scholarly journals Sequential Sampling for Incidence of Phomopsis Leaf Blight of Strawberry

2001 ◽  
Vol 91 (4) ◽  
pp. 336-347 ◽  
Author(s):  
W. W. Turechek ◽  
M. A. Ellis ◽  
L. V. Madden
Keyword(s):  
Disease Incidence ◽  
Sequential Sampling ◽  
Leaf Blight ◽  
Sufficient Evidence ◽  
Data Sets ◽  
Correct Classification ◽  
Validation Data ◽  
Sample Number ◽  
True Value ◽  
Sampling Protocols

Sequential sampling models for estimation and classification were developed for the incidence of strawberry leaflets infected by Phomopsis obscurans. Sampling protocols were based on a binary power law analysis of the spatial heterogeneity of Phomopsis leaf blight in commercial fields in Ohio. For sequential estimation, samples were collected until mean disease incidence could be estimated with a preselected coefficient of variation of the mean (C). For sequential classification, samples were collected until there was sufficient evidence to classify mean incidence as being below or above a threshold (pt) based on the sequential probability ratio test. Monte-Carlo simulations were used to determine the theoretical average sample number (ASN) and probability of classifying mean incidence as less than pt (operating characteristic) for any true value of incidence. Estimation and classification sampling models were both tested with bootstrap simulations of randomly selected data sets and validated by data sets from another year that were not utilized in developing the models. In general, achieved (or calculated) C after sequentially sampling for estimation was close to the preselected C of 0.2, and mean incidence was estimated with little bias. Achieving a C of 0.1 with less than 75 sampling units (the nominal value for many original data sets) was more problematical, especially with true incidence less than 0.2. ASN for classification was only 9 to 18 at disease incidence values near pt, and approximately five or less at incidence values far from pt. Correct classification decisions were made in over 88% of the validation data sets. Results indicated that it is possible to estimate Phomopsis leaf blight with high precision and with high correct classification probabilities.

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

Plant Disease ◽  
2021 ◽  
Author(s):  
Daniel Winter Heck ◽  
Julie R Kikkert ◽  
Linda Hanson ◽  
Sarah Jane Pethybridge
Keyword(s):  
New York ◽  
Leaf Spot ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Classification Models ◽  
Cercospora Leaf Spot ◽  
Sample Number ◽  
Sequential Sampling Plan ◽  
True Value

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.

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew II: Cone Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Powdery Mildew ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Model Construction ◽  
Data Sets ◽  
Data Set ◽  
Sampling Plans ◽  
Simulated Sampling

Sequential sampling models for estimation and classification of the incidence of powdery mildew (caused by Podosphaera macularis) on hop (Humulus lupulus) cones were developed using parameter estimates of the binary power law derived from the analysis of 221 transect data sets (model construction data set) collected from 41 hop yards sampled in Oregon and Washington from 2000 to 2005. Stop lines, models that determine when sufficient information has been collected to estimate mean disease incidence and stop sampling, for sequential estimation were validated by bootstrap simulation using a subset of 21 model construction data sets and simulated sampling of an additional 13 model construction data sets. Achieved coefficient of variation (C) approached the prespecified C as the estimated disease incidence, [Formula: see text], increased, although achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.03 with the number of sampling units evaluated in this study. The 95% confidence interval of the median difference between [Formula: see text] of each yard (achieved by sequential sampling) and the true p of the original data set included 0 for all 21 data sets evaluated at levels of C of 0.1 and 0.2. For sequential classification, operating characteristic (OC) and average sample number (ASN) curves of the sequential sampling plans obtained by bootstrap analysis and simulated sampling were similar to the OC and ASN values determined by Monte Carlo simulation. Correct decisions of whether disease incidence was above or below prespecified thresholds (pt) were made for 84.6 or 100% of the data sets during simulated sampling when stop lines were determined assuming a binomial or beta-binomial distribution of disease incidence, respectively. However, the higher proportion of correct decisions obtained by assuming a beta-binomial distribution of disease incidence required, on average, sampling 3.9 more plants per sampling round to classify disease incidence compared with the binomial distribution. Use of these sequential sampling plans may aid growers in deciding the order in which to harvest hop yards to minimize the risk of a condition called “cone early maturity” caused by late-season infection of cones by P. macularis. Also, sequential sampling could aid in research efforts, such as efficacy trials, where many hop cones are assessed to determine disease incidence.

