About

Sarah Jane Pethybridge is an Associate Professor in the School of Integrative Plant Science, Plant Pathology and Plant-Microbe Biology Section at Cornell AgriTech. Her primary research goal is to conduct novel studies that advance foundational knowledge surrounding pathogen biology and quantitative epidemiology, enabling transformational changes within plant pathology. Her research program is founded within the areas of quantitative epidemiology and disease management, focusing on using decision theory principles to inform strategic and tactical decisions made by growers to control plant diseases, minimize crop loss, and improve profitability and productivity. She aims to quantify the uncertainty surrounding these decisions and provide robust recommendations on the utility of various strategies and tools, with an emphasis on judicious pesticide use to reduce variable costs and environmental impacts, thereby supporting the resilience of agricultural systems in rural New York. In addition to her research, she is actively involved in extension activities aimed at encouraging the adoption of durable management strategies for soilborne vegetable diseases, working in collaboration with Cornell Cooperative Extension to understand sociological drivers and reduce uncertainty in decision-making among vegetable growers and industry stakeholders. Her work emphasizes communication, rapport-building, and understanding of the sociological context to facilitate the adoption of innovative disease management practices.

Research topics

• Biology
• Ecology
• Artificial Intelligence
• Agronomy
• Computer Science
• Mathematics
• Statistics
• Botany
• Social Science
• Sociology
• Genetics
• Horticulture
• Materials science
• Linguistics
• Geology
• Medicine
• Data science
• Remote sensing
• Acoustics
• Environmental science
• Agroforestry
• Biotechnology
• Pathology

Selected publications

• Advancing table beet root yield estimation via unmanned aerial systems (UAS) multi-modal sensing

  ISPRS Journal of Photogrammetry and Remote Sensing · 2026-01-06

  articleOpen access

  Unmanned aerial systems (UAS) offer significant potential to improve agricultural practices due to their multi-modal payload capacity, ease of deployment, and lower cost. However, there is a need to expand UAS capabilities by including root crops, offering robust, growth-stage-independent models, and providing a comprehensive assessment of various imaging systems, i.e., identifying application-specific sensing modalities. This study aims to tackle those challenges by presenting a unified Gaussian Process Regression (GPR) model for predicting end-of-season table beet (a subterranean root crop) yield using UAS-derived spectral and structural features, combined with meteorological data, while remaining robust to flight and harvest timing. Field trials were conducted at Cornell AgriTech in Geneva, NY during the 2021 and 2022 growing seasons. UAS flights captured five-band (475, 560, 668, 717, and 840 nm) multispectral imagery, hyperspectral imagery (400–1000 nm), and light detection and ranging (LiDAR) data at multiple times throughout the season. Our model achieved R 2 test = 0.81 and MAPE test = 15.7 % using only multispectral imagery, while the hyperspectral + LiDAR model attained R 2 test = 0.79 and MAPE test = 17.4 %, which is comparable to recent root yield modeling studies using UAS data. Shapely analysis was performed to gain further insight into model behavior. This analysis revealed canopy volume information to contain high relative importance, as compared to other features, for table beet root yield estimation. Our study demonstrated that UAS-based imaging, combined with a unified machine learning model, can effectively predict root crop yield, providing a scalable and transferable approach for precision agriculture.

  PublisherDOI

• Cereal rye ( <scp> <i>Secale cereale</i> </scp> ) and canola ( <scp> <i>Brassica napus</i> </scp> ) cover crops reduce dry bean ( <scp> <i>Phaseolus vulgaris</i> </scp> ) herbivore damage

