cercospora Leaf Spot of Table Beet

Two diagrammatic ordinal scales are available in the Estimate app (2017 version) for Cercospora leaf spot (CLS) severity on table beet: 10% linear (linear-based diagrammatic scale [LIN]) and logarithmic based (Horsfall-Barratt [HB]). These allow for estimating severity data of four types depending on the system used. A group of 30 raters assigned percentage severity on 30 photographs of diseased table beet leaves during five rounds first without an aid and then using each of the four rating systems in Estimate. In two, the perceived ordinal score of the HB or LIN scale was assigned where severity of the subject fit best. HB2 and LIN2 involved a second choice of unitary severity within the perceived score interval. There was large variation in unaided ability of raters to estimate severity: 13% were accurate (Lin's concordance correlation [LCC] > 0.9), 23% were inaccurate (LCC < 0.7), and the remaining had moderate accuracy. Larger disparities between assigned and actual ordinal scores (mostly overestimates) occurred using the LIN compared with the HB. The LIN2 produced the most accurate estimates (Lin's concordance correlation coefficient, ρ_c = 0.96; generalized bias parameter, C_b = 0.99; Pearson's correlation coefficient r = 0.95) and the greatest interrater reliability (overall concordance correlation coefficient and intraclass correlation coefficient > 0.93). The two-step process using the 10% linear scale is recommended for severity estimates of CLS in table beet.

Temporal Genetic Differentiation of Cercospora beticola Populations in New York Table Beet Fields

Annual epidemics of Cercospora leaf spot (CLS), caused by the fungus Cercospora beticola, can result in substantial defoliation in table beet fields in New York. High allelic and genotypic diversity have been described within C. beticola populations; however, information on the temporal stability of populations is lacking. C. beticola isolates were obtained from symptomatic leaves in three table beet fields in successive years. Two of the fields were organic mixed-cropping farms and the third was managed conventionally in a broad-acre cropping system. C. beticola isolates (n = 304) were genotyped using 12 microsatellite markers. Genotypic diversity (Simpson's complement index = 0.178 to 0.990), allele frequencies, and indices of differentiation between years varied. Pairwise index of differentiation values ranged from 0.02 to 0.25 for clone-corrected data, and indicated significant genetic differentiation at Farm 2. No multilocus genotype was shared between years. The shift in multilocus genotypes between years questions the role of clonally reproducing primary inoculum. Collectively, these results suggest that a dominant inoculum source for initiating annual CLS epidemics is external to the field of interest. These findings have implications for CLS disease management in conventional and organic table beet production.

IMA Genome-F 9: Draft genome sequence of Annulohypoxylon stygium, Aspergillus mulundensis, Berkeleyomyces basicola (syn. Thielaviopsis basicola), Ceratocystis smalleyi, two Cercospora beticola strains, Coleophoma cylindrospora, Fusarium fracticaudum, Phialophora cf. hyalina, and Morchella septimelata

Draft genomes of the species Annulohypoxylon stygium, Aspergillus mulundensis, Berkeleyomyces basicola (syn. Thielaviopsis basicola), Ceratocystis smalleyi, two Cercospora beticola strains, Coleophoma cylindrospora, Fusarium fracticaudum, Phialophora cf. hyalina and Morchella septimelata are presented. Both mating types (MAT1-1 and MAT1-2) of Cercospora beticola are included. Two strains of Coleophoma cylindrospora that produce sulfated homotyrosine echinocandin variants, FR209602, FR220897 and FR220899 are presented. The sequencing of Aspergillus mulundensis, Coleophoma cylindrospora and Phialophora cf. hyalina has enabled mapping of the gene clusters encoding the chemical diversity from the echinocandin pathways, providing data that reveals the complexity of secondary metabolism in these different species. Overall these genomes provide a valuable resource for understanding the molecular processes underlying pathogenicity (in some cases), biology and toxin production of these economically important fungi.

Cryptic diversity, pathogenicity, and evolutionary species boundaries in Cercospora populations associated with Cercospora leaf spot of Beta vulgaris

The taxonomy and evolutionary species boundaries in a global collection of Cercospora isolates from Beta vulgaris was investigated based on sequences of six loci. Species boundaries were assessed using concatenated multi-locus phylogenies, Generalized Mixed Yule Coalescent (GMYC), Poisson Tree Processes (PTP), and Bayes factor delimitation (BFD) framework. Cercospora beticola was confirmed as the primary cause of Cercospora leaf spot (CLS) on B. vulgaris. Cercospora apii, C. cf. flagellaris, Cercospora sp. G, and C. zebrina were also identified in association with CLS on B. vulgaris. Cercospora apii and C. cf. flagellaris were pathogenic to table beet but Cercospora sp. G and C. zebrina did not cause disease. Genealogical concordance phylogenetic species recognition, GMYC and PTP methods failed to differentiate C. apii and C. beticola as separate species. On the other hand, multi-species coalescent analysis based on BFD supported separation of C. apii and C. beticola into distinct species; and provided evidence of evolutionary independent lineages within C. beticola. Extensive intra- and intergenic recombination, incomplete lineage sorting and dominance of clonal reproduction complicate evolutionary species recognition in the genus Cercospora. The results warrant morphological and phylogenetic studies to disentangle cryptic speciation within C. beticola.

Estimate, a New iPad Application for Assessment of Plant Disease Severity Using Photographic Standard Area Diagrams

Assessment of disease severity is a foundational component of plant pathology and essential for robust disease management. Researchers often estimate disease severity using standard area diagrams (SADs) that are reference images representing disease severity in percentage increments. SADs provide assessments of disease severity that are more accurate, precise, and reliable than other methods. Although specific SADs have been constructed for many plant diseases, they often depict severity in unrealistic black-and-white or grayscale illustrations. SADs are also usually printed, static references that can burden data collection in the field and require data to be transferred manually to a computer spreadsheet for manipulation. This data entry process and verification are prone to errors and require additional inputs of time and labor. We developed a new iPad application (app) called Estimate for researchers and crop managers for their use on a mobile device at the field-level for assessing plant disease severity in order to collect data or aid in treatment decisions. The app is a repository for digital, photographic SADs and offers savings in time for data collection and processing. Estimate allows users to select a disease from a prepopulated list and specify the reference disease images in either logarithmic or linear intervals. Data may be collected as the midpoint of an interval (ordinal) or as 1% increments (continuous). Users then select among photographic images by touching those that best match the observed disease severity on successive samples. Estimate allows data entry at the plant and leaf hierarchical levels within plots and subplots. Alternatively, data may be collected on single sampling units with an undefined experimental design (i.e., 1 to x leaves). The user may inspect and e-mail the final data in comma-separated values format for analysis using conventional spreadsheet software. Estimate was released with SADs for assessing the severity of Cercospora leaf spot in red and yellow table beet cultivars. A list of collaborators and up-to-date list of SADs included in Estimate is available at http://evade.pppmb.cals.cornell.edu/estimate/ . SADs for other diseases will be added to Estimate as they become available. Estimate is available for free download from iTunes ( https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=1193605571&mt=8 ) and is compatible with an iPad Air 2 or equivalent using iOS 9.0 or greater.