scholarly journals Risk Assessment for Tomato Fruitworm in Processing Tomato Crop-Egg Location and Sequential Sampling

Insects ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 13
Author(s):  
Elisabete Figueiredo ◽  
Catarina Gonçalves ◽  
Sónia Duarte ◽  
Maria C. Godinho ◽  
António Mexia ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Sequential Sampling ◽  
Sampling Plan ◽  
Random Pattern ◽  
Growth Stages ◽  
Data Sets ◽  
Tomato Crop ◽  
Sample Number ◽  
Sequential Sampling Plan ◽  
Fixed Sample

Helicoverpa armigera is one of the key pests affecting processing tomatoes and many other crops. A three-year study was conducted to describe the oviposition preferences of this species on determinate tomato plants (mainly the stratum, leaf, leaflet, and leaf side) and the spatial pattern of the eggs in the field, to form a sequential sampling plan. Eggs were found mainly in the exposed canopy, on leaves a (upper stratum) and b (upper-middle stratum) and significantly fewer eggs on leaf c (middle-lower stratum) below flower clusters. This vertical pattern in the plant was found in all phenological growth stages. The spatial pattern was found to be aggregated, with a trend towards a random pattern at lower densities. A sequential sampling plan was developed, based on Iwao’s method with the parameters of Taylor’s power law, with minimum and maximum sample size of 20 and 80 sample units (plants), respectively (two leaves/plant). For its validation, operating characteristic (OC) and average sample number (ASN) curves were calculated by means of simulation with independent data sets. The β-error was higher than desirable in the vicinity of the economic threshold, but this sampling plan is regarded as an improvement both in effort and precision, compared with the fixed sample plan, and further improvements are discussed.

scholarly journals Spatial Pattern Analysis of Strawberry Leaf Blight in Perennial Production Systems

1999 ◽  
Vol 89 (5) ◽  
pp. 421-433 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden
Keyword(s):  
Spatial Pattern ◽  
Disease Incidence ◽  
Spatial Association ◽  
Disease Status ◽  
Leaf Blight ◽  
Data Sets ◽  
Ratio Test ◽  
Spatial Pattern Analysis ◽  
Test Statistic ◽  
Degree Of Heterogeneity

Spatial pattern of the incidence of strawberry leaf blight, caused by Phomopsis obscurans, was quantified in commercial strawberry fields in Ohio using statistics for heterogeneity and spatial correlation. For each strawberry planting, two transects were randomly chosen and the proportion of leaflets (out of 15) and leaves (out of five) with leaf blight symptoms was determined from N = 49 to 106 (typically 75) evenly spaced sampling units, thus establishing a natural spatial hierarchy to compare patterns of disease. The beta-binomial distribution fitted the data better than the binomial in 92 and 26% of the 121 data sets over 2 years at the leaflet and leaf levels, respectively, based on a likelihood ratio test. Heterogeneity in individual data sets was measured with the index of dispersion (variance ratio), C(α) test, a standard normal-based test statistic, and estimated θ parameter of the beta-binomial. Using these indices, overdispersion was detected in approximately 94 and 36% of the data sets at the leaflet and leaf levels, respectively. Estimates of the slope from the binary power law were significantly (P < 0.01) greater than 1 and estimates of the intercept were significantly greater than 0 (P < 0.01) at both the leaflet and leaf levels for both years, indicating that degree of heterogeneity was a function of incidence. A covariance analysis indicated that cultivar, time, and commercial farm location of sampling had little influence on the degree of heterogeneity. The measures of heterogeneity indicated that there was a positive correlation of disease status of leaflets (or leaves) within sampling units. Measures of spatial association in disease incidence among sampling units were determined based on autocorrelation coefficients, runs analysis, and a new class of tests known as spatial analysis by distance indices (SADIE). In general, from 9 to 22% of the data sets had a significant nonrandom spatial arrangement of disease incidence among sampling units, depending on which test was used. When significant associations existed, the magnitude of the association was small but was about the same for leaflets and leaves. Comparing test results, SADIE analysis was found to be a viable alternative to spatial autocorrelation analysis and has the advantage of being an extension of heterogeneity analysis rather than a separate approach. Collectively, results showed that incidence of Phomopsis leaf blight was primarily characterized by small, loosely aggregated clusters of diseased leaflets, typically confined within the borders of the sampling units.