  Pest Management Science · 2026-04-18

  article

  BACKGROUND: Cover crops can support soil health and increase habitats for beneficial insects in diverse cropping systems. The aim of this study was to evaluate the impact of cover crop-conditioned soil on plant defense responses and insect pest damage using dry bean (Phaseolus vulgaris L.) as a model. RESULTS: In a 2-year, multi-location field experiment comparing four cover crop treatments and a tilled control, we found that dry beans no-till planted into mechanically terminated cereal rye (Secale cereale L.) and canola (Brassica napus L.) experienced less insect damage than controls. In the laboratory, microbiome extracts from canola-enriched field soil increased dry bean jasmonic acid levels and reduced cowpea aphid (Aphis craccivora Koch) survival compared with other soil microbiome extracts. No differences in defense hormones were detected for dry bean grown in cereal rye soil microbiome extracts, however aphid survival was reduced on these plants compared to the controls. CONCLUSIONS: Cereal rye and canola cover crops reduced insect damage in dry bean fields. Soil microbiomes associated with canola enhanced defense-related compounds in dry bean, suggesting a potential mechanism for pest suppression in the field. However, the impacts of canola varied across microbiome sources, highlighting the need for additional studies. © 2026 Society of Chemical Industry.

  PublisherDOI

• Characterization of the Core Microbiome Associated with Table Beet Production in New York

  PhytoFrontiers™ · 2025-07-04

  articleOpen accessSenior author

  Host–microbiome interactions have been shown to moderate tolerance to multiple biotic and abiotic stressors, prompting interest in utilizing beneficial bacteria and fungi to manage plant diseases and improve crop production. Table beet is an important part of the agricultural landscape across New York, and increasing demand for organic production and alternatives to conventional fungicides for disease control has motivated interest in the associated microbial communities. The purpose of this project was to evaluate bacteria and fungi in the rhizosphere and phyllosphere microbiomes and identify the core taxa of table beet. In 2021 and 2022, microbiome samples were collected from nine table beet fields in New York representing the rhizosphere, phyllosphere, and bulk soil. Differences in the bacterial and fungal communities were associated with sample type, and additional variation was explained by field. Rhizosphere soil communities were similar to bulk soil communities, and both were distinct from phyllosphere microbiomes. Bulk soil and rhizosphere microbiomes had higher alpha diversity than phyllosphere microbiomes. Only bacteria and fungi in the foliar epiphyte community were present in over 90% of samples and had higher relative abundance compared with the bulk soil community and therefore were considered within the core microbiome. The core microbiome included members of the bacterial genera Sphingomonas, Methylobacterium, Pseudomonas, and Massilia, and members of the fungal genera were Alternaria, Epicoccum, and Cladosporium. Overall, a relatively small number of core bacteria and fungi made up the table beet microbiome. These results provide an important step toward the development of strategies to support plant health by harnessing the plant-associated microbiome. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

  PublisherOA PDFDOI

• Common soil invertebrate (Collembola: Isotomiella minor) reduces weed biomass and alters weed communities

  Applied Soil Ecology · 2025-05-19

  articleOpen access

  Soil microarthropods affect soil ecosystems in a manner that may contribute to balancing the goals of building soil health and controlling weeds in organic agricultural systems. While soil microarthropod feeding behavior can affect plant growth, their impacts on plant communities in agricultural systems are largely unknown. A greenhouse experiment was conducted to investigate the impacts of microarthropods on weed communities. A model weed seed bank was used in each mesocosm, which included yellow foxtail ( Setaria pumila (Poir.) Roem&Schult.), giant foxtail ( Setaria faberi Herrm.), Powell amaranth ( Amaranthus powellii S. Watson ), waterhemp ( Amaranthus tuberculatus (Moq.) Sauer), common lambsquarters ( Chenopodium album L.), and velvetleaf ( Abutilon theophrasti Medik.). The study included three treatments: Collembola ( Isotomiella minor , Schaffer 1896) abundance (none, low, high), soil microbial community (sterilized/non-sterilized), and fertilizer (presence/absence of compost). A lab experiment examining individual weed species interactions with I. minor was conducted to elucidate the mechanisms driving the greenhouse experiment findings. Twenty seeds of each weed species were placed on moistened germination paper in containers with varying I. minor abundance levels (none, low, high, very high). Seed germination was recorded after five and seven days. In the greenhouse, the presence of I. minor increased total weed emergence during the first two weeks, but this effect diminished after three weeks. Increasing I. minor abundances generally decreased weed biomass, though this effect was greater in the non-sterilized soil. In the non-sterilized soil, I. minor presence decreased total aboveground weed biomass production by up to 23 %. The Amaranthus species, Powell amaranth and waterhemp, drove this effect with a 55 % and 32 % reduction in biomass, respectively. In tandem, the Amaranthus species had reduced abundances in the presence of I. minor . I. minor increased yellow foxtail germination in the lab, while not affecting the other weed species. This suggests that their effects on the Amaranthus weeds in the greenhouse were likely not caused by direct effects on germination, but instead through nutrient cycling or root herbivory. The proposed mechanism underlying these interactions is that I. minor can initially stimulate germination by feeding on seed coats, but when the seed coats are minimal can damage the seedling. Our findings indicate I. minor could impact weed growth in a manner that affects management decisions and outcomes. • Isotomiella minor decreased total aboveground weed biomass production by up to 23 %. • Powell amaranth (55 %) and waterhemp (32 %) biomass were most decreased by I. minor . • I. minor directly increased yellow foxtail germination in lab study. • Seed coat composition may influence the outcomes of seed-Collembola interactions.

