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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #374721

Research Project: Genetic Characterization for Sugar Beet Improvement

Location: Sugarbeet and Bean Research

Title: Development of a sequential sampling plan using spatial attributes of Cercospora leaf spot epidemics of table beet in New York

Author
item HECK, DANIEL - Cornell University - New York
item KIKKERT, JULIE - Cornell University - New York
item Hanson, Linda
item PETHYBRIDGE, SARAH - Cornell University - New York

Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2021
Publication Date: 2/2/2021
Citation: Heck, D.W., Kikkert, J.R., Hanson, L.E., Pethybridge, S.J. 2021. Development of a sequential sampling plan using spatial attributes of Cercospora leaf spot epidemics of table beet in New York. Plant Disease. https://doi.org/10.1094/PDIS-07-20-1619-RE.
DOI: https://doi.org/10.1094/PDIS-07-20-1619-RE

Interpretive Summary: To make decisions for disease management, it is necessary to have good sampling strategies to determine disease in the field. This work was done to develop a strong sampling plan to examine for Cercospora leaf spot in table beet fields. The disease is a major constraint on production, causing yield loss including possible inability to harvest plants. To determine appropriate sampling methods, a total of 31 table beet fields were used over a two-year period (2017 and 2018) in western New York. Samples were taken along three to six lines in a field. The presence or absence and number of lesions caused by the disease was determined on six leaves at each of 51 sampling locations per transect at 0.32 m intervals. Spatial pattern analyses were performed, and the results were used to develop models for disease sampling. The models were evaluated with a variety of statistical tests. Disease incidence ranged from 13% to 92% over the duration and locations of the study. Results agreed with studies on other crops, showing clustering of diseased plants. Various levels of precision were tested to determine optimal number of samples to obtain reliable disease estimates. Models were developed that allowed for over 88% of sampling methods to accurately determine disease risk. Results indicated that sampling plans can be used to effectively assess Cercospora leaf spot disease severity in table beet fields. The methods showed potential to be adjusted for different management needs.

Technical Abstract: 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 2017 and 2018 in western New York. Assessments were performed in three to six linear, within row, transects per field. Along each transect, CLS incidence (presence/absence) was assessed on six arbitrarily selected leaves at each of 51 sampling locations at 0.32-m intervals. Spatial pattern analyses were performed for individual transects and results used to develop sequential sampling estimation and classification models. Bootstrap simulations evaluated the precision of the models. CLS incidence ranged from 13% to 92% with a median of 31%, and a beta-binomial distribution, that is reflective of aggregation, best described the spatial patterns observed. Statistical methods used to describe the spatial heterogeneity below or at the level of sampling unit detected aggregation in 95% of the transects, while the geostatistical-based approach, SADIE, detected aggregation in only 55.8%. Sequential estimation models estimated disease incidence (p) with a preselected coefficient of variation (C) of the mean. At the preselected level of precision, as the average sampling units (ASN) to be evaluated increased, the difference between the true p and achieved p^ decreased. For classification, models based on prespecified thresholds (pt) classified the datasets as above or below pt across a range of CLS incidence. As pt increased, the ASN and number of correct decisions decreased. The maximum ASN detected by Monte Carlo simulations using the model including pt values of 0.05 and 0.4 using error rates (a and ß) = 0.05 were 48.1 and 24.1, respectively. Evaluation by bootstrapping detected that incorrect decisions were more common when CLS incidence was closer to the critical threshold. Correct decisions occurred in at least 88.1% of the sampling plans and this rate was observed in the model using the highest critical threshold (pt = 0.4) and error rates (a = ß = 0.1). Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.