scholarly journals Spatial Pattern Analysis and Sequential Sampling for the Incidence of Leaf Spot on Strawberry in Ohio

Plant Disease ◽  
1999 ◽  
Vol 83 (11) ◽  
pp. 992-1000 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden
Keyword(s):  
Spatial Pattern ◽  
Leaf Spot ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Positive Association ◽  
Estimation Procedure ◽  
Data Sets ◽  
Ratio Test ◽  
Spatial Pattern Analysis ◽  
Significant Positive Association

Spatial pattern of the incidence of strawberry leaf spot, caused by Mycosphaerella fragariae (Ramularia brunnea), was quantified on commercial strawberry farms in Ohio. For each planting of strawberry, one or two transects were randomly chosen, and the proportion of leaflets (out of 15) with leaf spot was determined from N = 29 to 87 evenly spaced sampling units. Based on a likelihood ratio test, the beta-binomial distribution described the frequency of diseased leaflets better than the binomial in 93% of the 59 data sets over 3 years. Estimates of mean incidence ranged from 0.0009 to 0.82, with a median of 0.05. Estimates of the beta-binomial aggregation parameter, θ, ranged from 0 to 1.06, with a median of 0.20. Moreover, the estimate of the slope of the binary power law, fitted to the variance data for the 59 data sets, was significantly (P < 0.01) greater than one, indicating that heterogeneity, and hence the pattern of disease incidence at the spatial scale of the sampling units or smaller, was dependent on mean incidence. Spatial autocorrelation and Spatial Analysis by Distance IndicEs (SADIE) analyses detected significant positive association of disease incidence among sampling units in approximately 40% of the data sets, indicating that disease clusters extended beyond the borders of the sampling units in these fields. Collectively, the results show that strawberry leaf spot was characterized by relatively tight clusters of disease (based on θ) that extended beyond the borders of the sampling units in a little under half of the data sets (based on correlations). The information on heterogeneity was used to develop fixed and sequential sampling curves to precisely estimate disease incidence. The sequential-estimation procedure was evaluated using statistical bootstrap methods and performed well over the range of disease incidences encountered.

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew I: Leaf Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1002-1012 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Monte Carlo ◽  
Powdery Mildew ◽  
Monte Carlo Simulations ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Data Sets ◽  
Sampling Plans ◽  
Binomial Approximation

Hop powdery mildew (caused by Podosphaera macularis) is an important disease of hops (Humulus lupulus) in the Pacific Northwest. Sequential sampling models for estimation and classification of the incidence of powdery mildew on leaves of hop were developed based on the beta-binomial distribution, using parameter estimates of the binary power law determined in previous studies. Stop lines, models that indicate that enough information has been collected to estimate disease incidence and cease sampling, for sequential estimation were validated by bootstrap simulations of a select group of 18 data sets (out of a total of 198 data sets) from the model-construction data, and through simulated sampling of 104 data sets collected independently (i.e., validation data sets). The achieved coefficient of variation (C) approached prespecified C values as the achieved disease incidence ([Formula: see text]) increased. Achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.10. The 95% confidence interval of the median difference between the true p and [Formula: see text] included zero for 16 of 18 data sets evaluated at C = 0.2 and all data sets when C = 0.1. For sequential classification, Monte-Carlo simulations were used to determine the probability of classifying mean disease incidence as less than a threshold incidence, pt (operating characteristic [OC]), and average sample number (ASN) curves for 16 combinations of candidate stop lines and error levels (α and β). Four pairs of stop lines were selected for further evaluation based on the results of the Monte-Carlo simulations. Bootstrap simulations of the 18 selected data sets indicated that the OC and ASN curves of the sequential sampling plans for each of the four sets of stop lines were similar to OC and ASN values determined by Monte Carlo simulation. Correct classification of disease incidence as being above or below preselected thresholds was 2.0 to 7.7% higher when stop lines were determined by the beta-binomial approximation than when stop lines were calculated using the binomial distribution. Correct decision rates differed depending on the location where sampling was initiated in the hop yard; however, in all instances were greater than 86% when stop lines were determined using the beta-binomial approximation. The sequential sampling plans evaluated in this study should allow for rapid and accurate estimation and classification of the incidence of hop leaves with powdery mildew, and aid in sampling for pest management decision making.