  PublisherDOI

• Relative Contributions of Infected Transplants and Volunteers to the Population Biology of <i>Stemphylium vesicarium</i> in New York Onion Production

  Phytopathology · 2025-10-23

  articleSenior author

  Stemphylium leaf blight (SLB), caused by the fungus Stemphylium vesicarium, is a devastating foliar disease affecting onions in New York state. Primary inoculum sources for SLB epidemics potentially include infected transplants used to establish crops and volunteer onions remaining from the previous season, but little is known about their relative contribution to the population biology of S. vesicarium. In this study, 537 S. vesicarium isolates were obtained during 2022 and 2023 from infected transplants and volunteers, as well as symptomatic main crop plants collected during the mid- and late seasons. To evaluate the relative contributions of infected transplants and volunteers to S. vesicarium populations in New York, nine simple sequence repeat markers were used to characterize the genetic diversity and structure of populations by source and year. A total of 399 multilocus genotypes (MLGs) were identified, of which 27 MLGs were shared among two or more source populations and 28 between year populations. Structure analysis showed that populations from transplants were distinct from volunteers and main crop plants collected during the mid- and late seasons with low admixture. Populations had high genotypic diversity and genetic differentiation, also suggesting a minimal contribution of infected transplants to the New York S. vesicarium populations. The dominance of MLGs from volunteers to main crop populations suggests that the elimination of volunteers should be included in integrated disease management strategies. Crop rotation and hygiene practices to remove and destroy volunteer onions after harvest may be key to reducing the primary inoculum for SLB epidemics.

  PublisherDOI

• Role of Infested Seed as Primary Inoculum for Cercospora Leaf Spot in Table Beet

  Plant Disease · 2025-03-28 · 3 citations

  articleSenior author

  Cercospora leaf spot (CLS), caused by the fungus Cercospora beticola, is an important determinant of table beet foliar health. Primary inoculum sources include infested crop residues and alternative hosts, but seed-to-seedling transmission has also been reported. We evaluated the localization of C. beticola in table beet seeds and the contribution of infested seeds to CLS outbreaks in field studies. In seed dissection experiments, C. beticola was more frequently isolated from the pericarp (95.6%) and operculum (30.4%) compared with the true seed (17.4%). Field trials in Geneva, NY, and Freeville, NY, had significantly higher CLS incidence, severity, and disease progress in plots established from an infested seed lot than those from a noninfested lot. C. beticola populations collected from infested seeds and field plots were genotyped using 11 microsatellite markers. The population from an infested seed lot exhibited high genotypic diversity, mating type equilibrium, and linkage equilibrium, suggesting random mating. Two clonal lineages of C. beticola were identified. Populations from infested seeds and from plants that grew in plots planted with infested seed grouped into cluster 2, whereas cluster 1 contained populations from plants that grew in plots planted with noninfested seed. The C. beticola population not associated with genotypes from the infested seed in NY was dominated by a few multilocus genotypes and was genetically distinct from the infested seed lot population. Our findings highlight the potential of C. beticola–infested seed as a primary inoculum source.

