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

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 New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of the 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 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 less than 51 sampling units, and only observed on datasets with an incidence above 0.3. Reducing the level of precision, i.e. increasing C to 0.2, allowed the preselected C be achieved with a lower number of sampling units and with an estimated incidence (p̂) close to the true value of p. For classification, the goal was to classify the datasets above or below prespecified thresholds (pt) used for CLS management. The average sample number (ASN) was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to pt, and approximately 11 when far from pt. Correct decisions occurred in over 76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.

Incidence, Spatial Pattern and Temporal Progress of Fusarium Wilt of Bananas

“…However, this fact was only observed because the highest incidence was 41%. For foliar pathosystems where disease incidence above 50% is commonly observed, the binary power law’s aggregation parameter is low in datasets with either very low or very high incidences [ 11 , 16 ]. Diseases caused by pathogens dispersed mostly from plant-to-plant usually present this pattern [ 48 , 49 ].…”

“…Knowledge gained from the analysis of spatial patterns may help generate sound scientific hypotheses about pathogen dispersal mechanisms [ 10 , 11 ], and then can support mitigation strategies in the case of introduction of new variants of a pathogen into an area. Additionally, spatial analyses are useful for several purposes including the study of pathogen population dynamics [ 12 ], design of experiments [ 13 ], sampling programs for disease or pathogen monitoring [ 14 , 15 , 16 ], assessment of crop losses about disease intensity [ 13 ], and the development of management strategies [ 9 ].…”

Spatiotemporal Dynamics of Stemphylium Leaf Blight and Potential Inoculum Sources in New York Onion Fields

“…Quantifying the spatiotemporal characteristics of epidemics provides valuable information upon which to develop hypotheses surrounding inoculum sources, pathogen dissemination and epidemic progress (e.g., Madden 1980;Pethybridge et al 2005;Cieniewicz et al 2018;Gigot et al 2017;Heck et al 2021), which is lacking for SLB of onion. Spatiotemporal characteristics of SLB epidemics are also vital for evaluating crop loss (Madden and Hughes 1995;Madden et al 2018), experimental design (Madden and Hughes 1995;Madden et al 2018), the design of sampling methods Madden 1999, 2004;Turechek et al 2011;Jones et al 2011;Heck et al 2021;Madden et al 2018) and management strategies (Ristaino and Gumpertz 2000;Madden et al 2007).…”