scholarly journals Analysis of the Association Between the Incidence of Two Spatially Aggregated Foliar Diseases of Strawberry

2000 ◽  
Vol 90 (2) ◽  
pp. 157-170 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden
Keyword(s):  
Linear Combination ◽  
Binomial Distribution ◽  
Leaf Spot ◽  
Disease Incidence ◽  
Similarity Index ◽  
Rank Correlation ◽  
Jaccard Index ◽  
Leaf Blight ◽  
Sampling Unit ◽  
Data Sets

Association of the incidence of leaf blight (caused by Phomopsis obscurans) and leaf spot of strawberry (caused by Mycosphaerella fragariae) was assessed at multiple scales in perennial plantings at several commercial farms over 3 years (1996 to 1998). For each field, the presence or absence of each disease was recorded from n = 15 leaflets in each of N ≈ 70 evenly spaced sampling units, and the proportion of leaflets with blight, spot, and total disease (blight or spot) was determined. Individual diseases and total disease incidence were all well described by the beta-binomial distribution but not by the binomial distribution, indicating overdispersion of disease. The Jaccard similarity index was used to measure disease co-occurrence at the leaflet, sampling-unit, and field scales. Standard errors of this index for the lower two scales were obtained using the jackknife (resampling) procedure, and data randomizations were used to determine the expected Jaccard index for an independent arrangement of the two diseases, conditioned on the incidence and spatial heterogeneity of the observed disease data. Results based on these statistics showed that only 4 of 52 data sets at the leaflet level and no data sets at the sampling-unit level had Jaccard index values significantly different from that expected under an independent rearrangement of the two diseases. Rank correlation and cross-correlation statistics were calculated to determine the degree of covariation in incidence between the two diseases. Additionally, covariation between diseases was tested using a new procedure in the Spatial Analysis by Distance IndicEs (SADIE) class of tests. Covariation was detected in 21% of the data sets using rank correlation methods and in 15% of the data sets using the SADIE-based approach. The discrepancy between these two methods may be due to the rank correlation procedure not taking into account the effects of spatial pattern of disease incidence. There was no relationship between mean disease incidence per field of spot and blight or between degree of heterogeneity of the two diseases (as measured by θ of the beta-binomial distribution), demonstrating lack of covariation at the field scale. Incidence of leaflets with either disease (total disease incidence) could be well predicted using a linear combination of the estimated probabilities of leaf blight and leaf spot incidence based on independence of the two diseases. Heterogeneity of total disease incidence, measured with the estimated θ parameter of the beta-binomial distribution, could also be well predicted using a linear combination of the weighted θ values for leaf blight and leaf spot, with weights proportional to incidence of the individual diseases.

scholarly journals Spatial Heterogeneity of the Incidence of Powdery Mildew on Hop Cones

Plant Disease ◽  
2006 ◽  
Vol 90 (11) ◽  
pp. 1433-1440 ◽  
Author(s):  
David H. Gent ◽  
Walter F. Mahaffee ◽  
William W. Turechek
Keyword(s):  
Powdery Mildew ◽  
Spatial Heterogeneity ◽  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Disease Assessment ◽  
Cluster Sampling ◽  
Data Sets ◽  
Ratio Test ◽  
Frequency Distributions