  PublisherDOI

• A decade of grapevine red blotch disease epidemiology reveals zonal roguing as novel disease management

  npj Viruses · 2025-04-15 · 3 citations

  articleOpen access

  Red blotch disease, a threat to the grape industry, is caused by grapevine red blotch virus. This work is the first to study epidemiological patterns in a vineyard over the course of a decade, revealing an increase in disease incidence from 3.9% in 2014 to 36.4% in 2023 with rapid virus spread proximal to a transmission hotspot. Logistic and exponential models provided the best fit of spread in areas of high and low disease incidence and aggregation, respectively. An inverse spatial incidence of virus strains 1 and 2 suggested secondary spread mostly from diseased to neighboring vines and virus influx from background sources. Precipitation (3-4 years later) and air temperature (the same or 1 year later) significantly influenced epidemic parameters. Finally, asymptomatic infections contributed to spatial aggregations at increasing lags. These findings were salient for considering zonal roguing, the removal of diseased and surrounding vines, as a disease management option.

  PublisherOA PDFDOI

• Whole-Genome Sequence Resources for the <i>Cercospora beticola</i> Species Complex (<i>Cercospora beticola</i>, <i>Cercospora tecta</i>, and <i>Cercospora americana</i>) from <i>Beta vulgaris</i> in New York

  PhytoFrontiers™ · 2025-05-15

  articleOpen accessSenior author

  Cercospora leaf spot is a devastating foliar disease affecting table and sugar beet ( Beta vulgaris) worldwide and primarily caused by the hemibiotrophic fungus Cercospora beticola. Two new additional species, C. tecta and C. americana, have recently been identified as causal agents of Cercospora leaf spot in table beet in New York. The limited availability of the genomes of C. beticola and lack of genomic data for C. tecta and C. americana hinder studies into evolutionary relationships, pathogenicity mechanisms, and the development of species-specific tests. Here, we report the whole-genome sequences of each isolate of C. beticola (Ch16-Cb), C. tecta (Tb14-081), and C. americana (Tb14-140). Genome sizes ranged from 34.5 to 35 Mb, with high completeness scores (&gt;99%). Genome annotation revealed over 11,000 putative genes, 1,000 secreted proteins, and 2,300 transmembrane domains per genome. Effector prediction identified over 300 effectors, including both cytoplasmic and apoplastic types. Additionally, secondary metabolite analysis uncovered 62 to 67 gene clusters. Whole-genome average nucleotide identity analysis confirmed &gt;99% identity among the three species and high similarity (&gt;98%) to C. apii. These resources facilitate comparative analyses, improved species resolution within the Cercospora genus, and enhanced species-specific detection. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

  PublisherOA PDFDOI

• Safer and Smarter: Leveraging Interpretation-Guided Modeling and Data Merging of Disease and Environmental Data for Plant Disease Risk Prediction

  Phytopathology · 2025-08-02 · 2 citations

  articleSenior author

  Plant disease epidemiologists often work with datasets smaller than ideal for data-hungry machine-learning (ML) algorithms, thereby risking overfitting. We demonstrate how an interpretation-guided modeling approach, leveraging complex ML primarily for insight generation, can overcome this challenge, using white mold (caused by Sclerotinia sclerotiorum) in snap beans ( Phaseolus vulgaris) as a case study. An observational dataset of white mold prevalence across 356 commercial snap bean fields in central and western New York State (2006 to 2008) was augmented by merging georeferenced observations with POLARIS soils data and engineered features from downscaled ERA5-Land environmental data. Functional data analysis identified weather periods associated with white mold risk, and random forests (RFs), used interpretatively, identified key predictors. Although RF models showed high apparent performance, they exhibited significant overfitting and poor calibration. Insights from RF interpretation (via SHapley Additive exPlanations analysis) guided the development of a simpler, four-predictor logistic regression model using restricted cubic splines. This simpler model was better calibrated and had acceptable discrimination (internally validated C statistic = 0.77). For smaller epidemiological datasets, our results advocate for using ML primarily as an interpretive tool to guide the development of simpler, less data-intensive, yet robust predictive models better suited for practical disease management decisions.

  PublisherDOI

• Multiple Single-Point Mutations in <i>Stemphylium vesicarium</i> Are Associated with the Rapid Development of Resistance to Succinate Dehydrogenase Inhibitors in Onion Fields

  Plant Disease · 2025-09-19 · 1 citations

  articleSenior author

  Succinate dehydrogenase inhibitor (SDHI) fungicides are used to manage Stemphylium leaf blight (SLB) of onion caused by the fungus Stemphylium vesicarium. The SDHIs commonly used for SLB management in New York (NY) onion production are boscalid (first registered in 2005), fluxapyroxad (2015), fluopyram (2016), and pydiflumetofen (2019). However, reduced field performance of these products across multiple onion-producing regions within NY has been encountered. We quantified the in vitro sensitivity of S. vesicarium isolates collected from five onion-producing regions throughout NY in 2016, 2018, and 2020. To evaluate whether variations in in vitro sensitivity phenotypes were associated with target-site mutations, sequencing of the sdhB, sdhC, and sdhD genes associated with fungicide response was conducted. We identified a shift in sensitivity over a short period, that is, although more than 90% of isolates sampled in 2016 were sensitive to fluopyram and fluxapyroxad (EC 50 &lt; 1 mg/liter), more than 50% of isolates sampled in 2018 exhibited reduced sensitivity (EC 50 &gt; 1 mg/liter). This change in fungicide sensitivity was observed in three of the four main onion-producing regions of NY and emphasizes the need for improved disease management practices to preserve their efficacy. Primers were developed to sequence the full sdh genes of 176 isolates from all regions and years sampled. Thirteen single-nucleotide polymorphisms were identified in the sdhB, sdhC, and sdhD genes, and 11 were found to predict nonsynonymous amino acid (aa) substitutions. The isolates with genotypes P230H (SDHB) and G79R, H134N/R, and C135R (SDHC) were associated with the reduced sensitivity of S. vesicarium to fluopyram and fluxapyroxad. These putative aa substitutions were not associated with effects on mycelial growth at one temperature. Spatiotemporal analyses revealed a clear shift in population structure from wild-type populations in 2016 to diverse genotypes with multiple substitutions across onion-producing regions by 2020. The rapid, diverse, and widespread distribution of genotypes with putative aa substitutions suggests an ongoing adaptation and the presence of strong selective forces in S. vesicarium in NY.

  PublisherDOI

Frequent coauthors

• Frank S. Hay

  Cornell University

  206 shared

• David H. Gent

  National Forage Seed Production Research Center

  98 shared

• Julie R. Kikkert

  Cornell University

  93 shared

• Niloofar Vaghefi

  University of Melbourne

  91 shared

• Jason B. Scott

  Tasmanian Institute of Agriculture

  61 shared

• Tamieka L. Pearce

  University of Tasmania

  53 shared

• S Pilkington

  Tasmanian Institute of Agriculture

  46 shared

• Emerson M. Del Ponte

  Universidade Federal de Viçosa

  40 shared

Education

• Ph.D., Plant Pathology

  Cornell University

  2002

• M.S., Plant Pathology

  Cornell University

  1998

• B.S., Botany

  University of California, Davis

  1996

Awards & honors

• Schwartz Research Fund Graduate Student/Postdoc Travel Award…
• Schwartz Research Fund, Cornell University
• Affinito Stewart Award (2015)
• Presidents Council for Cornell Women
• American Phytopathological Society Syngenta Award (2010)