The spatial heterogeneity of the incidence of hop cones with powdery mildew (Podosphaera macularis) was characterized from transect surveys of 41 commercial hop yards in Oregon and Washington from 2000 to 2005. The proportion of sampled cones with powdery mildew ( p) was recorded for each of 221 transects, where N = 60 sampling units of n = 25 cones assessed in each transect according to a cluster sampling strategy. Disease incidence ranged from 0 to 0.92 among all yards and dates. The binomial and beta-binomial frequency distributions were fit to the N sampling units in a transect using maximum likelihood. The estimation procedure converged for 74% of the data sets where p > 0, and a loglikelihood ratio test indicated that the beta-binomial distribution provided a better fit to the data than the binomial distribution for 46% of the data sets, indicating an aggregated pattern of disease. Similarly, the C(α) test indicated that 54% could be described by the beta-binomial distribution. The heterogeneity parameter of the beta-binomial distribution, θ, a measure of variation among sampling units, ranged from 0.01 to 0.20, with a mean of 0.037 and a median of 0.015. Estimates of the index of dispersion ranged from 0.79 to 7.78, with a mean of 1.81 and a median of 1.37, and were significantly greater than 1 for 54% of the data sets. The binary power law provided an excellent fit to the data, with slope and intercept parameters significantly greater than 1, which indicated that heterogeneity varied systematically with the incidence of infected cones. A covariance analysis indicated that the geographic location (region) of the yards and the type of hop cultivar had little effect on heterogeneity; however, the year of sampling significantly influenced the intercept and slope parameters of the binary power law. Significant spatial autocorrelation was detected in only 11% of the data sets, with estimates of first-order autocorrelation, r1, ranging from -0.30 to 0.70, with a mean of 0.06 and a median of 0.04; however, correlation was detected in only 20 and 16% of the data sets by median and ordinary runs analysis, respectively. Together, these analyses suggest that the incidence of powdery mildew on cones was slightly aggregated among plants, but patterns of aggregation larger than the sampling unit were rare (20% or less of data sets). Knowledge of the heterogeneity of diseased cones was used to construct fixed sampling curves to precisely estimate the incidence of powdery mildew on cones at varying disease intensities. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and management decisions.

Non-Overlapping and Non–Cell-Type–Specific Gene Expression Signatures Predict Lung Cancer Survival

2008 ◽  
Vol 26 (6) ◽  
pp. 877-883 ◽  
Author(s):  
Zhifu Sun ◽  
Dennis A. Wigle ◽  
Ping Yang
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Gene Signature ◽  
Data Sets ◽  
Cell Type ◽  
Validation Data ◽  
Adjusted Risk ◽  
Gene Expression Signatures

Purpose Gene expression profiling for outcome prediction of non–small-cell lung cancer (NSCLC) remains clouded by heterogeneous and unvalidated results. This study applied multivariate approaches to identify and evaluate value-added gene expression signatures in two types of NSCLC. Materials and Methods Two NSCLC oligonucleotide microarray data sets of adenocarcinoma and squamous cell carcinoma were used as training sets to select prognostic genes independent of conventional predictors. The top 50 genes from each set were used to predict the outcomes of two independent validation data sets of 84 and 91 NSCLC cases. Results Adenocarcinomas with the 50-gene signature from adenocarcinoma in both validation data sets had a 2.4-fold (95% CI, 1.3 to 4.4 and 1.0 to 5.8) increased mortality after adjustment for conventional predictors. Squamous cell carcinoma with this high-risk signature had an adjusted risk of 1.1 (95% CI, 0.4 to 3.2) in one data set and 2.5 (95% CI, 1.1 to 5.8) in another consisting of stage I tumors. Adenocarcinoma with the 50-gene signature from squamous cell carcinoma had an elevated risk of 3.5 (95% CI, 1.4 to 9.0) after adjustment for conventional predictors. Squamous cell carcinoma with this high risk signature had an adjusted risk of 1.8 (95% CI, 0.7 to 4.6). Despite the little overlap in individual genes, the two gene signatures had significant functional connectedness in molecular pathways. Conclusion Two non-overlapping but functionally related gene expression signatures provide consistently improved survival prediction for NSCLC regardless of histologic cell type. Multiple sets of genes may exist for NSCLC with predictive value, but ones with independent predictive value beyond clinical predictors will be required for clinical translation.

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